diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d3a024d --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +The MIT License + +Copyright (c) 2012 Eugene Zatepyakin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..586e954 --- /dev/null +++ b/README.md @@ -0,0 +1,40 @@ +jsfeat +====== + +### JavaScript Computer Vision library ### + +The project aim is to explore JS/HTML5 possibilities using modern & state-of-art computer vision algorithms. + +[Examples and Documentation](https://inspirit.github.io/jsfeat/) + +### Features ### + +* Custom data structures +* Basic image processing methods (grayscale, derivatives, box-blur, resample, etc.) + * grayscale ([Demo](https://inspirit.github.io/jsfeat/sample_grayscale.html)) + * box blur ([Demo](https://inspirit.github.io/jsfeat/sample_boxblur.html)) + * gaussian blur ([Demo](https://inspirit.github.io/jsfeat/sample_gaussblur.html)) + * equalize histogram ([Demo](https://inspirit.github.io/jsfeat/sample_equalize_hist.html)) + * canny edges ([Demo](https://inspirit.github.io/jsfeat/sample_canny_edge.html)) + * sobel deriv ([Demo](https://inspirit.github.io/jsfeat/sample_sobel.html)) + * scharr deriv ([Demo](https://inspirit.github.io/jsfeat/sample_scharr.html)) + * find more at Examples and Documentation page +* Linear Algebra module + * LU (Gaussian elimination) solver + * Cholesky solver + * SVD decomposition, solver and pseudo-inverse + * Eigen Vectors and Values +* Multiview module ([Demo](https://inspirit.github.io/jsfeat/sample_videostab.html)) + * Affine2D motion kernel + * Homography2D motion kernel + * RANSAC motion estimator + * LMEDS motion estimator +* Matrix Math module for various matrix operation such as traspose, multiply etc. +* Features 2D + * Fast Corners feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_fast_corners.html)) + * YAPE06 feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_yape06.html)) + * YAPE feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_yape.html)) + * ORB feature descriptor ([Demo](https://inspirit.github.io/jsfeat/sample_orb.html)) +* Lucas-Kanade optical flow ([Demo](https://inspirit.github.io/jsfeat/sample_oflow_lk.html) - click to add points) +* HAAR object detector ([Demo](https://inspirit.github.io/jsfeat/sample_haar_face.html)) +* BBF object detector ([Demo](https://inspirit.github.io/jsfeat/sample_bbf_face.html)) diff --git a/bower.json b/bower.json new file mode 100644 index 0000000..83146f2 --- /dev/null +++ b/bower.json @@ -0,0 +1,14 @@ +{ + + "name": "jsfeat", + "version" : "0.0.8", + "description" : "JavaScript Computer Vision library", + "author" : "Eugene Zatepyakin (http://www.inspirit.ru/)", + "main" : "build/jsfeat-min.js", + + "ignore": [ + "src", + "compile", + "node_modules" + ] +} diff --git a/build/jsfeat-min.js b/build/jsfeat-min.js new file mode 100755 index 0000000..f1dc9da --- /dev/null +++ b/build/jsfeat-min.js @@ -0,0 +1 @@ +var jsfeat=jsfeat||{REVISION:"ALPHA"};(function(r){var o=1.192092896e-7;var l=1e-37;var m=256,i=512,h=1024,x=2048,w=4096;var A=1,n=2,b=3,p=4;var z=new Int32Array([-1,1,4,-1,4,-1,-1,-1,8,-1,-1,-1,-1,-1,-1,-1,8]);var y=(function(){return function(B){return(B&65280)}})();var k=(function(){return function(B){return(B&255)}})();var c=(function(){return function(B){return z[(B&65280)>>8]}})();var a=0;var f=1;var e=2;var u=3;var d=1;var s=1;var g=2;var v=(function(){function B(D,C){this.size=((D+7)|0)&-8;if(typeof C==="undefined"){this.buffer=new ArrayBuffer(this.size)}else{this.buffer=C;this.size=C.length}this.u8=new Uint8Array(this.buffer);this.i32=new Int32Array(this.buffer);this.f32=new Float32Array(this.buffer);this.f64=new Float64Array(this.buffer)}return B})();var q=(function(){function 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c=new jsfeat.matrix_t(3,3,jsfeat.F32_t|jsfeat.C1_t);var f=new jsfeat.matrix_t(32,32,jsfeat.U8_t|jsfeat.C1_t);var e=function(l,n,k,i,h,j){var m=Math.cos(k);var g=Math.sin(k);c.data[0]=m,c.data[1]=-g,c.data[2]=(-m+g)*j*0.5+i,c.data[3]=g,c.data[4]=m,c.data[5]=(-g-m)*j*0.5+h;jsfeat.imgproc.warp_affine(l,n,c,128)};return{describe:function(j,u,g,B){var r=32;var x=0,A=0,q=0,p=0,z=0;var o=0,m=0,D=0;var C=j.data,n=j.cols,y=j.rows;var t=f.data;var v=16*32+16;var k=0;if(!(B.type&jsfeat.U8_t)){B.type=jsfeat.U8_t;B.cols=r;B.rows=g;B.channel=1;B.allocate()}else{B.resize(r,g,1)}var l=B.data;var s=0;for(x=0;x=0;--ai){am=(1/(1<>ai;aH=aB>>ai;ab=ay<<1;g=r[ai].data;F=S[ai].data;A=(ay-N)|0;O=(aH-N)|0;c(r[ai],V);for(ak=0;ak=A)|(o<=d)|(o>=O);if(J!=0){if(ai==0){K[ak]=0}continue}U=aF-p;T=aD-o;aK=(((1-U)*(1-T)*ad)+0.5)|0;aJ=((U*(1-T)*ad)+0.5)|0;ar=(((1-U)*T*ad)+0.5)|0;aq=(ad-aK-aJ-ar);ao=0,an=0,ag=0;for(H=0;H>(Z));v=(aA[aM]*aK+aA[aM+2]*aJ+aA[aM+ab]*ar+aA[aM+ab+2]*aq);v=(((v)+m)>>(C));B=(aA[aM+1]*aK+aA[aM+3]*aJ+aA[aM+ab+1]*ar+aA[aM+ab+3]*aq);B=(((B)+m)>>(C));w[at]=al;ac[aI++]=v;ac[aI++]=B;ao+=v*v;an+=v*B;ag+=B*B}}ao*=X;an*=X;ag*=X;aj=ao*ag-an*an;aG=(ag+ao-Math.sqrt((ao-ag)*(ao-ag)+4*an*an))/aa;if(aG=A)|(aC<=d)|(aC>=O);if(J!=0){if(ai==0){K[ak]=0}break}U=af-aE;T=ae-aC;aK=(((1-U)*(1-T)*ad)+0.5)|0;aJ=((U*(1-T)*ad)+0.5)|0;ar=(((1-U)*T*ad)+0.5)|0;aq=(ad-aK-aJ-ar);aw=0,av=0;for(H=0;H>(Z));al=(al-w[at]);aw+=al*ac[aI++];av+=al*ac[aI++]}}aw*=X;av*=X;Y=((an*av-ag*aw)*aj);W=((an*aw-ao*av)*aj);af+=Y;ae+=W;aL[Q]=af+e;aL[P]=ae+e;if(Y*Y+W*W<=f){break}if(l>0&&Math.abs(Y+E)<0.01&&Math.abs(W+z)<0.01){aL[Q]-=Y*0.5;aL[P]-=W*0.5;break}E=Y;z=W}}}jsfeat.cache.put_buffer(az);jsfeat.cache.put_buffer(s);jsfeat.cache.put_buffer(t)}}})();b.optical_flow_lk=a})(jsfeat);(function(b){var a=(function(){var c=function(e,d){var f=(e.width*0.25+0.5)|0;return d.x<=e.x+f&&d.x>=e.x-f&&d.y<=e.y+f&&d.y>=e.y-f&&d.width<=(e.width*1.5+0.5)|0&&(d.width*1.5+0.5)|0>=e.width};return{edges_density:0.07,detect_single_scale:function(E,ad,af,q,d,f,D,B){var z=(B.size[0]*D)|0,N=(B.size[1]*D)|0,V=(0.5*D+1.5)|0,U=V;var Z,X,W,Q,O,T=(d-z)|0,R=(f-N)|0;var H=(d+1)|0,w,p,r,S;var e=1/(z*N);var t,o,l,u,s,ae,A,g=true,L,h,n,G,m;var M,K,J,I,v,C;var ac=0,ab=z,aa=N*H,Y=aa+z;var F=((z*N)*255*this.edges_density)|0;var P=[];for(O=0;O0?Math.sqrt(r):1;t=B.complexClassifiers;s=t.length;g=true;for(Z=0;Zr[s].rank){r[s].parent=t}else{r[t].parent=s;if(r[t].rank==r[s].rank){r[s].rank++}t=s}var A,d=v;while(r[d].parent!=-1){A=d;d=r[d].parent;r[A].parent=t}d=y;while(r[d].parent!=-1){A=d;d=r[d].parent;r[A].parent=t}}}}}var w=[];var o=0;for(y=0;y=0){r[e].rank=~o++}v=~r[e].rank}w[y]=v}var m=[];for(y=0;y=l){h.push({x:(m[y].x*2+q)/(2*q),y:(m[y].y*2+q)/(2*q),width:(m[y].width*2+q)/(2*q),height:(m[y].height*2+q)/(2*q),neighbors:m[y].neighbors,confidence:m[y].confidence})}}var p=[];q=h.length;for(y=0;y=u.x-f&&z.y>=u.y-f&&z.x+z.width<=u.x+u.width+f&&z.y+z.height<=u.y+u.height+f&&(u.neighbors>Math.max(3,z.neighbors)||z.neighbors<3)){x=false;break}}if(x){p.push(z)}}return p}}})();b.haar=a})(jsfeat);(function(a){var b=(function(){var c=function(f,e){var g=(f.width*0.25+0.5)|0;return e.x<=f.x+g&&e.x>=f.x-g&&e.y<=f.y+g&&e.y>=f.y-g&&e.width<=(f.width*1.5+0.5)|0&&(e.width*1.5+0.5)|0>=f.width};var d=new jsfeat.pyramid_t(1);return{interval:4,scale:1.1486,next:5,scale_to:1,prepare_cascade:function(g){var m=g.stage_classifier.length;for(var h=0;h>1;h=g.rows>>1;if(p||n!=j.cols||h!=j.rows){d.data[l<<2]=new jsfeat.matrix_t(n,h,r);j=d.data[l<<2]}jsfeat.imgproc.pyrdown(g,j)}for(l=this.next*2;l>1;h=g.rows>>1;j=d.data[(l<<2)+1];if(p||n!=j.cols||h!=j.rows){d.data[(l<<2)+1]=new jsfeat.matrix_t(n,h,r);j=d.data[(l<<2)+1]}jsfeat.imgproc.pyrdown(g,j,1,0);j=d.data[(l<<2)+2];if(p||n!=j.cols||h!=j.rows){d.data[(l<<2)+2]=new jsfeat.matrix_t(n,h,r);j=d.data[(l<<2)+2]}jsfeat.imgproc.pyrdown(g,j,0,1);j=d.data[(l<<2)+3];if(p||n!=j.cols||h!=j.rows){d.data[(l<<2)+3]=new jsfeat.matrix_t(n,h,r);j=d.data[(l<<2)+3]}jsfeat.imgproc.pyrdown(g,j,1,1)}return d},detect:function(G,L){var h=this.interval;var N=this.scale;var m=this.next;var l=this.scale_to;var ab=0,aa=0,Z=0,W=0,S=0,R=0,U=0,B=0,J=0,I=0,V=0,ae=0,M=0,ad=0,w=0,Y=0,g=0;var E=0,X,Q,D,H,F,O=true,o=true;var z=1,v=1;var s=[0,1,0,1];var r=[0,0,1,1];var K=[];var C=G.data,ac=1,u=2,t=4;var A=[],e=[0,0,0];var P=[0,0,0];var T=[0,0,0];for(ab=0;ab>2);g=C[w+(m<<3)].rows-(L.height>>2);P[0]=C[w].cols*ac;P[1]=C[w+(m<<2)].cols*ac;P[2]=C[w+(m<<3)].cols*ac;T[0]=(C[w].cols*t)-(Y*t);T[1]=(C[w+(m<<2)].cols*u)-(Y*u);T[2]=(C[w+(m<<3)].cols*ac)-(Y*ac);B=L.stage_classifier.length;for(aa=0;aa=0){V=A[H.pz[ad]][e[H.pz[ad]]+H.px[ad]];if(V=0){W=A[H.nz[ad]][e[H.nz[ad]]+H.nx[ad]];if(W>M){if(ae<=W){o=false;break}M=W}}}E+=(o)?X[(Z<<1)+1]:X[Z<<1]}}if(Es[t].rank){s[t].parent=u}else{s[u].parent=t;if(s[u].rank==s[t].rank){s[t].rank++}u=t}var B,e=w;while(s[e].parent!=-1){B=e;e=s[e].parent;s[B].parent=u}e=z;while(s[e].parent!=-1){B=e;e=s[e].parent;s[B].parent=u}}}}}var x=[];var p=0;for(z=0;z=0){s[f].rank=~p++}w=~s[f].rank}x[z]=w}var o=[];for(z=0;z=m){k.push({x:(o[z].x*2+r)/(2*r),y:(o[z].y*2+r)/(2*r),width:(o[z].width*2+r)/(2*r),height:(o[z].height*2+r)/(2*r),neighbors:o[z].neighbors,confidence:o[z].confidence})}}var q=[];r=k.length;for(z=0;z=v.x-g&&A.y>=v.y-g&&A.x+A.width<=v.x+v.width+g&&A.y+A.height<=v.y+v.height+g&&(v.neighbors>Math.max(3,A.neighbors)||A.neighbors<3)){y=false;break}}if(y){q.push(A)}}return q}}})();a.bbf=b})(jsfeat);(function(a){if(typeof module==="undefined"||typeof module.exports==="undefined"){window.jsfeat=a}else{module.exports=a}})(jsfeat); \ No newline at end of file diff --git a/build/jsfeat.js b/build/jsfeat.js new file mode 100644 index 0000000..f9cc93b --- /dev/null +++ b/build/jsfeat.js @@ -0,0 +1,5639 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +// namespace ? +var jsfeat = jsfeat || { REVISION: 'ALPHA' }; +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + // CONSTANTS + var EPSILON = 0.0000001192092896; + var FLT_MIN = 1E-37; + + // implementation from CCV project + // currently working only with u8,s32,f32 + var U8_t = 0x0100, + S32_t = 0x0200, + F32_t = 0x0400, + S64_t = 0x0800, + F64_t = 0x1000; + + var C1_t = 0x01, + C2_t = 0x02, + C3_t = 0x03, + C4_t = 0x04; + + var _data_type_size = new Int32Array([ -1, 1, 4, -1, 4, -1, -1, -1, 8, -1, -1, -1, -1, -1, -1, -1, 8 ]); + + var get_data_type = (function () { + return function(type) { + return (type & 0xFF00); + } + })(); + + var get_channel = (function () { + return function(type) { + return (type & 0xFF); + } + })(); + + var get_data_type_size = (function () { + return function(type) { + return _data_type_size[(type & 0xFF00) >> 8]; + } + })(); + + // color conversion + var COLOR_RGBA2GRAY = 0; + var COLOR_RGB2GRAY = 1; + var COLOR_BGRA2GRAY = 2; + var COLOR_BGR2GRAY = 3; + + // box blur option + var BOX_BLUR_NOSCALE = 0x01; + // svd options + var SVD_U_T = 0x01; + var SVD_V_T = 0x02; + + var data_t = (function () { + function data_t(size_in_bytes, buffer) { + // we need align size to multiple of 8 + this.size = ((size_in_bytes + 7) | 0) & -8; + if (typeof buffer === "undefined") { + this.buffer = new ArrayBuffer(this.size); + } else { + this.buffer = buffer; + this.size = buffer.length; + } + this.u8 = new Uint8Array(this.buffer); + this.i32 = new Int32Array(this.buffer); + this.f32 = new Float32Array(this.buffer); + this.f64 = new Float64Array(this.buffer); + } + return data_t; + })(); + + var matrix_t = (function () { + // columns, rows, data_type + function matrix_t(c, r, data_type, data_buffer) { + this.type = get_data_type(data_type)|0; + this.channel = get_channel(data_type)|0; + this.cols = c|0; + this.rows = r|0; + if (typeof data_buffer === "undefined") { + this.allocate(); + } else { + this.buffer = data_buffer; + // data user asked for + this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); + } + } + matrix_t.prototype.allocate = function() { + // clear references + delete this.data; + delete this.buffer; + // + this.buffer = new data_t((this.cols * get_data_type_size(this.type) * this.channel) * this.rows); + this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); + } + matrix_t.prototype.copy_to = function(other) { + var od = other.data, td = this.data; + var i = 0, n = (this.cols*this.rows*this.channel)|0; + for(; i < n-4; i+=4) { + od[i] = td[i]; + od[i+1] = td[i+1]; + od[i+2] = td[i+2]; + od[i+3] = td[i+3]; + } + for(; i < n; ++i) { + od[i] = td[i]; + } + } + matrix_t.prototype.resize = function(c, r, ch) { + if (typeof ch === "undefined") { ch = this.channel; } + // relocate buffer only if new size doesnt fit + var new_size = (c * get_data_type_size(this.type) * ch) * r; + if(new_size > this.buffer.size) { + this.cols = c; + this.rows = r; + this.channel = ch; + this.allocate(); + } else { + this.cols = c; + this.rows = r; + this.channel = ch; + } + } + + return matrix_t; + })(); + + var pyramid_t = (function () { + + function pyramid_t(levels) { + this.levels = levels|0; + this.data = new Array(levels); + this.pyrdown = jsfeat.imgproc.pyrdown; + } + + pyramid_t.prototype.allocate = function(start_w, start_h, data_type) { + var i = this.levels; + while(--i >= 0) { + this.data[i] = new matrix_t(start_w >> i, start_h >> i, data_type); + } + } + + pyramid_t.prototype.build = function(input, skip_first_level) { + if (typeof skip_first_level === "undefined") { skip_first_level = true; } + // just copy data to first level + var i = 2, a = input, b = this.data[0]; + if(!skip_first_level) { + var j=input.cols*input.rows; + while(--j >= 0) { + b.data[j] = input.data[j]; + } + } + b = this.data[1]; + this.pyrdown(a, b); + for(; i < this.levels; ++i) { + a = b; + b = this.data[i]; + this.pyrdown(a, b); + } + } + + return pyramid_t; + })(); + + var keypoint_t = (function () { + function keypoint_t(x,y,score,level,angle) { + if (typeof x === "undefined") { x=0; } + if (typeof y === "undefined") { y=0; } + if (typeof score === "undefined") { score=0; } + if (typeof level === "undefined") { level=0; } + if (typeof angle === "undefined") { angle=-1.0; } + + this.x = x; + this.y = y; + this.score = score; + this.level = level; + this.angle = angle; + } + return keypoint_t; + })(); + + + // data types + global.U8_t = U8_t; + global.S32_t = S32_t; + global.F32_t = F32_t; + global.S64_t = S64_t; + global.F64_t = F64_t; + // data channels + global.C1_t = C1_t; + global.C2_t = C2_t; + global.C3_t = C3_t; + global.C4_t = C4_t; + + // popular formats + global.U8C1_t = U8_t | C1_t; + global.U8C3_t = U8_t | C3_t; + global.U8C4_t = U8_t | C4_t; + + global.F32C1_t = F32_t | C1_t; + global.F32C2_t = F32_t | C2_t; + global.S32C1_t = S32_t | C1_t; + global.S32C2_t = S32_t | C2_t; + + // constants + global.EPSILON = EPSILON; + global.FLT_MIN = FLT_MIN; + + // color convert + global.COLOR_RGBA2GRAY = COLOR_RGBA2GRAY; + global.COLOR_RGB2GRAY = COLOR_RGB2GRAY; + global.COLOR_BGRA2GRAY = COLOR_BGRA2GRAY; + global.COLOR_BGR2GRAY = COLOR_BGR2GRAY; + + // options + global.BOX_BLUR_NOSCALE = BOX_BLUR_NOSCALE; + global.SVD_U_T = SVD_U_T; + global.SVD_V_T = SVD_V_T; + + global.get_data_type = get_data_type; + global.get_channel = get_channel; + global.get_data_type_size = get_data_type_size; + + global.data_t = data_t; + global.matrix_t = matrix_t; + global.pyramid_t = pyramid_t; + global.keypoint_t = keypoint_t; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var cache = (function() { + + // very primitive array cache, still need testing if it helps + // of course V8 has its own powerful cache sys but i'm not sure + // it caches several multichannel 640x480 buffer creations each frame + + var _pool_node_t = (function () { + function _pool_node_t(size_in_bytes) { + this.next = null; + this.data = new jsfeat.data_t(size_in_bytes); + this.size = this.data.size; + this.buffer = this.data.buffer; + this.u8 = this.data.u8; + this.i32 = this.data.i32; + this.f32 = this.data.f32; + this.f64 = this.data.f64; + } + _pool_node_t.prototype.resize = function(size_in_bytes) { + delete this.data; + this.data = new jsfeat.data_t(size_in_bytes); + this.size = this.data.size; + this.buffer = this.data.buffer; + this.u8 = this.data.u8; + this.i32 = this.data.i32; + this.f32 = this.data.f32; + this.f64 = this.data.f64; + } + return _pool_node_t; + })(); + + var _pool_head, _pool_tail; + var _pool_size = 0; + + return { + + allocate: function(capacity, data_size) { + _pool_head = _pool_tail = new _pool_node_t(data_size); + for (var i = 0; i < capacity; ++i) { + var node = new _pool_node_t(data_size); + _pool_tail = _pool_tail.next = node; + + _pool_size++; + } + }, + + get_buffer: function(size_in_bytes) { + // assume we have enough free nodes + var node = _pool_head; + _pool_head = _pool_head.next; + _pool_size--; + + if(size_in_bytes > node.size) { + node.resize(size_in_bytes); + } + + return node; + }, + + put_buffer: function(node) { + _pool_tail = _pool_tail.next = node; + _pool_size++; + } + }; + })(); + + global.cache = cache; + // for now we dont need more than 30 buffers + // if having cache sys really helps we can add auto extending sys + cache.allocate(30, 640*4); + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var math = (function() { + + var qsort_stack = new Int32Array(48*2); + + return { + get_gaussian_kernel: function(size, sigma, kernel, data_type) { + var i=0,x=0.0,t=0.0,sigma_x=0.0,scale_2x=0.0; + var sum = 0.0; + var kern_node = jsfeat.cache.get_buffer(size<<2); + var _kernel = kern_node.f32;//new Float32Array(size); + + if((size&1) == 1 && size <= 7 && sigma <= 0) { + switch(size>>1) { + case 0: + _kernel[0] = 1.0; + sum = 1.0; + break; + case 1: + _kernel[0] = 0.25, _kernel[1] = 0.5, _kernel[2] = 0.25; + sum = 0.25+0.5+0.25; + break; + case 2: + _kernel[0] = 0.0625, _kernel[1] = 0.25, _kernel[2] = 0.375, + _kernel[3] = 0.25, _kernel[4] = 0.0625; + sum = 0.0625+0.25+0.375+0.25+0.0625; + break; + case 3: + _kernel[0] = 0.03125, _kernel[1] = 0.109375, _kernel[2] = 0.21875, + _kernel[3] = 0.28125, _kernel[4] = 0.21875, _kernel[5] = 0.109375, _kernel[6] = 0.03125; + sum = 0.03125+0.109375+0.21875+0.28125+0.21875+0.109375+0.03125; + break; + } + } else { + sigma_x = sigma > 0 ? sigma : ((size-1)*0.5 - 1.0)*0.3 + 0.8; + scale_2x = -0.5/(sigma_x*sigma_x); + + for( ; i < size; ++i ) + { + x = i - (size-1)*0.5; + t = Math.exp(scale_2x*x*x); + + _kernel[i] = t; + sum += t; + } + } + + if(data_type & jsfeat.U8_t) { + // int based kernel + sum = 256.0/sum; + for (i = 0; i < size; ++i) { + kernel[i] = (_kernel[i] * sum + 0.5)|0; + } + } else { + // classic kernel + sum = 1.0/sum; + for (i = 0; i < size; ++i) { + kernel[i] = _kernel[i] * sum; + } + } + + jsfeat.cache.put_buffer(kern_node); + }, + + // model is 3x3 matrix_t + perspective_4point_transform: function(model, src_x0, src_y0, dst_x0, dst_y0, + src_x1, src_y1, dst_x1, dst_y1, + src_x2, src_y2, dst_x2, dst_y2, + src_x3, src_y3, dst_x3, dst_y3) { + var t1 = src_x0; + var t2 = src_x2; + var t4 = src_y1; + var t5 = t1 * t2 * t4; + var t6 = src_y3; + var t7 = t1 * t6; + var t8 = t2 * t7; + var t9 = src_y2; + var t10 = t1 * t9; + var t11 = src_x1; + var t14 = src_y0; + var t15 = src_x3; + var t16 = t14 * t15; + var t18 = t16 * t11; + var t20 = t15 * t11 * t9; + var t21 = t15 * t4; + var t24 = t15 * t9; + var t25 = t2 * t4; + var t26 = t6 * t2; + var t27 = t6 * t11; + var t28 = t9 * t11; + var t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + var t32 = t1 * t15; + var t35 = t14 * t11; + var t41 = t4 * t1; + var t42 = t6 * t41; + var t43 = t14 * t2; + var t46 = t16 * t9; + var t48 = t14 * t9 * t11; + var t51 = t4 * t6 * t2; + var t55 = t6 * t14; + var Hr0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hr1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hr2 = t1; + var Hr3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hr4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hr5 = t14; + var Hr6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hr7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + t1 = dst_x0; + t2 = dst_x2; + t4 = dst_y1; + t5 = t1 * t2 * t4; + t6 = dst_y3; + t7 = t1 * t6; + t8 = t2 * t7; + t9 = dst_y2; + t10 = t1 * t9; + t11 = dst_x1; + t14 = dst_y0; + t15 = dst_x3; + t16 = t14 * t15; + t18 = t16 * t11; + t20 = t15 * t11 * t9; + t21 = t15 * t4; + t24 = t15 * t9; + t25 = t2 * t4; + t26 = t6 * t2; + t27 = t6 * t11; + t28 = t9 * t11; + t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + t32 = t1 * t15; + t35 = t14 * t11; + t41 = t4 * t1; + t42 = t6 * t41; + t43 = t14 * t2; + t46 = t16 * t9; + t48 = t14 * t9 * t11; + t51 = t4 * t6 * t2; + t55 = t6 * t14; + var Hl0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hl1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hl2 = t1; + var Hl3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hl4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hl5 = t14; + var Hl6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hl7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + // the following code computes R = Hl * inverse Hr + t2 = Hr4-Hr7*Hr5; + t4 = Hr0*Hr4; + t5 = Hr0*Hr5; + t7 = Hr3*Hr1; + t8 = Hr2*Hr3; + t10 = Hr1*Hr6; + var t12 = Hr2*Hr6; + t15 = 1.0 / (t4-t5*Hr7-t7+t8*Hr7+t10*Hr5-t12*Hr4); + t18 = -Hr3+Hr5*Hr6; + var t23 = -Hr3*Hr7+Hr4*Hr6; + t28 = -Hr1+Hr2*Hr7; + var t31 = Hr0-t12; + t35 = Hr0*Hr7-t10; + t41 = -Hr1*Hr5+Hr2*Hr4; + var t44 = t5-t8; + var t47 = t4-t7; + t48 = t2*t15; + var t49 = t28*t15; + var t50 = t41*t15; + var mat = model.data; + mat[0] = Hl0*t48+Hl1*(t18*t15)-Hl2*(t23*t15); + mat[1] = Hl0*t49+Hl1*(t31*t15)-Hl2*(t35*t15); + mat[2] = -Hl0*t50-Hl1*(t44*t15)+Hl2*(t47*t15); + mat[3] = Hl3*t48+Hl4*(t18*t15)-Hl5*(t23*t15); + mat[4] = Hl3*t49+Hl4*(t31*t15)-Hl5*(t35*t15); + mat[5] = -Hl3*t50-Hl4*(t44*t15)+Hl5*(t47*t15); + mat[6] = Hl6*t48+Hl7*(t18*t15)-t23*t15; + mat[7] = Hl6*t49+Hl7*(t31*t15)-t35*t15; + mat[8] = -Hl6*t50-Hl7*(t44*t15)+t47*t15; + }, + + // The current implementation was derived from *BSD system qsort(): + // Copyright (c) 1992, 1993 + // The Regents of the University of California. All rights reserved. + qsort: function(array, low, high, cmp) { + var isort_thresh = 7; + var t,ta,tb,tc; + var sp = 0,left=0,right=0,i=0,n=0,m=0,ptr=0,ptr2=0,d=0; + var left0=0,left1=0,right0=0,right1=0,pivot=0,a=0,b=0,c=0,swap_cnt=0; + + var stack = qsort_stack; + + if( (high-low+1) <= 1 ) return; + + stack[0] = low; + stack[1] = high; + + while( sp >= 0 ) { + + left = stack[sp<<1]; + right = stack[(sp<<1)+1]; + sp--; + + for(;;) { + n = (right - left) + 1; + + if( n <= isort_thresh ) { + //insert_sort: + for( ptr = left + 1; ptr <= right; ptr++ ) { + for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { + t = array[ptr2]; + array[ptr2] = array[ptr2-1]; + array[ptr2-1] = t; + } + } + break; + } else { + swap_cnt = 0; + + left0 = left; + right0 = right; + pivot = left + (n>>1); + + if( n > 40 ) { + d = n >> 3; + a = left, b = left + d, c = left + (d<<1); + ta = array[a],tb = array[b],tc = array[c]; + left = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + + a = pivot - d, b = pivot, c = pivot + d; + ta = array[a],tb = array[b],tc = array[c]; + pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + + a = right - (d<<1), b = right - d, c = right; + ta = array[a],tb = array[b],tc = array[c]; + right = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + } + + a = left, b = pivot, c = right; + ta = array[a],tb = array[b],tc = array[c]; + pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + if( pivot != left0 ) { + t = array[pivot]; + array[pivot] = array[left0]; + array[left0] = t; + pivot = left0; + } + left = left1 = left0 + 1; + right = right1 = right0; + + ta = array[pivot]; + for(;;) { + while( left <= right && !cmp(ta, array[left]) ) { + if( !cmp(array[left], ta) ) { + if( left > left1 ) { + t = array[left1]; + array[left1] = array[left]; + array[left] = t; + } + swap_cnt = 1; + left1++; + } + left++; + } + + while( left <= right && !cmp(array[right], ta) ) { + if( !cmp(ta, array[right]) ) { + if( right < right1 ) { + t = array[right1]; + array[right1] = array[right]; + array[right] = t; + } + swap_cnt = 1; + right1--; + } + right--; + } + + if( left > right ) break; + + t = array[left]; + array[left] = array[right]; + array[right] = t; + swap_cnt = 1; + left++; + right--; + } + + if( swap_cnt == 0 ) { + left = left0, right = right0; + //goto insert_sort; + for( ptr = left + 1; ptr <= right; ptr++ ) { + for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { + t = array[ptr2]; + array[ptr2] = array[ptr2-1]; + array[ptr2-1] = t; + } + } + break; + } + + n = Math.min( (left1 - left0), (left - left1) ); + m = (left-n)|0; + for( i = 0; i < n; ++i,++m ) { + t = array[left0+i]; + array[left0+i] = array[m]; + array[m] = t; + } + + n = Math.min( (right0 - right1), (right1 - right) ); + m = (right0-n+1)|0; + for( i = 0; i < n; ++i,++m ) { + t = array[left+i]; + array[left+i] = array[m]; + array[m] = t; + } + n = (left - left1); + m = (right1 - right); + if( n > 1 ) { + if( m > 1 ) { + if( n > m ) { + ++sp; + stack[sp<<1] = left0; + stack[(sp<<1)+1] = left0 + n - 1; + left = right0 - m + 1, right = right0; + } else { + ++sp; + stack[sp<<1] = right0 - m + 1; + stack[(sp<<1)+1] = right0; + left = left0, right = left0 + n - 1; + } + } else { + left = left0, right = left0 + n - 1; + } + } + else if( m > 1 ) + left = right0 - m + 1, right = right0; + else + break; + } + } + } + }, + + median: function(array, low, high) { + var w; + var middle=0,ll=0,hh=0,median=(low+high)>>1; + for (;;) { + if (high <= low) return array[median]; + if (high == (low + 1)) { + if (array[low] > array[high]) { + w = array[low]; + array[low] = array[high]; + array[high] = w; + } + return array[median]; + } + middle = ((low + high) >> 1); + if (array[middle] > array[high]) { + w = array[middle]; + array[middle] = array[high]; + array[high] = w; + } + if (array[low] > array[high]) { + w = array[low]; + array[low] = array[high]; + array[high] = w; + } + if (array[middle] > array[low]) { + w = array[middle]; + array[middle] = array[low]; + array[low] = w; + } + ll = (low + 1); + w = array[middle]; + array[middle] = array[ll]; + array[ll] = w; + hh = high; + for (;;) { + do ++ll; while (array[low] > array[ll]); + do --hh; while (array[hh] > array[low]); + if (hh < ll) break; + w = array[ll]; + array[ll] = array[hh]; + array[hh] = w; + } + w = array[low]; + array[low] = array[hh]; + array[hh] = w; + if (hh <= median) + low = ll; + else if (hh >= median) + high = (hh - 1); + } + return 0; + } + }; + + })(); + + global.math = math; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var matmath = (function() { + + return { + identity: function(M, value) { + if (typeof value === "undefined") { value=1; } + var src=M.data; + var rows=M.rows, cols=M.cols, cols_1=(cols+1)|0; + var len = rows * cols; + var k = len; + while(--len >= 0) src[len] = 0.0; + len = k; + k = 0; + while(k < len) { + src[k] = value; + k = k + cols_1; + } + }, + + transpose: function(At, A) { + var i=0,j=0,nrows=A.rows,ncols=A.cols; + var Ai=0,Ati=0,pAt=0; + var ad=A.data,atd=At.data; + + for (; i < nrows; Ati += 1, Ai += ncols, i++) { + pAt = Ati; + for (j = 0; j < ncols; pAt += nrows, j++) atd[pAt] = ad[Ai+j]; + } + }, + + // C = A * B + multiply: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,p_B=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mcols=B.cols; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; Ap += ncols, i++) { + for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { + pB = p_B; + pA = Ap; + sum = 0.0; + for (k = 0; k < ncols; pA++, pB += mcols, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A * B' + multiply_ABt: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mrows=B.rows; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; Ap += ncols, i++) { + for (pB = 0, j = 0; j < mrows; Cp++, j++) { + pA = Ap; + sum = 0.0; + for (k = 0; k < ncols; pA++, pB++, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A' * B + multiply_AtB: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,p_B=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mcols=B.cols; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < ncols; Ap++, i++) { + for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { + pB = p_B; + pA = Ap; + sum = 0.0; + for (k = 0; k < nrows; pA += ncols, pB += mcols, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A * A' + multiply_AAt: function(C, A) { + var i=0,j=0,k=0; + var pCdiag=0,p_A=0,pA=0,pB=0,pC=0,pCt=0; + var ncols=A.cols,nrows=A.rows; + var ad=A.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; pCdiag += nrows + 1, p_A = pA, i++) { + pC = pCdiag; + pCt = pCdiag; + pB = p_A; + for (j = i; j < nrows; pC++, pCt += nrows, j++) { + pA = p_A; + sum = 0.0; + for (k = 0; k < ncols; k++) { + sum += ad[pA++] * ad[pB++]; + } + cd[pC] = sum + cd[pCt] = sum; + } + } + }, + + // C = A' * A + multiply_AtA: function(C, A) { + var i=0,j=0,k=0; + var p_A=0,pA=0,pB=0,p_C=0,pC=0,p_CC=0; + var ncols=A.cols,nrows=A.rows; + var ad=A.data,cd=C.data; + var sum=0.0; + + for (; i < ncols; p_C += ncols, i++) { + p_A = i; + p_CC = p_C + i; + pC = p_CC; + for (j = i; j < ncols; pC++, p_CC += ncols, j++) { + pA = p_A; + pB = j; + sum = 0.0; + for (k = 0; k < nrows; pA += ncols, pB += ncols, k++) { + sum += ad[pA] * ad[pB]; + } + cd[pC] = sum + cd[p_CC] = sum; + } + } + }, + + // various small matrix operations + identity_3x3: function(M, value) { + if (typeof value === "undefined") { value=1; } + var dt=M.data; + dt[0] = dt[4] = dt[8] = value; + dt[1] = dt[2] = dt[3] = 0; + dt[5] = dt[6] = dt[7] = 0; + }, + + invert_3x3: function(from, to) { + var A = from.data, invA = to.data; + var t1 = A[4]; + var t2 = A[8]; + var t4 = A[5]; + var t5 = A[7]; + var t8 = A[0]; + + var t9 = t8*t1; + var t11 = t8*t4; + var t13 = A[3]; + var t14 = A[1]; + var t15 = t13*t14; + var t17 = A[2]; + var t18 = t13*t17; + var t20 = A[6]; + var t21 = t20*t14; + var t23 = t20*t17; + var t26 = 1.0/(t9*t2-t11*t5-t15*t2+t18*t5+t21*t4-t23*t1); + invA[0] = (t1*t2-t4*t5)*t26; + invA[1] = -(t14*t2-t17*t5)*t26; + invA[2] = -(-t14*t4+t17*t1)*t26; + invA[3] = -(t13*t2-t4*t20)*t26; + invA[4] = (t8*t2-t23)*t26; + invA[5] = -(t11-t18)*t26; + invA[6] = -(-t13*t5+t1*t20)*t26; + invA[7] = -(t8*t5-t21)*t26; + invA[8] = (t9-t15)*t26; + }, + // C = A * B + multiply_3x3: function(C, A, B) { + var Cd=C.data, Ad=A.data, Bd=B.data; + var m1_0 = Ad[0], m1_1 = Ad[1], m1_2 = Ad[2]; + var m1_3 = Ad[3], m1_4 = Ad[4], m1_5 = Ad[5]; + var m1_6 = Ad[6], m1_7 = Ad[7], m1_8 = Ad[8]; + + var m2_0 = Bd[0], m2_1 = Bd[1], m2_2 = Bd[2]; + var m2_3 = Bd[3], m2_4 = Bd[4], m2_5 = Bd[5]; + var m2_6 = Bd[6], m2_7 = Bd[7], m2_8 = Bd[8]; + + Cd[0] = m1_0 * m2_0 + m1_1 * m2_3 + m1_2 * m2_6; + Cd[1] = m1_0 * m2_1 + m1_1 * m2_4 + m1_2 * m2_7; + Cd[2] = m1_0 * m2_2 + m1_1 * m2_5 + m1_2 * m2_8; + Cd[3] = m1_3 * m2_0 + m1_4 * m2_3 + m1_5 * m2_6; + Cd[4] = m1_3 * m2_1 + m1_4 * m2_4 + m1_5 * m2_7; + Cd[5] = m1_3 * m2_2 + m1_4 * m2_5 + m1_5 * m2_8; + Cd[6] = m1_6 * m2_0 + m1_7 * m2_3 + m1_8 * m2_6; + Cd[7] = m1_6 * m2_1 + m1_7 * m2_4 + m1_8 * m2_7; + Cd[8] = m1_6 * m2_2 + m1_7 * m2_5 + m1_8 * m2_8; + }, + + mat3x3_determinant: function(M) { + var md=M.data; + return md[0] * md[4] * md[8] - + md[0] * md[5] * md[7] - + md[3] * md[1] * md[8] + + md[3] * md[2] * md[7] + + md[6] * md[1] * md[5] - + md[6] * md[2] * md[4]; + }, + + determinant_3x3: function(M11, M12, M13, + M21, M22, M23, + M31, M32, M33) { + return M11 * M22 * M33 - M11 * M23 * M32 - + M21 * M12 * M33 + M21 * M13 * M32 + + M31 * M12 * M23 - M31 * M13 * M22; + } + }; + + })(); + + global.matmath = matmath; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var linalg = (function() { + + var swap = function(A, i0, i1, t) { + t = A[i0]; + A[i0] = A[i1]; + A[i1] = t; + } + + var hypot = function(a, b) { + a = Math.abs(a); + b = Math.abs(b); + if( a > b ) { + b /= a; + return a*Math.sqrt(1.0 + b*b); + } + if( b > 0 ) { + a /= b; + return b*Math.sqrt(1.0 + a*a); + } + return 0.0; + } + + var JacobiImpl = function(A, astep, W, V, vstep, n) { + var eps = jsfeat.EPSILON; + var i=0,j=0,k=0,m=0,l=0,idx=0,_in=0,_in2=0; + var iters=0,max_iter=n*n*30; + var mv=0.0,val=0.0,p=0.0,y=0.0,t=0.0,s=0.0,c=0.0,a0=0.0,b0=0.0; + + var indR_buff = jsfeat.cache.get_buffer(n<<2); + var indC_buff = jsfeat.cache.get_buffer(n<<2); + var indR = indR_buff.i32; + var indC = indC_buff.i32; + + if(V) { + for(; i < n; i++) { + k = i*vstep; + for(j = 0; j < n; j++) { + V[k + j] = 0.0; + } + V[k + i] = 1.0; + } + } + + for(k = 0; k < n; k++) { + W[k] = A[(astep + 1)*k]; + if(k < n - 1) { + for(m = k+1, mv = Math.abs(A[astep*k + m]), i = k+2; i < n; i++) { + val = Math.abs(A[astep*k+i]); + if(mv < val) + mv = val, m = i; + } + indR[k] = m; + } + if(k > 0) { + for(m = 0, mv = Math.abs(A[k]), i = 1; i < k; i++) { + val = Math.abs(A[astep*i+k]); + if(mv < val) + mv = val, m = i; + } + indC[k] = m; + } + } + + if(n > 1) for( ; iters < max_iter; iters++) { + // find index (k,l) of pivot p + for(k = 0, mv = Math.abs(A[indR[0]]), i = 1; i < n-1; i++) { + val = Math.abs(A[astep*i + indR[i]]); + if( mv < val ) + mv = val, k = i; + } + l = indR[k]; + for(i = 1; i < n; i++) { + val = Math.abs(A[astep*indC[i] + i]); + if( mv < val ) + mv = val, k = indC[i], l = i; + } + + p = A[astep*k + l]; + + if(Math.abs(p) <= eps) break; + + y = (W[l] - W[k])*0.5; + t = Math.abs(y) + hypot(p, y); + s = hypot(p, t); + c = t/s; + s = p/s; t = (p/t)*p; + if(y < 0) + s = -s, t = -t; + A[astep*k + l] = 0; + + W[k] -= t; + W[l] += t; + + // rotate rows and columns k and l + for (i = 0; i < k; i++) { + _in = (astep * i + k); + _in2 = (astep * i + l); + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + for (i = (k + 1); i < l; i++) { + _in = (astep * k + i); + _in2 = (astep * i + l); + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + i = l + 1; + _in = (astep * k + i); + _in2 = (astep * l + i); + for (; i < n; i++, _in++, _in2++) { + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + + // rotate eigenvectors + if (V) { + _in = vstep * k; + _in2 = vstep * l; + for (i = 0; i < n; i++, _in++, _in2++) { + a0 = V[_in]; + b0 = V[_in2]; + V[_in] = a0 * c - b0 * s; + V[_in2] = a0 * s + b0 * c; + } + } + + for(j = 0; j < 2; j++) { + idx = j == 0 ? k : l; + if(idx < n - 1) { + for(m = idx+1, mv = Math.abs(A[astep*idx + m]), i = idx+2; i < n; i++) { + val = Math.abs(A[astep*idx+i]); + if( mv < val ) + mv = val, m = i; + } + indR[idx] = m; + } + if(idx > 0) { + for(m = 0, mv = Math.abs(A[idx]), i = 1; i < idx; i++) { + val = Math.abs(A[astep*i+idx]); + if( mv < val ) + mv = val, m = i; + } + indC[idx] = m; + } + } + } + + // sort eigenvalues & eigenvectors + for(k = 0; k < n-1; k++) { + m = k; + for(i = k+1; i < n; i++) { + if(W[m] < W[i]) + m = i; + } + if(k != m) { + swap(W, m, k, mv); + if(V) { + for(i = 0; i < n; i++) { + swap(V, vstep*m + i, vstep*k + i, mv); + } + } + } + } + + + jsfeat.cache.put_buffer(indR_buff); + jsfeat.cache.put_buffer(indC_buff); + } + + var JacobiSVDImpl = function(At, astep, _W, Vt, vstep, m, n, n1) { + var eps = jsfeat.EPSILON * 2.0; + var minval = jsfeat.FLT_MIN; + var i=0,j=0,k=0,iter=0,max_iter=Math.max(m, 30); + var Ai=0,Aj=0,Vi=0,Vj=0,changed=0; + var c=0.0, s=0.0, t=0.0; + var t0=0.0,t1=0.0,sd=0.0,beta=0.0,gamma=0.0,delta=0.0,a=0.0,p=0.0,b=0.0; + var seed = 0x1234; + var val=0.0,val0=0.0,asum=0.0; + + var W_buff = jsfeat.cache.get_buffer(n<<3); + var W = W_buff.f64; + + for(; i < n; i++) { + for(k = 0, sd = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + W[i] = sd; + + if(Vt) { + for(k = 0; k < n; k++) { + Vt[i*vstep + k] = 0; + } + Vt[i*vstep + i] = 1; + } + } + + for(; iter < max_iter; iter++) { + changed = 0; + + for(i = 0; i < n-1; i++) { + for(j = i+1; j < n; j++) { + Ai = (i*astep)|0, Aj = (j*astep)|0; + a = W[i], p = 0, b = W[j]; + + k = 2; + p += At[Ai]*At[Aj]; + p += At[Ai+1]*At[Aj+1]; + + for(; k < m; k++) + p += At[Ai+k]*At[Aj+k]; + + if(Math.abs(p) <= eps*Math.sqrt(a*b)) continue; + + p *= 2.0; + beta = a - b, gamma = hypot(p, beta); + if( beta < 0 ) { + delta = (gamma - beta)*0.5; + s = Math.sqrt(delta/gamma); + c = (p/(gamma*s*2.0)); + } else { + c = Math.sqrt((gamma + beta)/(gamma*2.0)); + s = (p/(gamma*c*2.0)); + } + + a=0.0, b=0.0; + + k = 2; // unroll + t0 = c*At[Ai] + s*At[Aj]; + t1 = -s*At[Ai] + c*At[Aj]; + At[Ai] = t0; At[Aj] = t1; + a += t0*t0; b += t1*t1; + + t0 = c*At[Ai+1] + s*At[Aj+1]; + t1 = -s*At[Ai+1] + c*At[Aj+1]; + At[Ai+1] = t0; At[Aj+1] = t1; + a += t0*t0; b += t1*t1; + + for( ; k < m; k++ ) + { + t0 = c*At[Ai+k] + s*At[Aj+k]; + t1 = -s*At[Ai+k] + c*At[Aj+k]; + At[Ai+k] = t0; At[Aj+k] = t1; + + a += t0*t0; b += t1*t1; + } + + W[i] = a; W[j] = b; + + changed = 1; + + if(Vt) { + Vi = (i*vstep)|0, Vj = (j*vstep)|0; + + k = 2; + t0 = c*Vt[Vi] + s*Vt[Vj]; + t1 = -s*Vt[Vi] + c*Vt[Vj]; + Vt[Vi] = t0; Vt[Vj] = t1; + + t0 = c*Vt[Vi+1] + s*Vt[Vj+1]; + t1 = -s*Vt[Vi+1] + c*Vt[Vj+1]; + Vt[Vi+1] = t0; Vt[Vj+1] = t1; + + for(; k < n; k++) { + t0 = c*Vt[Vi+k] + s*Vt[Vj+k]; + t1 = -s*Vt[Vi+k] + c*Vt[Vj+k]; + Vt[Vi+k] = t0; Vt[Vj+k] = t1; + } + } + } + } + if(changed == 0) break; + } + + for(i = 0; i < n; i++) { + for(k = 0, sd = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + W[i] = Math.sqrt(sd); + } + + for(i = 0; i < n-1; i++) { + j = i; + for(k = i+1; k < n; k++) { + if(W[j] < W[k]) + j = k; + } + if(i != j) { + swap(W, i, j, sd); + if(Vt) { + for(k = 0; k < m; k++) { + swap(At, i*astep + k, j*astep + k, t); + } + + for(k = 0; k < n; k++) { + swap(Vt, i*vstep + k, j*vstep + k, t); + } + } + } + } + + for(i = 0; i < n; i++) { + _W[i] = W[i]; + } + + if(!Vt) { + jsfeat.cache.put_buffer(W_buff); + return; + } + + for(i = 0; i < n1; i++) { + + sd = i < n ? W[i] : 0; + + while(sd <= minval) { + // if we got a zero singular value, then in order to get the corresponding left singular vector + // we generate a random vector, project it to the previously computed left singular vectors, + // subtract the projection and normalize the difference. + val0 = (1.0/m); + for(k = 0; k < m; k++) { + seed = (seed * 214013 + 2531011); + val = (((seed >> 16) & 0x7fff) & 256) != 0 ? val0 : -val0; + At[i*astep + k] = val; + } + for(iter = 0; iter < 2; iter++) { + for(j = 0; j < i; j++) { + sd = 0; + for(k = 0; k < m; k++) { + sd += At[i*astep + k]*At[j*astep + k]; + } + asum = 0.0; + for(k = 0; k < m; k++) { + t = (At[i*astep + k] - sd*At[j*astep + k]); + At[i*astep + k] = t; + asum += Math.abs(t); + } + asum = asum ? 1.0/asum : 0; + for(k = 0; k < m; k++) { + At[i*astep + k] *= asum; + } + } + } + sd = 0; + for(k = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + sd = Math.sqrt(sd); + } + + s = (1.0/sd); + for(k = 0; k < m; k++) { + At[i*astep + k] *= s; + } + } + + jsfeat.cache.put_buffer(W_buff); + } + + return { + + lu_solve: function(A, B) { + var i=0,j=0,k=0,p=1,astep=A.cols; + var ad=A.data, bd=B.data; + var t,alpha,d,s; + + for(i = 0; i < astep; i++) { + k = i; + for(j = i+1; j < astep; j++) { + if(Math.abs(ad[j*astep + i]) > Math.abs(ad[k*astep+i])) { + k = j; + } + } + + if(Math.abs(ad[k*astep+i]) < jsfeat.EPSILON) { + return 0; // FAILED + } + + if(k != i) { + for(j = i; j < astep; j++ ) { + swap(ad, i*astep+j, k*astep+j, t); + } + + swap(bd, i, k, t); + p = -p; + } + + d = -1.0/ad[i*astep+i]; + + for(j = i+1; j < astep; j++) { + alpha = ad[j*astep+i]*d; + + for(k = i+1; k < astep; k++) { + ad[j*astep+k] += alpha*ad[i*astep+k]; + } + + bd[j] += alpha*bd[i]; + } + + ad[i*astep+i] = -d; + } + + for(i = astep-1; i >= 0; i--) { + s = bd[i]; + for(k = i+1; k < astep; k++) { + s -= ad[i*astep+k]*bd[k]; + } + bd[i] = s*ad[i*astep+i]; + } + + return 1; // OK + }, + + cholesky_solve: function(A, B) { + var col=0,row=0,col2=0,cs=0,rs=0,i=0,j=0; + var size = A.cols; + var ad=A.data, bd=B.data; + var val,inv_diag; + + for (col = 0; col < size; col++) { + inv_diag = 1.0; + cs = (col * size); + rs = cs; + for (row = col; row < size; row++) + { + // correct for the parts of cholesky already computed + val = ad[(rs+col)]; + for (col2 = 0; col2 < col; col2++) { + val -= ad[(col2*size+col)] * ad[(rs+col2)]; + } + if (row == col) { + // this is the diagonal element so don't divide + ad[(rs+col)] = val; + if(val == 0) { + return 0; + } + inv_diag = 1.0 / val; + } else { + // cache the value without division in the upper half + ad[(cs+row)] = val; + // divide my the diagonal element for all others + ad[(rs+col)] = val * inv_diag; + } + rs = (rs + size); + } + } + + // first backsub through L + cs = 0; + for (i = 0; i < size; i++) { + val = bd[i]; + for (j = 0; j < i; j++) { + val -= ad[(cs+j)] * bd[j]; + } + bd[i] = val; + cs = (cs + size); + } + // backsub through diagonal + cs = 0; + for (i = 0; i < size; i++) { + bd[i] /= ad[(cs + i)]; + cs = (cs + size); + } + // backsub through L Transpose + i = (size-1); + for (; i >= 0; i--) { + val = bd[i]; + j = (i + 1); + cs = (j * size); + for (; j < size; j++) { + val -= ad[(cs + i)] * bd[j]; + cs = (cs + size); + } + bd[i] = val; + } + + return 1; + }, + + svd_decompose: function(A, W, U, V, options) { + if (typeof options === "undefined") { options = 0; }; + var at=0,i=0,j=0,_m=A.rows,_n=A.cols,m=_m,n=_n; + var dt = A.type | jsfeat.C1_t; // we only work with single channel + + if(m < n) { + at = 1; + i = m; + m = n; + n = i; + } + + var a_buff = jsfeat.cache.get_buffer((m*m)<<3); + var w_buff = jsfeat.cache.get_buffer(n<<3); + var v_buff = jsfeat.cache.get_buffer((n*n)<<3); + + var a_mt = new jsfeat.matrix_t(m, m, dt, a_buff.data); + var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(n, n, dt, v_buff.data); + + if(at == 0) { + // transpose + jsfeat.matmath.transpose(a_mt, A); + } else { + for(i = 0; i < _n*_m; i++) { + a_mt.data[i] = A.data[i]; + } + for(; i < n*m; i++) { + a_mt.data[i] = 0; + } + } + + JacobiSVDImpl(a_mt.data, m, w_mt.data, v_mt.data, n, m, n, m); + + if(W) { + for(i=0; i < n; i++) { + W.data[i] = w_mt.data[i]; + } + for(; i < _n; i++) { + W.data[i] = 0; + } + } + + if (at == 0) { + if(U && (options & jsfeat.SVD_U_T)) { + i = m*m; + while(--i >= 0) { + U.data[i] = a_mt.data[i]; + } + } else if(U) { + jsfeat.matmath.transpose(U, a_mt); + } + + if(V && (options & jsfeat.SVD_V_T)) { + i = n*n; + while(--i >= 0) { + V.data[i] = v_mt.data[i]; + } + } else if(V) { + jsfeat.matmath.transpose(V, v_mt); + } + } else { + if(U && (options & jsfeat.SVD_U_T)) { + i = n*n; + while(--i >= 0) { + U.data[i] = v_mt.data[i]; + } + } else if(U) { + jsfeat.matmath.transpose(U, v_mt); + } + + if(V && (options & jsfeat.SVD_V_T)) { + i = m*m; + while(--i >= 0) { + V.data[i] = a_mt.data[i]; + } + } else if(V) { + jsfeat.matmath.transpose(V, a_mt); + } + } + + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + + }, + + svd_solve: function(A, X, B) { + var i=0,j=0,k=0; + var pu=0,pv=0; + var nrows=A.rows,ncols=A.cols; + var sum=0.0,xsum=0.0,tol=0.0; + var dt = A.type | jsfeat.C1_t; + + var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); + var w_buff = jsfeat.cache.get_buffer(ncols<<3); + var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); + + var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); + var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); + + var bd = B.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; + + this.svd_decompose(A, w_mt, u_mt, v_mt, 0); + + tol = jsfeat.EPSILON * wd[0] * ncols; + + for (; i < ncols; i++, pv += ncols) { + xsum = 0.0; + for(j = 0; j < ncols; j++) { + if(wd[j] > tol) { + for(k = 0, sum = 0.0, pu = 0; k < nrows; k++, pu += ncols) { + sum += ud[pu + j] * bd[k]; + } + xsum += sum * vd[pv + j] / wd[j]; + } + } + X.data[i] = xsum; + } + + jsfeat.cache.put_buffer(u_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + }, + + svd_invert: function(Ai, A) { + var i=0,j=0,k=0; + var pu=0,pv=0,pa=0; + var nrows=A.rows,ncols=A.cols; + var sum=0.0,tol=0.0; + var dt = A.type | jsfeat.C1_t; + + var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); + var w_buff = jsfeat.cache.get_buffer(ncols<<3); + var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); + + var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); + var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); + + var id = Ai.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; + + this.svd_decompose(A, w_mt, u_mt, v_mt, 0); + + tol = jsfeat.EPSILON * wd[0] * ncols; + + for (; i < ncols; i++, pv += ncols) { + for (j = 0, pu = 0; j < nrows; j++, pa++) { + for (k = 0, sum = 0.0; k < ncols; k++, pu++) { + if (wd[k] > tol) sum += vd[pv + k] * ud[pu] / wd[k]; + } + id[pa] = sum; + } + } + + jsfeat.cache.put_buffer(u_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + }, + + eigenVV: function(A, vects, vals) { + var n=A.cols,i=n*n; + var dt = A.type | jsfeat.C1_t; + + var a_buff = jsfeat.cache.get_buffer((n*n)<<3); + var w_buff = jsfeat.cache.get_buffer(n<<3); + var a_mt = new jsfeat.matrix_t(n, n, dt, a_buff.data); + var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); + + while(--i >= 0) { + a_mt.data[i] = A.data[i]; + } + + JacobiImpl(a_mt.data, n, w_mt.data, vects ? vects.data : null, n, n); + + if(vals) { + while(--n >= 0) { + vals.data[n] = w_mt.data[n]; + } + } + + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(w_buff); + } + + }; + + })(); + + global.linalg = linalg; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var motion_model = (function() { + + var sqr = function(x) { + return x*x; + } + + // does isotropic normalization + var iso_normalize_points = function(from, to, T0, T1, count) { + var i=0; + var cx0=0.0, cy0=0.0, d0=0.0, s0=0.0; + var cx1=0.0, cy1=0.0, d1=0.0, s1=0.0; + var dx=0.0,dy=0.0; + + for (; i < count; ++i) { + cx0 += from[i].x; + cy0 += from[i].y; + cx1 += to[i].x; + cy1 += to[i].y; + } + + cx0 /= count; cy0 /= count; + cx1 /= count; cy1 /= count; + + for (i = 0; i < count; ++i) { + dx = from[i].x - cx0; + dy = from[i].y - cy0; + d0 += Math.sqrt(dx*dx + dy*dy); + dx = to[i].x - cx1; + dy = to[i].y - cy1; + d1 += Math.sqrt(dx*dx + dy*dy); + } + + d0 /= count; d1 /= count; + + s0 = Math.SQRT2 / d0; s1 = Math.SQRT2 / d1; + + T0[0] = T0[4] = s0; + T0[2] = -cx0*s0; + T0[5] = -cy0*s0; + T0[1] = T0[3] = T0[6] = T0[7] = 0.0; + T0[8] = 1.0; + + T1[0] = T1[4] = s1; + T1[2] = -cx1*s1; + T1[5] = -cy1*s1; + T1[1] = T1[3] = T1[6] = T1[7] = 0.0; + T1[8] = 1.0; + } + + var have_collinear_points = function(points, count) { + var j=0,k=0,i=(count-1)|0; + var dx1=0.0,dy1=0.0,dx2=0.0,dy2=0.0; + + // check that the i-th selected point does not belong + // to a line connecting some previously selected points + for(; j < i; ++j) { + dx1 = points[j].x - points[i].x; + dy1 = points[j].y - points[i].y; + for(k = 0; k < j; ++k) { + dx2 = points[k].x - points[i].x; + dy2 = points[k].y - points[i].y; + if( Math.abs(dx2*dy1 - dy2*dx1) <= jsfeat.EPSILON*(Math.abs(dx1) + Math.abs(dy1) + Math.abs(dx2) + Math.abs(dy2))) + return true; + } + } + return false; + } + + var T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var AtA = new jsfeat.matrix_t(6, 6, jsfeat.F32_t|jsfeat.C1_t); + var AtB = new jsfeat.matrix_t(6, 1, jsfeat.F32_t|jsfeat.C1_t); + + var affine2d = (function () { + + function affine2d() { + // empty constructor + } + + affine2d.prototype.run = function(from, to, model, count) { + var i=0,j=0; + var dt=model.type|jsfeat.C1_t; + var md=model.data, t0d=T0.data, t1d=T1.data; + var pt0,pt1,px=0.0,py=0.0; + + iso_normalize_points(from, to, t0d, t1d, count); + + var a_buff = jsfeat.cache.get_buffer((2*count*6)<<3); + var b_buff = jsfeat.cache.get_buffer((2*count)<<3); + + var a_mt = new jsfeat.matrix_t(6, 2*count, dt, a_buff.data); + var b_mt = new jsfeat.matrix_t(1, 2*count, dt, b_buff.data); + var ad=a_mt.data, bd=b_mt.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + px = t0d[0]*pt0.x + t0d[1]*pt0.y + t0d[2]; + py = t0d[3]*pt0.x + t0d[4]*pt0.y + t0d[5]; + + j = i*2*6; + ad[j]=px, ad[j+1]=py, ad[j+2]=1.0, ad[j+3]=0.0, ad[j+4]=0.0, ad[j+5]=0.0; + + j += 6; + ad[j]=0.0, ad[j+1]=0.0, ad[j+2]=0.0, ad[j+3]=px, ad[j+4]=py, ad[j+5]=1.0; + + bd[i<<1] = t1d[0]*pt1.x + t1d[1]*pt1.y + t1d[2]; + bd[(i<<1)+1] = t1d[3]*pt1.x + t1d[4]*pt1.y + t1d[5]; + } + + jsfeat.matmath.multiply_AtA(AtA, a_mt); + jsfeat.matmath.multiply_AtB(AtB, a_mt, b_mt); + + jsfeat.linalg.lu_solve(AtA, AtB); + + md[0] = AtB.data[0], md[1]=AtB.data[1], md[2]=AtB.data[2]; + md[3] = AtB.data[3], md[4]=AtB.data[4], md[5]=AtB.data[5]; + md[6] = 0.0, md[7] = 0.0, md[8] = 1.0; // fill last row + + // denormalize + jsfeat.matmath.invert_3x3(T1, T1); + jsfeat.matmath.multiply_3x3(model, T1, model); + jsfeat.matmath.multiply_3x3(model, model, T0); + + // free buffer + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(b_buff); + + return 1; + } + + affine2d.prototype.error = function(from, to, model, err, count) { + var i=0; + var pt0,pt1; + var m=model.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + err[i] = sqr(pt1.x - m[0]*pt0.x - m[1]*pt0.y - m[2]) + + sqr(pt1.y - m[3]*pt0.x - m[4]*pt0.y - m[5]); + } + } + + affine2d.prototype.check_subset = function(from, to, count) { + return true; // all good + } + + return affine2d; + })(); + + var mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + var Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + + var homography2d = (function () { + + function homography2d() { + // empty constructor + //this.T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + //this.T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + //this.mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + //this.Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + } + + homography2d.prototype.run = function(from, to, model, count) { + var i=0,j=0; + var md=model.data, t0d=T0.data, t1d=T1.data; + var LtL=mLtL.data, evd=Evec.data; + var x=0.0,y=0.0,X=0.0,Y=0.0; + + // norm + var smx=0.0, smy=0.0, cmx=0.0, cmy=0.0, sMx=0.0, sMy=0.0, cMx=0.0, cMy=0.0; + + for(; i < count; ++i) { + cmx += to[i].x; + cmy += to[i].y; + cMx += from[i].x; + cMy += from[i].y; + } + + cmx /= count; cmy /= count; + cMx /= count; cMy /= count; + + for(i = 0; i < count; ++i) + { + smx += Math.abs(to[i].x - cmx); + smy += Math.abs(to[i].y - cmy); + sMx += Math.abs(from[i].x - cMx); + sMy += Math.abs(from[i].y - cMy); + } + + if( Math.abs(smx) < jsfeat.EPSILON + || Math.abs(smy) < jsfeat.EPSILON + || Math.abs(sMx) < jsfeat.EPSILON + || Math.abs(sMy) < jsfeat.EPSILON ) return 0; + + smx = count/smx; smy = count/smy; + sMx = count/sMx; sMy = count/sMy; + + t0d[0] = sMx; t0d[1] = 0; t0d[2] = -cMx*sMx; + t0d[3] = 0; t0d[4] = sMy; t0d[5] = -cMy*sMy; + t0d[6] = 0; t0d[7] = 0; t0d[8] = 1; + + t1d[0] = 1.0/smx; t1d[1] = 0; t1d[2] = cmx; + t1d[3] = 0; t1d[4] = 1.0/smy; t1d[5] = cmy; + t1d[6] = 0; t1d[7] = 0; t1d[8] = 1; + // + + // construct system + i = 81; + while(--i >= 0) { + LtL[i] = 0.0; + } + for(i = 0; i < count; ++i) { + x = (to[i].x - cmx) * smx; + y = (to[i].y - cmy) * smy; + X = (from[i].x - cMx) * sMx; + Y = (from[i].y - cMy) * sMy; + + LtL[0] += X*X; + LtL[1] += X*Y; + LtL[2] += X; + + LtL[6] += X*-x*X; + LtL[7] += X*-x*Y; + LtL[8] += X*-x; + LtL[10] += Y*Y; + LtL[11] += Y; + + LtL[15] += Y*-x*X; + LtL[16] += Y*-x*Y; + LtL[17] += Y*-x; + LtL[20] += 1.0; + + LtL[24] += -x*X; + LtL[25] += -x*Y; + LtL[26] += -x; + LtL[30] += X*X; + LtL[31] += X*Y; + LtL[32] += X; + LtL[33] += X*-y*X; + LtL[34] += X*-y*Y; + LtL[35] += X*-y; + LtL[40] += Y*Y; + LtL[41] += Y; + LtL[42] += Y*-y*X; + LtL[43] += Y*-y*Y; + LtL[44] += Y*-y; + LtL[50] += 1.0; + LtL[51] += -y*X; + LtL[52] += -y*Y; + LtL[53] += -y; + LtL[60] += -x*X*-x*X + -y*X*-y*X; + LtL[61] += -x*X*-x*Y + -y*X*-y*Y; + LtL[62] += -x*X*-x + -y*X*-y; + LtL[70] += -x*Y*-x*Y + -y*Y*-y*Y; + LtL[71] += -x*Y*-x + -y*Y*-y; + LtL[80] += -x*-x + -y*-y; + } + // + + // symmetry + for(i = 0; i < 9; ++i) { + for(j = 0; j < i; ++j) + LtL[i*9+j] = LtL[j*9+i]; + } + + jsfeat.linalg.eigenVV(mLtL, Evec); + + md[0]=evd[72], md[1]=evd[73], md[2]=evd[74]; + md[3]=evd[75], md[4]=evd[76], md[5]=evd[77]; + md[6]=evd[78], md[7]=evd[79], md[8]=evd[80]; + + // denormalize + jsfeat.matmath.multiply_3x3(model, T1, model); + jsfeat.matmath.multiply_3x3(model, model, T0); + + // set bottom right to 1.0 + x = 1.0/md[8]; + md[0] *= x; md[1] *= x; md[2] *= x; + md[3] *= x; md[4] *= x; md[5] *= x; + md[6] *= x; md[7] *= x; md[8] = 1.0; + + return 1; + } + + homography2d.prototype.error = function(from, to, model, err, count) { + var i=0; + var pt0,pt1,ww=0.0,dx=0.0,dy=0.0; + var m=model.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + ww = 1.0/(m[6]*pt0.x + m[7]*pt0.y + 1.0); + dx = (m[0]*pt0.x + m[1]*pt0.y + m[2])*ww - pt1.x; + dy = (m[3]*pt0.x + m[4]*pt0.y + m[5])*ww - pt1.y; + err[i] = (dx*dx + dy*dy); + } + } + + homography2d.prototype.check_subset = function(from, to, count) { + // seems to reject good subsets actually + //if( have_collinear_points(from, count) || have_collinear_points(to, count) ) { + //return false; + //} + if( count == 4 ) { + var negative = 0; + + var fp0=from[0],fp1=from[1],fp2=from[2],fp3=from[3]; + var tp0=to[0],tp1=to[1],tp2=to[2],tp3=to[3]; + + // set1 + var A11=fp0.x, A12=fp0.y, A13=1.0; + var A21=fp1.x, A22=fp1.y, A23=1.0; + var A31=fp2.x, A32=fp2.y, A33=1.0; + + var B11=tp0.x, B12=tp0.y, B13=1.0; + var B21=tp1.x, B22=tp1.y, B23=1.0; + var B31=tp2.x, B32=tp2.y, B33=1.0; + + var detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + var detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set2 + A11=fp1.x, A12=fp1.y; + A21=fp2.x, A22=fp2.y; + A31=fp3.x, A32=fp3.y; + + B11=tp1.x, B12=tp1.y; + B21=tp2.x, B22=tp2.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set3 + A11=fp0.x, A12=fp0.y; + A21=fp2.x, A22=fp2.y; + A31=fp3.x, A32=fp3.y; + + B11=tp0.x, B12=tp0.y; + B21=tp2.x, B22=tp2.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set4 + A11=fp0.x, A12=fp0.y; + A21=fp1.x, A22=fp1.y; + A31=fp3.x, A32=fp3.y; + + B11=tp0.x, B12=tp0.y; + B21=tp1.x, B22=tp1.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + if(negative != 0 && negative != 4) { + return false; + } + } + return true; // all good + } + + return homography2d; + })(); + + return { + + affine2d:affine2d, + homography2d:homography2d + + }; + + })(); + + var ransac_params_t = (function () { + function ransac_params_t(size, thresh, eps, prob) { + if (typeof size === "undefined") { size=0; } + if (typeof thresh === "undefined") { thresh=0.5; } + if (typeof eps === "undefined") { eps=0.5; } + if (typeof prob === "undefined") { prob=0.99; } + + this.size = size; + this.thresh = thresh; + this.eps = eps; + this.prob = prob; + }; + ransac_params_t.prototype.update_iters = function(_eps, max_iters) { + var num = Math.log(1 - this.prob); + var denom = Math.log(1 - Math.pow(1 - _eps, this.size)); + return (denom >= 0 || -num >= max_iters*(-denom) ? max_iters : Math.round(num/denom))|0; + }; + return ransac_params_t; + })(); + + var motion_estimator = (function() { + + var get_subset = function(kernel, from, to, need_cnt, max_cnt, from_sub, to_sub) { + var max_try = 1000; + var indices = []; + var i=0, j=0, ssiter=0, idx_i=0, ok=false; + for(; ssiter < max_try; ++ssiter) { + i = 0; + for (; i < need_cnt && ssiter < max_try;) { + ok = false; + idx_i = 0; + while (!ok) { + ok = true; + idx_i = indices[i] = Math.floor(Math.random() * max_cnt)|0; + for (j = 0; j < i; ++j) { + if (idx_i == indices[j]) + { ok = false; break; } + } + } + from_sub[i] = from[idx_i]; + to_sub[i] = to[idx_i]; + if( !kernel.check_subset( from_sub, to_sub, i+1 ) ) { + ssiter++; + continue; + } + ++i; + } + break; + } + + return (i == need_cnt && ssiter < max_try); + } + + var find_inliers = function(kernel, model, from, to, count, thresh, err, mask) { + var numinliers = 0, i=0, f=0; + var t = thresh*thresh; + + kernel.error(from, to, model, err, count); + + for(; i < count; ++i) { + f = err[i] <= t; + mask[i] = f; + numinliers += f; + } + return numinliers; + } + + return { + + ransac: function(params, kernel, from, to, count, model, mask, max_iters) { + if (typeof max_iters === "undefined") { max_iters=1000; } + + if(count < params.size) return false; + + var model_points = params.size; + var niters = max_iters, iter=0; + var result = false; + + var subset0 = []; + var subset1 = []; + var found = false; + + var mc=model.cols,mr=model.rows; + var dt = model.type | jsfeat.C1_t; + + var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); + var ms_buff = jsfeat.cache.get_buffer(count); + var err_buff = jsfeat.cache.get_buffer(count<<2); + var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); + var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8C1_t, ms_buff.data); + + var inliers_max = -1, numinliers=0; + var nmodels = 0; + + var err = err_buff.f32; + + // special case + if(count == model_points) { + if(kernel.run(from, to, M, count) <= 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + + M.copy_to(model); + if(mask) { + while(--count >= 0) { + mask.data[count] = 1; + } + } + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return true; + } + + for (; iter < niters; ++iter) { + // generate subset + found = get_subset(kernel, from, to, model_points, count, subset0, subset1); + if(!found) { + if(iter == 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + break; + } + + nmodels = kernel.run( subset0, subset1, M, model_points ); + if(nmodels <= 0) + continue; + + // TODO handle multimodel output + + numinliers = find_inliers(kernel, M, from, to, count, params.thresh, err, curr_mask.data); + + if( numinliers > Math.max(inliers_max, model_points-1) ) { + M.copy_to(model); + inliers_max = numinliers; + if(mask) curr_mask.copy_to(mask); + niters = params.update_iters((count - numinliers)/count, niters); + result = true; + } + } + + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + + return result; + }, + + lmeds: function(params, kernel, from, to, count, model, mask, max_iters) { + if (typeof max_iters === "undefined") { max_iters=1000; } + + if(count < params.size) return false; + + var model_points = params.size; + var niters = max_iters, iter=0; + var result = false; + + var subset0 = []; + var subset1 = []; + var found = false; + + var mc=model.cols,mr=model.rows; + var dt = model.type | jsfeat.C1_t; + + var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); + var ms_buff = jsfeat.cache.get_buffer(count); + var err_buff = jsfeat.cache.get_buffer(count<<2); + var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); + var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8_t|jsfeat.C1_t, ms_buff.data); + + var numinliers=0; + var nmodels = 0; + + var err = err_buff.f32; + var min_median = 1000000000.0, sigma=0.0, median=0.0; + + params.eps = 0.45; + niters = params.update_iters(params.eps, niters); + + // special case + if(count == model_points) { + if(kernel.run(from, to, M, count) <= 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + + M.copy_to(model); + if(mask) { + while(--count >= 0) { + mask.data[count] = 1; + } + } + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return true; + } + + for (; iter < niters; ++iter) { + // generate subset + found = get_subset(kernel, from, to, model_points, count, subset0, subset1); + if(!found) { + if(iter == 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + break; + } + + nmodels = kernel.run( subset0, subset1, M, model_points ); + if(nmodels <= 0) + continue; + + // TODO handle multimodel output + + kernel.error(from, to, M, err, count); + median = jsfeat.math.median(err, 0, count-1); + + if(median < min_median) { + min_median = median; + M.copy_to(model); + result = true; + } + } + + if(result) { + sigma = 2.5*1.4826*(1 + 5.0/(count - model_points))*Math.sqrt(min_median); + sigma = Math.max(sigma, 0.001); + + numinliers = find_inliers(kernel, model, from, to, count, sigma, err, curr_mask.data); + if(mask) curr_mask.copy_to(mask); + + result = numinliers >= model_points; + } + + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + + return result; + } + + }; + + })(); + + global.ransac_params_t = ransac_params_t; + global.motion_model = motion_model; + global.motion_estimator = motion_estimator; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var imgproc = (function() { + + var _resample_u8 = function(src, dst, nw, nh) { + var xofs_count=0; + var ch=src.channel,w=src.cols,h=src.rows; + var src_d=src.data,dst_d=dst.data; + var scale_x = w / nw, scale_y = h / nh; + var inv_scale_256 = (scale_x * scale_y * 0x10000)|0; + var dx=0,dy=0,sx=0,sy=0,sx1=0,sx2=0,i=0,k=0,fsx1=0.0,fsx2=0.0; + var a=0,b=0,dxn=0,alpha=0,beta=0,beta1=0; + + var buf_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var sum_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var xofs_node = jsfeat.cache.get_buffer((w*2*3)<<2); + + var buf = buf_node.i32; + var sum = sum_node.i32; + var xofs = xofs_node.i32; + + for (; dx < nw; dx++) { + fsx1 = dx * scale_x, fsx2 = fsx1 + scale_x; + sx1 = (fsx1 + 1.0 - 1e-6)|0, sx2 = fsx2|0; + sx1 = Math.min(sx1, w - 1); + sx2 = Math.min(sx2, w - 1); + + if(sx1 > fsx1) { + xofs[k++] = (dx * ch)|0; + xofs[k++] = ((sx1 - 1)*ch)|0; + xofs[k++] = ((sx1 - fsx1) * 0x100)|0; + xofs_count++; + } + for(sx = sx1; sx < sx2; sx++){ + xofs_count++; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx * ch)|0; + xofs[k++] = 256; + } + if(fsx2 - sx2 > 1e-3) { + xofs_count++; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx2 * ch)|0; + xofs[k++] = ((fsx2 - sx2) * 256)|0; + } + } + + for (dx = 0; dx < nw * ch; dx++) { + buf[dx] = sum[dx] = 0; + } + dy = 0; + for (sy = 0; sy < h; sy++) { + a = w * sy; + for (k = 0; k < xofs_count; k++) { + dxn = xofs[k*3]; + sx1 = xofs[k*3+1]; + alpha = xofs[k*3+2]; + for (i = 0; i < ch; i++) { + buf[dxn + i] += src_d[a+sx1+i] * alpha; + } + } + if ((dy + 1) * scale_y <= sy + 1 || sy == h - 1) { + beta = (Math.max(sy + 1 - (dy + 1) * scale_y, 0.0) * 256)|0; + beta1 = 256 - beta; + b = nw * dy; + if (beta <= 0) { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = Math.min(Math.max((sum[dx] + buf[dx] * 256) / inv_scale_256, 0), 255); + sum[dx] = buf[dx] = 0; + } + } else { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = Math.min(Math.max((sum[dx] + buf[dx] * beta1) / inv_scale_256, 0), 255); + sum[dx] = buf[dx] * beta; + buf[dx] = 0; + } + } + dy++; + } else { + for(dx = 0; dx < nw * ch; dx++) { + sum[dx] += buf[dx] * 256; + buf[dx] = 0; + } + } + } + + jsfeat.cache.put_buffer(sum_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(xofs_node); + } + + var _resample = function(src, dst, nw, nh) { + var xofs_count=0; + var ch=src.channel,w=src.cols,h=src.rows; + var src_d=src.data,dst_d=dst.data; + var scale_x = w / nw, scale_y = h / nh; + var scale = 1.0 / (scale_x * scale_y); + var dx=0,dy=0,sx=0,sy=0,sx1=0,sx2=0,i=0,k=0,fsx1=0.0,fsx2=0.0; + var a=0,b=0,dxn=0,alpha=0.0,beta=0.0,beta1=0.0; + + var buf_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var sum_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var xofs_node = jsfeat.cache.get_buffer((w*2*3)<<2); + + var buf = buf_node.f32; + var sum = sum_node.f32; + var xofs = xofs_node.f32; + + for (; dx < nw; dx++) { + fsx1 = dx * scale_x, fsx2 = fsx1 + scale_x; + sx1 = (fsx1 + 1.0 - 1e-6)|0, sx2 = fsx2|0; + sx1 = Math.min(sx1, w - 1); + sx2 = Math.min(sx2, w - 1); + + if(sx1 > fsx1) { + xofs_count++; + xofs[k++] = ((sx1 - 1)*ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx1 - fsx1) * scale; + } + for(sx = sx1; sx < sx2; sx++){ + xofs_count++; + xofs[k++] = (sx * ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = scale; + } + if(fsx2 - sx2 > 1e-3) { + xofs_count++; + xofs[k++] = (sx2 * ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (fsx2 - sx2) * scale; + } + } + + for (dx = 0; dx < nw * ch; dx++) { + buf[dx] = sum[dx] = 0; + } + dy = 0; + for (sy = 0; sy < h; sy++) { + a = w * sy; + for (k = 0; k < xofs_count; k++) { + sx1 = xofs[k*3]|0; + dxn = xofs[k*3+1]|0; + alpha = xofs[k*3+2]; + for (i = 0; i < ch; i++) { + buf[dxn + i] += src_d[a+sx1+i] * alpha; + } + } + if ((dy + 1) * scale_y <= sy + 1 || sy == h - 1) { + beta = Math.max(sy + 1 - (dy + 1) * scale_y, 0.0); + beta1 = 1.0 - beta; + b = nw * dy; + if (Math.abs(beta) < 1e-3) { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = sum[dx] + buf[dx]; + sum[dx] = buf[dx] = 0; + } + } else { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = sum[dx] + buf[dx] * beta1; + sum[dx] = buf[dx] * beta; + buf[dx] = 0; + } + } + dy++; + } else { + for(dx = 0; dx < nw * ch; dx++) { + sum[dx] += buf[dx]; + buf[dx] = 0; + } + } + } + jsfeat.cache.put_buffer(sum_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(xofs_node); + } + + var _convol_u8 = function(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel) { + var i=0,j=0,k=0,sp=0,dp=0,sum=0,sum1=0,sum2=0,sum3=0,f0=filter[0],fk=0; + var w2=w<<1,w3=w*3,w4=w<<2; + // hor pass + for (; i < h; ++i) { + sum = src_d[sp]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + for (j = 0; j <= w-2; j+=2) { + buf[j + half_kernel] = src_d[sp+j]; + buf[j + half_kernel+1] = src_d[sp+j+1]; + } + for (; j < w; ++j) { + buf[j + half_kernel] = src_d[sp+j]; + } + sum = src_d[sp+w-1]; + for (j = w; j < half_kernel + w; ++j) { + buf[j + half_kernel] = sum; + } + for (j = 0; j <= w-4; j+=4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp+j] = Math.min(sum >> 8, 255); + dst_d[dp+j+1] = Math.min(sum1 >> 8, 255); + dst_d[dp+j+2] = Math.min(sum2 >> 8, 255); + dst_d[dp+j+3] = Math.min(sum3 >> 8, 255); + } + for (; j < w; ++j) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp+j] = Math.min(sum >> 8, 255); + } + sp += w; + dp += w; + } + + // vert pass + for (i = 0; i < w; ++i) { + sum = dst_d[i]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + k = i; + for (j = 0; j <= h-2; j+=2, k+=w2) { + buf[j+half_kernel] = dst_d[k]; + buf[j+half_kernel+1] = dst_d[k+w]; + } + for (; j < h; ++j, k+=w) { + buf[j+half_kernel] = dst_d[k]; + } + sum = dst_d[(h-1)*w + i]; + for (j = h; j < half_kernel + h; ++j) { + buf[j + half_kernel] = sum; + } + dp = i; + for (j = 0; j <= h-4; j+=4, dp+=w4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp] = Math.min(sum >> 8, 255); + dst_d[dp+w] = Math.min(sum1 >> 8, 255); + dst_d[dp+w2] = Math.min(sum2 >> 8, 255); + dst_d[dp+w3] = Math.min(sum3 >> 8, 255); + } + for (; j < h; ++j, dp+=w) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp] = Math.min(sum >> 8, 255); + } + } + } + + var _convol = function(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel) { + var i=0,j=0,k=0,sp=0,dp=0,sum=0.0,sum1=0.0,sum2=0.0,sum3=0.0,f0=filter[0],fk=0.0; + var w2=w<<1,w3=w*3,w4=w<<2; + // hor pass + for (; i < h; ++i) { + sum = src_d[sp]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + for (j = 0; j <= w-2; j+=2) { + buf[j + half_kernel] = src_d[sp+j]; + buf[j + half_kernel+1] = src_d[sp+j+1]; + } + for (; j < w; ++j) { + buf[j + half_kernel] = src_d[sp+j]; + } + sum = src_d[sp+w-1]; + for (j = w; j < half_kernel + w; ++j) { + buf[j + half_kernel] = sum; + } + for (j = 0; j <= w-4; j+=4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp+j] = sum; + dst_d[dp+j+1] = sum1; + dst_d[dp+j+2] = sum2; + dst_d[dp+j+3] = sum3; + } + for (; j < w; ++j) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp+j] = sum; + } + sp += w; + dp += w; + } + + // vert pass + for (i = 0; i < w; ++i) { + sum = dst_d[i]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + k = i; + for (j = 0; j <= h-2; j+=2, k+=w2) { + buf[j+half_kernel] = dst_d[k]; + buf[j+half_kernel+1] = dst_d[k+w]; + } + for (; j < h; ++j, k+=w) { + buf[j+half_kernel] = dst_d[k]; + } + sum = dst_d[(h-1)*w + i]; + for (j = h; j < half_kernel + h; ++j) { + buf[j + half_kernel] = sum; + } + dp = i; + for (j = 0; j <= h-4; j+=4, dp+=w4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp] = sum; + dst_d[dp+w] = sum1; + dst_d[dp+w2] = sum2; + dst_d[dp+w3] = sum3; + } + for (; j < h; ++j, dp+=w) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp] = sum; + } + } + } + + return { + // TODO: add support for RGB/BGR order + // for raw arrays + grayscale: function(src, w, h, dst, code) { + // this is default image data representation in browser + if (typeof code === "undefined") { code = jsfeat.COLOR_RGBA2GRAY; } + var x=0, y=0, i=0, j=0, ir=0,jr=0; + var coeff_r = 4899, coeff_g = 9617, coeff_b = 1868, cn = 4; + + if(code == jsfeat.COLOR_BGRA2GRAY || code == jsfeat.COLOR_BGR2GRAY) { + coeff_r = 1868; + coeff_b = 4899; + } + if(code == jsfeat.COLOR_RGB2GRAY || code == jsfeat.COLOR_BGR2GRAY) { + cn = 3; + } + var cn2 = cn<<1, cn3 = (cn*3)|0; + + dst.resize(w, h, 1); + var dst_u8 = dst.data; + + for(y = 0; y < h; ++y, j+=w, i+=w*cn) { + for(x = 0, ir = i, jr = j; x <= w-4; x+=4, ir+=cn<<2, jr+=4) { + dst_u8[jr] = (src[ir] * coeff_r + src[ir+1] * coeff_g + src[ir+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 1] = (src[ir+cn] * coeff_r + src[ir+cn+1] * coeff_g + src[ir+cn+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 2] = (src[ir+cn2] * coeff_r + src[ir+cn2+1] * coeff_g + src[ir+cn2+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 3] = (src[ir+cn3] * coeff_r + src[ir+cn3+1] * coeff_g + src[ir+cn3+2] * coeff_b + 8192) >> 14; + } + for (; x < w; ++x, ++jr, ir+=cn) { + dst_u8[jr] = (src[ir] * coeff_r + src[ir+1] * coeff_g + src[ir+2] * coeff_b + 8192) >> 14; + } + } + }, + // derived from CCV library + resample: function(src, dst, nw, nh) { + var h=src.rows,w=src.cols; + if (h > nh && w > nw) { + dst.resize(nw, nh, src.channel); + // using the fast alternative (fix point scale, 0x100 to avoid overflow) + if (src.type&jsfeat.U8_t && dst.type&jsfeat.U8_t && h * w / (nh * nw) < 0x100) { + _resample_u8(src, dst, nw, nh); + } else { + _resample(src, dst, nw, nh); + } + } + }, + + box_blur_gray: function(src, dst, radius, options) { + if (typeof options === "undefined") { options = 0; } + var w=src.cols, h=src.rows, h2=h<<1, w2=w<<1; + var i=0,x=0,y=0,end=0; + var windowSize = ((radius << 1) + 1)|0; + var radiusPlusOne = (radius + 1)|0, radiusPlus2 = (radiusPlusOne+1)|0; + var scale = options&jsfeat.BOX_BLUR_NOSCALE ? 1 : (1.0 / (windowSize*windowSize)); + + var tmp_buff = jsfeat.cache.get_buffer((w*h)<<2); + + var sum=0, dstIndex=0, srcIndex = 0, nextPixelIndex=0, previousPixelIndex=0; + var data_i32 = tmp_buff.i32; // to prevent overflow + var data_u8 = src.data; + var hold=0; + + dst.resize(w, h, src.channel); + + // first pass + // no need to scale + //data_u8 = src.data; + //data_i32 = tmp; + for (y = 0; y < h; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_u8[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_u8[i]; + } + + nextPixelIndex = (srcIndex + radiusPlusOne)|0; + previousPixelIndex = srcIndex; + hold = data_u8[previousPixelIndex]; + for(x = 0; x < radius; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < w-radiusPlus2; x+=2, dstIndex += h2) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- data_u8[previousPixelIndex]; + + data_i32[dstIndex+h] = sum; + sum += data_u8[nextPixelIndex+1]- data_u8[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < w-radiusPlusOne; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- data_u8[previousPixelIndex]; + + nextPixelIndex ++; + previousPixelIndex ++; + } + + hold = data_u8[nextPixelIndex-1]; + for(; x < w; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + + sum += hold- data_u8[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += w; + } + // + // second pass + srcIndex = 0; + //data_i32 = tmp; // this is a transpose + data_u8 = dst.data; + + // dont scale result + if(scale == 1) { + for (y = 0; y < w; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_i32[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_i32[i]; + } + + nextPixelIndex = srcIndex + radiusPlusOne; + previousPixelIndex = srcIndex; + hold = data_i32[previousPixelIndex]; + + for(x = 0; x < radius; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + sum += data_i32[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < h-radiusPlus2; x+=2, dstIndex += w2) { + data_u8[dstIndex] = sum; + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + + data_u8[dstIndex+w] = sum; + sum += data_i32[nextPixelIndex+1]- data_i32[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < h-radiusPlusOne; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + nextPixelIndex ++; + previousPixelIndex ++; + } + hold = data_i32[nextPixelIndex-1]; + for(; x < h; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + + sum += hold- data_i32[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += h; + } + } else { + for (y = 0; y < w; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_i32[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_i32[i]; + } + + nextPixelIndex = srcIndex + radiusPlusOne; + previousPixelIndex = srcIndex; + hold = data_i32[previousPixelIndex]; + + for(x = 0; x < radius; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + sum += data_i32[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < h-radiusPlus2; x+=2, dstIndex += w2) { + data_u8[dstIndex] = sum*scale; + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + + data_u8[dstIndex+w] = sum*scale; + sum += data_i32[nextPixelIndex+1]- data_i32[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < h-radiusPlusOne; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + nextPixelIndex ++; + previousPixelIndex ++; + } + hold = data_i32[nextPixelIndex-1]; + for(; x < h; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + + sum += hold- data_i32[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += h; + } + } + + jsfeat.cache.put_buffer(tmp_buff); + }, + + gaussian_blur: function(src, dst, kernel_size, sigma) { + if (typeof sigma === "undefined") { sigma = 0.0; } + if (typeof kernel_size === "undefined") { kernel_size = 0; } + kernel_size = kernel_size == 0 ? (Math.max(1, (4.0 * sigma + 1.0 - 1e-8)) * 2 + 1)|0 : kernel_size; + var half_kernel = kernel_size >> 1; + var w = src.cols, h = src.rows; + var data_type = src.type, is_u8 = data_type&jsfeat.U8_t; + + dst.resize(w, h, src.channel); + + var src_d = src.data, dst_d = dst.data; + var buf,filter,buf_sz=(kernel_size + Math.max(h, w))|0; + + var buf_node = jsfeat.cache.get_buffer(buf_sz<<2); + var filt_node = jsfeat.cache.get_buffer(kernel_size<<2); + + if(is_u8) { + buf = buf_node.i32; + filter = filt_node.i32; + } else if(data_type&jsfeat.S32_t) { + buf = buf_node.i32; + filter = filt_node.f32; + } else { + buf = buf_node.f32; + filter = filt_node.f32; + } + + jsfeat.math.get_gaussian_kernel(kernel_size, sigma, filter, data_type); + + if(is_u8) { + _convol_u8(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel); + } else { + _convol(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel); + } + + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(filt_node); + }, + hough_transform: function( img, rho_res, theta_res, threshold ) { + var image = img.data; + + var width = img.cols; + var height = img.rows; + var step = width; + + min_theta = 0.0; + max_theta = Math.PI; + + numangle = Math.round((max_theta - min_theta) / theta_res); + numrho = Math.round(((width + height) * 2 + 1) / rho_res); + irho = 1.0 / rho_res; + + var accum = new Int32Array((numangle+2) * (numrho+2)); //typed arrays are initialized to 0 + var tabSin = new Float32Array(numangle); + var tabCos = new Float32Array(numangle); + + var n=0; + var ang = min_theta; + for(; n < numangle; n++ ) { + tabSin[n] = Math.sin(ang) * irho; + tabCos[n] = Math.cos(ang) * irho; + ang += theta_res + } + + // stage 1. fill accumulator + for( var i = 0; i < height; i++ ) { + for( var j = 0; j < width; j++ ) { + if( image[i * step + j] != 0 ) { + //console.log(r, (n+1) * (numrho+2) + r+1, tabCos[n], tabSin[n]); + for(var n = 0; n < numangle; n++ ) { + var r = Math.round( j * tabCos[n] + i * tabSin[n] ); + r += (numrho - 1) / 2; + accum[(n+1) * (numrho+2) + r+1] += 1; + } + } + } + } + + // stage 2. find local maximums + //TODO: Consider making a vector class that uses typed arrays + _sort_buf = new Array(); + for(var r = 0; r < numrho; r++ ) { + for(var n = 0; n < numangle; n++ ) { + var base = (n+1) * (numrho+2) + r+1; + if( accum[base] > threshold && + accum[base] > accum[base - 1] && accum[base] >= accum[base + 1] && + accum[base] > accum[base - numrho - 2] && accum[base] >= accum[base + numrho + 2] ) { + _sort_buf.push(base); + } + } + } + + // stage 3. sort the detected lines by accumulator value + _sort_buf.sort(function(l1, l2) { + return accum[l1] > accum[l2] || (accum[l1] == accum[l2] && l1 < l2); + }); + + // stage 4. store the first min(total,linesMax) lines to the output buffer + linesMax = Math.min(numangle*numrho, _sort_buf.length); + scale = 1.0 / (numrho+2); + lines = new Array(); + for( var i = 0; i < linesMax; i++ ) { + var idx = _sort_buf[i]; + var n = Math.floor(idx*scale) - 1; + var r = idx - (n+1)*(numrho+2) - 1; + var lrho = (r - (numrho - 1)*0.5) * rho_res; + var langle = n * theta_res; + lines.push([lrho, langle]); + } + return lines; + }, + // assume we always need it for u8 image + pyrdown: function(src, dst, sx, sy) { + // this is needed for bbf + if (typeof sx === "undefined") { sx = 0; } + if (typeof sy === "undefined") { sy = 0; } + + var w = src.cols, h = src.rows; + var w2 = w >> 1, h2 = h >> 1; + var _w2 = w2 - (sx << 1), _h2 = h2 - (sy << 1); + var x=0,y=0,sptr=sx+sy*w,sline=0,dptr=0,dline=0; + + dst.resize(w2, h2, src.channel); + + var src_d = src.data, dst_d = dst.data; + + for(y = 0; y < _h2; ++y) { + sline = sptr; + dline = dptr; + for(x = 0; x <= _w2-2; x+=2, dline+=2, sline += 4) { + dst_d[dline] = (src_d[sline] + src_d[sline+1] + + src_d[sline+w] + src_d[sline+w+1] + 2) >> 2; + dst_d[dline+1] = (src_d[sline+2] + src_d[sline+3] + + src_d[sline+w+2] + src_d[sline+w+3] + 2) >> 2; + } + for(; x < _w2; ++x, ++dline, sline += 2) { + dst_d[dline] = (src_d[sline] + src_d[sline+1] + + src_d[sline+w] + src_d[sline+w+1] + 2) >> 2; + } + sptr += w << 1; + dptr += w2; + } + }, + + // dst: [gx,gy,...] + scharr_derivatives: function(src, dst) { + var w = src.cols, h = src.rows; + var dstep = w<<1,x=0,y=0,x1=0,a,b,c,d,e,f; + var srow0=0,srow1=0,srow2=0,drow=0; + var trow0,trow1; + + dst.resize(w, h, 2); // 2 channel output gx, gy + + var img = src.data, gxgy=dst.data; + + var buf0_node = jsfeat.cache.get_buffer((w+2)<<2); + var buf1_node = jsfeat.cache.get_buffer((w+2)<<2); + + if(src.type&jsfeat.U8_t || src.type&jsfeat.S32_t) { + trow0 = buf0_node.i32; + trow1 = buf1_node.i32; + } else { + trow0 = buf0_node.f32; + trow1 = buf1_node.f32; + } + + for(; y < h; ++y, srow1+=w) { + srow0 = ((y > 0 ? y-1 : 1)*w)|0; + srow2 = ((y < h-1 ? y+1 : h-2)*w)|0; + drow = (y*dstep)|0; + // do vertical convolution + for(x = 0, x1 = 1; x <= w-2; x+=2, x1+=2) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b)*3 + (img[srow1+x])*10 ); + trow1[x1] = ( b - a ); + // + a = img[srow0+x+1], b = img[srow2+x+1]; + trow0[x1+1] = ( (a + b)*3 + (img[srow1+x+1])*10 ); + trow1[x1+1] = ( b - a ); + } + for(; x < w; ++x, ++x1) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b)*3 + (img[srow1+x])*10 ); + trow1[x1] = ( b - a ); + } + // make border + x = (w + 1)|0; + trow0[0] = trow0[1]; trow0[x] = trow0[w]; + trow1[0] = trow1[1]; trow1[x] = trow1[w]; + // do horizontal convolution, interleave the results and store them + for(x = 0; x <= w-4; x+=4) { + a = trow1[x+2], b = trow1[x+1], c = trow1[x+3], d = trow1[x+4], + e = trow0[x+2], f = trow0[x+3]; + gxgy[drow++] = ( e - trow0[x] ); + gxgy[drow++] = ( (a + trow1[x])*3 + b*10 ); + gxgy[drow++] = ( f - trow0[x+1] ); + gxgy[drow++] = ( (c + b)*3 + a*10 ); + + gxgy[drow++] = ( (trow0[x+4] - e) ); + gxgy[drow++] = ( ((d + a)*3 + c*10) ); + gxgy[drow++] = ( (trow0[x+5] - f) ); + gxgy[drow++] = ( ((trow1[x+5] + c)*3 + d*10) ); + } + for(; x < w; ++x) { + gxgy[drow++] = ( (trow0[x+2] - trow0[x]) ); + gxgy[drow++] = ( ((trow1[x+2] + trow1[x])*3 + trow1[x+1]*10) ); + } + } + jsfeat.cache.put_buffer(buf0_node); + jsfeat.cache.put_buffer(buf1_node); + }, + + // compute gradient using Sobel kernel [1 2 1] * [-1 0 1]^T + // dst: [gx,gy,...] + sobel_derivatives: function(src, dst) { + var w = src.cols, h = src.rows; + var dstep = w<<1,x=0,y=0,x1=0,a,b,c,d,e,f; + var srow0=0,srow1=0,srow2=0,drow=0; + var trow0,trow1; + + dst.resize(w, h, 2); // 2 channel output gx, gy + + var img = src.data, gxgy=dst.data; + + var buf0_node = jsfeat.cache.get_buffer((w+2)<<2); + var buf1_node = jsfeat.cache.get_buffer((w+2)<<2); + + if(src.type&jsfeat.U8_t || src.type&jsfeat.S32_t) { + trow0 = buf0_node.i32; + trow1 = buf1_node.i32; + } else { + trow0 = buf0_node.f32; + trow1 = buf1_node.f32; + } + + for(; y < h; ++y, srow1+=w) { + srow0 = ((y > 0 ? y-1 : 1)*w)|0; + srow2 = ((y < h-1 ? y+1 : h-2)*w)|0; + drow = (y*dstep)|0; + // do vertical convolution + for(x = 0, x1 = 1; x <= w-2; x+=2, x1+=2) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b) + (img[srow1+x]*2) ); + trow1[x1] = ( b - a ); + // + a = img[srow0+x+1], b = img[srow2+x+1]; + trow0[x1+1] = ( (a + b) + (img[srow1+x+1]*2) ); + trow1[x1+1] = ( b - a ); + } + for(; x < w; ++x, ++x1) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b) + (img[srow1+x]*2) ); + trow1[x1] = ( b - a ); + } + // make border + x = (w + 1)|0; + trow0[0] = trow0[1]; trow0[x] = trow0[w]; + trow1[0] = trow1[1]; trow1[x] = trow1[w]; + // do horizontal convolution, interleave the results and store them + for(x = 0; x <= w-4; x+=4) { + a = trow1[x+2], b = trow1[x+1], c = trow1[x+3], d = trow1[x+4], + e = trow0[x+2], f = trow0[x+3]; + gxgy[drow++] = ( e - trow0[x] ); + gxgy[drow++] = ( a + trow1[x] + b*2 ); + gxgy[drow++] = ( f - trow0[x+1] ); + gxgy[drow++] = ( c + b + a*2 ); + + gxgy[drow++] = ( trow0[x+4] - e ); + gxgy[drow++] = ( d + a + c*2 ); + gxgy[drow++] = ( trow0[x+5] - f ); + gxgy[drow++] = ( trow1[x+5] + c + d*2 ); + } + for(; x < w; ++x) { + gxgy[drow++] = ( trow0[x+2] - trow0[x] ); + gxgy[drow++] = ( trow1[x+2] + trow1[x] + trow1[x+1]*2 ); + } + } + jsfeat.cache.put_buffer(buf0_node); + jsfeat.cache.put_buffer(buf1_node); + }, + + // please note: + // dst_(type) size should be cols = src.cols+1, rows = src.rows+1 + compute_integral_image: function(src, dst_sum, dst_sqsum, dst_tilted) { + var w0=src.cols|0,h0=src.rows|0,src_d=src.data; + var w1=(w0+1)|0; + var s=0,s2=0,p=0,pup=0,i=0,j=0,v=0,k=0; + + if(dst_sum && dst_sqsum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sum[i] = 0, dst_sqsum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s = s2 = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + v = src_d[k]; + s += v, s2 += v*v; + dst_sum[p] = dst_sum[pup] + s; + dst_sqsum[p] = dst_sqsum[pup] + s2; + + v = src_d[k+1]; + s += v, s2 += v*v; + dst_sum[p+1] = dst_sum[pup+1] + s; + dst_sqsum[p+1] = dst_sqsum[pup+1] + s2; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + v = src_d[k]; + s += v, s2 += v*v; + dst_sum[p] = dst_sum[pup] + s; + dst_sqsum[p] = dst_sqsum[pup] + s2; + } + } + } else if(dst_sum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + s += src_d[k]; + dst_sum[p] = dst_sum[pup] + s; + s += src_d[k+1]; + dst_sum[p+1] = dst_sum[pup+1] + s; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + s += src_d[k]; + dst_sum[p] = dst_sum[pup] + s; + } + } + } else if(dst_sqsum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sqsum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s2 = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + v = src_d[k]; + s2 += v*v; + dst_sqsum[p] = dst_sqsum[pup] + s2; + v = src_d[k+1]; + s2 += v*v; + dst_sqsum[p+1] = dst_sqsum[pup+1] + s2; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + v = src_d[k]; + s2 += v*v; + dst_sqsum[p] = dst_sqsum[pup] + s2; + } + } + } + + if(dst_tilted) { + // fill first row with zeros + for(i = 0; i < w1; ++i) { + dst_tilted[i] = 0; + } + // diagonal + p = (w1+1)|0, pup = 0; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + dst_tilted[p] = src_d[k] + dst_tilted[pup]; + dst_tilted[p+1] = src_d[k+1] + dst_tilted[pup+1]; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + dst_tilted[p] = src_d[k] + dst_tilted[pup]; + } + } + // diagonal + p = (w1+w0)|0, pup = w0; + for(i = 0; i < h0; ++i, p+=w1, pup+=w1) { + dst_tilted[p] += dst_tilted[pup]; + } + + for(j = w0-1; j > 0; --j) { + p = j+h0*w1, pup=p-w1; + for(i = h0; i > 0; --i, p-=w1, pup-=w1) { + dst_tilted[p] += dst_tilted[pup] + dst_tilted[pup+1]; + } + } + } + }, + equalize_histogram: function(src, dst) { + var w=src.cols,h=src.rows,src_d=src.data; + + dst.resize(w, h, src.channel); + + var dst_d=dst.data,size=w*h; + var i=0,prev=0,hist0,norm; + + var hist0_node = jsfeat.cache.get_buffer(256<<2); + hist0 = hist0_node.i32; + for(; i < 256; ++i) hist0[i] = 0; + for (i = 0; i < size; ++i) { + ++hist0[src_d[i]]; + } + + prev = hist0[0]; + for (i = 1; i < 256; ++i) { + prev = hist0[i] += prev; + } + + norm = 255 / size; + for (i = 0; i < size; ++i) { + dst_d[i] = (hist0[src_d[i]] * norm + 0.5)|0; + } + jsfeat.cache.put_buffer(hist0_node); + }, + + canny: function(src, dst, low_thresh, high_thresh) { + var w=src.cols,h=src.rows,src_d=src.data; + + dst.resize(w, h, src.channel); + + var dst_d=dst.data; + var i=0,j=0,grad=0,w2=w<<1,_grad=0,suppress=0,f=0,x=0,y=0,s=0; + var tg22x=0,tg67x=0; + + // cache buffers + var dxdy_node = jsfeat.cache.get_buffer((h * w2)<<2); + var buf_node = jsfeat.cache.get_buffer((3 * (w + 2))<<2); + var map_node = jsfeat.cache.get_buffer(((h+2) * (w + 2))<<2); + var stack_node = jsfeat.cache.get_buffer((h * w)<<2); + + + var buf = buf_node.i32; + var map = map_node.i32; + var stack = stack_node.i32; + var dxdy = dxdy_node.i32; + var dxdy_m = new jsfeat.matrix_t(w, h, jsfeat.S32C2_t, dxdy_node.data); + var row0=1,row1=(w+2+1)|0,row2=(2*(w+2)+1)|0,map_w=(w+2)|0,map_i=(map_w+1)|0,stack_i=0; + + this.sobel_derivatives(src, dxdy_m); + + if(low_thresh > high_thresh) { + i = low_thresh; + low_thresh = high_thresh; + high_thresh = i; + } + + i = (3 * (w + 2))|0; + while(--i>=0) { + buf[i] = 0; + } + + i = ((h+2) * (w + 2))|0; + while(--i>=0) { + map[i] = 0; + } + + for (; j < w; ++j, grad+=2) { + //buf[row1+j] = Math.abs(dxdy[grad]) + Math.abs(dxdy[grad+1]); + x = dxdy[grad], y = dxdy[grad+1]; + //buf[row1+j] = x*x + y*y; + buf[row1+j] = ((x ^ (x >> 31)) - (x >> 31)) + ((y ^ (y >> 31)) - (y >> 31)); + } + + for(i=1; i <= h; ++i, grad+=w2) { + if(i == h) { + j = row2+w; + while(--j>=row2) { + buf[j] = 0; + } + } else { + for (j = 0; j < w; j++) { + //buf[row2+j] = Math.abs(dxdy[grad+(j<<1)]) + Math.abs(dxdy[grad+(j<<1)+1]); + x = dxdy[grad+(j<<1)], y = dxdy[grad+(j<<1)+1]; + //buf[row2+j] = x*x + y*y; + buf[row2+j] = ((x ^ (x >> 31)) - (x >> 31)) + ((y ^ (y >> 31)) - (y >> 31)); + } + } + _grad = (grad - w2)|0; + map[map_i-1] = 0; + suppress = 0; + for(j = 0; j < w; ++j, _grad+=2) { + f = buf[row1+j]; + if (f > low_thresh) { + x = dxdy[_grad]; + y = dxdy[_grad+1]; + s = x ^ y; + // seems ot be faster than Math.abs + x = ((x ^ (x >> 31)) - (x >> 31))|0; + y = ((y ^ (y >> 31)) - (y >> 31))|0; + //x * tan(22.5) x * tan(67.5) == 2 * x + x * tan(22.5) + tg22x = x * 13573; + tg67x = tg22x + ((x + x) << 15); + y <<= 15; + if (y < tg22x) { + if (f > buf[row1+j-1] && f >= buf[row1+j+1]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } else if (y > tg67x) { + if (f > buf[row0+j] && f >= buf[row2+j]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } else { + s = s < 0 ? -1 : 1; + if (f > buf[row0+j-s] && f > buf[row2+j+s]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } + } + map[map_i+j] = 0; + suppress = 0; + } + map[map_i+w] = 0; + map_i += map_w; + j = row0; + row0 = row1; + row1 = row2; + row2 = j; + } + + j = map_i - map_w - 1; + for(i = 0; i < map_w; ++i, ++j) { + map[j] = 0; + } + // path following + while(stack_i > 0) { + map_i = stack[--stack_i]; + map_i -= map_w+1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += map_w; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i -= 2; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += map_w; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + } + + map_i = map_w + 1; + row0 = 0; + for(i = 0; i < h; ++i, map_i+=map_w) { + for(j = 0; j < w; ++j) { + dst_d[row0++] = (map[map_i+j] == 2) * 0xff; + } + } + + // free buffers + jsfeat.cache.put_buffer(dxdy_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(map_node); + jsfeat.cache.put_buffer(stack_node); + }, + // transform is 3x3 matrix_t + warp_perspective: function(src, dst, transform, fill_value) { + if (typeof fill_value === "undefined") { fill_value = 0; } + var src_width=src.cols|0, src_height=src.rows|0, dst_width=dst.cols|0, dst_height=dst.rows|0; + var src_d=src.data, dst_d=dst.data; + var x=0,y=0,off=0,ixs=0,iys=0,xs=0.0,ys=0.0,xs0=0.0,ys0=0.0,ws=0.0,sc=0.0,a=0.0,b=0.0,p0=0.0,p1=0.0; + var td=transform.data; + var m00=td[0],m01=td[1],m02=td[2], + m10=td[3],m11=td[4],m12=td[5], + m20=td[6],m21=td[7],m22=td[8]; + + for(var dptr = 0; y < dst_height; ++y) { + xs0 = m01 * y + m02, + ys0 = m11 * y + m12, + ws = m21 * y + m22; + for(x = 0; x < dst_width; ++x, ++dptr, xs0+=m00, ys0+=m10, ws+=m20) { + sc = 1.0 / ws; + xs = xs0 * sc, ys = ys0 * sc; + ixs = xs | 0, iys = ys | 0; + + if(xs > 0 && ys > 0 && ixs < (src_width - 1) && iys < (src_height - 1)) { + a = Math.max(xs - ixs, 0.0); + b = Math.max(ys - iys, 0.0); + off = (src_width*iys + ixs)|0; + + p0 = src_d[off] + a * (src_d[off+1] - src_d[off]); + p1 = src_d[off+src_width] + a * (src_d[off+src_width+1] - src_d[off+src_width]); + + dst_d[dptr] = p0 + b * (p1 - p0); + } + else dst_d[dptr] = fill_value; + } + } + }, + // transform is 3x3 or 2x3 matrix_t only first 6 values referenced + warp_affine: function(src, dst, transform, fill_value) { + if (typeof fill_value === "undefined") { fill_value = 0; } + var src_width=src.cols, src_height=src.rows, dst_width=dst.cols, dst_height=dst.rows; + var src_d=src.data, dst_d=dst.data; + var x=0,y=0,off=0,ixs=0,iys=0,xs=0.0,ys=0.0,a=0.0,b=0.0,p0=0.0,p1=0.0; + var td=transform.data; + var m00=td[0],m01=td[1],m02=td[2], + m10=td[3],m11=td[4],m12=td[5]; + + for(var dptr = 0; y < dst_height; ++y) { + xs = m01 * y + m02; + ys = m11 * y + m12; + for(x = 0; x < dst_width; ++x, ++dptr, xs+=m00, ys+=m10) { + ixs = xs | 0; iys = ys | 0; + + if(ixs >= 0 && iys >= 0 && ixs < (src_width - 1) && iys < (src_height - 1)) { + a = xs - ixs; + b = ys - iys; + off = src_width*iys + ixs; + + p0 = src_d[off] + a * (src_d[off+1] - src_d[off]); + p1 = src_d[off+src_width] + a * (src_d[off+src_width+1] - src_d[off+src_width]); + + dst_d[dptr] = p0 + b * (p1 - p0); + } + else dst_d[dptr] = fill_value; + } + } + }, + + // Basic RGB Skin detection filter + // from http://popscan.blogspot.fr/2012/08/skin-detection-in-digital-images.html + skindetector: function(src,dst) { + var r,g,b,j; + var i = src.width*src.height; + while(i--){ + j = i*4; + r = src.data[j]; + g = src.data[j+1]; + b = src.data[j+2]; + if((r>95)&&(g>40)&&(b>20) + &&(r>g)&&(r>b) + &&(r-Math.min(g,b)>15) + &&(Math.abs(r-g)>15)){ + dst[i] = 255; + } else { + dst[i] = 0; + } + } + } + }; + })(); + + global.imgproc = imgproc; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten. + */ + +/* +The references are: + * Machine learning for high-speed corner detection, + E. Rosten and T. Drummond, ECCV 2006 + * Faster and better: A machine learning approach to corner detection + E. Rosten, R. Porter and T. Drummond, PAMI, 2009 +*/ + +(function(global) { + "use strict"; + // + var fast_corners = (function() { + + var offsets16 = new Int32Array([0, 3, 1, 3, 2, 2, 3, 1, 3, 0, 3, -1, 2, -2, 1, -3, 0, -3, -1, -3, -2, -2, -3, -1, -3, 0, -3, 1, -2, 2, -1, 3]); + + var threshold_tab = new Uint8Array(512); + var pixel_off = new Int32Array(25); + var score_diff = new Int32Array(25); + + // private functions + var _cmp_offsets = function(pixel, step, pattern_size) { + var k = 0; + var offsets = offsets16; + for( ; k < pattern_size; ++k ) { + pixel[k] = offsets[k<<1] + offsets[(k<<1)+1] * step; + } + for( ; k < 25; ++k ) { + pixel[k] = pixel[k - pattern_size]; + } + }, + + _cmp_score_16 = function(src, off, pixel, d, threshold) { + var N = 25, k = 0, v = src[off]; + var a0 = threshold,a=0,b0=0,b=0; + + for( ; k < N; ++k ) { + d[k] = v - src[off+pixel[k]]; + } + + for( k = 0; k < 16; k += 2 ) { + a = Math.min(d[k+1], d[k+2]); + a = Math.min(a, d[k+3]); + + if( a <= a0 ) continue; + + a = Math.min(a, d[k+4]); + a = Math.min(a, d[k+5]); + a = Math.min(a, d[k+6]); + a = Math.min(a, d[k+7]); + a = Math.min(a, d[k+8]); + a0 = Math.max(a0, Math.min(a, d[k])); + a0 = Math.max(a0, Math.min(a, d[k+9])); + } + + b0 = -a0; + for( k = 0; k < 16; k += 2 ) { + b = Math.max(d[k+1], d[k+2]); + b = Math.max(b, d[k+3]); + b = Math.max(b, d[k+4]); + b = Math.max(b, d[k+5]); + + if( b >= b0 ) continue; + b = Math.max(b, d[k+6]); + b = Math.max(b, d[k+7]); + b = Math.max(b, d[k+8]); + b0 = Math.min(b0, Math.max(b, d[k])); + b0 = Math.min(b0, Math.max(b, d[k+9])); + } + + return -b0-1; + }; + + var _threshold = 20; + + return { + set_threshold: function(threshold) { + _threshold = Math.min(Math.max(threshold, 0), 255); + for (var i = -255; i <= 255; ++i) { + threshold_tab[(i + 255)] = (i < -_threshold ? 1 : (i > _threshold ? 2 : 0)); + } + return _threshold; + }, + + detect: function(src, corners, border) { + if (typeof border === "undefined") { border = 3; } + + var K = 8, N = 25; + var img = src.data, w = src.cols, h = src.rows; + var i=0, j=0, k=0, vt=0, x=0, m3=0; + var buf_node = jsfeat.cache.get_buffer(3 * w); + var cpbuf_node = jsfeat.cache.get_buffer(((w+1)*3)<<2); + var buf = buf_node.u8; + var cpbuf = cpbuf_node.i32; + var pixel = pixel_off; + var sd = score_diff; + var sy = Math.max(3, border); + var ey = Math.min((h-2), (h-border)); + var sx = Math.max(3, border); + var ex = Math.min((w - 3), (w - border)); + var _count = 0, corners_cnt = 0, pt; + var score_func = _cmp_score_16; + var thresh_tab = threshold_tab; + var threshold = _threshold; + + var v=0,tab=0,d=0,ncorners=0,cornerpos=0,curr=0,ptr=0,prev=0,pprev=0; + var jp1=0,jm1=0,score=0; + + _cmp_offsets(pixel, w, 16); + + // local vars are faster? + var pixel0 = pixel[0]; + var pixel1 = pixel[1]; + var pixel2 = pixel[2]; + var pixel3 = pixel[3]; + var pixel4 = pixel[4]; + var pixel5 = pixel[5]; + var pixel6 = pixel[6]; + var pixel7 = pixel[7]; + var pixel8 = pixel[8]; + var pixel9 = pixel[9]; + var pixel10 = pixel[10]; + var pixel11 = pixel[11]; + var pixel12 = pixel[12]; + var pixel13 = pixel[13]; + var pixel14 = pixel[14]; + var pixel15 = pixel[15]; + + for(i = 0; i < w*3; ++i) { + buf[i] = 0; + } + + for(i = sy; i < ey; ++i) { + ptr = ((i * w) + sx)|0; + m3 = (i - 3)%3; + curr = (m3*w)|0; + cornerpos = (m3*(w+1))|0; + for (j = 0; j < w; ++j) buf[curr+j] = 0; + ncorners = 0; + + if( i < (ey - 1) ) { + j = sx; + + for( ; j < ex; ++j, ++ptr ) { + v = img[ptr]; + tab = ( - v + 255 ); + d = ( thresh_tab[tab+img[ptr+pixel0]] | thresh_tab[tab+img[ptr+pixel8]] ); + + if( d == 0 ) { + continue; + } + + d &= ( thresh_tab[tab+img[ptr+pixel2]] | thresh_tab[tab+img[ptr+pixel10]] ); + d &= ( thresh_tab[tab+img[ptr+pixel4]] | thresh_tab[tab+img[ptr+pixel12]] ); + d &= ( thresh_tab[tab+img[ptr+pixel6]] | thresh_tab[tab+img[ptr+pixel14]] ); + + if( d == 0 ) { + continue; + } + + d &= ( thresh_tab[tab+img[ptr+pixel1]] | thresh_tab[tab+img[ptr+pixel9]] ); + d &= ( thresh_tab[tab+img[ptr+pixel3]] | thresh_tab[tab+img[ptr+pixel11]] ); + d &= ( thresh_tab[tab+img[ptr+pixel5]] | thresh_tab[tab+img[ptr+pixel13]] ); + d &= ( thresh_tab[tab+img[ptr+pixel7]] | thresh_tab[tab+img[ptr+pixel15]] ); + + if( d & 1 ) { + vt = (v - threshold); + _count = 0; + + for( k = 0; k < N; ++k ) { + x = img[(ptr+pixel[k])]; + if(x < vt) { + ++_count; + if( _count > K ) { + ++ncorners; + cpbuf[cornerpos+ncorners] = j; + buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); + break; + } + } + else { + _count = 0; + } + } + } + + if( d & 2 ) { + vt = (v + threshold); + _count = 0; + + for( k = 0; k < N; ++k ) { + x = img[(ptr+pixel[k])]; + if(x > vt) { + ++_count; + if( _count > K ) { + ++ncorners; + cpbuf[cornerpos+ncorners] = j; + buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); + break; + } + } + else { + _count = 0; + } + } + } + } + } + + cpbuf[cornerpos+w] = ncorners; + + if ( i == sy ) { + continue; + } + + m3 = (i - 4 + 3)%3; + prev = (m3*w)|0; + cornerpos = (m3*(w+1))|0; + m3 = (i - 5 + 3)%3; + pprev = (m3*w)|0; + + ncorners = cpbuf[cornerpos+w]; + + for( k = 0; k < ncorners; ++k ) { + j = cpbuf[cornerpos+k]; + jp1 = (j+1)|0; + jm1 = (j-1)|0; + score = buf[prev+j]; + if( (score > buf[prev+jp1] && score > buf[prev+jm1] && + score > buf[pprev+jm1] && score > buf[pprev+j] && score > buf[pprev+jp1] && + score > buf[curr+jm1] && score > buf[curr+j] && score > buf[curr+jp1]) ) { + // save corner + pt = corners[corners_cnt]; + pt.x = j, pt.y = (i-1), pt.score = score; + corners_cnt++; + } + } + } // y loop + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(cpbuf_node); + return corners_cnt; + } + }; + })(); + + global.fast_corners = fast_corners; + fast_corners.set_threshold(20); // set default + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Copyright 2007 Computer Vision Lab, + * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. + * @author Vincent Lepetit (http://cvlab.epfl.ch/~lepetit) + */ + +(function(global) { + "use strict"; + // + + var yape06 = (function() { + + var compute_laplacian = function(src, dst, w, h, Dxx, Dyy, sx,sy, ex,ey) { + var y=0,x=0,yrow=(sy*w+sx)|0,row=yrow; + + for(y = sy; y < ey; ++y, yrow+=w, row = yrow) { + for(x = sx; x < ex; ++x, ++row) { + dst[row] = -4 * src[row] + src[row+Dxx] + src[row-Dxx] + src[row+Dyy] + src[row-Dyy]; + } + } + } + + var hessian_min_eigen_value = function(src, off, tr, Dxx, Dyy, Dxy, Dyx) { + var Ixx = -2 * src[off] + src[off + Dxx] + src[off - Dxx]; + var Iyy = -2 * src[off] + src[off + Dyy] + src[off - Dyy]; + var Ixy = src[off + Dxy] + src[off - Dxy] - src[off + Dyx] - src[off - Dyx]; + var sqrt_delta = ( Math.sqrt(((Ixx - Iyy) * (Ixx - Iyy) + 4 * Ixy * Ixy) ) )|0; + + return Math.min(Math.abs(tr - sqrt_delta), Math.abs(-(tr + sqrt_delta))); + } + + return { + + laplacian_threshold: 30, + min_eigen_value_threshold: 25, + + detect: function(src, points, border) { + if (typeof border === "undefined") { border = 5; } + var x=0,y=0; + var w=src.cols, h=src.rows, srd_d=src.data; + var Dxx = 5, Dyy = (5 * w)|0; + var Dxy = (3 + 3 * w)|0, Dyx = (3 - 3 * w)|0; + var lap_buf = jsfeat.cache.get_buffer((w*h)<<2); + var laplacian = lap_buf.i32; + var lv=0, row=0,rowx=0,min_eigen_value=0,pt; + var number_of_points = 0; + var lap_thresh = this.laplacian_threshold; + var eigen_thresh = this.min_eigen_value_threshold; + + var sx = Math.max(5, border)|0; + var sy = Math.max(3, border)|0; + var ex = Math.min(w-5, w-border)|0; + var ey = Math.min(h-3, h-border)|0; + + x = w*h; + while(--x>=0) {laplacian[x]=0;} + compute_laplacian(srd_d, laplacian, w, h, Dxx, Dyy, sx,sy, ex,ey); + + row = (sy*w+sx)|0; + for(y = sy; y < ey; ++y, row += w) { + for(x = sx, rowx=row; x < ex; ++x, ++rowx) { + + lv = laplacian[rowx]; + if ((lv < -lap_thresh && + lv < laplacian[rowx - 1] && lv < laplacian[rowx + 1] && + lv < laplacian[rowx - w] && lv < laplacian[rowx + w] && + lv < laplacian[rowx - w - 1] && lv < laplacian[rowx + w - 1] && + lv < laplacian[rowx - w + 1] && lv < laplacian[rowx + w + 1]) + || + (lv > lap_thresh && + lv > laplacian[rowx - 1] && lv > laplacian[rowx + 1] && + lv > laplacian[rowx - w] && lv > laplacian[rowx + w] && + lv > laplacian[rowx - w - 1] && lv > laplacian[rowx + w - 1] && + lv > laplacian[rowx - w + 1] && lv > laplacian[rowx + w + 1]) + ) { + + min_eigen_value = hessian_min_eigen_value(srd_d, rowx, lv, Dxx, Dyy, Dxy, Dyx); + if (min_eigen_value > eigen_thresh) { + pt = points[number_of_points]; + pt.x = x, pt.y = y, pt.score = min_eigen_value; + ++number_of_points; + ++x, ++rowx; // skip next pixel since this is maxima in 3x3 + } + } + } + } + + jsfeat.cache.put_buffer(lap_buf); + + return number_of_points; + } + + }; + })(); + + global.yape06 = yape06; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Copyright 2007 Computer Vision Lab, + * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. + */ + +(function(global) { + "use strict"; + // + + var yape = (function() { + + var precompute_directions = function(step, dirs, R) { + var i = 0; + var x, y; + + x = R; + for(y = 0; y < x; y++, i++) + { + x = (Math.sqrt((R * R - y * y)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for(x-- ; x < y && x >= 0; x--, i++) + { + y = (Math.sqrt((R * R - x * x)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for( ; -x < y; x--, i++) + { + y = (Math.sqrt((R * R - x * x)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for(y-- ; y >= 0; y--, i++) + { + x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(; y > x; y--, i++) + { + x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(x++ ; x <= 0; x++, i++) + { + y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for( ; x < -y; x++, i++) + { + y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(y++ ; y < 0; y++, i++) + { + x = (Math.sqrt((R * R - y * y)) + 0.5)|0; + dirs[i] = (x + step * y); + } + + dirs[i] = dirs[0]; + dirs[i + 1] = dirs[1]; + return i; + } + + var third_check = function (Sb, off, step) { + var n = 0; + if(Sb[off+1] != 0) n++; + if(Sb[off-1] != 0) n++; + if(Sb[off+step] != 0) n++; + if(Sb[off+step+1] != 0) n++; + if(Sb[off+step-1] != 0) n++; + if(Sb[off-step] != 0) n++; + if(Sb[off-step+1] != 0) n++; + if(Sb[off-step-1] != 0) n++; + + return n; + } + + var is_local_maxima = function(p, off, v, step, neighborhood) { + var x, y; + + if (v > 0) { + off -= step*neighborhood; + for (y= -neighborhood; y<=neighborhood; ++y) { + for (x= -neighborhood; x<=neighborhood; ++x) { + if (p[off+x] > v) return false; + } + off += step; + } + } else { + off -= step*neighborhood; + for (y= -neighborhood; y<=neighborhood; ++y) { + for (x= -neighborhood; x<=neighborhood; ++x) { + if (p[off+x] < v) return false; + } + off += step; + } + } + return true; + } + + var perform_one_point = function(I, x, Scores, Im, Ip, dirs, opposite, dirs_nb) { + var score = 0; + var a = 0, b = (opposite - 1)|0; + var A=0, B0=0, B1=0, B2=0; + var state=0; + + // WE KNOW THAT NOT(A ~ I0 & B1 ~ I0): + A = I[x+dirs[a]]; + if ((A <= Ip)) { + if ((A >= Im)) { // A ~ I0 + B0 = I[x+dirs[b]]; + if ((B0 <= Ip)) { + if ((B0 >= Im)) { Scores[x] = 0; return; } + else { + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 3; + else if ((B2 < Im)) state = 6; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else/* if ((B1 < Im))*/ { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 7; + else if ((B2 < Im)) state = 2; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + //else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 + } + } + else { // B0 < I0 + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 3; + else if ((B2 < Im)) state = 6; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else if ((B1 < Im)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 7; + else if ((B2 < Im)) state = 2; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 + } + } + else { // A > I0 + B0 = I[x+dirs[b]]; + if ((B0 > Ip)) { Scores[x] = 0; return; } + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { Scores[x] = 0; return; } + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + state = 1; + } + } + else // A < I0 + { + B0 = I[x+dirs[b]]; + if ((B0 < Im)) { Scores[x] = 0; return; } + b++; B1 = I[x+dirs[b]]; + if ((B1 < Im)) { Scores[x] = 0; return; } + b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + state = 0; + } + + for(a = 1; a <= opposite; a++) + { + A = I[x+dirs[a]]; + + switch(state) + { + case 0: + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + if ((A < Im)) { + if ((B1 > Ip)) { Scores[x] = 0; return; } + if ((B2 > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 8; break; }; + } + // A ~ I0 + if ((B1 <= Ip)) { Scores[x] = 0; return; } + if ((B2 <= Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 1: + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + if ((A > Ip)) { + if ((B1 < Im)) { Scores[x] = 0; return; } + if ((B2 < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 9; break; }; + } + // A ~ I0 + if ((B1 >= Im)) { Scores[x] = 0; return; } + if ((B2 >= Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } + + case 2: + if ((A > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((A < Im)) + { + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 4; break; }; + } + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 3: + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((A > Ip)) { + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 5; break; }; + } + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 4: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + if ((B2 >= Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } + + case 5: + if ((A < Im)) { Scores[x] = 0; return; } + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + // A ~ I0 + if ((B2 <= Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 7: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 6: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + // A ~ I0 + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 8: + if ((A > Ip)) { + if ((B2 < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 9; break; }; + } + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + { Scores[x] = 0; return; } + + case 9: + if ((A < Im)) { + if ((B2 > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 8; break; }; + } + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + { Scores[x] = 0; return; } + + default: + //"PB default"; + break; + } // switch(state) + } // for(a...) + + Scores[x] = (score + dirs_nb * I[x]); + } + + var lev_table_t = (function () { + function lev_table_t(w, h, r) { + this.dirs = new Int32Array(1024); + this.dirs_count = precompute_directions(w, this.dirs, r)|0; + this.scores = new Int32Array(w*h); + this.radius = r|0; + } + return lev_table_t; + })(); + + return { + + level_tables: [], + tau: 7, + + init: function(width, height, radius, pyramid_levels) { + if (typeof pyramid_levels === "undefined") { pyramid_levels = 1; } + var i; + radius = Math.min(radius, 7); + radius = Math.max(radius, 3); + for(i = 0; i < pyramid_levels; ++i) { + this.level_tables[i] = new lev_table_t(width>>i, height>>i, radius); + } + }, + + detect: function(src, points, border) { + if (typeof border === "undefined") { border = 4; } + var t = this.level_tables[0]; + var R = t.radius|0, Rm1 = (R-1)|0; + var dirs = t.dirs; + var dirs_count = t.dirs_count|0; + var opposite = dirs_count >> 1; + var img = src.data, w=src.cols|0, h=src.rows|0,hw=w>>1; + var scores = t.scores; + var x=0,y=0,row=0,rowx=0,ip=0,im=0,abs_score=0, score=0; + var tau = this.tau|0; + var number_of_points = 0, pt; + + var sx = Math.max(R+1, border)|0; + var sy = Math.max(R+1, border)|0; + var ex = Math.min(w-R-2, w-border)|0; + var ey = Math.min(h-R-2, h-border)|0; + + row = (sy*w+sx)|0; + for(y = sy; y < ey; ++y, row+=w) { + for(x = sx, rowx = row; x < ex; ++x, ++rowx) { + ip = img[rowx] + tau, im = img[rowx] - tau; + + if (im= 3 && is_local_maxima(scores, rowx, score, hw, R)) { + pt = points[number_of_points]; + pt.x = x, pt.y = y, pt.score = abs_score; + ++number_of_points; + + x += Rm1, rowx += Rm1; + } + } + } + } + + return number_of_points; + } + }; + + })(); + + global.yape = yape; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Original implementation derived from OpenCV, + * @authors Ethan Rublee, Vincent Rabaud, Gary Bradski + */ + +(function(global) { + "use strict"; + // + + var orb = (function() { + + var bit_pattern_31_ = new Int32Array([ + 8,-3, 9,5/*mean (0), correlation (0)*/, + 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/, + -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/, + 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/, + 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/, + 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/, + -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/, + -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/, + -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/, + 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/, + -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/, + -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/, + 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/, + -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/, + -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/, + -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/, + 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/, + -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/, + -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/, + 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/, + 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/, + 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/, + 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/, + -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/, + -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/, + -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/, + -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/, + -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/, + -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/, + 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/, + 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/, + 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/, + 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/, + 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/, + 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/, + -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/, + -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/, + 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/, + 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/, + -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/, + -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/, + -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/, + 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/, + 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/, + 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/, + -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/, + 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/, + -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/, + 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/, + -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/, + -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/, + 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/, + 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/, + -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/, + 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/, + 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/, + -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/, + -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/, + -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/, + -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/, + 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/, + -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/, + -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/, + -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/, + 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/, + -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/, + -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/, + 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/, + -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/, + -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/, + 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/, + -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/, + -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/, + -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/, + 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/, + 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/, + -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/, + -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/, + 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/, + -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/, + -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/, + -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/, + 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/, + -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/, + 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/, + 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/, + -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/, + -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/, + 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/, + 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/, + 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/, + -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/, + -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/, + 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/, + 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/, + 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/, + 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/, + 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/, + 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/, + 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/, + 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/, + -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/, + -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/, + 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/, + 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/, + 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/, + 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/, + 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/, + 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/, + -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/, + -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/, + -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/, + -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/, + 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/, + 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/, + -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/, + 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/, + -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/, + -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/, + 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/, + -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/, + -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/, + 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/, + -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/, + 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/, + 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/, + -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/, + 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/, + -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/, + 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/, + 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/, + 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/, + -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/, + 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/, + -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/, + 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/, + 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/, + -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/, + -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/, + -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/, + 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/, + -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/, + -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/, + 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/, + 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/, + -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/, + 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/, + -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/, + 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/, + -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/, + -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/, + 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/, + 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/, + -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/, + 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/, + 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/, + -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/, + -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/, + -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/, + 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/, + 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/, + -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/, + 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/, + 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/, + -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/, + -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/, + -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/, + 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/, + -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/, + -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/, + -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, + -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, + -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, + 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, + -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/, + -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/, + -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/, + -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, + 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, + -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, + 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, + 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, + -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, + -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, + -7,1, -6,7/*mean (0.175), correlation (0.500024)*/, + -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/, + -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/, + -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/, + -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/, + -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/, + 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/, + 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/, + 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/, + 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/, + -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/, + -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/, + -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/, + -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/, + 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/, + 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/, + 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/, + -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/, + -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, + 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/, + 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/, + -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/, + 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/, + 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/, + -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/, + 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/, + -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/, + 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/, + -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/, + 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/, + -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/, + 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/, + 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/, + 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/, + -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/, + -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/, + -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/, + -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/, + -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/, + 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/, + 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/, + 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/, + 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/, + 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/, + -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/, + 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/, + 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/, + -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/, + -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/, + -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/, + 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/, + 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/, + -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/, + -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/, + -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/, + 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/, + 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/, + -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/, + 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/, + 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/, + 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/, + -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/, + 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/, + -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/, + 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/, + 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/, + 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/, + -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/, + 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/, + 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/, + 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/, + -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/ + ]); + + var H = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var patch_img = new jsfeat.matrix_t(32, 32, jsfeat.U8_t|jsfeat.C1_t); + + var rectify_patch = function(src, dst, angle, px, py, psize) { + var cosine = Math.cos(angle); + var sine = Math.sin(angle); + + H.data[0] = cosine, H.data[1] = -sine, H.data[2] = (-cosine + sine ) * psize*0.5 + px, + H.data[3] = sine, H.data[4] = cosine, H.data[5] = (-sine - cosine) * psize*0.5 + py; + + jsfeat.imgproc.warp_affine(src, dst, H, 128); + } + + return { + + describe: function(src, corners, count, descriptors) { + var DESCR_SIZE = 32; // bytes; + var i=0,b=0,px=0.0,py=0.0,angle=0.0; + var t0=0, t1=0, val=0; + var img = src.data, w = src.cols, h = src.rows; + var patch_d = patch_img.data; + var patch_off = 16*32 + 16; // center of patch + var patt=0; + + if(!(descriptors.type&jsfeat.U8_t)) { + // relocate to U8 type + descriptors.type = jsfeat.U8_t; + descriptors.cols = DESCR_SIZE; + descriptors.rows = count; + descriptors.channel = 1; + descriptors.allocate(); + } else { + descriptors.resize(DESCR_SIZE, count, 1); + } + + var descr_d = descriptors.data; + var descr_off = 0; + + for(i = 0; i < count; ++i) { + px = corners[i].x; + py = corners[i].y; + angle = corners[i].angle; + + rectify_patch(src, patch_img, angle, px, py, 32); + + // describe the patch + patt = 0; + for (b = 0; b < DESCR_SIZE; ++b) { + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val = (t0 < t1)|0; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 1; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 2; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 3; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 4; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 5; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 6; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 7; + + descr_d[descr_off+b] = val; + } + descr_off += DESCR_SIZE; + } + } + }; + })(); + + global.orb = orb; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from OpenCV's Lucas-Kanade optical flow implementation + */ + +(function(global) { + "use strict"; + // + var optical_flow_lk = (function() { + + // short link to shar deriv + var scharr_deriv = jsfeat.imgproc.scharr_derivatives; + + return { + track: function(prev_pyr, curr_pyr, prev_xy, curr_xy, count, win_size, max_iter, status, eps, min_eigen_threshold) { + if (typeof max_iter === "undefined") { max_iter = 30; } + if (typeof status === "undefined") { status = new Uint8Array(count); } + if (typeof eps === "undefined") { eps = 0.01; } + if (typeof min_eigen_threshold === "undefined") { min_eigen_threshold = 0.0001; } + + var half_win = (win_size-1)*0.5; + var win_area = (win_size*win_size)|0; + var win_area2 = win_area << 1; + var prev_imgs = prev_pyr.data, next_imgs = curr_pyr.data; + var img_prev=prev_imgs[0].data,img_next=next_imgs[0].data; + var w0 = prev_imgs[0].cols, h0 = prev_imgs[0].rows,lw=0,lh=0; + + var iwin_node = jsfeat.cache.get_buffer(win_area<<2); + var deriv_iwin_node = jsfeat.cache.get_buffer(win_area2<<2); + var deriv_lev_node = jsfeat.cache.get_buffer((h0*(w0<<1))<<2); + + var deriv_m = new jsfeat.matrix_t(w0, h0, jsfeat.S32C2_t, deriv_lev_node.data); + + var iwin_buf = iwin_node.i32; + var deriv_iwin = deriv_iwin_node.i32; + var deriv_lev = deriv_lev_node.i32; + + var dstep=0,src=0,dsrc=0,iptr=0,diptr=0,jptr=0; + var lev_sc=0.0,prev_x=0.0,prev_y=0.0,next_x=0.0,next_y=0.0; + var prev_delta_x=0.0,prev_delta_y=0.0,delta_x=0.0,delta_y=0.0; + var iprev_x=0,iprev_y=0,inext_x=0,inext_y=0; + var i=0,j=0,x=0,y=0,level=0,ptid=0,iter=0; + var brd_tl=0,brd_r=0,brd_b=0; + var a=0.0,b=0.0,b1=0.0,b2=0.0; + + // fixed point math + var W_BITS14 = 14; + var W_BITS4 = 14; + var W_BITS1m5 = W_BITS4 - 5; + var W_BITS1m51 = (1 << ((W_BITS1m5) - 1)); + var W_BITS14_ = (1 << W_BITS14); + var W_BITS41 = (1 << ((W_BITS4) - 1)); + var FLT_SCALE = 1.0/(1 << 20); + var iw00=0,iw01=0,iw10=0,iw11=0,ival=0,ixval=0,iyval=0; + var A11=0.0,A12=0.0,A22=0.0,D=0.0,min_eig=0.0; + + var FLT_EPSILON = 0.00000011920929; + eps *= eps; + + // reset status + for(; i < count; ++i) { + status[i] = 1; + } + + var max_level = (prev_pyr.levels - 1)|0; + level = max_level; + + for(; level >= 0; --level) { + lev_sc = (1.0/(1 << level)); + lw = w0 >> level; + lh = h0 >> level; + dstep = lw << 1; + img_prev = prev_imgs[level].data; + img_next = next_imgs[level].data; + + brd_r = (lw - win_size)|0; + brd_b = (lh - win_size)|0; + + // calculate level derivatives + scharr_deriv(prev_imgs[level], deriv_m); + + // iterate through points + for(ptid = 0; ptid < count; ++ptid) { + i = ptid << 1; + j = i + 1; + prev_x = prev_xy[i]*lev_sc; + prev_y = prev_xy[j]*lev_sc; + + if( level == max_level ) { + next_x = prev_x; + next_y = prev_y; + } else { + next_x = curr_xy[i]*2.0; + next_y = curr_xy[j]*2.0; + } + curr_xy[i] = next_x; + curr_xy[j] = next_y; + + prev_x -= half_win; + prev_y -= half_win; + iprev_x = prev_x|0; + iprev_y = prev_y|0; + + // border check + x = (iprev_x <= brd_tl)|(iprev_x >= brd_r)|(iprev_y <= brd_tl)|(iprev_y >= brd_b); + if( x != 0 ) { + if( level == 0 ) { + status[ptid] = 0; + } + continue; + } + + a = prev_x - iprev_x; + b = prev_y - iprev_y; + iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; + iw11 = (W_BITS14_ - iw00 - iw01 - iw10); + + A11 = 0.0, A12 = 0.0, A22 = 0.0; + + // extract the patch from the first image, compute covariation matrix of derivatives + for( y = 0; y < win_size; ++y ) { + src = ( (y + iprev_y)*lw + iprev_x )|0; + dsrc = src << 1; + + iptr = (y*win_size)|0; + diptr = iptr << 1; + for(x = 0 ; x < win_size; ++x, ++src, ++iptr, dsrc += 2) { + ival = ( (img_prev[src])*iw00 + (img_prev[src+1])*iw01 + + (img_prev[src+lw])*iw10 + (img_prev[src+lw+1])*iw11 ); + ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); + + ixval = ( deriv_lev[dsrc]*iw00 + deriv_lev[dsrc+2]*iw01 + + deriv_lev[dsrc+dstep]*iw10 + deriv_lev[dsrc+dstep+2]*iw11 ); + ixval = (((ixval) + W_BITS41) >> (W_BITS4)); + + iyval = ( deriv_lev[dsrc+1]*iw00 + deriv_lev[dsrc+3]*iw01 + deriv_lev[dsrc+dstep+1]*iw10 + + deriv_lev[dsrc+dstep+3]*iw11 ); + iyval = (((iyval) + W_BITS41) >> (W_BITS4)); + + iwin_buf[iptr] = ival; + deriv_iwin[diptr++] = ixval; + deriv_iwin[diptr++] = iyval; + + A11 += ixval*ixval; + A12 += ixval*iyval; + A22 += iyval*iyval; + } + } + + A11 *= FLT_SCALE; A12 *= FLT_SCALE; A22 *= FLT_SCALE; + + D = A11*A22 - A12*A12; + min_eig = (A22 + A11 - Math.sqrt((A11-A22)*(A11-A22) + 4.0*A12*A12)) / win_area2; + + if( min_eig < min_eigen_threshold || D < FLT_EPSILON ) + { + if( level == 0 ) { + status[ptid] = 0; + } + continue; + } + + D = 1.0/D; + + next_x -= half_win; + next_y -= half_win; + prev_delta_x = 0.0; + prev_delta_y = 0.0; + + for( iter = 0; iter < max_iter; ++iter ) { + inext_x = next_x|0; + inext_y = next_y|0; + + x = (inext_x <= brd_tl)|(inext_x >= brd_r)|(inext_y <= brd_tl)|(inext_y >= brd_b); + if( x != 0 ) { + if( level == 0 ) { + status[ptid] = 0; + } + break; + } + + a = next_x - inext_x; + b = next_y - inext_y; + iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; + iw11 = (W_BITS14_ - iw00 - iw01 - iw10); + b1 = 0.0, b2 = 0.0; + + for( y = 0; y < win_size; ++y ) { + jptr = ( (y + inext_y)*lw + inext_x )|0; + + iptr = (y*win_size)|0; + diptr = iptr << 1; + for( x = 0 ; x < win_size; ++x, ++jptr, ++iptr ) { + ival = ( (img_next[jptr])*iw00 + (img_next[jptr+1])*iw01 + + (img_next[jptr+lw])*iw10 + (img_next[jptr+lw+1])*iw11 ); + ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); + ival = (ival - iwin_buf[iptr]); + + b1 += ival * deriv_iwin[diptr++]; + b2 += ival * deriv_iwin[diptr++]; + } + } + + b1 *= FLT_SCALE; + b2 *= FLT_SCALE; + + delta_x = ((A12*b2 - A22*b1) * D); + delta_y = ((A12*b1 - A11*b2) * D); + + next_x += delta_x; + next_y += delta_y; + curr_xy[i] = next_x + half_win; + curr_xy[j] = next_y + half_win; + + if( delta_x*delta_x + delta_y*delta_y <= eps ) { + break; + } + + if( iter > 0 && Math.abs(delta_x + prev_delta_x) < 0.01 && + Math.abs(delta_y + prev_delta_y) < 0.01 ) { + curr_xy[i] -= delta_x*0.5; + curr_xy[j] -= delta_y*0.5; + break; + } + + prev_delta_x = delta_x; + prev_delta_y = delta_y; + } + } // points loop + } // levels loop + + jsfeat.cache.put_buffer(iwin_node); + jsfeat.cache.put_buffer(deriv_iwin_node); + jsfeat.cache.put_buffer(deriv_lev_node); + } + }; + })(); + + global.optical_flow_lk = optical_flow_lk; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ + +(function(global) { + "use strict"; + // + var haar = (function() { + + var _group_func = function(r1, r2) { + var distance = (r1.width * 0.25 + 0.5)|0; + + return r2.x <= r1.x + distance && + r2.x >= r1.x - distance && + r2.y <= r1.y + distance && + r2.y >= r1.y - distance && + r2.width <= (r1.width * 1.5 + 0.5)|0 && + (r2.width * 1.5 + 0.5)|0 >= r1.width; + } + + return { + + edges_density: 0.07, + + detect_single_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale, classifier) { + var win_w = (classifier.size[0] * scale)|0, + win_h = (classifier.size[1] * scale)|0, + step_x = (0.5 * scale + 1.5)|0, + step_y = step_x; + var i,j,k,x,y,ex=(width-win_w)|0,ey=(height-win_h)|0; + var w1=(width+1)|0,edge_dens,mean,variance,std; + var inv_area = 1.0 / (win_w * win_h); + var stages,stage,trees,tree,sn,tn,fn,found=true,stage_thresh,stage_sum,tree_sum,feature,features; + var fi_a,fi_b,fi_c,fi_d,fw,fh; + + var ii_a=0,ii_b=win_w,ii_c=win_h*w1,ii_d=ii_c+win_w; + var edges_thresh = ((win_w*win_h) * 0xff * this.edges_density)|0; + // if too much gradient we also can skip + //var edges_thresh_high = ((win_w*win_h) * 0xff * 0.3)|0; + + var rects = []; + for(y = 0; y < ey; y += step_y) { + ii_a = y * w1; + for(x = 0; x < ex; x += step_x, ii_a += step_x) { + + mean = int_sum[ii_a] + - int_sum[ii_a+ii_b] + - int_sum[ii_a+ii_c] + + int_sum[ii_a+ii_d]; + + // canny prune + if(int_canny_sum) { + edge_dens = (int_canny_sum[ii_a] + - int_canny_sum[ii_a+ii_b] + - int_canny_sum[ii_a+ii_c] + + int_canny_sum[ii_a+ii_d]); + if(edge_dens < edges_thresh || mean < 20) { + x += step_x, ii_a += step_x; + continue; + } + } + + mean *= inv_area; + variance = (int_sqsum[ii_a] + - int_sqsum[ii_a+ii_b] + - int_sqsum[ii_a+ii_c] + + int_sqsum[ii_a+ii_d]) * inv_area - mean * mean; + + std = variance > 0. ? Math.sqrt(variance) : 1; + + stages = classifier.complexClassifiers; + sn = stages.length; + found = true; + for(i = 0; i < sn; ++i) { + stage = stages[i]; + stage_thresh = stage.threshold; + trees = stage.simpleClassifiers; + tn = trees.length; + stage_sum = 0; + for(j = 0; j < tn; ++j) { + tree = trees[j]; + tree_sum = 0; + features = tree.features; + fn = features.length; + if(tree.tilted === 1) { + for(k=0; k < fn; ++k) { + feature = features[k]; + fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; + fw = ~~(feature[2] * scale); + fh = ~~(feature[3] * scale); + fi_b = fw * w1; + fi_c = fh * w1; + + tree_sum += (int_tilted[fi_a] + - int_tilted[fi_a + fw + fi_b] + - int_tilted[fi_a - fh + fi_c] + + int_tilted[fi_a + fw - fh + fi_b + fi_c]) * feature[4]; + } + } else { + for(k=0; k < fn; ++k) { + feature = features[k]; + fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; + fw = ~~(feature[2] * scale); + fh = ~~(feature[3] * scale); + fi_c = fh * w1; + + tree_sum += (int_sum[fi_a] + - int_sum[fi_a+fw] + - int_sum[fi_a+fi_c] + + int_sum[fi_a+fi_c+fw]) * feature[4]; + } + } + stage_sum += (tree_sum * inv_area < tree.threshold * std) ? tree.left_val : tree.right_val; + } + if (stage_sum < stage_thresh) { + found = false; + break; + } + } + + if(found) { + rects.push({"x" : x, + "y" : y, + "width" : win_w, + "height" : win_h, + "neighbor" : 1, + "confidence" : stage_sum}); + x += step_x, ii_a += step_x; + } + } + } + return rects; + }, + + detect_multi_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, classifier, scale_factor, scale_min) { + if (typeof scale_factor === "undefined") { scale_factor = 1.2; } + if (typeof scale_min === "undefined") { scale_min = 1.0; } + var win_w = classifier.size[0]; + var win_h = classifier.size[1]; + var rects = []; + while (scale_min * win_w < width && scale_min * win_h < height) { + rects = rects.concat(this.detect_single_scale(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale_min, classifier)); + scale_min *= scale_factor; + } + return rects; + }, + + // OpenCV method to group detected rectangles + group_rectangles: function(rects, min_neighbors) { + if (typeof min_neighbors === "undefined") { min_neighbors = 1; } + var i, j, n = rects.length; + var node = []; + for (i = 0; i < n; ++i) { + node[i] = {"parent" : -1, + "element" : rects[i], + "rank" : 0}; + } + for (i = 0; i < n; ++i) { + if (!node[i].element) + continue; + var root = i; + while (node[root].parent != -1) + root = node[root].parent; + for (j = 0; j < n; ++j) { + if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { + var root2 = j; + + while (node[root2].parent != -1) + root2 = node[root2].parent; + + if(root2 != root) { + if(node[root].rank > node[root2].rank) + node[root2].parent = root; + else { + node[root].parent = root2; + if (node[root].rank == node[root2].rank) + node[root2].rank++; + root = root2; + } + + /* compress path from node2 to the root: */ + var temp, node2 = j; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + + /* compress path from node to the root: */ + node2 = i; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + } + } + } + } + var idx_seq = []; + var class_idx = 0; + for(i = 0; i < n; i++) { + j = -1; + var node1 = i; + if(node[node1].element) { + while (node[node1].parent != -1) + node1 = node[node1].parent; + if(node[node1].rank >= 0) + node[node1].rank = ~class_idx++; + j = ~node[node1].rank; + } + idx_seq[i] = j; + } + + var comps = []; + for (i = 0; i < class_idx+1; ++i) { + comps[i] = {"neighbors" : 0, + "x" : 0, + "y" : 0, + "width" : 0, + "height" : 0, + "confidence" : 0}; + } + + // count number of neighbors + for(i = 0; i < n; ++i) { + var r1 = rects[i]; + var idx = idx_seq[i]; + + if (comps[idx].neighbors == 0) + comps[idx].confidence = r1.confidence; + + ++comps[idx].neighbors; + + comps[idx].x += r1.x; + comps[idx].y += r1.y; + comps[idx].width += r1.width; + comps[idx].height += r1.height; + comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); + } + + var seq2 = []; + // calculate average bounding box + for(i = 0; i < class_idx; ++i) { + n = comps[i].neighbors; + if (n >= min_neighbors) + seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), + "y" : (comps[i].y * 2 + n) / (2 * n), + "width" : (comps[i].width * 2 + n) / (2 * n), + "height" : (comps[i].height * 2 + n) / (2 * n), + "neighbors" : comps[i].neighbors, + "confidence" : comps[i].confidence}); + } + + var result_seq = []; + n = seq2.length; + // filter out small face rectangles inside large face rectangles + for(i = 0; i < n; ++i) { + var r1 = seq2[i]; + var flag = true; + for(j = 0; j < n; ++j) { + var r2 = seq2[j]; + var distance = (r2.width * 0.25 + 0.5)|0; + + if(i != j && + r1.x >= r2.x - distance && + r1.y >= r2.y - distance && + r1.x + r1.width <= r2.x + r2.width + distance && + r1.y + r1.height <= r2.y + r2.height + distance && + (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { + flag = false; + break; + } + } + + if(flag) + result_seq.push(r1); + } + return result_seq; + } + }; + + })(); + + global.haar = haar; + +})(jsfeat); +/** + * BBF: Brightness Binary Feature + * + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from https://github.com/liuliu/ccv implementation + * @author Liu Liu / http://liuliu.me/ + * + * The original paper refers to: YEF∗ Real-Time Object Detection, Yotam Abramson and Bruno Steux + */ + +(function(global) { + "use strict"; + // + var bbf = (function() { + + var _group_func = function(r1, r2) { + var distance = (r1.width * 0.25 + 0.5)|0; + + return r2.x <= r1.x + distance && + r2.x >= r1.x - distance && + r2.y <= r1.y + distance && + r2.y >= r1.y - distance && + r2.width <= (r1.width * 1.5 + 0.5)|0 && + (r2.width * 1.5 + 0.5)|0 >= r1.width; + } + + var img_pyr = new jsfeat.pyramid_t(1); + + return { + + interval: 4, + scale: 1.1486, + next: 5, + scale_to: 1, + + // make features local copy + // to avoid array allocation with each scale + // this is strange but array works faster than Int32 version??? + prepare_cascade: function(cascade) { + var sn = cascade.stage_classifier.length; + for (var j = 0; j < sn; j++) { + var orig_feature = cascade.stage_classifier[j].feature; + var f_cnt = cascade.stage_classifier[j].count; + var feature = cascade.stage_classifier[j]._feature = new Array(f_cnt); + for (var k = 0; k < f_cnt; k++) { + feature[k] = {"size" : orig_feature[k].size, + "px" : new Array(orig_feature[k].size), + "pz" : new Array(orig_feature[k].size), + "nx" : new Array(orig_feature[k].size), + "nz" : new Array(orig_feature[k].size)}; + } + } + }, + + build_pyramid: function(src, min_width, min_height, interval) { + if (typeof interval === "undefined") { interval = 4; } + + var sw=src.cols,sh=src.rows; + var i=0,nw=0,nh=0; + var new_pyr=false; + var src0=src,src1=src; + var data_type = jsfeat.U8_t | jsfeat.C1_t; + + this.interval = interval; + this.scale = Math.pow(2, 1 / (this.interval + 1)); + this.next = (this.interval + 1)|0; + this.scale_to = (Math.log(Math.min(sw / min_width, sh / min_height)) / Math.log(this.scale))|0; + + var pyr_l = ((this.scale_to + this.next * 2) * 4) | 0; + if(img_pyr.levels != pyr_l) { + img_pyr.levels = pyr_l; + img_pyr.data = new Array(pyr_l); + new_pyr = true; + img_pyr.data[0] = src; // first is src + } + + for (i = 1; i <= this.interval; ++i) { + nw = (sw / Math.pow(this.scale, i))|0; + nh = (sh / Math.pow(this.scale, i))|0; + src0 = img_pyr.data[i<<2]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[i<<2]; + } + jsfeat.imgproc.resample(src, src0, nw, nh); + } + for (i = this.next; i < this.scale_to + this.next * 2; ++i) { + src1 = img_pyr.data[(i << 2) - (this.next << 2)]; + src0 = img_pyr.data[i<<2]; + nw = src1.cols >> 1; + nh = src1.rows >> 1; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[i<<2]; + } + jsfeat.imgproc.pyrdown(src1, src0); + } + for (i = this.next * 2; i < this.scale_to + this.next * 2; ++i) { + src1 = img_pyr.data[(i << 2) - (this.next << 2)]; + nw = src1.cols >> 1; + nh = src1.rows >> 1; + src0 = img_pyr.data[(i<<2)+1]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+1] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+1]; + } + jsfeat.imgproc.pyrdown(src1, src0, 1, 0); + // + src0 = img_pyr.data[(i<<2)+2]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+2]; + } + jsfeat.imgproc.pyrdown(src1, src0, 0, 1); + // + src0 = img_pyr.data[(i<<2)+3]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+3] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+3]; + } + jsfeat.imgproc.pyrdown(src1, src0, 1, 1); + } + return img_pyr; + }, + + detect: function(pyramid, cascade) { + var interval = this.interval; + var scale = this.scale; + var next = this.next; + var scale_upto = this.scale_to; + var i=0,j=0,k=0,n=0,x=0,y=0,q=0,sn=0,f_cnt=0,q_cnt=0,p=0,pmin=0,nmax=0,f=0,i4=0,qw=0,qh=0; + var sum=0.0, alpha, feature, orig_feature, feature_k, feature_o, flag = true, shortcut=true; + var scale_x = 1.0, scale_y = 1.0; + var dx = [0, 1, 0, 1]; + var dy = [0, 0, 1, 1]; + var seq = []; + var pyr=pyramid.data, bpp = 1, bpp2 = 2, bpp4 = 4; + + var u8 = [], u8o = [0,0,0]; + var step = [0,0,0]; + var paddings = [0,0,0]; + + for (i = 0; i < scale_upto; i++) { + i4 = (i<<2); + qw = pyr[i4 + (next << 3)].cols - (cascade.width >> 2); + qh = pyr[i4 + (next << 3)].rows - (cascade.height >> 2); + step[0] = pyr[i4].cols * bpp; + step[1] = pyr[i4 + (next << 2)].cols * bpp; + step[2] = pyr[i4 + (next << 3)].cols * bpp; + paddings[0] = (pyr[i4].cols * bpp4) - (qw * bpp4); + paddings[1] = (pyr[i4 + (next << 2)].cols * bpp2) - (qw * bpp2); + paddings[2] = (pyr[i4 + (next << 3)].cols * bpp) - (qw * bpp); + sn = cascade.stage_classifier.length; + for (j = 0; j < sn; j++) { + orig_feature = cascade.stage_classifier[j].feature; + feature = cascade.stage_classifier[j]._feature; + f_cnt = cascade.stage_classifier[j].count; + for (k = 0; k < f_cnt; k++) { + feature_k = feature[k]; + feature_o = orig_feature[k]; + q_cnt = feature_o.size|0; + for (q = 0; q < q_cnt; q++) { + feature_k.px[q] = (feature_o.px[q] * bpp) + feature_o.py[q] * step[feature_o.pz[q]]; + feature_k.pz[q] = feature_o.pz[q]; + feature_k.nx[q] = (feature_o.nx[q] * bpp) + feature_o.ny[q] * step[feature_o.nz[q]]; + feature_k.nz[q] = feature_o.nz[q]; + } + } + } + u8[0] = pyr[i4].data; u8[1] = pyr[i4 + (next<<2)].data; + for (q = 0; q < 4; q++) { + u8[2] = pyr[i4 + (next<<3) + q].data; + u8o[0] = (dx[q]*bpp2) + dy[q] * (pyr[i4].cols*bpp2); + u8o[1] = (dx[q]*bpp) + dy[q] * (pyr[i4 + (next<<2)].cols*bpp); + u8o[2] = 0; + for (y = 0; y < qh; y++) { + for (x = 0; x < qw; x++) { + sum = 0; + flag = true; + sn = cascade.stage_classifier.length; + for (j = 0; j < sn; j++) { + sum = 0; + alpha = cascade.stage_classifier[j].alpha; + feature = cascade.stage_classifier[j]._feature; + f_cnt = cascade.stage_classifier[j].count; + for (k = 0; k < f_cnt; k++) { + feature_k = feature[k]; + pmin = u8[feature_k.pz[0]][u8o[feature_k.pz[0]] + feature_k.px[0]]; + nmax = u8[feature_k.nz[0]][u8o[feature_k.nz[0]] + feature_k.nx[0]]; + if (pmin <= nmax) { + sum += alpha[k << 1]; + } else { + shortcut = true; + q_cnt = feature_k.size; + for (f = 1; f < q_cnt; f++) { + if (feature_k.pz[f] >= 0) { + p = u8[feature_k.pz[f]][u8o[feature_k.pz[f]] + feature_k.px[f]]; + if (p < pmin) { + if (p <= nmax) { + shortcut = false; + break; + } + pmin = p; + } + } + if (feature_k.nz[f] >= 0) { + n = u8[feature_k.nz[f]][u8o[feature_k.nz[f]] + feature_k.nx[f]]; + if (n > nmax) { + if (pmin <= n) { + shortcut = false; + break; + } + nmax = n; + } + } + } + sum += (shortcut) ? alpha[(k << 1) + 1] : alpha[k << 1]; + } + } + if (sum < cascade.stage_classifier[j].threshold) { + flag = false; + break; + } + } + if (flag) { + seq.push({"x" : (x * 4 + dx[q] * 2) * scale_x, + "y" : (y * 4 + dy[q] * 2) * scale_y, + "width" : cascade.width * scale_x, + "height" : cascade.height * scale_y, + "neighbor" : 1, + "confidence" : sum}); + ++x; + u8o[0] += bpp4; + u8o[1] += bpp2; + u8o[2] += bpp; + } + u8o[0] += bpp4; + u8o[1] += bpp2; + u8o[2] += bpp; + } + u8o[0] += paddings[0]; + u8o[1] += paddings[1]; + u8o[2] += paddings[2]; + } + } + scale_x *= scale; + scale_y *= scale; + } + + return seq; + }, + + // OpenCV method to group detected rectangles + group_rectangles: function(rects, min_neighbors) { + if (typeof min_neighbors === "undefined") { min_neighbors = 1; } + var i, j, n = rects.length; + var node = []; + for (i = 0; i < n; ++i) { + node[i] = {"parent" : -1, + "element" : rects[i], + "rank" : 0}; + } + for (i = 0; i < n; ++i) { + if (!node[i].element) + continue; + var root = i; + while (node[root].parent != -1) + root = node[root].parent; + for (j = 0; j < n; ++j) { + if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { + var root2 = j; + + while (node[root2].parent != -1) + root2 = node[root2].parent; + + if(root2 != root) { + if(node[root].rank > node[root2].rank) + node[root2].parent = root; + else { + node[root].parent = root2; + if (node[root].rank == node[root2].rank) + node[root2].rank++; + root = root2; + } + + /* compress path from node2 to the root: */ + var temp, node2 = j; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + + /* compress path from node to the root: */ + node2 = i; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + } + } + } + } + var idx_seq = []; + var class_idx = 0; + for(i = 0; i < n; i++) { + j = -1; + var node1 = i; + if(node[node1].element) { + while (node[node1].parent != -1) + node1 = node[node1].parent; + if(node[node1].rank >= 0) + node[node1].rank = ~class_idx++; + j = ~node[node1].rank; + } + idx_seq[i] = j; + } + + var comps = []; + for (i = 0; i < class_idx+1; ++i) { + comps[i] = {"neighbors" : 0, + "x" : 0, + "y" : 0, + "width" : 0, + "height" : 0, + "confidence" : 0}; + } + + // count number of neighbors + for(i = 0; i < n; ++i) { + var r1 = rects[i]; + var idx = idx_seq[i]; + + if (comps[idx].neighbors == 0) + comps[idx].confidence = r1.confidence; + + ++comps[idx].neighbors; + + comps[idx].x += r1.x; + comps[idx].y += r1.y; + comps[idx].width += r1.width; + comps[idx].height += r1.height; + comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); + } + + var seq2 = []; + // calculate average bounding box + for(i = 0; i < class_idx; ++i) { + n = comps[i].neighbors; + if (n >= min_neighbors) + seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), + "y" : (comps[i].y * 2 + n) / (2 * n), + "width" : (comps[i].width * 2 + n) / (2 * n), + "height" : (comps[i].height * 2 + n) / (2 * n), + "neighbors" : comps[i].neighbors, + "confidence" : comps[i].confidence}); + } + + var result_seq = []; + n = seq2.length; + // filter out small face rectangles inside large face rectangles + for(i = 0; i < n; ++i) { + var r1 = seq2[i]; + var flag = true; + for(j = 0; j < n; ++j) { + var r2 = seq2[j]; + var distance = (r2.width * 0.25 + 0.5)|0; + + if(i != j && + r1.x >= r2.x - distance && + r1.y >= r2.y - distance && + r1.x + r1.width <= r2.x + r2.width + distance && + r1.y + r1.height <= r2.y + r2.height + distance && + (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { + flag = false; + break; + } + } + + if(flag) + result_seq.push(r1); + } + return result_seq; + } + + }; + + })(); + + global.bbf = bbf; + +})(jsfeat); +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(lib) { + "use strict"; + + if (typeof module === "undefined" || typeof module.exports === "undefined") { + // in a browser, define its namespaces in global + window.jsfeat = lib; + } else { + // in commonjs, or when AMD wrapping has been applied, define its namespaces as exports + module.exports = lib; + } +})(jsfeat); diff --git a/cascades/bbf_face.js b/cascades/bbf_face.js new file mode 100755 index 0000000..1f2ca01 --- /dev/null +++ b/cascades/bbf_face.js @@ -0,0 +1,3 @@ +(function(global) { + global.face_cascade = {"count" : 16, "width" : 24, "height" : 24, "stage_classifier" : 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+})(jsfeat.bbf); diff --git a/cascades/eye.js b/cascades/eye.js new file mode 100755 index 0000000..b9e805b --- /dev/null +++ b/cascades/eye.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.eye = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/frontalface.js b/cascades/frontalface.js new file mode 100755 index 0000000..3a77ee6 --- /dev/null +++ b/cascades/frontalface.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.frontalface = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/handfist.js b/cascades/handfist.js new file mode 100755 index 0000000..ca623d5 --- /dev/null +++ b/cascades/handfist.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.handfist = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/handopen.js b/cascades/handopen.js new file mode 100755 index 0000000..f587675 --- /dev/null +++ b/cascades/handopen.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.handopen = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/mouth.js b/cascades/mouth.js new file mode 100755 index 0000000..2244665 --- /dev/null +++ b/cascades/mouth.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.mouth = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/profileface.js b/cascades/profileface.js new file mode 100755 index 0000000..a32cbfd --- /dev/null +++ b/cascades/profileface.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.profileface = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/cascades/upperbody.js b/cascades/upperbody.js new file mode 100755 index 0000000..8e88c8f --- /dev/null +++ b/cascades/upperbody.js @@ -0,0 +1,7 @@ +/** + * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ +(function(global) { + global.upperbody = 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+})(jsfeat.haar); \ No newline at end of file diff --git a/compile/build.xml b/compile/build.xml new file mode 100755 index 0000000..8225c78 --- /dev/null +++ b/compile/build.xml @@ -0,0 +1,35 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/compile/yuicompressor-2.4.2.jar b/compile/yuicompressor-2.4.2.jar new file mode 100644 index 0000000..c29470b Binary files /dev/null and b/compile/yuicompressor-2.4.2.jar differ diff --git a/package.json b/package.json new file mode 100644 index 0000000..6bc3310 --- /dev/null +++ b/package.json @@ -0,0 +1,8 @@ +{ + "name" : "jsfeat", + "version" : "0.0.8", + "description" : "JavaScript Computer Vision library", + "author" : "Eugene Zatepyakin (http://www.inspirit.ru/)", + "files" : "build/jsfeat.js", + "main" : "build/jsfeat" +} diff --git a/sample_bbf_face.html b/sample_bbf_face.html index d0185e3..efe4e0e 100755 --- a/sample_bbf_face.html +++ b/sample_bbf_face.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_boxblur.html b/sample_boxblur.html index b5cb0ff..084ed1d 100755 --- a/sample_boxblur.html +++ b/sample_boxblur.html @@ -72,9 +72,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_canny_edge.html b/sample_canny_edge.html index 202583b..017a227 100755 --- a/sample_canny_edge.html +++ b/sample_canny_edge.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_equalize_hist.html b/sample_equalize_hist.html index e342791..57cf8b0 100755 --- a/sample_equalize_hist.html +++ b/sample_equalize_hist.html @@ -70,9 +70,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_fast_corners.html b/sample_fast_corners.html index a7d7c56..5e0dce0 100755 --- a/sample_fast_corners.html +++ b/sample_fast_corners.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_gaussblur.html b/sample_gaussblur.html index a8c7db4..8113f84 100755 --- a/sample_gaussblur.html +++ b/sample_gaussblur.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_grayscale.html b/sample_grayscale.html index 70aa0f7..48c2edc 100755 --- a/sample_grayscale.html +++ b/sample_grayscale.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_haar_face.html b/sample_haar_face.html index c9b9fcd..6d11c9d 100755 --- a/sample_haar_face.html +++ b/sample_haar_face.html @@ -72,9 +72,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_oflow_lk.html b/sample_oflow_lk.html index 69f1092..fad2972 100755 --- a/sample_oflow_lk.html +++ b/sample_oflow_lk.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_orb.html b/sample_orb.html index 6443ee8..b31c1c7 100644 --- a/sample_orb.html +++ b/sample_orb.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_pyrdown.html b/sample_pyrdown.html index 7e1ba2b..fdde283 100755 --- a/sample_pyrdown.html +++ b/sample_pyrdown.html @@ -70,9 +70,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_scharr.html b/sample_scharr.html index 62b8cf3..ce6c56a 100644 --- a/sample_scharr.html +++ b/sample_scharr.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_sobel.html b/sample_sobel.html index b877beb..51bbc47 100644 --- a/sample_sobel.html +++ b/sample_sobel.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_sobel_edge.html b/sample_sobel_edge.html index 9fd833a..66cbbb5 100644 --- a/sample_sobel_edge.html +++ b/sample_sobel_edge.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_videostab.html b/sample_videostab.html index e239d06..73889b4 100644 --- a/sample_videostab.html +++ b/sample_videostab.html @@ -222,9 +222,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_warp_affine.html b/sample_warp_affine.html index a86be3e..111fb47 100755 --- a/sample_warp_affine.html +++ b/sample_warp_affine.html @@ -70,9 +70,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_warp_perspective.html b/sample_warp_perspective.html index 3b6c7b4..b11df4c 100755 --- a/sample_warp_perspective.html +++ b/sample_warp_perspective.html @@ -70,9 +70,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_yape.html b/sample_yape.html index a8e4b3c..d636e70 100755 --- a/sample_yape.html +++ b/sample_yape.html @@ -70,9 +70,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/sample_yape06.html b/sample_yape06.html index b1711a2..b26121b 100755 --- a/sample_yape06.html +++ b/sample_yape06.html @@ -71,9 +71,9 @@ compatibility.getUserMedia({video: true}, function(stream) { try { - video.src = compatibility.URL.createObjectURL(stream); + video.srcObject = stream; } catch (error) { - video.src = stream; + video.src = compatibility.URL.createObjectURL(stream); } setTimeout(function() { video.play(); diff --git a/src/jsfeat.js b/src/jsfeat.js new file mode 100755 index 0000000..86526d0 --- /dev/null +++ b/src/jsfeat.js @@ -0,0 +1,6 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +// namespace ? +var jsfeat = jsfeat || { REVISION: 'ALPHA' }; diff --git a/src/jsfeat_bbf.js b/src/jsfeat_bbf.js new file mode 100755 index 0000000..e4e5878 --- /dev/null +++ b/src/jsfeat_bbf.js @@ -0,0 +1,394 @@ +/** + * BBF: Brightness Binary Feature + * + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from https://github.com/liuliu/ccv implementation + * @author Liu Liu / http://liuliu.me/ + * + * The original paper refers to: YEF∗ Real-Time Object Detection, Yotam Abramson and Bruno Steux + */ + +(function(global) { + "use strict"; + // + var bbf = (function() { + + var _group_func = function(r1, r2) { + var distance = (r1.width * 0.25 + 0.5)|0; + + return r2.x <= r1.x + distance && + r2.x >= r1.x - distance && + r2.y <= r1.y + distance && + r2.y >= r1.y - distance && + r2.width <= (r1.width * 1.5 + 0.5)|0 && + (r2.width * 1.5 + 0.5)|0 >= r1.width; + } + + var img_pyr = new jsfeat.pyramid_t(1); + + return { + + interval: 4, + scale: 1.1486, + next: 5, + scale_to: 1, + + // make features local copy + // to avoid array allocation with each scale + // this is strange but array works faster than Int32 version??? + prepare_cascade: function(cascade) { + var sn = cascade.stage_classifier.length; + for (var j = 0; j < sn; j++) { + var orig_feature = cascade.stage_classifier[j].feature; + var f_cnt = cascade.stage_classifier[j].count; + var feature = cascade.stage_classifier[j]._feature = new Array(f_cnt); + for (var k = 0; k < f_cnt; k++) { + feature[k] = {"size" : orig_feature[k].size, + "px" : new Array(orig_feature[k].size), + "pz" : new Array(orig_feature[k].size), + "nx" : new Array(orig_feature[k].size), + "nz" : new Array(orig_feature[k].size)}; + } + } + }, + + build_pyramid: function(src, min_width, min_height, interval) { + if (typeof interval === "undefined") { interval = 4; } + + var sw=src.cols,sh=src.rows; + var i=0,nw=0,nh=0; + var new_pyr=false; + var src0=src,src1=src; + var data_type = jsfeat.U8_t | jsfeat.C1_t; + + this.interval = interval; + this.scale = Math.pow(2, 1 / (this.interval + 1)); + this.next = (this.interval + 1)|0; + this.scale_to = (Math.log(Math.min(sw / min_width, sh / min_height)) / Math.log(this.scale))|0; + + var pyr_l = ((this.scale_to + this.next * 2) * 4) | 0; + if(img_pyr.levels != pyr_l) { + img_pyr.levels = pyr_l; + img_pyr.data = new Array(pyr_l); + new_pyr = true; + img_pyr.data[0] = src; // first is src + } + + for (i = 1; i <= this.interval; ++i) { + nw = (sw / Math.pow(this.scale, i))|0; + nh = (sh / Math.pow(this.scale, i))|0; + src0 = img_pyr.data[i<<2]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[i<<2]; + } + jsfeat.imgproc.resample(src, src0, nw, nh); + } + for (i = this.next; i < this.scale_to + this.next * 2; ++i) { + src1 = img_pyr.data[(i << 2) - (this.next << 2)]; + src0 = img_pyr.data[i<<2]; + nw = src1.cols >> 1; + nh = src1.rows >> 1; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[i<<2]; + } + jsfeat.imgproc.pyrdown(src1, src0); + } + for (i = this.next * 2; i < this.scale_to + this.next * 2; ++i) { + src1 = img_pyr.data[(i << 2) - (this.next << 2)]; + nw = src1.cols >> 1; + nh = src1.rows >> 1; + src0 = img_pyr.data[(i<<2)+1]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+1] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+1]; + } + jsfeat.imgproc.pyrdown(src1, src0, 1, 0); + // + src0 = img_pyr.data[(i<<2)+2]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+2] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+2]; + } + jsfeat.imgproc.pyrdown(src1, src0, 0, 1); + // + src0 = img_pyr.data[(i<<2)+3]; + if(new_pyr || nw != src0.cols || nh != src0.rows) { + img_pyr.data[(i<<2)+3] = new jsfeat.matrix_t(nw, nh, data_type); + src0 = img_pyr.data[(i<<2)+3]; + } + jsfeat.imgproc.pyrdown(src1, src0, 1, 1); + } + return img_pyr; + }, + + detect: function(pyramid, cascade) { + var interval = this.interval; + var scale = this.scale; + var next = this.next; + var scale_upto = this.scale_to; + var i=0,j=0,k=0,n=0,x=0,y=0,q=0,sn=0,f_cnt=0,q_cnt=0,p=0,pmin=0,nmax=0,f=0,i4=0,qw=0,qh=0; + var sum=0.0, alpha, feature, orig_feature, feature_k, feature_o, flag = true, shortcut=true; + var scale_x = 1.0, scale_y = 1.0; + var dx = [0, 1, 0, 1]; + var dy = [0, 0, 1, 1]; + var seq = []; + var pyr=pyramid.data, bpp = 1, bpp2 = 2, bpp4 = 4; + + var u8 = [], u8o = [0,0,0]; + var step = [0,0,0]; + var paddings = [0,0,0]; + + for (i = 0; i < scale_upto; i++) { + i4 = (i<<2); + qw = pyr[i4 + (next << 3)].cols - (cascade.width >> 2); + qh = pyr[i4 + (next << 3)].rows - (cascade.height >> 2); + step[0] = pyr[i4].cols * bpp; + step[1] = pyr[i4 + (next << 2)].cols * bpp; + step[2] = pyr[i4 + (next << 3)].cols * bpp; + paddings[0] = (pyr[i4].cols * bpp4) - (qw * bpp4); + paddings[1] = (pyr[i4 + (next << 2)].cols * bpp2) - (qw * bpp2); + paddings[2] = (pyr[i4 + (next << 3)].cols * bpp) - (qw * bpp); + sn = cascade.stage_classifier.length; + for (j = 0; j < sn; j++) { + orig_feature = cascade.stage_classifier[j].feature; + feature = cascade.stage_classifier[j]._feature; + f_cnt = cascade.stage_classifier[j].count; + for (k = 0; k < f_cnt; k++) { + feature_k = feature[k]; + feature_o = orig_feature[k]; + q_cnt = feature_o.size|0; + for (q = 0; q < q_cnt; q++) { + feature_k.px[q] = (feature_o.px[q] * bpp) + feature_o.py[q] * step[feature_o.pz[q]]; + feature_k.pz[q] = feature_o.pz[q]; + feature_k.nx[q] = (feature_o.nx[q] * bpp) + feature_o.ny[q] * step[feature_o.nz[q]]; + feature_k.nz[q] = feature_o.nz[q]; + } + } + } + u8[0] = pyr[i4].data; u8[1] = pyr[i4 + (next<<2)].data; + for (q = 0; q < 4; q++) { + u8[2] = pyr[i4 + (next<<3) + q].data; + u8o[0] = (dx[q]*bpp2) + dy[q] * (pyr[i4].cols*bpp2); + u8o[1] = (dx[q]*bpp) + dy[q] * (pyr[i4 + (next<<2)].cols*bpp); + u8o[2] = 0; + for (y = 0; y < qh; y++) { + for (x = 0; x < qw; x++) { + sum = 0; + flag = true; + sn = cascade.stage_classifier.length; + for (j = 0; j < sn; j++) { + sum = 0; + alpha = cascade.stage_classifier[j].alpha; + feature = cascade.stage_classifier[j]._feature; + f_cnt = cascade.stage_classifier[j].count; + for (k = 0; k < f_cnt; k++) { + feature_k = feature[k]; + pmin = u8[feature_k.pz[0]][u8o[feature_k.pz[0]] + feature_k.px[0]]; + nmax = u8[feature_k.nz[0]][u8o[feature_k.nz[0]] + feature_k.nx[0]]; + if (pmin <= nmax) { + sum += alpha[k << 1]; + } else { + shortcut = true; + q_cnt = feature_k.size; + for (f = 1; f < q_cnt; f++) { + if (feature_k.pz[f] >= 0) { + p = u8[feature_k.pz[f]][u8o[feature_k.pz[f]] + feature_k.px[f]]; + if (p < pmin) { + if (p <= nmax) { + shortcut = false; + break; + } + pmin = p; + } + } + if (feature_k.nz[f] >= 0) { + n = u8[feature_k.nz[f]][u8o[feature_k.nz[f]] + feature_k.nx[f]]; + if (n > nmax) { + if (pmin <= n) { + shortcut = false; + break; + } + nmax = n; + } + } + } + sum += (shortcut) ? alpha[(k << 1) + 1] : alpha[k << 1]; + } + } + if (sum < cascade.stage_classifier[j].threshold) { + flag = false; + break; + } + } + if (flag) { + seq.push({"x" : (x * 4 + dx[q] * 2) * scale_x, + "y" : (y * 4 + dy[q] * 2) * scale_y, + "width" : cascade.width * scale_x, + "height" : cascade.height * scale_y, + "neighbor" : 1, + "confidence" : sum}); + ++x; + u8o[0] += bpp4; + u8o[1] += bpp2; + u8o[2] += bpp; + } + u8o[0] += bpp4; + u8o[1] += bpp2; + u8o[2] += bpp; + } + u8o[0] += paddings[0]; + u8o[1] += paddings[1]; + u8o[2] += paddings[2]; + } + } + scale_x *= scale; + scale_y *= scale; + } + + return seq; + }, + + // OpenCV method to group detected rectangles + group_rectangles: function(rects, min_neighbors) { + if (typeof min_neighbors === "undefined") { min_neighbors = 1; } + var i, j, n = rects.length; + var node = []; + for (i = 0; i < n; ++i) { + node[i] = {"parent" : -1, + "element" : rects[i], + "rank" : 0}; + } + for (i = 0; i < n; ++i) { + if (!node[i].element) + continue; + var root = i; + while (node[root].parent != -1) + root = node[root].parent; + for (j = 0; j < n; ++j) { + if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { + var root2 = j; + + while (node[root2].parent != -1) + root2 = node[root2].parent; + + if(root2 != root) { + if(node[root].rank > node[root2].rank) + node[root2].parent = root; + else { + node[root].parent = root2; + if (node[root].rank == node[root2].rank) + node[root2].rank++; + root = root2; + } + + /* compress path from node2 to the root: */ + var temp, node2 = j; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + + /* compress path from node to the root: */ + node2 = i; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + } + } + } + } + var idx_seq = []; + var class_idx = 0; + for(i = 0; i < n; i++) { + j = -1; + var node1 = i; + if(node[node1].element) { + while (node[node1].parent != -1) + node1 = node[node1].parent; + if(node[node1].rank >= 0) + node[node1].rank = ~class_idx++; + j = ~node[node1].rank; + } + idx_seq[i] = j; + } + + var comps = []; + for (i = 0; i < class_idx+1; ++i) { + comps[i] = {"neighbors" : 0, + "x" : 0, + "y" : 0, + "width" : 0, + "height" : 0, + "confidence" : 0}; + } + + // count number of neighbors + for(i = 0; i < n; ++i) { + var r1 = rects[i]; + var idx = idx_seq[i]; + + if (comps[idx].neighbors == 0) + comps[idx].confidence = r1.confidence; + + ++comps[idx].neighbors; + + comps[idx].x += r1.x; + comps[idx].y += r1.y; + comps[idx].width += r1.width; + comps[idx].height += r1.height; + comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); + } + + var seq2 = []; + // calculate average bounding box + for(i = 0; i < class_idx; ++i) { + n = comps[i].neighbors; + if (n >= min_neighbors) + seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), + "y" : (comps[i].y * 2 + n) / (2 * n), + "width" : (comps[i].width * 2 + n) / (2 * n), + "height" : (comps[i].height * 2 + n) / (2 * n), + "neighbors" : comps[i].neighbors, + "confidence" : comps[i].confidence}); + } + + var result_seq = []; + n = seq2.length; + // filter out small face rectangles inside large face rectangles + for(i = 0; i < n; ++i) { + var r1 = seq2[i]; + var flag = true; + for(j = 0; j < n; ++j) { + var r2 = seq2[j]; + var distance = (r2.width * 0.25 + 0.5)|0; + + if(i != j && + r1.x >= r2.x - distance && + r1.y >= r2.y - distance && + r1.x + r1.width <= r2.x + r2.width + distance && + r1.y + r1.height <= r2.y + r2.height + distance && + (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { + flag = false; + break; + } + } + + if(flag) + result_seq.push(r1); + } + return result_seq; + } + + }; + + })(); + + global.bbf = bbf; + +})(jsfeat); diff --git a/src/jsfeat_cache.js b/src/jsfeat_cache.js new file mode 100755 index 0000000..3bbe170 --- /dev/null +++ b/src/jsfeat_cache.js @@ -0,0 +1,79 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var cache = (function() { + + // very primitive array cache, still need testing if it helps + // of course V8 has its own powerful cache sys but i'm not sure + // it caches several multichannel 640x480 buffer creations each frame + + var _pool_node_t = (function () { + function _pool_node_t(size_in_bytes) { + this.next = null; + this.data = new jsfeat.data_t(size_in_bytes); + this.size = this.data.size; + this.buffer = this.data.buffer; + this.u8 = this.data.u8; + this.i32 = this.data.i32; + this.f32 = this.data.f32; + this.f64 = this.data.f64; + } + _pool_node_t.prototype.resize = function(size_in_bytes) { + delete this.data; + this.data = new jsfeat.data_t(size_in_bytes); + this.size = this.data.size; + this.buffer = this.data.buffer; + this.u8 = this.data.u8; + this.i32 = this.data.i32; + this.f32 = this.data.f32; + this.f64 = this.data.f64; + } + return _pool_node_t; + })(); + + var _pool_head, _pool_tail; + var _pool_size = 0; + + return { + + allocate: function(capacity, data_size) { + _pool_head = _pool_tail = new _pool_node_t(data_size); + for (var i = 0; i < capacity; ++i) { + var node = new _pool_node_t(data_size); + _pool_tail = _pool_tail.next = node; + + _pool_size++; + } + }, + + get_buffer: function(size_in_bytes) { + // assume we have enough free nodes + var node = _pool_head; + _pool_head = _pool_head.next; + _pool_size--; + + if(size_in_bytes > node.size) { + node.resize(size_in_bytes); + } + + return node; + }, + + put_buffer: function(node) { + _pool_tail = _pool_tail.next = node; + _pool_size++; + } + }; + })(); + + global.cache = cache; + // for now we dont need more than 30 buffers + // if having cache sys really helps we can add auto extending sys + cache.allocate(30, 640*4); + +})(jsfeat); diff --git a/src/jsfeat_export.js b/src/jsfeat_export.js new file mode 100755 index 0000000..0ee0507 --- /dev/null +++ b/src/jsfeat_export.js @@ -0,0 +1,15 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(lib) { + "use strict"; + + if (typeof module === "undefined" || typeof module.exports === "undefined") { + // in a browser, define its namespaces in global + window.jsfeat = lib; + } else { + // in commonjs, or when AMD wrapping has been applied, define its namespaces as exports + module.exports = lib; + } +})(jsfeat); diff --git a/src/jsfeat_fast_corners.js b/src/jsfeat_fast_corners.js new file mode 100755 index 0000000..5689439 --- /dev/null +++ b/src/jsfeat_fast_corners.js @@ -0,0 +1,254 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten. + */ + +/* +The references are: + * Machine learning for high-speed corner detection, + E. Rosten and T. Drummond, ECCV 2006 + * Faster and better: A machine learning approach to corner detection + E. Rosten, R. Porter and T. Drummond, PAMI, 2009 +*/ + +(function(global) { + "use strict"; + // + var fast_corners = (function() { + + var offsets16 = new Int32Array([0, 3, 1, 3, 2, 2, 3, 1, 3, 0, 3, -1, 2, -2, 1, -3, 0, -3, -1, -3, -2, -2, -3, -1, -3, 0, -3, 1, -2, 2, -1, 3]); + + var threshold_tab = new Uint8Array(512); + var pixel_off = new Int32Array(25); + var score_diff = new Int32Array(25); + + // private functions + var _cmp_offsets = function(pixel, step, pattern_size) { + var k = 0; + var offsets = offsets16; + for( ; k < pattern_size; ++k ) { + pixel[k] = offsets[k<<1] + offsets[(k<<1)+1] * step; + } + for( ; k < 25; ++k ) { + pixel[k] = pixel[k - pattern_size]; + } + }, + + _cmp_score_16 = function(src, off, pixel, d, threshold) { + var N = 25, k = 0, v = src[off]; + var a0 = threshold,a=0,b0=0,b=0; + + for( ; k < N; ++k ) { + d[k] = v - src[off+pixel[k]]; + } + + for( k = 0; k < 16; k += 2 ) { + a = Math.min(d[k+1], d[k+2]); + a = Math.min(a, d[k+3]); + + if( a <= a0 ) continue; + + a = Math.min(a, d[k+4]); + a = Math.min(a, d[k+5]); + a = Math.min(a, d[k+6]); + a = Math.min(a, d[k+7]); + a = Math.min(a, d[k+8]); + a0 = Math.max(a0, Math.min(a, d[k])); + a0 = Math.max(a0, Math.min(a, d[k+9])); + } + + b0 = -a0; + for( k = 0; k < 16; k += 2 ) { + b = Math.max(d[k+1], d[k+2]); + b = Math.max(b, d[k+3]); + b = Math.max(b, d[k+4]); + b = Math.max(b, d[k+5]); + + if( b >= b0 ) continue; + b = Math.max(b, d[k+6]); + b = Math.max(b, d[k+7]); + b = Math.max(b, d[k+8]); + b0 = Math.min(b0, Math.max(b, d[k])); + b0 = Math.min(b0, Math.max(b, d[k+9])); + } + + return -b0-1; + }; + + var _threshold = 20; + + return { + set_threshold: function(threshold) { + _threshold = Math.min(Math.max(threshold, 0), 255); + for (var i = -255; i <= 255; ++i) { + threshold_tab[(i + 255)] = (i < -_threshold ? 1 : (i > _threshold ? 2 : 0)); + } + return _threshold; + }, + + detect: function(src, corners, border) { + if (typeof border === "undefined") { border = 3; } + + var K = 8, N = 25; + var img = src.data, w = src.cols, h = src.rows; + var i=0, j=0, k=0, vt=0, x=0, m3=0; + var buf_node = jsfeat.cache.get_buffer(3 * w); + var cpbuf_node = jsfeat.cache.get_buffer(((w+1)*3)<<2); + var buf = buf_node.u8; + var cpbuf = cpbuf_node.i32; + var pixel = pixel_off; + var sd = score_diff; + var sy = Math.max(3, border); + var ey = Math.min((h-2), (h-border)); + var sx = Math.max(3, border); + var ex = Math.min((w - 3), (w - border)); + var _count = 0, corners_cnt = 0, pt; + var score_func = _cmp_score_16; + var thresh_tab = threshold_tab; + var threshold = _threshold; + + var v=0,tab=0,d=0,ncorners=0,cornerpos=0,curr=0,ptr=0,prev=0,pprev=0; + var jp1=0,jm1=0,score=0; + + _cmp_offsets(pixel, w, 16); + + // local vars are faster? + var pixel0 = pixel[0]; + var pixel1 = pixel[1]; + var pixel2 = pixel[2]; + var pixel3 = pixel[3]; + var pixel4 = pixel[4]; + var pixel5 = pixel[5]; + var pixel6 = pixel[6]; + var pixel7 = pixel[7]; + var pixel8 = pixel[8]; + var pixel9 = pixel[9]; + var pixel10 = pixel[10]; + var pixel11 = pixel[11]; + var pixel12 = pixel[12]; + var pixel13 = pixel[13]; + var pixel14 = pixel[14]; + var pixel15 = pixel[15]; + + for(i = 0; i < w*3; ++i) { + buf[i] = 0; + } + + for(i = sy; i < ey; ++i) { + ptr = ((i * w) + sx)|0; + m3 = (i - 3)%3; + curr = (m3*w)|0; + cornerpos = (m3*(w+1))|0; + for (j = 0; j < w; ++j) buf[curr+j] = 0; + ncorners = 0; + + if( i < (ey - 1) ) { + j = sx; + + for( ; j < ex; ++j, ++ptr ) { + v = img[ptr]; + tab = ( - v + 255 ); + d = ( thresh_tab[tab+img[ptr+pixel0]] | thresh_tab[tab+img[ptr+pixel8]] ); + + if( d == 0 ) { + continue; + } + + d &= ( thresh_tab[tab+img[ptr+pixel2]] | thresh_tab[tab+img[ptr+pixel10]] ); + d &= ( thresh_tab[tab+img[ptr+pixel4]] | thresh_tab[tab+img[ptr+pixel12]] ); + d &= ( thresh_tab[tab+img[ptr+pixel6]] | thresh_tab[tab+img[ptr+pixel14]] ); + + if( d == 0 ) { + continue; + } + + d &= ( thresh_tab[tab+img[ptr+pixel1]] | thresh_tab[tab+img[ptr+pixel9]] ); + d &= ( thresh_tab[tab+img[ptr+pixel3]] | thresh_tab[tab+img[ptr+pixel11]] ); + d &= ( thresh_tab[tab+img[ptr+pixel5]] | thresh_tab[tab+img[ptr+pixel13]] ); + d &= ( thresh_tab[tab+img[ptr+pixel7]] | thresh_tab[tab+img[ptr+pixel15]] ); + + if( d & 1 ) { + vt = (v - threshold); + _count = 0; + + for( k = 0; k < N; ++k ) { + x = img[(ptr+pixel[k])]; + if(x < vt) { + ++_count; + if( _count > K ) { + ++ncorners; + cpbuf[cornerpos+ncorners] = j; + buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); + break; + } + } + else { + _count = 0; + } + } + } + + if( d & 2 ) { + vt = (v + threshold); + _count = 0; + + for( k = 0; k < N; ++k ) { + x = img[(ptr+pixel[k])]; + if(x > vt) { + ++_count; + if( _count > K ) { + ++ncorners; + cpbuf[cornerpos+ncorners] = j; + buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); + break; + } + } + else { + _count = 0; + } + } + } + } + } + + cpbuf[cornerpos+w] = ncorners; + + if ( i == sy ) { + continue; + } + + m3 = (i - 4 + 3)%3; + prev = (m3*w)|0; + cornerpos = (m3*(w+1))|0; + m3 = (i - 5 + 3)%3; + pprev = (m3*w)|0; + + ncorners = cpbuf[cornerpos+w]; + + for( k = 0; k < ncorners; ++k ) { + j = cpbuf[cornerpos+k]; + jp1 = (j+1)|0; + jm1 = (j-1)|0; + score = buf[prev+j]; + if( (score > buf[prev+jp1] && score > buf[prev+jm1] && + score > buf[pprev+jm1] && score > buf[pprev+j] && score > buf[pprev+jp1] && + score > buf[curr+jm1] && score > buf[curr+j] && score > buf[curr+jp1]) ) { + // save corner + pt = corners[corners_cnt]; + pt.x = j, pt.y = (i-1), pt.score = score; + corners_cnt++; + } + } + } // y loop + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(cpbuf_node); + return corners_cnt; + } + }; + })(); + + global.fast_corners = fast_corners; + fast_corners.set_threshold(20); // set default + +})(jsfeat); diff --git a/src/jsfeat_haar.js b/src/jsfeat_haar.js new file mode 100755 index 0000000..7d29544 --- /dev/null +++ b/src/jsfeat_haar.js @@ -0,0 +1,290 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from https://github.com/mtschirs/js-objectdetect implementation + * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t + */ + +(function(global) { + "use strict"; + // + var haar = (function() { + + var _group_func = function(r1, r2) { + var distance = (r1.width * 0.25 + 0.5)|0; + + return r2.x <= r1.x + distance && + r2.x >= r1.x - distance && + r2.y <= r1.y + distance && + r2.y >= r1.y - distance && + r2.width <= (r1.width * 1.5 + 0.5)|0 && + (r2.width * 1.5 + 0.5)|0 >= r1.width; + } + + return { + + edges_density: 0.07, + + detect_single_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale, classifier) { + var win_w = (classifier.size[0] * scale)|0, + win_h = (classifier.size[1] * scale)|0, + step_x = (0.5 * scale + 1.5)|0, + step_y = step_x; + var i,j,k,x,y,ex=(width-win_w)|0,ey=(height-win_h)|0; + var w1=(width+1)|0,edge_dens,mean,variance,std; + var inv_area = 1.0 / (win_w * win_h); + var stages,stage,trees,tree,sn,tn,fn,found=true,stage_thresh,stage_sum,tree_sum,feature,features; + var fi_a,fi_b,fi_c,fi_d,fw,fh; + + var ii_a=0,ii_b=win_w,ii_c=win_h*w1,ii_d=ii_c+win_w; + var edges_thresh = ((win_w*win_h) * 0xff * this.edges_density)|0; + // if too much gradient we also can skip + //var edges_thresh_high = ((win_w*win_h) * 0xff * 0.3)|0; + + var rects = []; + for(y = 0; y < ey; y += step_y) { + ii_a = y * w1; + for(x = 0; x < ex; x += step_x, ii_a += step_x) { + + mean = int_sum[ii_a] + - int_sum[ii_a+ii_b] + - int_sum[ii_a+ii_c] + + int_sum[ii_a+ii_d]; + + // canny prune + if(int_canny_sum) { + edge_dens = (int_canny_sum[ii_a] + - int_canny_sum[ii_a+ii_b] + - int_canny_sum[ii_a+ii_c] + + int_canny_sum[ii_a+ii_d]); + if(edge_dens < edges_thresh || mean < 20) { + x += step_x, ii_a += step_x; + continue; + } + } + + mean *= inv_area; + variance = (int_sqsum[ii_a] + - int_sqsum[ii_a+ii_b] + - int_sqsum[ii_a+ii_c] + + int_sqsum[ii_a+ii_d]) * inv_area - mean * mean; + + std = variance > 0. ? Math.sqrt(variance) : 1; + + stages = classifier.complexClassifiers; + sn = stages.length; + found = true; + for(i = 0; i < sn; ++i) { + stage = stages[i]; + stage_thresh = stage.threshold; + trees = stage.simpleClassifiers; + tn = trees.length; + stage_sum = 0; + for(j = 0; j < tn; ++j) { + tree = trees[j]; + tree_sum = 0; + features = tree.features; + fn = features.length; + if(tree.tilted === 1) { + for(k=0; k < fn; ++k) { + feature = features[k]; + fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; + fw = ~~(feature[2] * scale); + fh = ~~(feature[3] * scale); + fi_b = fw * w1; + fi_c = fh * w1; + + tree_sum += (int_tilted[fi_a] + - int_tilted[fi_a + fw + fi_b] + - int_tilted[fi_a - fh + fi_c] + + int_tilted[fi_a + fw - fh + fi_b + fi_c]) * feature[4]; + } + } else { + for(k=0; k < fn; ++k) { + feature = features[k]; + fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; + fw = ~~(feature[2] * scale); + fh = ~~(feature[3] * scale); + fi_c = fh * w1; + + tree_sum += (int_sum[fi_a] + - int_sum[fi_a+fw] + - int_sum[fi_a+fi_c] + + int_sum[fi_a+fi_c+fw]) * feature[4]; + } + } + stage_sum += (tree_sum * inv_area < tree.threshold * std) ? tree.left_val : tree.right_val; + } + if (stage_sum < stage_thresh) { + found = false; + break; + } + } + + if(found) { + rects.push({"x" : x, + "y" : y, + "width" : win_w, + "height" : win_h, + "neighbor" : 1, + "confidence" : stage_sum}); + x += step_x, ii_a += step_x; + } + } + } + return rects; + }, + + detect_multi_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, classifier, scale_factor, scale_min) { + if (typeof scale_factor === "undefined") { scale_factor = 1.2; } + if (typeof scale_min === "undefined") { scale_min = 1.0; } + var win_w = classifier.size[0]; + var win_h = classifier.size[1]; + var rects = []; + while (scale_min * win_w < width && scale_min * win_h < height) { + rects = rects.concat(this.detect_single_scale(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale_min, classifier)); + scale_min *= scale_factor; + } + return rects; + }, + + // OpenCV method to group detected rectangles + group_rectangles: function(rects, min_neighbors) { + if (typeof min_neighbors === "undefined") { min_neighbors = 1; } + var i, j, n = rects.length; + var node = []; + for (i = 0; i < n; ++i) { + node[i] = {"parent" : -1, + "element" : rects[i], + "rank" : 0}; + } + for (i = 0; i < n; ++i) { + if (!node[i].element) + continue; + var root = i; + while (node[root].parent != -1) + root = node[root].parent; + for (j = 0; j < n; ++j) { + if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { + var root2 = j; + + while (node[root2].parent != -1) + root2 = node[root2].parent; + + if(root2 != root) { + if(node[root].rank > node[root2].rank) + node[root2].parent = root; + else { + node[root].parent = root2; + if (node[root].rank == node[root2].rank) + node[root2].rank++; + root = root2; + } + + /* compress path from node2 to the root: */ + var temp, node2 = j; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + + /* compress path from node to the root: */ + node2 = i; + while (node[node2].parent != -1) { + temp = node2; + node2 = node[node2].parent; + node[temp].parent = root; + } + } + } + } + } + var idx_seq = []; + var class_idx = 0; + for(i = 0; i < n; i++) { + j = -1; + var node1 = i; + if(node[node1].element) { + while (node[node1].parent != -1) + node1 = node[node1].parent; + if(node[node1].rank >= 0) + node[node1].rank = ~class_idx++; + j = ~node[node1].rank; + } + idx_seq[i] = j; + } + + var comps = []; + for (i = 0; i < class_idx+1; ++i) { + comps[i] = {"neighbors" : 0, + "x" : 0, + "y" : 0, + "width" : 0, + "height" : 0, + "confidence" : 0}; + } + + // count number of neighbors + for(i = 0; i < n; ++i) { + var r1 = rects[i]; + var idx = idx_seq[i]; + + if (comps[idx].neighbors == 0) + comps[idx].confidence = r1.confidence; + + ++comps[idx].neighbors; + + comps[idx].x += r1.x; + comps[idx].y += r1.y; + comps[idx].width += r1.width; + comps[idx].height += r1.height; + comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); + } + + var seq2 = []; + // calculate average bounding box + for(i = 0; i < class_idx; ++i) { + n = comps[i].neighbors; + if (n >= min_neighbors) + seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), + "y" : (comps[i].y * 2 + n) / (2 * n), + "width" : (comps[i].width * 2 + n) / (2 * n), + "height" : (comps[i].height * 2 + n) / (2 * n), + "neighbors" : comps[i].neighbors, + "confidence" : comps[i].confidence}); + } + + var result_seq = []; + n = seq2.length; + // filter out small face rectangles inside large face rectangles + for(i = 0; i < n; ++i) { + var r1 = seq2[i]; + var flag = true; + for(j = 0; j < n; ++j) { + var r2 = seq2[j]; + var distance = (r2.width * 0.25 + 0.5)|0; + + if(i != j && + r1.x >= r2.x - distance && + r1.y >= r2.y - distance && + r1.x + r1.width <= r2.x + r2.width + distance && + r1.y + r1.height <= r2.y + r2.height + distance && + (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { + flag = false; + break; + } + } + + if(flag) + result_seq.push(r1); + } + return result_seq; + } + }; + + })(); + + global.haar = haar; + +})(jsfeat); diff --git a/src/jsfeat_imgproc.js b/src/jsfeat_imgproc.js new file mode 100755 index 0000000..5462684 --- /dev/null +++ b/src/jsfeat_imgproc.js @@ -0,0 +1,1254 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var imgproc = (function() { + + var _resample_u8 = function(src, dst, nw, nh) { + var xofs_count=0; + var ch=src.channel,w=src.cols,h=src.rows; + var src_d=src.data,dst_d=dst.data; + var scale_x = w / nw, scale_y = h / nh; + var inv_scale_256 = (scale_x * scale_y * 0x10000)|0; + var dx=0,dy=0,sx=0,sy=0,sx1=0,sx2=0,i=0,k=0,fsx1=0.0,fsx2=0.0; + var a=0,b=0,dxn=0,alpha=0,beta=0,beta1=0; + + var buf_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var sum_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var xofs_node = jsfeat.cache.get_buffer((w*2*3)<<2); + + var buf = buf_node.i32; + var sum = sum_node.i32; + var xofs = xofs_node.i32; + + for (; dx < nw; dx++) { + fsx1 = dx * scale_x, fsx2 = fsx1 + scale_x; + sx1 = (fsx1 + 1.0 - 1e-6)|0, sx2 = fsx2|0; + sx1 = Math.min(sx1, w - 1); + sx2 = Math.min(sx2, w - 1); + + if(sx1 > fsx1) { + xofs[k++] = (dx * ch)|0; + xofs[k++] = ((sx1 - 1)*ch)|0; + xofs[k++] = ((sx1 - fsx1) * 0x100)|0; + xofs_count++; + } + for(sx = sx1; sx < sx2; sx++){ + xofs_count++; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx * ch)|0; + xofs[k++] = 256; + } + if(fsx2 - sx2 > 1e-3) { + xofs_count++; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx2 * ch)|0; + xofs[k++] = ((fsx2 - sx2) * 256)|0; + } + } + + for (dx = 0; dx < nw * ch; dx++) { + buf[dx] = sum[dx] = 0; + } + dy = 0; + for (sy = 0; sy < h; sy++) { + a = w * sy; + for (k = 0; k < xofs_count; k++) { + dxn = xofs[k*3]; + sx1 = xofs[k*3+1]; + alpha = xofs[k*3+2]; + for (i = 0; i < ch; i++) { + buf[dxn + i] += src_d[a+sx1+i] * alpha; + } + } + if ((dy + 1) * scale_y <= sy + 1 || sy == h - 1) { + beta = (Math.max(sy + 1 - (dy + 1) * scale_y, 0.0) * 256)|0; + beta1 = 256 - beta; + b = nw * dy; + if (beta <= 0) { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = Math.min(Math.max((sum[dx] + buf[dx] * 256) / inv_scale_256, 0), 255); + sum[dx] = buf[dx] = 0; + } + } else { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = Math.min(Math.max((sum[dx] + buf[dx] * beta1) / inv_scale_256, 0), 255); + sum[dx] = buf[dx] * beta; + buf[dx] = 0; + } + } + dy++; + } else { + for(dx = 0; dx < nw * ch; dx++) { + sum[dx] += buf[dx] * 256; + buf[dx] = 0; + } + } + } + + jsfeat.cache.put_buffer(sum_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(xofs_node); + } + + var _resample = function(src, dst, nw, nh) { + var xofs_count=0; + var ch=src.channel,w=src.cols,h=src.rows; + var src_d=src.data,dst_d=dst.data; + var scale_x = w / nw, scale_y = h / nh; + var scale = 1.0 / (scale_x * scale_y); + var dx=0,dy=0,sx=0,sy=0,sx1=0,sx2=0,i=0,k=0,fsx1=0.0,fsx2=0.0; + var a=0,b=0,dxn=0,alpha=0.0,beta=0.0,beta1=0.0; + + var buf_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var sum_node = jsfeat.cache.get_buffer((nw*ch)<<2); + var xofs_node = jsfeat.cache.get_buffer((w*2*3)<<2); + + var buf = buf_node.f32; + var sum = sum_node.f32; + var xofs = xofs_node.f32; + + for (; dx < nw; dx++) { + fsx1 = dx * scale_x, fsx2 = fsx1 + scale_x; + sx1 = (fsx1 + 1.0 - 1e-6)|0, sx2 = fsx2|0; + sx1 = Math.min(sx1, w - 1); + sx2 = Math.min(sx2, w - 1); + + if(sx1 > fsx1) { + xofs_count++; + xofs[k++] = ((sx1 - 1)*ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (sx1 - fsx1) * scale; + } + for(sx = sx1; sx < sx2; sx++){ + xofs_count++; + xofs[k++] = (sx * ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = scale; + } + if(fsx2 - sx2 > 1e-3) { + xofs_count++; + xofs[k++] = (sx2 * ch)|0; + xofs[k++] = (dx * ch)|0; + xofs[k++] = (fsx2 - sx2) * scale; + } + } + + for (dx = 0; dx < nw * ch; dx++) { + buf[dx] = sum[dx] = 0; + } + dy = 0; + for (sy = 0; sy < h; sy++) { + a = w * sy; + for (k = 0; k < xofs_count; k++) { + sx1 = xofs[k*3]|0; + dxn = xofs[k*3+1]|0; + alpha = xofs[k*3+2]; + for (i = 0; i < ch; i++) { + buf[dxn + i] += src_d[a+sx1+i] * alpha; + } + } + if ((dy + 1) * scale_y <= sy + 1 || sy == h - 1) { + beta = Math.max(sy + 1 - (dy + 1) * scale_y, 0.0); + beta1 = 1.0 - beta; + b = nw * dy; + if (Math.abs(beta) < 1e-3) { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = sum[dx] + buf[dx]; + sum[dx] = buf[dx] = 0; + } + } else { + for (dx = 0; dx < nw * ch; dx++) { + dst_d[b+dx] = sum[dx] + buf[dx] * beta1; + sum[dx] = buf[dx] * beta; + buf[dx] = 0; + } + } + dy++; + } else { + for(dx = 0; dx < nw * ch; dx++) { + sum[dx] += buf[dx]; + buf[dx] = 0; + } + } + } + jsfeat.cache.put_buffer(sum_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(xofs_node); + } + + var _convol_u8 = function(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel) { + var i=0,j=0,k=0,sp=0,dp=0,sum=0,sum1=0,sum2=0,sum3=0,f0=filter[0],fk=0; + var w2=w<<1,w3=w*3,w4=w<<2; + // hor pass + for (; i < h; ++i) { + sum = src_d[sp]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + for (j = 0; j <= w-2; j+=2) { + buf[j + half_kernel] = src_d[sp+j]; + buf[j + half_kernel+1] = src_d[sp+j+1]; + } + for (; j < w; ++j) { + buf[j + half_kernel] = src_d[sp+j]; + } + sum = src_d[sp+w-1]; + for (j = w; j < half_kernel + w; ++j) { + buf[j + half_kernel] = sum; + } + for (j = 0; j <= w-4; j+=4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp+j] = Math.min(sum >> 8, 255); + dst_d[dp+j+1] = Math.min(sum1 >> 8, 255); + dst_d[dp+j+2] = Math.min(sum2 >> 8, 255); + dst_d[dp+j+3] = Math.min(sum3 >> 8, 255); + } + for (; j < w; ++j) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp+j] = Math.min(sum >> 8, 255); + } + sp += w; + dp += w; + } + + // vert pass + for (i = 0; i < w; ++i) { + sum = dst_d[i]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + k = i; + for (j = 0; j <= h-2; j+=2, k+=w2) { + buf[j+half_kernel] = dst_d[k]; + buf[j+half_kernel+1] = dst_d[k+w]; + } + for (; j < h; ++j, k+=w) { + buf[j+half_kernel] = dst_d[k]; + } + sum = dst_d[(h-1)*w + i]; + for (j = h; j < half_kernel + h; ++j) { + buf[j + half_kernel] = sum; + } + dp = i; + for (j = 0; j <= h-4; j+=4, dp+=w4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp] = Math.min(sum >> 8, 255); + dst_d[dp+w] = Math.min(sum1 >> 8, 255); + dst_d[dp+w2] = Math.min(sum2 >> 8, 255); + dst_d[dp+w3] = Math.min(sum3 >> 8, 255); + } + for (; j < h; ++j, dp+=w) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp] = Math.min(sum >> 8, 255); + } + } + } + + var _convol = function(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel) { + var i=0,j=0,k=0,sp=0,dp=0,sum=0.0,sum1=0.0,sum2=0.0,sum3=0.0,f0=filter[0],fk=0.0; + var w2=w<<1,w3=w*3,w4=w<<2; + // hor pass + for (; i < h; ++i) { + sum = src_d[sp]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + for (j = 0; j <= w-2; j+=2) { + buf[j + half_kernel] = src_d[sp+j]; + buf[j + half_kernel+1] = src_d[sp+j+1]; + } + for (; j < w; ++j) { + buf[j + half_kernel] = src_d[sp+j]; + } + sum = src_d[sp+w-1]; + for (j = w; j < half_kernel + w; ++j) { + buf[j + half_kernel] = sum; + } + for (j = 0; j <= w-4; j+=4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp+j] = sum; + dst_d[dp+j+1] = sum1; + dst_d[dp+j+2] = sum2; + dst_d[dp+j+3] = sum3; + } + for (; j < w; ++j) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp+j] = sum; + } + sp += w; + dp += w; + } + + // vert pass + for (i = 0; i < w; ++i) { + sum = dst_d[i]; + for (j = 0; j < half_kernel; ++j) { + buf[j] = sum; + } + k = i; + for (j = 0; j <= h-2; j+=2, k+=w2) { + buf[j+half_kernel] = dst_d[k]; + buf[j+half_kernel+1] = dst_d[k+w]; + } + for (; j < h; ++j, k+=w) { + buf[j+half_kernel] = dst_d[k]; + } + sum = dst_d[(h-1)*w + i]; + for (j = h; j < half_kernel + h; ++j) { + buf[j + half_kernel] = sum; + } + dp = i; + for (j = 0; j <= h-4; j+=4, dp+=w4) { + sum = buf[j] * f0, + sum1 = buf[j+1] * f0, + sum2 = buf[j+2] * f0, + sum3 = buf[j+3] * f0; + for (k = 1; k < kernel_size; ++k) { + fk = filter[k]; + sum += buf[k + j] * fk; + sum1 += buf[k + j+1] * fk; + sum2 += buf[k + j+2] * fk; + sum3 += buf[k + j+3] * fk; + } + dst_d[dp] = sum; + dst_d[dp+w] = sum1; + dst_d[dp+w2] = sum2; + dst_d[dp+w3] = sum3; + } + for (; j < h; ++j, dp+=w) { + sum = buf[j] * f0; + for (k = 1; k < kernel_size; ++k) { + sum += buf[k + j] * filter[k]; + } + dst_d[dp] = sum; + } + } + } + + return { + // TODO: add support for RGB/BGR order + // for raw arrays + grayscale: function(src, w, h, dst, code) { + // this is default image data representation in browser + if (typeof code === "undefined") { code = jsfeat.COLOR_RGBA2GRAY; } + var x=0, y=0, i=0, j=0, ir=0,jr=0; + var coeff_r = 4899, coeff_g = 9617, coeff_b = 1868, cn = 4; + + if(code == jsfeat.COLOR_BGRA2GRAY || code == jsfeat.COLOR_BGR2GRAY) { + coeff_r = 1868; + coeff_b = 4899; + } + if(code == jsfeat.COLOR_RGB2GRAY || code == jsfeat.COLOR_BGR2GRAY) { + cn = 3; + } + var cn2 = cn<<1, cn3 = (cn*3)|0; + + dst.resize(w, h, 1); + var dst_u8 = dst.data; + + for(y = 0; y < h; ++y, j+=w, i+=w*cn) { + for(x = 0, ir = i, jr = j; x <= w-4; x+=4, ir+=cn<<2, jr+=4) { + dst_u8[jr] = (src[ir] * coeff_r + src[ir+1] * coeff_g + src[ir+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 1] = (src[ir+cn] * coeff_r + src[ir+cn+1] * coeff_g + src[ir+cn+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 2] = (src[ir+cn2] * coeff_r + src[ir+cn2+1] * coeff_g + src[ir+cn2+2] * coeff_b + 8192) >> 14; + dst_u8[jr + 3] = (src[ir+cn3] * coeff_r + src[ir+cn3+1] * coeff_g + src[ir+cn3+2] * coeff_b + 8192) >> 14; + } + for (; x < w; ++x, ++jr, ir+=cn) { + dst_u8[jr] = (src[ir] * coeff_r + src[ir+1] * coeff_g + src[ir+2] * coeff_b + 8192) >> 14; + } + } + }, + // derived from CCV library + resample: function(src, dst, nw, nh) { + var h=src.rows,w=src.cols; + if (h > nh && w > nw) { + dst.resize(nw, nh, src.channel); + // using the fast alternative (fix point scale, 0x100 to avoid overflow) + if (src.type&jsfeat.U8_t && dst.type&jsfeat.U8_t && h * w / (nh * nw) < 0x100) { + _resample_u8(src, dst, nw, nh); + } else { + _resample(src, dst, nw, nh); + } + } + }, + + box_blur_gray: function(src, dst, radius, options) { + if (typeof options === "undefined") { options = 0; } + var w=src.cols, h=src.rows, h2=h<<1, w2=w<<1; + var i=0,x=0,y=0,end=0; + var windowSize = ((radius << 1) + 1)|0; + var radiusPlusOne = (radius + 1)|0, radiusPlus2 = (radiusPlusOne+1)|0; + var scale = options&jsfeat.BOX_BLUR_NOSCALE ? 1 : (1.0 / (windowSize*windowSize)); + + var tmp_buff = jsfeat.cache.get_buffer((w*h)<<2); + + var sum=0, dstIndex=0, srcIndex = 0, nextPixelIndex=0, previousPixelIndex=0; + var data_i32 = tmp_buff.i32; // to prevent overflow + var data_u8 = src.data; + var hold=0; + + dst.resize(w, h, src.channel); + + // first pass + // no need to scale + //data_u8 = src.data; + //data_i32 = tmp; + for (y = 0; y < h; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_u8[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_u8[i]; + } + + nextPixelIndex = (srcIndex + radiusPlusOne)|0; + previousPixelIndex = srcIndex; + hold = data_u8[previousPixelIndex]; + for(x = 0; x < radius; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < w-radiusPlus2; x+=2, dstIndex += h2) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- data_u8[previousPixelIndex]; + + data_i32[dstIndex+h] = sum; + sum += data_u8[nextPixelIndex+1]- data_u8[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < w-radiusPlusOne; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + sum += data_u8[nextPixelIndex]- data_u8[previousPixelIndex]; + + nextPixelIndex ++; + previousPixelIndex ++; + } + + hold = data_u8[nextPixelIndex-1]; + for(; x < w; ++x, dstIndex += h) { + data_i32[dstIndex] = sum; + + sum += hold- data_u8[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += w; + } + // + // second pass + srcIndex = 0; + //data_i32 = tmp; // this is a transpose + data_u8 = dst.data; + + // dont scale result + if(scale == 1) { + for (y = 0; y < w; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_i32[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_i32[i]; + } + + nextPixelIndex = srcIndex + radiusPlusOne; + previousPixelIndex = srcIndex; + hold = data_i32[previousPixelIndex]; + + for(x = 0; x < radius; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + sum += data_i32[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < h-radiusPlus2; x+=2, dstIndex += w2) { + data_u8[dstIndex] = sum; + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + + data_u8[dstIndex+w] = sum; + sum += data_i32[nextPixelIndex+1]- data_i32[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < h-radiusPlusOne; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + nextPixelIndex ++; + previousPixelIndex ++; + } + hold = data_i32[nextPixelIndex-1]; + for(; x < h; ++x, dstIndex += w) { + data_u8[dstIndex] = sum; + + sum += hold- data_i32[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += h; + } + } else { + for (y = 0; y < w; ++y) { + dstIndex = y; + sum = radiusPlusOne * data_i32[srcIndex]; + + for(i = (srcIndex+1)|0, end=(srcIndex+radius)|0; i <= end; ++i) { + sum += data_i32[i]; + } + + nextPixelIndex = srcIndex + radiusPlusOne; + previousPixelIndex = srcIndex; + hold = data_i32[previousPixelIndex]; + + for(x = 0; x < radius; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + sum += data_i32[nextPixelIndex]- hold; + nextPixelIndex ++; + } + for(; x < h-radiusPlus2; x+=2, dstIndex += w2) { + data_u8[dstIndex] = sum*scale; + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + + data_u8[dstIndex+w] = sum*scale; + sum += data_i32[nextPixelIndex+1]- data_i32[previousPixelIndex+1]; + + nextPixelIndex +=2; + previousPixelIndex +=2; + } + for(; x < h-radiusPlusOne; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + + sum += data_i32[nextPixelIndex]- data_i32[previousPixelIndex]; + nextPixelIndex ++; + previousPixelIndex ++; + } + hold = data_i32[nextPixelIndex-1]; + for(; x < h; ++x, dstIndex += w) { + data_u8[dstIndex] = sum*scale; + + sum += hold- data_i32[previousPixelIndex]; + previousPixelIndex ++; + } + + srcIndex += h; + } + } + + jsfeat.cache.put_buffer(tmp_buff); + }, + + gaussian_blur: function(src, dst, kernel_size, sigma) { + if (typeof sigma === "undefined") { sigma = 0.0; } + if (typeof kernel_size === "undefined") { kernel_size = 0; } + kernel_size = kernel_size == 0 ? (Math.max(1, (4.0 * sigma + 1.0 - 1e-8)) * 2 + 1)|0 : kernel_size; + var half_kernel = kernel_size >> 1; + var w = src.cols, h = src.rows; + var data_type = src.type, is_u8 = data_type&jsfeat.U8_t; + + dst.resize(w, h, src.channel); + + var src_d = src.data, dst_d = dst.data; + var buf,filter,buf_sz=(kernel_size + Math.max(h, w))|0; + + var buf_node = jsfeat.cache.get_buffer(buf_sz<<2); + var filt_node = jsfeat.cache.get_buffer(kernel_size<<2); + + if(is_u8) { + buf = buf_node.i32; + filter = filt_node.i32; + } else if(data_type&jsfeat.S32_t) { + buf = buf_node.i32; + filter = filt_node.f32; + } else { + buf = buf_node.f32; + filter = filt_node.f32; + } + + jsfeat.math.get_gaussian_kernel(kernel_size, sigma, filter, data_type); + + if(is_u8) { + _convol_u8(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel); + } else { + _convol(buf, src_d, dst_d, w, h, filter, kernel_size, half_kernel); + } + + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(filt_node); + }, + hough_transform: function( img, rho_res, theta_res, threshold ) { + var image = img.data; + + var width = img.cols; + var height = img.rows; + var step = width; + + var min_theta = 0.0; + var max_theta = Math.PI; + + var numangle = Math.round((max_theta - min_theta) / theta_res); + var numrho = Math.round(((width + height) * 2 + 1) / rho_res); + var irho = 1.0 / rho_res; + + var accum = new Int32Array((numangle+2) * (numrho+2)); //typed arrays are initialized to 0 + var tabSin = new Float32Array(numangle); + var tabCos = new Float32Array(numangle); + + var n=0; + var ang = min_theta; + for(; n < numangle; n++ ) { + tabSin[n] = Math.sin(ang) * irho; + tabCos[n] = Math.cos(ang) * irho; + ang += theta_res + } + + // stage 1. fill accumulator + for( var i = 0; i < height; i++ ) { + for( var j = 0; j < width; j++ ) { + if( image[i * step + j] != 0 ) { + //console.log(r, (n+1) * (numrho+2) + r+1, tabCos[n], tabSin[n]); + for(var n = 0; n < numangle; n++ ) { + var r = Math.round( j * tabCos[n] + i * tabSin[n] ); + r += (numrho - 1) / 2; + accum[(n+1) * (numrho+2) + r+1] += 1; + } + } + } + } + + // stage 2. find local maximums + //TODO: Consider making a vector class that uses typed arrays + var _sort_buf = new Array(); + for(var r = 0; r < numrho; r++ ) { + for(var n = 0; n < numangle; n++ ) { + var base = (n+1) * (numrho+2) + r+1; + if( accum[base] > threshold && + accum[base] > accum[base - 1] && accum[base] >= accum[base + 1] && + accum[base] > accum[base - numrho - 2] && accum[base] >= accum[base + numrho + 2] ) { + _sort_buf.push(base); + } + } + } + + // stage 3. sort the detected lines by accumulator value + _sort_buf.sort(function(l1, l2) { + return accum[l1] > accum[l2] || (accum[l1] == accum[l2] && l1 < l2); + }); + + // stage 4. store the first min(total,linesMax) lines to the output buffer + var linesMax = Math.min(numangle*numrho, _sort_buf.length); + var scale = 1.0 / (numrho+2); + var lines = new Array(); + for( var i = 0; i < linesMax; i++ ) { + var idx = _sort_buf[i]; + var n = Math.floor(idx*scale) - 1; + var r = idx - (n+1)*(numrho+2) - 1; + var lrho = (r - (numrho - 1)*0.5) * rho_res; + var langle = n * theta_res; + lines.push([lrho, langle]); + } + return lines; + }, + // assume we always need it for u8 image + pyrdown: function(src, dst, sx, sy) { + // this is needed for bbf + if (typeof sx === "undefined") { sx = 0; } + if (typeof sy === "undefined") { sy = 0; } + + var w = src.cols, h = src.rows; + var w2 = w >> 1, h2 = h >> 1; + var _w2 = w2 - (sx << 1), _h2 = h2 - (sy << 1); + var x=0,y=0,sptr=sx+sy*w,sline=0,dptr=0,dline=0; + + dst.resize(w2, h2, src.channel); + + var src_d = src.data, dst_d = dst.data; + + for(y = 0; y < _h2; ++y) { + sline = sptr; + dline = dptr; + for(x = 0; x <= _w2-2; x+=2, dline+=2, sline += 4) { + dst_d[dline] = (src_d[sline] + src_d[sline+1] + + src_d[sline+w] + src_d[sline+w+1] + 2) >> 2; + dst_d[dline+1] = (src_d[sline+2] + src_d[sline+3] + + src_d[sline+w+2] + src_d[sline+w+3] + 2) >> 2; + } + for(; x < _w2; ++x, ++dline, sline += 2) { + dst_d[dline] = (src_d[sline] + src_d[sline+1] + + src_d[sline+w] + src_d[sline+w+1] + 2) >> 2; + } + sptr += w << 1; + dptr += w2; + } + }, + + // dst: [gx,gy,...] + scharr_derivatives: function(src, dst) { + var w = src.cols, h = src.rows; + var dstep = w<<1,x=0,y=0,x1=0,a,b,c,d,e,f; + var srow0=0,srow1=0,srow2=0,drow=0; + var trow0,trow1; + + dst.resize(w, h, 2); // 2 channel output gx, gy + + var img = src.data, gxgy=dst.data; + + var buf0_node = jsfeat.cache.get_buffer((w+2)<<2); + var buf1_node = jsfeat.cache.get_buffer((w+2)<<2); + + if(src.type&jsfeat.U8_t || src.type&jsfeat.S32_t) { + trow0 = buf0_node.i32; + trow1 = buf1_node.i32; + } else { + trow0 = buf0_node.f32; + trow1 = buf1_node.f32; + } + + for(; y < h; ++y, srow1+=w) { + srow0 = ((y > 0 ? y-1 : 1)*w)|0; + srow2 = ((y < h-1 ? y+1 : h-2)*w)|0; + drow = (y*dstep)|0; + // do vertical convolution + for(x = 0, x1 = 1; x <= w-2; x+=2, x1+=2) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b)*3 + (img[srow1+x])*10 ); + trow1[x1] = ( b - a ); + // + a = img[srow0+x+1], b = img[srow2+x+1]; + trow0[x1+1] = ( (a + b)*3 + (img[srow1+x+1])*10 ); + trow1[x1+1] = ( b - a ); + } + for(; x < w; ++x, ++x1) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b)*3 + (img[srow1+x])*10 ); + trow1[x1] = ( b - a ); + } + // make border + x = (w + 1)|0; + trow0[0] = trow0[1]; trow0[x] = trow0[w]; + trow1[0] = trow1[1]; trow1[x] = trow1[w]; + // do horizontal convolution, interleave the results and store them + for(x = 0; x <= w-4; x+=4) { + a = trow1[x+2], b = trow1[x+1], c = trow1[x+3], d = trow1[x+4], + e = trow0[x+2], f = trow0[x+3]; + gxgy[drow++] = ( e - trow0[x] ); + gxgy[drow++] = ( (a + trow1[x])*3 + b*10 ); + gxgy[drow++] = ( f - trow0[x+1] ); + gxgy[drow++] = ( (c + b)*3 + a*10 ); + + gxgy[drow++] = ( (trow0[x+4] - e) ); + gxgy[drow++] = ( ((d + a)*3 + c*10) ); + gxgy[drow++] = ( (trow0[x+5] - f) ); + gxgy[drow++] = ( ((trow1[x+5] + c)*3 + d*10) ); + } + for(; x < w; ++x) { + gxgy[drow++] = ( (trow0[x+2] - trow0[x]) ); + gxgy[drow++] = ( ((trow1[x+2] + trow1[x])*3 + trow1[x+1]*10) ); + } + } + jsfeat.cache.put_buffer(buf0_node); + jsfeat.cache.put_buffer(buf1_node); + }, + + // compute gradient using Sobel kernel [1 2 1] * [-1 0 1]^T + // dst: [gx,gy,...] + sobel_derivatives: function(src, dst) { + var w = src.cols, h = src.rows; + var dstep = w<<1,x=0,y=0,x1=0,a,b,c,d,e,f; + var srow0=0,srow1=0,srow2=0,drow=0; + var trow0,trow1; + + dst.resize(w, h, 2); // 2 channel output gx, gy + + var img = src.data, gxgy=dst.data; + + var buf0_node = jsfeat.cache.get_buffer((w+2)<<2); + var buf1_node = jsfeat.cache.get_buffer((w+2)<<2); + + if(src.type&jsfeat.U8_t || src.type&jsfeat.S32_t) { + trow0 = buf0_node.i32; + trow1 = buf1_node.i32; + } else { + trow0 = buf0_node.f32; + trow1 = buf1_node.f32; + } + + for(; y < h; ++y, srow1+=w) { + srow0 = ((y > 0 ? y-1 : 1)*w)|0; + srow2 = ((y < h-1 ? y+1 : h-2)*w)|0; + drow = (y*dstep)|0; + // do vertical convolution + for(x = 0, x1 = 1; x <= w-2; x+=2, x1+=2) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b) + (img[srow1+x]*2) ); + trow1[x1] = ( b - a ); + // + a = img[srow0+x+1], b = img[srow2+x+1]; + trow0[x1+1] = ( (a + b) + (img[srow1+x+1]*2) ); + trow1[x1+1] = ( b - a ); + } + for(; x < w; ++x, ++x1) { + a = img[srow0+x], b = img[srow2+x]; + trow0[x1] = ( (a + b) + (img[srow1+x]*2) ); + trow1[x1] = ( b - a ); + } + // make border + x = (w + 1)|0; + trow0[0] = trow0[1]; trow0[x] = trow0[w]; + trow1[0] = trow1[1]; trow1[x] = trow1[w]; + // do horizontal convolution, interleave the results and store them + for(x = 0; x <= w-4; x+=4) { + a = trow1[x+2], b = trow1[x+1], c = trow1[x+3], d = trow1[x+4], + e = trow0[x+2], f = trow0[x+3]; + gxgy[drow++] = ( e - trow0[x] ); + gxgy[drow++] = ( a + trow1[x] + b*2 ); + gxgy[drow++] = ( f - trow0[x+1] ); + gxgy[drow++] = ( c + b + a*2 ); + + gxgy[drow++] = ( trow0[x+4] - e ); + gxgy[drow++] = ( d + a + c*2 ); + gxgy[drow++] = ( trow0[x+5] - f ); + gxgy[drow++] = ( trow1[x+5] + c + d*2 ); + } + for(; x < w; ++x) { + gxgy[drow++] = ( trow0[x+2] - trow0[x] ); + gxgy[drow++] = ( trow1[x+2] + trow1[x] + trow1[x+1]*2 ); + } + } + jsfeat.cache.put_buffer(buf0_node); + jsfeat.cache.put_buffer(buf1_node); + }, + + // please note: + // dst_(type) size should be cols = src.cols+1, rows = src.rows+1 + compute_integral_image: function(src, dst_sum, dst_sqsum, dst_tilted) { + var w0=src.cols|0,h0=src.rows|0,src_d=src.data; + var w1=(w0+1)|0; + var s=0,s2=0,p=0,pup=0,i=0,j=0,v=0,k=0; + + if(dst_sum && dst_sqsum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sum[i] = 0, dst_sqsum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s = s2 = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + v = src_d[k]; + s += v, s2 += v*v; + dst_sum[p] = dst_sum[pup] + s; + dst_sqsum[p] = dst_sqsum[pup] + s2; + + v = src_d[k+1]; + s += v, s2 += v*v; + dst_sum[p+1] = dst_sum[pup+1] + s; + dst_sqsum[p+1] = dst_sqsum[pup+1] + s2; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + v = src_d[k]; + s += v, s2 += v*v; + dst_sum[p] = dst_sum[pup] + s; + dst_sqsum[p] = dst_sqsum[pup] + s2; + } + } + } else if(dst_sum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + s += src_d[k]; + dst_sum[p] = dst_sum[pup] + s; + s += src_d[k+1]; + dst_sum[p+1] = dst_sum[pup+1] + s; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + s += src_d[k]; + dst_sum[p] = dst_sum[pup] + s; + } + } + } else if(dst_sqsum) { + // fill first row with zeros + for(; i < w1; ++i) { + dst_sqsum[i] = 0; + } + p = (w1+1)|0, pup = 1; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + s2 = 0; + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + v = src_d[k]; + s2 += v*v; + dst_sqsum[p] = dst_sqsum[pup] + s2; + v = src_d[k+1]; + s2 += v*v; + dst_sqsum[p+1] = dst_sqsum[pup+1] + s2; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + v = src_d[k]; + s2 += v*v; + dst_sqsum[p] = dst_sqsum[pup] + s2; + } + } + } + + if(dst_tilted) { + // fill first row with zeros + for(i = 0; i < w1; ++i) { + dst_tilted[i] = 0; + } + // diagonal + p = (w1+1)|0, pup = 0; + for(i = 0, k = 0; i < h0; ++i, ++p, ++pup) { + for(j = 0; j <= w0-2; j+=2, k+=2, p+=2, pup+=2) { + dst_tilted[p] = src_d[k] + dst_tilted[pup]; + dst_tilted[p+1] = src_d[k+1] + dst_tilted[pup+1]; + } + for(; j < w0; ++j, ++k, ++p, ++pup) { + dst_tilted[p] = src_d[k] + dst_tilted[pup]; + } + } + // diagonal + p = (w1+w0)|0, pup = w0; + for(i = 0; i < h0; ++i, p+=w1, pup+=w1) { + dst_tilted[p] += dst_tilted[pup]; + } + + for(j = w0-1; j > 0; --j) { + p = j+h0*w1, pup=p-w1; + for(i = h0; i > 0; --i, p-=w1, pup-=w1) { + dst_tilted[p] += dst_tilted[pup] + dst_tilted[pup+1]; + } + } + } + }, + equalize_histogram: function(src, dst) { + var w=src.cols,h=src.rows,src_d=src.data; + + dst.resize(w, h, src.channel); + + var dst_d=dst.data,size=w*h; + var i=0,prev=0,hist0,norm; + + var hist0_node = jsfeat.cache.get_buffer(256<<2); + hist0 = hist0_node.i32; + for(; i < 256; ++i) hist0[i] = 0; + for (i = 0; i < size; ++i) { + ++hist0[src_d[i]]; + } + + prev = hist0[0]; + for (i = 1; i < 256; ++i) { + prev = hist0[i] += prev; + } + + norm = 255 / size; + for (i = 0; i < size; ++i) { + dst_d[i] = (hist0[src_d[i]] * norm + 0.5)|0; + } + jsfeat.cache.put_buffer(hist0_node); + }, + + canny: function(src, dst, low_thresh, high_thresh) { + var w=src.cols,h=src.rows,src_d=src.data; + + dst.resize(w, h, src.channel); + + var dst_d=dst.data; + var i=0,j=0,grad=0,w2=w<<1,_grad=0,suppress=0,f=0,x=0,y=0,s=0; + var tg22x=0,tg67x=0; + + // cache buffers + var dxdy_node = jsfeat.cache.get_buffer((h * w2)<<2); + var buf_node = jsfeat.cache.get_buffer((3 * (w + 2))<<2); + var map_node = jsfeat.cache.get_buffer(((h+2) * (w + 2))<<2); + var stack_node = jsfeat.cache.get_buffer((h * w)<<2); + + + var buf = buf_node.i32; + var map = map_node.i32; + var stack = stack_node.i32; + var dxdy = dxdy_node.i32; + var dxdy_m = new jsfeat.matrix_t(w, h, jsfeat.S32C2_t, dxdy_node.data); + var row0=1,row1=(w+2+1)|0,row2=(2*(w+2)+1)|0,map_w=(w+2)|0,map_i=(map_w+1)|0,stack_i=0; + + this.sobel_derivatives(src, dxdy_m); + + if(low_thresh > high_thresh) { + i = low_thresh; + low_thresh = high_thresh; + high_thresh = i; + } + + i = (3 * (w + 2))|0; + while(--i>=0) { + buf[i] = 0; + } + + i = ((h+2) * (w + 2))|0; + while(--i>=0) { + map[i] = 0; + } + + for (; j < w; ++j, grad+=2) { + //buf[row1+j] = Math.abs(dxdy[grad]) + Math.abs(dxdy[grad+1]); + x = dxdy[grad], y = dxdy[grad+1]; + //buf[row1+j] = x*x + y*y; + buf[row1+j] = ((x ^ (x >> 31)) - (x >> 31)) + ((y ^ (y >> 31)) - (y >> 31)); + } + + for(i=1; i <= h; ++i, grad+=w2) { + if(i == h) { + j = row2+w; + while(--j>=row2) { + buf[j] = 0; + } + } else { + for (j = 0; j < w; j++) { + //buf[row2+j] = Math.abs(dxdy[grad+(j<<1)]) + Math.abs(dxdy[grad+(j<<1)+1]); + x = dxdy[grad+(j<<1)], y = dxdy[grad+(j<<1)+1]; + //buf[row2+j] = x*x + y*y; + buf[row2+j] = ((x ^ (x >> 31)) - (x >> 31)) + ((y ^ (y >> 31)) - (y >> 31)); + } + } + _grad = (grad - w2)|0; + map[map_i-1] = 0; + suppress = 0; + for(j = 0; j < w; ++j, _grad+=2) { + f = buf[row1+j]; + if (f > low_thresh) { + x = dxdy[_grad]; + y = dxdy[_grad+1]; + s = x ^ y; + // seems ot be faster than Math.abs + x = ((x ^ (x >> 31)) - (x >> 31))|0; + y = ((y ^ (y >> 31)) - (y >> 31))|0; + //x * tan(22.5) x * tan(67.5) == 2 * x + x * tan(22.5) + tg22x = x * 13573; + tg67x = tg22x + ((x + x) << 15); + y <<= 15; + if (y < tg22x) { + if (f > buf[row1+j-1] && f >= buf[row1+j+1]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } else if (y > tg67x) { + if (f > buf[row0+j] && f >= buf[row2+j]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } else { + s = s < 0 ? -1 : 1; + if (f > buf[row0+j-s] && f > buf[row2+j+s]) { + if (f > high_thresh && !suppress && map[map_i+j-map_w] != 2) { + map[map_i+j] = 2; + suppress = 1; + stack[stack_i++] = map_i + j; + } else { + map[map_i+j] = 1; + } + continue; + } + } + } + map[map_i+j] = 0; + suppress = 0; + } + map[map_i+w] = 0; + map_i += map_w; + j = row0; + row0 = row1; + row1 = row2; + row2 = j; + } + + j = map_i - map_w - 1; + for(i = 0; i < map_w; ++i, ++j) { + map[j] = 0; + } + // path following + while(stack_i > 0) { + map_i = stack[--stack_i]; + map_i -= map_w+1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += map_w; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i -= 2; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += map_w; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + map_i += 1; + if(map[map_i] == 1) map[map_i] = 2, stack[stack_i++] = map_i; + } + + map_i = map_w + 1; + row0 = 0; + for(i = 0; i < h; ++i, map_i+=map_w) { + for(j = 0; j < w; ++j) { + dst_d[row0++] = (map[map_i+j] == 2) * 0xff; + } + } + + // free buffers + jsfeat.cache.put_buffer(dxdy_node); + jsfeat.cache.put_buffer(buf_node); + jsfeat.cache.put_buffer(map_node); + jsfeat.cache.put_buffer(stack_node); + }, + // transform is 3x3 matrix_t + warp_perspective: function(src, dst, transform, fill_value) { + if (typeof fill_value === "undefined") { fill_value = 0; } + var src_width=src.cols|0, src_height=src.rows|0, dst_width=dst.cols|0, dst_height=dst.rows|0; + var src_d=src.data, dst_d=dst.data; + var x=0,y=0,off=0,ixs=0,iys=0,xs=0.0,ys=0.0,xs0=0.0,ys0=0.0,ws=0.0,sc=0.0,a=0.0,b=0.0,p0=0.0,p1=0.0; + var td=transform.data; + var m00=td[0],m01=td[1],m02=td[2], + m10=td[3],m11=td[4],m12=td[5], + m20=td[6],m21=td[7],m22=td[8]; + + for(var dptr = 0; y < dst_height; ++y) { + xs0 = m01 * y + m02, + ys0 = m11 * y + m12, + ws = m21 * y + m22; + for(x = 0; x < dst_width; ++x, ++dptr, xs0+=m00, ys0+=m10, ws+=m20) { + sc = 1.0 / ws; + xs = xs0 * sc, ys = ys0 * sc; + ixs = xs | 0, iys = ys | 0; + + if(xs > 0 && ys > 0 && ixs < (src_width - 1) && iys < (src_height - 1)) { + a = Math.max(xs - ixs, 0.0); + b = Math.max(ys - iys, 0.0); + off = (src_width*iys + ixs)|0; + + p0 = src_d[off] + a * (src_d[off+1] - src_d[off]); + p1 = src_d[off+src_width] + a * (src_d[off+src_width+1] - src_d[off+src_width]); + + dst_d[dptr] = p0 + b * (p1 - p0); + } + else dst_d[dptr] = fill_value; + } + } + }, + // transform is 3x3 or 2x3 matrix_t only first 6 values referenced + warp_affine: function(src, dst, transform, fill_value) { + if (typeof fill_value === "undefined") { fill_value = 0; } + var src_width=src.cols, src_height=src.rows, dst_width=dst.cols, dst_height=dst.rows; + var src_d=src.data, dst_d=dst.data; + var x=0,y=0,off=0,ixs=0,iys=0,xs=0.0,ys=0.0,a=0.0,b=0.0,p0=0.0,p1=0.0; + var td=transform.data; + var m00=td[0],m01=td[1],m02=td[2], + m10=td[3],m11=td[4],m12=td[5]; + + for(var dptr = 0; y < dst_height; ++y) { + xs = m01 * y + m02; + ys = m11 * y + m12; + for(x = 0; x < dst_width; ++x, ++dptr, xs+=m00, ys+=m10) { + ixs = xs | 0; iys = ys | 0; + + if(ixs >= 0 && iys >= 0 && ixs < (src_width - 1) && iys < (src_height - 1)) { + a = xs - ixs; + b = ys - iys; + off = src_width*iys + ixs; + + p0 = src_d[off] + a * (src_d[off+1] - src_d[off]); + p1 = src_d[off+src_width] + a * (src_d[off+src_width+1] - src_d[off+src_width]); + + dst_d[dptr] = p0 + b * (p1 - p0); + } + else dst_d[dptr] = fill_value; + } + } + }, + + // Basic RGB Skin detection filter + // from http://popscan.blogspot.fr/2012/08/skin-detection-in-digital-images.html + skindetector: function(src,dst) { + var r,g,b,j; + var i = src.width*src.height; + while(i--){ + j = i*4; + r = src.data[j]; + g = src.data[j+1]; + b = src.data[j+2]; + if((r>95)&&(g>40)&&(b>20) + &&(r>g)&&(r>b) + &&(r-Math.min(g,b)>15) + &&(Math.abs(r-g)>15)){ + dst[i] = 255; + } else { + dst[i] = 0; + } + } + } + }; + })(); + + global.imgproc = imgproc; + +})(jsfeat); diff --git a/src/jsfeat_linalg.js b/src/jsfeat_linalg.js new file mode 100755 index 0000000..a695b3e --- /dev/null +++ b/src/jsfeat_linalg.js @@ -0,0 +1,688 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var linalg = (function() { + + var swap = function(A, i0, i1, t) { + t = A[i0]; + A[i0] = A[i1]; + A[i1] = t; + } + + var hypot = function(a, b) { + a = Math.abs(a); + b = Math.abs(b); + if( a > b ) { + b /= a; + return a*Math.sqrt(1.0 + b*b); + } + if( b > 0 ) { + a /= b; + return b*Math.sqrt(1.0 + a*a); + } + return 0.0; + } + + var JacobiImpl = function(A, astep, W, V, vstep, n) { + var eps = jsfeat.EPSILON; + var i=0,j=0,k=0,m=0,l=0,idx=0,_in=0,_in2=0; + var iters=0,max_iter=n*n*30; + var mv=0.0,val=0.0,p=0.0,y=0.0,t=0.0,s=0.0,c=0.0,a0=0.0,b0=0.0; + + var indR_buff = jsfeat.cache.get_buffer(n<<2); + var indC_buff = jsfeat.cache.get_buffer(n<<2); + var indR = indR_buff.i32; + var indC = indC_buff.i32; + + if(V) { + for(; i < n; i++) { + k = i*vstep; + for(j = 0; j < n; j++) { + V[k + j] = 0.0; + } + V[k + i] = 1.0; + } + } + + for(k = 0; k < n; k++) { + W[k] = A[(astep + 1)*k]; + if(k < n - 1) { + for(m = k+1, mv = Math.abs(A[astep*k + m]), i = k+2; i < n; i++) { + val = Math.abs(A[astep*k+i]); + if(mv < val) + mv = val, m = i; + } + indR[k] = m; + } + if(k > 0) { + for(m = 0, mv = Math.abs(A[k]), i = 1; i < k; i++) { + val = Math.abs(A[astep*i+k]); + if(mv < val) + mv = val, m = i; + } + indC[k] = m; + } + } + + if(n > 1) for( ; iters < max_iter; iters++) { + // find index (k,l) of pivot p + for(k = 0, mv = Math.abs(A[indR[0]]), i = 1; i < n-1; i++) { + val = Math.abs(A[astep*i + indR[i]]); + if( mv < val ) + mv = val, k = i; + } + l = indR[k]; + for(i = 1; i < n; i++) { + val = Math.abs(A[astep*indC[i] + i]); + if( mv < val ) + mv = val, k = indC[i], l = i; + } + + p = A[astep*k + l]; + + if(Math.abs(p) <= eps) break; + + y = (W[l] - W[k])*0.5; + t = Math.abs(y) + hypot(p, y); + s = hypot(p, t); + c = t/s; + s = p/s; t = (p/t)*p; + if(y < 0) + s = -s, t = -t; + A[astep*k + l] = 0; + + W[k] -= t; + W[l] += t; + + // rotate rows and columns k and l + for (i = 0; i < k; i++) { + _in = (astep * i + k); + _in2 = (astep * i + l); + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + for (i = (k + 1); i < l; i++) { + _in = (astep * k + i); + _in2 = (astep * i + l); + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + i = l + 1; + _in = (astep * k + i); + _in2 = (astep * l + i); + for (; i < n; i++, _in++, _in2++) { + a0 = A[_in]; + b0 = A[_in2]; + A[_in] = a0 * c - b0 * s; + A[_in2] = a0 * s + b0 * c; + } + + // rotate eigenvectors + if (V) { + _in = vstep * k; + _in2 = vstep * l; + for (i = 0; i < n; i++, _in++, _in2++) { + a0 = V[_in]; + b0 = V[_in2]; + V[_in] = a0 * c - b0 * s; + V[_in2] = a0 * s + b0 * c; + } + } + + for(j = 0; j < 2; j++) { + idx = j == 0 ? k : l; + if(idx < n - 1) { + for(m = idx+1, mv = Math.abs(A[astep*idx + m]), i = idx+2; i < n; i++) { + val = Math.abs(A[astep*idx+i]); + if( mv < val ) + mv = val, m = i; + } + indR[idx] = m; + } + if(idx > 0) { + for(m = 0, mv = Math.abs(A[idx]), i = 1; i < idx; i++) { + val = Math.abs(A[astep*i+idx]); + if( mv < val ) + mv = val, m = i; + } + indC[idx] = m; + } + } + } + + // sort eigenvalues & eigenvectors + for(k = 0; k < n-1; k++) { + m = k; + for(i = k+1; i < n; i++) { + if(W[m] < W[i]) + m = i; + } + if(k != m) { + swap(W, m, k, mv); + if(V) { + for(i = 0; i < n; i++) { + swap(V, vstep*m + i, vstep*k + i, mv); + } + } + } + } + + + jsfeat.cache.put_buffer(indR_buff); + jsfeat.cache.put_buffer(indC_buff); + } + + var JacobiSVDImpl = function(At, astep, _W, Vt, vstep, m, n, n1) { + var eps = jsfeat.EPSILON * 2.0; + var minval = jsfeat.FLT_MIN; + var i=0,j=0,k=0,iter=0,max_iter=Math.max(m, 30); + var Ai=0,Aj=0,Vi=0,Vj=0,changed=0; + var c=0.0, s=0.0, t=0.0; + var t0=0.0,t1=0.0,sd=0.0,beta=0.0,gamma=0.0,delta=0.0,a=0.0,p=0.0,b=0.0; + var seed = 0x1234; + var val=0.0,val0=0.0,asum=0.0; + + var W_buff = jsfeat.cache.get_buffer(n<<3); + var W = W_buff.f64; + + for(; i < n; i++) { + for(k = 0, sd = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + W[i] = sd; + + if(Vt) { + for(k = 0; k < n; k++) { + Vt[i*vstep + k] = 0; + } + Vt[i*vstep + i] = 1; + } + } + + for(; iter < max_iter; iter++) { + changed = 0; + + for(i = 0; i < n-1; i++) { + for(j = i+1; j < n; j++) { + Ai = (i*astep)|0, Aj = (j*astep)|0; + a = W[i], p = 0, b = W[j]; + + k = 2; + p += At[Ai]*At[Aj]; + p += At[Ai+1]*At[Aj+1]; + + for(; k < m; k++) + p += At[Ai+k]*At[Aj+k]; + + if(Math.abs(p) <= eps*Math.sqrt(a*b)) continue; + + p *= 2.0; + beta = a - b, gamma = hypot(p, beta); + if( beta < 0 ) { + delta = (gamma - beta)*0.5; + s = Math.sqrt(delta/gamma); + c = (p/(gamma*s*2.0)); + } else { + c = Math.sqrt((gamma + beta)/(gamma*2.0)); + s = (p/(gamma*c*2.0)); + } + + a=0.0, b=0.0; + + k = 2; // unroll + t0 = c*At[Ai] + s*At[Aj]; + t1 = -s*At[Ai] + c*At[Aj]; + At[Ai] = t0; At[Aj] = t1; + a += t0*t0; b += t1*t1; + + t0 = c*At[Ai+1] + s*At[Aj+1]; + t1 = -s*At[Ai+1] + c*At[Aj+1]; + At[Ai+1] = t0; At[Aj+1] = t1; + a += t0*t0; b += t1*t1; + + for( ; k < m; k++ ) + { + t0 = c*At[Ai+k] + s*At[Aj+k]; + t1 = -s*At[Ai+k] + c*At[Aj+k]; + At[Ai+k] = t0; At[Aj+k] = t1; + + a += t0*t0; b += t1*t1; + } + + W[i] = a; W[j] = b; + + changed = 1; + + if(Vt) { + Vi = (i*vstep)|0, Vj = (j*vstep)|0; + + k = 2; + t0 = c*Vt[Vi] + s*Vt[Vj]; + t1 = -s*Vt[Vi] + c*Vt[Vj]; + Vt[Vi] = t0; Vt[Vj] = t1; + + t0 = c*Vt[Vi+1] + s*Vt[Vj+1]; + t1 = -s*Vt[Vi+1] + c*Vt[Vj+1]; + Vt[Vi+1] = t0; Vt[Vj+1] = t1; + + for(; k < n; k++) { + t0 = c*Vt[Vi+k] + s*Vt[Vj+k]; + t1 = -s*Vt[Vi+k] + c*Vt[Vj+k]; + Vt[Vi+k] = t0; Vt[Vj+k] = t1; + } + } + } + } + if(changed == 0) break; + } + + for(i = 0; i < n; i++) { + for(k = 0, sd = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + W[i] = Math.sqrt(sd); + } + + for(i = 0; i < n-1; i++) { + j = i; + for(k = i+1; k < n; k++) { + if(W[j] < W[k]) + j = k; + } + if(i != j) { + swap(W, i, j, sd); + if(Vt) { + for(k = 0; k < m; k++) { + swap(At, i*astep + k, j*astep + k, t); + } + + for(k = 0; k < n; k++) { + swap(Vt, i*vstep + k, j*vstep + k, t); + } + } + } + } + + for(i = 0; i < n; i++) { + _W[i] = W[i]; + } + + if(!Vt) { + jsfeat.cache.put_buffer(W_buff); + return; + } + + for(i = 0; i < n1; i++) { + + sd = i < n ? W[i] : 0; + + while(sd <= minval) { + // if we got a zero singular value, then in order to get the corresponding left singular vector + // we generate a random vector, project it to the previously computed left singular vectors, + // subtract the projection and normalize the difference. + val0 = (1.0/m); + for(k = 0; k < m; k++) { + seed = (seed * 214013 + 2531011); + val = (((seed >> 16) & 0x7fff) & 256) != 0 ? val0 : -val0; + At[i*astep + k] = val; + } + for(iter = 0; iter < 2; iter++) { + for(j = 0; j < i; j++) { + sd = 0; + for(k = 0; k < m; k++) { + sd += At[i*astep + k]*At[j*astep + k]; + } + asum = 0.0; + for(k = 0; k < m; k++) { + t = (At[i*astep + k] - sd*At[j*astep + k]); + At[i*astep + k] = t; + asum += Math.abs(t); + } + asum = asum ? 1.0/asum : 0; + for(k = 0; k < m; k++) { + At[i*astep + k] *= asum; + } + } + } + sd = 0; + for(k = 0; k < m; k++) { + t = At[i*astep + k]; + sd += t*t; + } + sd = Math.sqrt(sd); + } + + s = (1.0/sd); + for(k = 0; k < m; k++) { + At[i*astep + k] *= s; + } + } + + jsfeat.cache.put_buffer(W_buff); + } + + return { + + lu_solve: function(A, B) { + var i=0,j=0,k=0,p=1,astep=A.cols; + var ad=A.data, bd=B.data; + var t,alpha,d,s; + + for(i = 0; i < astep; i++) { + k = i; + for(j = i+1; j < astep; j++) { + if(Math.abs(ad[j*astep + i]) > Math.abs(ad[k*astep+i])) { + k = j; + } + } + + if(Math.abs(ad[k*astep+i]) < jsfeat.EPSILON) { + return 0; // FAILED + } + + if(k != i) { + for(j = i; j < astep; j++ ) { + swap(ad, i*astep+j, k*astep+j, t); + } + + swap(bd, i, k, t); + p = -p; + } + + d = -1.0/ad[i*astep+i]; + + for(j = i+1; j < astep; j++) { + alpha = ad[j*astep+i]*d; + + for(k = i+1; k < astep; k++) { + ad[j*astep+k] += alpha*ad[i*astep+k]; + } + + bd[j] += alpha*bd[i]; + } + + ad[i*astep+i] = -d; + } + + for(i = astep-1; i >= 0; i--) { + s = bd[i]; + for(k = i+1; k < astep; k++) { + s -= ad[i*astep+k]*bd[k]; + } + bd[i] = s*ad[i*astep+i]; + } + + return 1; // OK + }, + + cholesky_solve: function(A, B) { + var col=0,row=0,col2=0,cs=0,rs=0,i=0,j=0; + var size = A.cols; + var ad=A.data, bd=B.data; + var val,inv_diag; + + for (col = 0; col < size; col++) { + inv_diag = 1.0; + cs = (col * size); + rs = cs; + for (row = col; row < size; row++) + { + // correct for the parts of cholesky already computed + val = ad[(rs+col)]; + for (col2 = 0; col2 < col; col2++) { + val -= ad[(col2*size+col)] * ad[(rs+col2)]; + } + if (row == col) { + // this is the diagonal element so don't divide + ad[(rs+col)] = val; + if(val == 0) { + return 0; + } + inv_diag = 1.0 / val; + } else { + // cache the value without division in the upper half + ad[(cs+row)] = val; + // divide my the diagonal element for all others + ad[(rs+col)] = val * inv_diag; + } + rs = (rs + size); + } + } + + // first backsub through L + cs = 0; + for (i = 0; i < size; i++) { + val = bd[i]; + for (j = 0; j < i; j++) { + val -= ad[(cs+j)] * bd[j]; + } + bd[i] = val; + cs = (cs + size); + } + // backsub through diagonal + cs = 0; + for (i = 0; i < size; i++) { + bd[i] /= ad[(cs + i)]; + cs = (cs + size); + } + // backsub through L Transpose + i = (size-1); + for (; i >= 0; i--) { + val = bd[i]; + j = (i + 1); + cs = (j * size); + for (; j < size; j++) { + val -= ad[(cs + i)] * bd[j]; + cs = (cs + size); + } + bd[i] = val; + } + + return 1; + }, + + svd_decompose: function(A, W, U, V, options) { + if (typeof options === "undefined") { options = 0; }; + var at=0,i=0,j=0,_m=A.rows,_n=A.cols,m=_m,n=_n; + var dt = A.type | jsfeat.C1_t; // we only work with single channel + + if(m < n) { + at = 1; + i = m; + m = n; + n = i; + } + + var a_buff = jsfeat.cache.get_buffer((m*m)<<3); + var w_buff = jsfeat.cache.get_buffer(n<<3); + var v_buff = jsfeat.cache.get_buffer((n*n)<<3); + + var a_mt = new jsfeat.matrix_t(m, m, dt, a_buff.data); + var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(n, n, dt, v_buff.data); + + if(at == 0) { + // transpose + jsfeat.matmath.transpose(a_mt, A); + } else { + for(i = 0; i < _n*_m; i++) { + a_mt.data[i] = A.data[i]; + } + for(; i < n*m; i++) { + a_mt.data[i] = 0; + } + } + + JacobiSVDImpl(a_mt.data, m, w_mt.data, v_mt.data, n, m, n, m); + + if(W) { + for(i=0; i < n; i++) { + W.data[i] = w_mt.data[i]; + } + for(; i < _n; i++) { + W.data[i] = 0; + } + } + + if (at == 0) { + if(U && (options & jsfeat.SVD_U_T)) { + i = m*m; + while(--i >= 0) { + U.data[i] = a_mt.data[i]; + } + } else if(U) { + jsfeat.matmath.transpose(U, a_mt); + } + + if(V && (options & jsfeat.SVD_V_T)) { + i = n*n; + while(--i >= 0) { + V.data[i] = v_mt.data[i]; + } + } else if(V) { + jsfeat.matmath.transpose(V, v_mt); + } + } else { + if(U && (options & jsfeat.SVD_U_T)) { + i = n*n; + while(--i >= 0) { + U.data[i] = v_mt.data[i]; + } + } else if(U) { + jsfeat.matmath.transpose(U, v_mt); + } + + if(V && (options & jsfeat.SVD_V_T)) { + i = m*m; + while(--i >= 0) { + V.data[i] = a_mt.data[i]; + } + } else if(V) { + jsfeat.matmath.transpose(V, a_mt); + } + } + + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + + }, + + svd_solve: function(A, X, B) { + var i=0,j=0,k=0; + var pu=0,pv=0; + var nrows=A.rows,ncols=A.cols; + var sum=0.0,xsum=0.0,tol=0.0; + var dt = A.type | jsfeat.C1_t; + + var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); + var w_buff = jsfeat.cache.get_buffer(ncols<<3); + var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); + + var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); + var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); + + var bd = B.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; + + this.svd_decompose(A, w_mt, u_mt, v_mt, 0); + + tol = jsfeat.EPSILON * wd[0] * ncols; + + for (; i < ncols; i++, pv += ncols) { + xsum = 0.0; + for(j = 0; j < ncols; j++) { + if(wd[j] > tol) { + for(k = 0, sum = 0.0, pu = 0; k < nrows; k++, pu += ncols) { + sum += ud[pu + j] * bd[k]; + } + xsum += sum * vd[pv + j] / wd[j]; + } + } + X.data[i] = xsum; + } + + jsfeat.cache.put_buffer(u_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + }, + + svd_invert: function(Ai, A) { + var i=0,j=0,k=0; + var pu=0,pv=0,pa=0; + var nrows=A.rows,ncols=A.cols; + var sum=0.0,tol=0.0; + var dt = A.type | jsfeat.C1_t; + + var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); + var w_buff = jsfeat.cache.get_buffer(ncols<<3); + var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); + + var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); + var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); + var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); + + var id = Ai.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; + + this.svd_decompose(A, w_mt, u_mt, v_mt, 0); + + tol = jsfeat.EPSILON * wd[0] * ncols; + + for (; i < ncols; i++, pv += ncols) { + for (j = 0, pu = 0; j < nrows; j++, pa++) { + for (k = 0, sum = 0.0; k < ncols; k++, pu++) { + if (wd[k] > tol) sum += vd[pv + k] * ud[pu] / wd[k]; + } + id[pa] = sum; + } + } + + jsfeat.cache.put_buffer(u_buff); + jsfeat.cache.put_buffer(w_buff); + jsfeat.cache.put_buffer(v_buff); + }, + + eigenVV: function(A, vects, vals) { + var n=A.cols,i=n*n; + var dt = A.type | jsfeat.C1_t; + + var a_buff = jsfeat.cache.get_buffer((n*n)<<3); + var w_buff = jsfeat.cache.get_buffer(n<<3); + var a_mt = new jsfeat.matrix_t(n, n, dt, a_buff.data); + var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); + + while(--i >= 0) { + a_mt.data[i] = A.data[i]; + } + + JacobiImpl(a_mt.data, n, w_mt.data, vects ? vects.data : null, n, n); + + if(vals) { + while(--n >= 0) { + vals.data[n] = w_mt.data[n]; + } + } + + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(w_buff); + } + + }; + + })(); + + global.linalg = linalg; + +})(jsfeat); \ No newline at end of file diff --git a/src/jsfeat_mat_math.js b/src/jsfeat_mat_math.js new file mode 100755 index 0000000..9dc25ec --- /dev/null +++ b/src/jsfeat_mat_math.js @@ -0,0 +1,233 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var matmath = (function() { + + return { + identity: function(M, value) { + if (typeof value === "undefined") { value=1; } + var src=M.data; + var rows=M.rows, cols=M.cols, cols_1=(cols+1)|0; + var len = rows * cols; + var k = len; + while(--len >= 0) src[len] = 0.0; + len = k; + k = 0; + while(k < len) { + src[k] = value; + k = k + cols_1; + } + }, + + transpose: function(At, A) { + var i=0,j=0,nrows=A.rows,ncols=A.cols; + var Ai=0,Ati=0,pAt=0; + var ad=A.data,atd=At.data; + + for (; i < nrows; Ati += 1, Ai += ncols, i++) { + pAt = Ati; + for (j = 0; j < ncols; pAt += nrows, j++) atd[pAt] = ad[Ai+j]; + } + }, + + // C = A * B + multiply: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,p_B=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mcols=B.cols; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; Ap += ncols, i++) { + for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { + pB = p_B; + pA = Ap; + sum = 0.0; + for (k = 0; k < ncols; pA++, pB += mcols, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A * B' + multiply_ABt: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mrows=B.rows; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; Ap += ncols, i++) { + for (pB = 0, j = 0; j < mrows; Cp++, j++) { + pA = Ap; + sum = 0.0; + for (k = 0; k < ncols; pA++, pB++, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A' * B + multiply_AtB: function(C, A, B) { + var i=0,j=0,k=0; + var Ap=0,pA=0,pB=0,p_B=0,Cp=0; + var ncols=A.cols,nrows=A.rows,mcols=B.cols; + var ad=A.data,bd=B.data,cd=C.data; + var sum=0.0; + + for (; i < ncols; Ap++, i++) { + for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { + pB = p_B; + pA = Ap; + sum = 0.0; + for (k = 0; k < nrows; pA += ncols, pB += mcols, k++) { + sum += ad[pA] * bd[pB]; + } + cd[Cp] = sum; + } + } + }, + + // C = A * A' + multiply_AAt: function(C, A) { + var i=0,j=0,k=0; + var pCdiag=0,p_A=0,pA=0,pB=0,pC=0,pCt=0; + var ncols=A.cols,nrows=A.rows; + var ad=A.data,cd=C.data; + var sum=0.0; + + for (; i < nrows; pCdiag += nrows + 1, p_A = pA, i++) { + pC = pCdiag; + pCt = pCdiag; + pB = p_A; + for (j = i; j < nrows; pC++, pCt += nrows, j++) { + pA = p_A; + sum = 0.0; + for (k = 0; k < ncols; k++) { + sum += ad[pA++] * ad[pB++]; + } + cd[pC] = sum + cd[pCt] = sum; + } + } + }, + + // C = A' * A + multiply_AtA: function(C, A) { + var i=0,j=0,k=0; + var p_A=0,pA=0,pB=0,p_C=0,pC=0,p_CC=0; + var ncols=A.cols,nrows=A.rows; + var ad=A.data,cd=C.data; + var sum=0.0; + + for (; i < ncols; p_C += ncols, i++) { + p_A = i; + p_CC = p_C + i; + pC = p_CC; + for (j = i; j < ncols; pC++, p_CC += ncols, j++) { + pA = p_A; + pB = j; + sum = 0.0; + for (k = 0; k < nrows; pA += ncols, pB += ncols, k++) { + sum += ad[pA] * ad[pB]; + } + cd[pC] = sum + cd[p_CC] = sum; + } + } + }, + + // various small matrix operations + identity_3x3: function(M, value) { + if (typeof value === "undefined") { value=1; } + var dt=M.data; + dt[0] = dt[4] = dt[8] = value; + dt[1] = dt[2] = dt[3] = 0; + dt[5] = dt[6] = dt[7] = 0; + }, + + invert_3x3: function(from, to) { + var A = from.data, invA = to.data; + var t1 = A[4]; + var t2 = A[8]; + var t4 = A[5]; + var t5 = A[7]; + var t8 = A[0]; + + var t9 = t8*t1; + var t11 = t8*t4; + var t13 = A[3]; + var t14 = A[1]; + var t15 = t13*t14; + var t17 = A[2]; + var t18 = t13*t17; + var t20 = A[6]; + var t21 = t20*t14; + var t23 = t20*t17; + var t26 = 1.0/(t9*t2-t11*t5-t15*t2+t18*t5+t21*t4-t23*t1); + invA[0] = (t1*t2-t4*t5)*t26; + invA[1] = -(t14*t2-t17*t5)*t26; + invA[2] = -(-t14*t4+t17*t1)*t26; + invA[3] = -(t13*t2-t4*t20)*t26; + invA[4] = (t8*t2-t23)*t26; + invA[5] = -(t11-t18)*t26; + invA[6] = -(-t13*t5+t1*t20)*t26; + invA[7] = -(t8*t5-t21)*t26; + invA[8] = (t9-t15)*t26; + }, + // C = A * B + multiply_3x3: function(C, A, B) { + var Cd=C.data, Ad=A.data, Bd=B.data; + var m1_0 = Ad[0], m1_1 = Ad[1], m1_2 = Ad[2]; + var m1_3 = Ad[3], m1_4 = Ad[4], m1_5 = Ad[5]; + var m1_6 = Ad[6], m1_7 = Ad[7], m1_8 = Ad[8]; + + var m2_0 = Bd[0], m2_1 = Bd[1], m2_2 = Bd[2]; + var m2_3 = Bd[3], m2_4 = Bd[4], m2_5 = Bd[5]; + var m2_6 = Bd[6], m2_7 = Bd[7], m2_8 = Bd[8]; + + Cd[0] = m1_0 * m2_0 + m1_1 * m2_3 + m1_2 * m2_6; + Cd[1] = m1_0 * m2_1 + m1_1 * m2_4 + m1_2 * m2_7; + Cd[2] = m1_0 * m2_2 + m1_1 * m2_5 + m1_2 * m2_8; + Cd[3] = m1_3 * m2_0 + m1_4 * m2_3 + m1_5 * m2_6; + Cd[4] = m1_3 * m2_1 + m1_4 * m2_4 + m1_5 * m2_7; + Cd[5] = m1_3 * m2_2 + m1_4 * m2_5 + m1_5 * m2_8; + Cd[6] = m1_6 * m2_0 + m1_7 * m2_3 + m1_8 * m2_6; + Cd[7] = m1_6 * m2_1 + m1_7 * m2_4 + m1_8 * m2_7; + Cd[8] = m1_6 * m2_2 + m1_7 * m2_5 + m1_8 * m2_8; + }, + + mat3x3_determinant: function(M) { + var md=M.data; + return md[0] * md[4] * md[8] - + md[0] * md[5] * md[7] - + md[3] * md[1] * md[8] + + md[3] * md[2] * md[7] + + md[6] * md[1] * md[5] - + md[6] * md[2] * md[4]; + }, + + determinant_3x3: function(M11, M12, M13, + M21, M22, M23, + M31, M32, M33) { + return M11 * M22 * M33 - M11 * M23 * M32 - + M21 * M12 * M33 + M21 * M13 * M32 + + M31 * M12 * M23 - M31 * M13 * M22; + } + }; + + })(); + + global.matmath = matmath; + +})(jsfeat); \ No newline at end of file diff --git a/src/jsfeat_math.js b/src/jsfeat_math.js new file mode 100755 index 0000000..f5ce94b --- /dev/null +++ b/src/jsfeat_math.js @@ -0,0 +1,414 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var math = (function() { + + var qsort_stack = new Int32Array(48*2); + + return { + get_gaussian_kernel: function(size, sigma, kernel, data_type) { + var i=0,x=0.0,t=0.0,sigma_x=0.0,scale_2x=0.0; + var sum = 0.0; + var kern_node = jsfeat.cache.get_buffer(size<<2); + var _kernel = kern_node.f32;//new Float32Array(size); + + if((size&1) == 1 && size <= 7 && sigma <= 0) { + switch(size>>1) { + case 0: + _kernel[0] = 1.0; + sum = 1.0; + break; + case 1: + _kernel[0] = 0.25, _kernel[1] = 0.5, _kernel[2] = 0.25; + sum = 0.25+0.5+0.25; + break; + case 2: + _kernel[0] = 0.0625, _kernel[1] = 0.25, _kernel[2] = 0.375, + _kernel[3] = 0.25, _kernel[4] = 0.0625; + sum = 0.0625+0.25+0.375+0.25+0.0625; + break; + case 3: + _kernel[0] = 0.03125, _kernel[1] = 0.109375, _kernel[2] = 0.21875, + _kernel[3] = 0.28125, _kernel[4] = 0.21875, _kernel[5] = 0.109375, _kernel[6] = 0.03125; + sum = 0.03125+0.109375+0.21875+0.28125+0.21875+0.109375+0.03125; + break; + } + } else { + sigma_x = sigma > 0 ? sigma : ((size-1)*0.5 - 1.0)*0.3 + 0.8; + scale_2x = -0.5/(sigma_x*sigma_x); + + for( ; i < size; ++i ) + { + x = i - (size-1)*0.5; + t = Math.exp(scale_2x*x*x); + + _kernel[i] = t; + sum += t; + } + } + + if(data_type & jsfeat.U8_t) { + // int based kernel + sum = 256.0/sum; + for (i = 0; i < size; ++i) { + kernel[i] = (_kernel[i] * sum + 0.5)|0; + } + } else { + // classic kernel + sum = 1.0/sum; + for (i = 0; i < size; ++i) { + kernel[i] = _kernel[i] * sum; + } + } + + jsfeat.cache.put_buffer(kern_node); + }, + + // model is 3x3 matrix_t + perspective_4point_transform: function(model, src_x0, src_y0, dst_x0, dst_y0, + src_x1, src_y1, dst_x1, dst_y1, + src_x2, src_y2, dst_x2, dst_y2, + src_x3, src_y3, dst_x3, dst_y3) { + var t1 = src_x0; + var t2 = src_x2; + var t4 = src_y1; + var t5 = t1 * t2 * t4; + var t6 = src_y3; + var t7 = t1 * t6; + var t8 = t2 * t7; + var t9 = src_y2; + var t10 = t1 * t9; + var t11 = src_x1; + var t14 = src_y0; + var t15 = src_x3; + var t16 = t14 * t15; + var t18 = t16 * t11; + var t20 = t15 * t11 * t9; + var t21 = t15 * t4; + var t24 = t15 * t9; + var t25 = t2 * t4; + var t26 = t6 * t2; + var t27 = t6 * t11; + var t28 = t9 * t11; + var t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + var t32 = t1 * t15; + var t35 = t14 * t11; + var t41 = t4 * t1; + var t42 = t6 * t41; + var t43 = t14 * t2; + var t46 = t16 * t9; + var t48 = t14 * t9 * t11; + var t51 = t4 * t6 * t2; + var t55 = t6 * t14; + var Hr0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hr1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hr2 = t1; + var Hr3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hr4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hr5 = t14; + var Hr6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hr7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + t1 = dst_x0; + t2 = dst_x2; + t4 = dst_y1; + t5 = t1 * t2 * t4; + t6 = dst_y3; + t7 = t1 * t6; + t8 = t2 * t7; + t9 = dst_y2; + t10 = t1 * t9; + t11 = dst_x1; + t14 = dst_y0; + t15 = dst_x3; + t16 = t14 * t15; + t18 = t16 * t11; + t20 = t15 * t11 * t9; + t21 = t15 * t4; + t24 = t15 * t9; + t25 = t2 * t4; + t26 = t6 * t2; + t27 = t6 * t11; + t28 = t9 * t11; + t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + t32 = t1 * t15; + t35 = t14 * t11; + t41 = t4 * t1; + t42 = t6 * t41; + t43 = t14 * t2; + t46 = t16 * t9; + t48 = t14 * t9 * t11; + t51 = t4 * t6 * t2; + t55 = t6 * t14; + var Hl0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hl1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hl2 = t1; + var Hl3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hl4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hl5 = t14; + var Hl6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hl7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + // the following code computes R = Hl * inverse Hr + t2 = Hr4-Hr7*Hr5; + t4 = Hr0*Hr4; + t5 = Hr0*Hr5; + t7 = Hr3*Hr1; + t8 = Hr2*Hr3; + t10 = Hr1*Hr6; + var t12 = Hr2*Hr6; + t15 = 1.0 / (t4-t5*Hr7-t7+t8*Hr7+t10*Hr5-t12*Hr4); + t18 = -Hr3+Hr5*Hr6; + var t23 = -Hr3*Hr7+Hr4*Hr6; + t28 = -Hr1+Hr2*Hr7; + var t31 = Hr0-t12; + t35 = Hr0*Hr7-t10; + t41 = -Hr1*Hr5+Hr2*Hr4; + var t44 = t5-t8; + var t47 = t4-t7; + t48 = t2*t15; + var t49 = t28*t15; + var t50 = t41*t15; + var mat = model.data; + mat[0] = Hl0*t48+Hl1*(t18*t15)-Hl2*(t23*t15); + mat[1] = Hl0*t49+Hl1*(t31*t15)-Hl2*(t35*t15); + mat[2] = -Hl0*t50-Hl1*(t44*t15)+Hl2*(t47*t15); + mat[3] = Hl3*t48+Hl4*(t18*t15)-Hl5*(t23*t15); + mat[4] = Hl3*t49+Hl4*(t31*t15)-Hl5*(t35*t15); + mat[5] = -Hl3*t50-Hl4*(t44*t15)+Hl5*(t47*t15); + mat[6] = Hl6*t48+Hl7*(t18*t15)-t23*t15; + mat[7] = Hl6*t49+Hl7*(t31*t15)-t35*t15; + mat[8] = -Hl6*t50-Hl7*(t44*t15)+t47*t15; + }, + + // The current implementation was derived from *BSD system qsort(): + // Copyright (c) 1992, 1993 + // The Regents of the University of California. All rights reserved. + qsort: function(array, low, high, cmp) { + var isort_thresh = 7; + var t,ta,tb,tc; + var sp = 0,left=0,right=0,i=0,n=0,m=0,ptr=0,ptr2=0,d=0; + var left0=0,left1=0,right0=0,right1=0,pivot=0,a=0,b=0,c=0,swap_cnt=0; + + var stack = qsort_stack; + + if( (high-low+1) <= 1 ) return; + + stack[0] = low; + stack[1] = high; + + while( sp >= 0 ) { + + left = stack[sp<<1]; + right = stack[(sp<<1)+1]; + sp--; + + for(;;) { + n = (right - left) + 1; + + if( n <= isort_thresh ) { + //insert_sort: + for( ptr = left + 1; ptr <= right; ptr++ ) { + for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { + t = array[ptr2]; + array[ptr2] = array[ptr2-1]; + array[ptr2-1] = t; + } + } + break; + } else { + swap_cnt = 0; + + left0 = left; + right0 = right; + pivot = left + (n>>1); + + if( n > 40 ) { + d = n >> 3; + a = left, b = left + d, c = left + (d<<1); + ta = array[a],tb = array[b],tc = array[c]; + left = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + + a = pivot - d, b = pivot, c = pivot + d; + ta = array[a],tb = array[b],tc = array[c]; + pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + + a = right - (d<<1), b = right - d, c = right; + ta = array[a],tb = array[b],tc = array[c]; + right = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + } + + a = left, b = pivot, c = right; + ta = array[a],tb = array[b],tc = array[c]; + pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) + : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); + if( pivot != left0 ) { + t = array[pivot]; + array[pivot] = array[left0]; + array[left0] = t; + pivot = left0; + } + left = left1 = left0 + 1; + right = right1 = right0; + + ta = array[pivot]; + for(;;) { + while( left <= right && !cmp(ta, array[left]) ) { + if( !cmp(array[left], ta) ) { + if( left > left1 ) { + t = array[left1]; + array[left1] = array[left]; + array[left] = t; + } + swap_cnt = 1; + left1++; + } + left++; + } + + while( left <= right && !cmp(array[right], ta) ) { + if( !cmp(ta, array[right]) ) { + if( right < right1 ) { + t = array[right1]; + array[right1] = array[right]; + array[right] = t; + } + swap_cnt = 1; + right1--; + } + right--; + } + + if( left > right ) break; + + t = array[left]; + array[left] = array[right]; + array[right] = t; + swap_cnt = 1; + left++; + right--; + } + + if( swap_cnt == 0 ) { + left = left0, right = right0; + //goto insert_sort; + for( ptr = left + 1; ptr <= right; ptr++ ) { + for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { + t = array[ptr2]; + array[ptr2] = array[ptr2-1]; + array[ptr2-1] = t; + } + } + break; + } + + n = Math.min( (left1 - left0), (left - left1) ); + m = (left-n)|0; + for( i = 0; i < n; ++i,++m ) { + t = array[left0+i]; + array[left0+i] = array[m]; + array[m] = t; + } + + n = Math.min( (right0 - right1), (right1 - right) ); + m = (right0-n+1)|0; + for( i = 0; i < n; ++i,++m ) { + t = array[left+i]; + array[left+i] = array[m]; + array[m] = t; + } + n = (left - left1); + m = (right1 - right); + if( n > 1 ) { + if( m > 1 ) { + if( n > m ) { + ++sp; + stack[sp<<1] = left0; + stack[(sp<<1)+1] = left0 + n - 1; + left = right0 - m + 1, right = right0; + } else { + ++sp; + stack[sp<<1] = right0 - m + 1; + stack[(sp<<1)+1] = right0; + left = left0, right = left0 + n - 1; + } + } else { + left = left0, right = left0 + n - 1; + } + } + else if( m > 1 ) + left = right0 - m + 1, right = right0; + else + break; + } + } + } + }, + + median: function(array, low, high) { + var w; + var middle=0,ll=0,hh=0,median=(low+high)>>1; + for (;;) { + if (high <= low) return array[median]; + if (high == (low + 1)) { + if (array[low] > array[high]) { + w = array[low]; + array[low] = array[high]; + array[high] = w; + } + return array[median]; + } + middle = ((low + high) >> 1); + if (array[middle] > array[high]) { + w = array[middle]; + array[middle] = array[high]; + array[high] = w; + } + if (array[low] > array[high]) { + w = array[low]; + array[low] = array[high]; + array[high] = w; + } + if (array[middle] > array[low]) { + w = array[middle]; + array[middle] = array[low]; + array[low] = w; + } + ll = (low + 1); + w = array[middle]; + array[middle] = array[ll]; + array[ll] = w; + hh = high; + for (;;) { + do ++ll; while (array[low] > array[ll]); + do --hh; while (array[hh] > array[low]); + if (hh < ll) break; + w = array[ll]; + array[ll] = array[hh]; + array[hh] = w; + } + w = array[low]; + array[low] = array[hh]; + array[hh] = w; + if (hh <= median) + low = ll; + else if (hh >= median) + high = (hh - 1); + } + return 0; + } + }; + + })(); + + global.math = math; + +})(jsfeat); diff --git a/src/jsfeat_motion_estimator.js b/src/jsfeat_motion_estimator.js new file mode 100755 index 0000000..b9eeea5 --- /dev/null +++ b/src/jsfeat_motion_estimator.js @@ -0,0 +1,660 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + */ + +(function(global) { + "use strict"; + // + + var motion_model = (function() { + + var sqr = function(x) { + return x*x; + } + + // does isotropic normalization + var iso_normalize_points = function(from, to, T0, T1, count) { + var i=0; + var cx0=0.0, cy0=0.0, d0=0.0, s0=0.0; + var cx1=0.0, cy1=0.0, d1=0.0, s1=0.0; + var dx=0.0,dy=0.0; + + for (; i < count; ++i) { + cx0 += from[i].x; + cy0 += from[i].y; + cx1 += to[i].x; + cy1 += to[i].y; + } + + cx0 /= count; cy0 /= count; + cx1 /= count; cy1 /= count; + + for (i = 0; i < count; ++i) { + dx = from[i].x - cx0; + dy = from[i].y - cy0; + d0 += Math.sqrt(dx*dx + dy*dy); + dx = to[i].x - cx1; + dy = to[i].y - cy1; + d1 += Math.sqrt(dx*dx + dy*dy); + } + + d0 /= count; d1 /= count; + + s0 = Math.SQRT2 / d0; s1 = Math.SQRT2 / d1; + + T0[0] = T0[4] = s0; + T0[2] = -cx0*s0; + T0[5] = -cy0*s0; + T0[1] = T0[3] = T0[6] = T0[7] = 0.0; + T0[8] = 1.0; + + T1[0] = T1[4] = s1; + T1[2] = -cx1*s1; + T1[5] = -cy1*s1; + T1[1] = T1[3] = T1[6] = T1[7] = 0.0; + T1[8] = 1.0; + } + + var have_collinear_points = function(points, count) { + var j=0,k=0,i=(count-1)|0; + var dx1=0.0,dy1=0.0,dx2=0.0,dy2=0.0; + + // check that the i-th selected point does not belong + // to a line connecting some previously selected points + for(; j < i; ++j) { + dx1 = points[j].x - points[i].x; + dy1 = points[j].y - points[i].y; + for(k = 0; k < j; ++k) { + dx2 = points[k].x - points[i].x; + dy2 = points[k].y - points[i].y; + if( Math.abs(dx2*dy1 - dy2*dx1) <= jsfeat.EPSILON*(Math.abs(dx1) + Math.abs(dy1) + Math.abs(dx2) + Math.abs(dy2))) + return true; + } + } + return false; + } + + var T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var AtA = new jsfeat.matrix_t(6, 6, jsfeat.F32_t|jsfeat.C1_t); + var AtB = new jsfeat.matrix_t(6, 1, jsfeat.F32_t|jsfeat.C1_t); + + var affine2d = (function () { + + function affine2d() { + // empty constructor + } + + affine2d.prototype.run = function(from, to, model, count) { + var i=0,j=0; + var dt=model.type|jsfeat.C1_t; + var md=model.data, t0d=T0.data, t1d=T1.data; + var pt0,pt1,px=0.0,py=0.0; + + iso_normalize_points(from, to, t0d, t1d, count); + + var a_buff = jsfeat.cache.get_buffer((2*count*6)<<3); + var b_buff = jsfeat.cache.get_buffer((2*count)<<3); + + var a_mt = new jsfeat.matrix_t(6, 2*count, dt, a_buff.data); + var b_mt = new jsfeat.matrix_t(1, 2*count, dt, b_buff.data); + var ad=a_mt.data, bd=b_mt.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + px = t0d[0]*pt0.x + t0d[1]*pt0.y + t0d[2]; + py = t0d[3]*pt0.x + t0d[4]*pt0.y + t0d[5]; + + j = i*2*6; + ad[j]=px, ad[j+1]=py, ad[j+2]=1.0, ad[j+3]=0.0, ad[j+4]=0.0, ad[j+5]=0.0; + + j += 6; + ad[j]=0.0, ad[j+1]=0.0, ad[j+2]=0.0, ad[j+3]=px, ad[j+4]=py, ad[j+5]=1.0; + + bd[i<<1] = t1d[0]*pt1.x + t1d[1]*pt1.y + t1d[2]; + bd[(i<<1)+1] = t1d[3]*pt1.x + t1d[4]*pt1.y + t1d[5]; + } + + jsfeat.matmath.multiply_AtA(AtA, a_mt); + jsfeat.matmath.multiply_AtB(AtB, a_mt, b_mt); + + jsfeat.linalg.lu_solve(AtA, AtB); + + md[0] = AtB.data[0], md[1]=AtB.data[1], md[2]=AtB.data[2]; + md[3] = AtB.data[3], md[4]=AtB.data[4], md[5]=AtB.data[5]; + md[6] = 0.0, md[7] = 0.0, md[8] = 1.0; // fill last row + + // denormalize + jsfeat.matmath.invert_3x3(T1, T1); + jsfeat.matmath.multiply_3x3(model, T1, model); + jsfeat.matmath.multiply_3x3(model, model, T0); + + // free buffer + jsfeat.cache.put_buffer(a_buff); + jsfeat.cache.put_buffer(b_buff); + + return 1; + } + + affine2d.prototype.error = function(from, to, model, err, count) { + var i=0; + var pt0,pt1; + var m=model.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + err[i] = sqr(pt1.x - m[0]*pt0.x - m[1]*pt0.y - m[2]) + + sqr(pt1.y - m[3]*pt0.x - m[4]*pt0.y - m[5]); + } + } + + affine2d.prototype.check_subset = function(from, to, count) { + return true; // all good + } + + return affine2d; + })(); + + var mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + var Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + + var homography2d = (function () { + + function homography2d() { + // empty constructor + //this.T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + //this.T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + //this.mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + //this.Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); + } + + homography2d.prototype.run = function(from, to, model, count) { + var i=0,j=0; + var md=model.data, t0d=T0.data, t1d=T1.data; + var LtL=mLtL.data, evd=Evec.data; + var x=0.0,y=0.0,X=0.0,Y=0.0; + + // norm + var smx=0.0, smy=0.0, cmx=0.0, cmy=0.0, sMx=0.0, sMy=0.0, cMx=0.0, cMy=0.0; + + for(; i < count; ++i) { + cmx += to[i].x; + cmy += to[i].y; + cMx += from[i].x; + cMy += from[i].y; + } + + cmx /= count; cmy /= count; + cMx /= count; cMy /= count; + + for(i = 0; i < count; ++i) + { + smx += Math.abs(to[i].x - cmx); + smy += Math.abs(to[i].y - cmy); + sMx += Math.abs(from[i].x - cMx); + sMy += Math.abs(from[i].y - cMy); + } + + if( Math.abs(smx) < jsfeat.EPSILON + || Math.abs(smy) < jsfeat.EPSILON + || Math.abs(sMx) < jsfeat.EPSILON + || Math.abs(sMy) < jsfeat.EPSILON ) return 0; + + smx = count/smx; smy = count/smy; + sMx = count/sMx; sMy = count/sMy; + + t0d[0] = sMx; t0d[1] = 0; t0d[2] = -cMx*sMx; + t0d[3] = 0; t0d[4] = sMy; t0d[5] = -cMy*sMy; + t0d[6] = 0; t0d[7] = 0; t0d[8] = 1; + + t1d[0] = 1.0/smx; t1d[1] = 0; t1d[2] = cmx; + t1d[3] = 0; t1d[4] = 1.0/smy; t1d[5] = cmy; + t1d[6] = 0; t1d[7] = 0; t1d[8] = 1; + // + + // construct system + i = 81; + while(--i >= 0) { + LtL[i] = 0.0; + } + for(i = 0; i < count; ++i) { + x = (to[i].x - cmx) * smx; + y = (to[i].y - cmy) * smy; + X = (from[i].x - cMx) * sMx; + Y = (from[i].y - cMy) * sMy; + + LtL[0] += X*X; + LtL[1] += X*Y; + LtL[2] += X; + + LtL[6] += X*-x*X; + LtL[7] += X*-x*Y; + LtL[8] += X*-x; + LtL[10] += Y*Y; + LtL[11] += Y; + + LtL[15] += Y*-x*X; + LtL[16] += Y*-x*Y; + LtL[17] += Y*-x; + LtL[20] += 1.0; + + LtL[24] += -x*X; + LtL[25] += -x*Y; + LtL[26] += -x; + LtL[30] += X*X; + LtL[31] += X*Y; + LtL[32] += X; + LtL[33] += X*-y*X; + LtL[34] += X*-y*Y; + LtL[35] += X*-y; + LtL[40] += Y*Y; + LtL[41] += Y; + LtL[42] += Y*-y*X; + LtL[43] += Y*-y*Y; + LtL[44] += Y*-y; + LtL[50] += 1.0; + LtL[51] += -y*X; + LtL[52] += -y*Y; + LtL[53] += -y; + LtL[60] += -x*X*-x*X + -y*X*-y*X; + LtL[61] += -x*X*-x*Y + -y*X*-y*Y; + LtL[62] += -x*X*-x + -y*X*-y; + LtL[70] += -x*Y*-x*Y + -y*Y*-y*Y; + LtL[71] += -x*Y*-x + -y*Y*-y; + LtL[80] += -x*-x + -y*-y; + } + // + + // symmetry + for(i = 0; i < 9; ++i) { + for(j = 0; j < i; ++j) + LtL[i*9+j] = LtL[j*9+i]; + } + + jsfeat.linalg.eigenVV(mLtL, Evec); + + md[0]=evd[72], md[1]=evd[73], md[2]=evd[74]; + md[3]=evd[75], md[4]=evd[76], md[5]=evd[77]; + md[6]=evd[78], md[7]=evd[79], md[8]=evd[80]; + + // denormalize + jsfeat.matmath.multiply_3x3(model, T1, model); + jsfeat.matmath.multiply_3x3(model, model, T0); + + // set bottom right to 1.0 + x = 1.0/md[8]; + md[0] *= x; md[1] *= x; md[2] *= x; + md[3] *= x; md[4] *= x; md[5] *= x; + md[6] *= x; md[7] *= x; md[8] = 1.0; + + return 1; + } + + homography2d.prototype.error = function(from, to, model, err, count) { + var i=0; + var pt0,pt1,ww=0.0,dx=0.0,dy=0.0; + var m=model.data; + + for (; i < count; ++i) { + pt0 = from[i]; + pt1 = to[i]; + + ww = 1.0/(m[6]*pt0.x + m[7]*pt0.y + 1.0); + dx = (m[0]*pt0.x + m[1]*pt0.y + m[2])*ww - pt1.x; + dy = (m[3]*pt0.x + m[4]*pt0.y + m[5])*ww - pt1.y; + err[i] = (dx*dx + dy*dy); + } + } + + homography2d.prototype.check_subset = function(from, to, count) { + // seems to reject good subsets actually + //if( have_collinear_points(from, count) || have_collinear_points(to, count) ) { + //return false; + //} + if( count == 4 ) { + var negative = 0; + + var fp0=from[0],fp1=from[1],fp2=from[2],fp3=from[3]; + var tp0=to[0],tp1=to[1],tp2=to[2],tp3=to[3]; + + // set1 + var A11=fp0.x, A12=fp0.y, A13=1.0; + var A21=fp1.x, A22=fp1.y, A23=1.0; + var A31=fp2.x, A32=fp2.y, A33=1.0; + + var B11=tp0.x, B12=tp0.y, B13=1.0; + var B21=tp1.x, B22=tp1.y, B23=1.0; + var B31=tp2.x, B32=tp2.y, B33=1.0; + + var detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + var detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set2 + A11=fp1.x, A12=fp1.y; + A21=fp2.x, A22=fp2.y; + A31=fp3.x, A32=fp3.y; + + B11=tp1.x, B12=tp1.y; + B21=tp2.x, B22=tp2.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set3 + A11=fp0.x, A12=fp0.y; + A21=fp2.x, A22=fp2.y; + A31=fp3.x, A32=fp3.y; + + B11=tp0.x, B12=tp0.y; + B21=tp2.x, B22=tp2.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + // set4 + A11=fp0.x, A12=fp0.y; + A21=fp1.x, A22=fp1.y; + A31=fp3.x, A32=fp3.y; + + B11=tp0.x, B12=tp0.y; + B21=tp1.x, B22=tp1.y; + B31=tp3.x, B32=tp3.y; + + detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); + detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); + + if(detA*detB < 0) negative++; + + if(negative != 0 && negative != 4) { + return false; + } + } + return true; // all good + } + + return homography2d; + })(); + + return { + + affine2d:affine2d, + homography2d:homography2d + + }; + + })(); + + var ransac_params_t = (function () { + function ransac_params_t(size, thresh, eps, prob) { + if (typeof size === "undefined") { size=0; } + if (typeof thresh === "undefined") { thresh=0.5; } + if (typeof eps === "undefined") { eps=0.5; } + if (typeof prob === "undefined") { prob=0.99; } + + this.size = size; + this.thresh = thresh; + this.eps = eps; + this.prob = prob; + }; + ransac_params_t.prototype.update_iters = function(_eps, max_iters) { + var num = Math.log(1 - this.prob); + var denom = Math.log(1 - Math.pow(1 - _eps, this.size)); + return (denom >= 0 || -num >= max_iters*(-denom) ? max_iters : Math.round(num/denom))|0; + }; + return ransac_params_t; + })(); + + var motion_estimator = (function() { + + var get_subset = function(kernel, from, to, need_cnt, max_cnt, from_sub, to_sub) { + var max_try = 1000; + var indices = []; + var i=0, j=0, ssiter=0, idx_i=0, ok=false; + for(; ssiter < max_try; ++ssiter) { + i = 0; + for (; i < need_cnt && ssiter < max_try;) { + ok = false; + idx_i = 0; + while (!ok) { + ok = true; + idx_i = indices[i] = Math.floor(Math.random() * max_cnt)|0; + for (j = 0; j < i; ++j) { + if (idx_i == indices[j]) + { ok = false; break; } + } + } + from_sub[i] = from[idx_i]; + to_sub[i] = to[idx_i]; + if( !kernel.check_subset( from_sub, to_sub, i+1 ) ) { + ssiter++; + continue; + } + ++i; + } + break; + } + + return (i == need_cnt && ssiter < max_try); + } + + var find_inliers = function(kernel, model, from, to, count, thresh, err, mask) { + var numinliers = 0, i=0, f=0; + var t = thresh*thresh; + + kernel.error(from, to, model, err, count); + + for(; i < count; ++i) { + f = err[i] <= t; + mask[i] = f; + numinliers += f; + } + return numinliers; + } + + return { + + ransac: function(params, kernel, from, to, count, model, mask, max_iters) { + if (typeof max_iters === "undefined") { max_iters=1000; } + + if(count < params.size) return false; + + var model_points = params.size; + var niters = max_iters, iter=0; + var result = false; + + var subset0 = []; + var subset1 = []; + var found = false; + + var mc=model.cols,mr=model.rows; + var dt = model.type | jsfeat.C1_t; + + var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); + var ms_buff = jsfeat.cache.get_buffer(count); + var err_buff = jsfeat.cache.get_buffer(count<<2); + var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); + var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8C1_t, ms_buff.data); + + var inliers_max = -1, numinliers=0; + var nmodels = 0; + + var err = err_buff.f32; + + // special case + if(count == model_points) { + if(kernel.run(from, to, M, count) <= 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + + M.copy_to(model); + if(mask) { + while(--count >= 0) { + mask.data[count] = 1; + } + } + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return true; + } + + for (; iter < niters; ++iter) { + // generate subset + found = get_subset(kernel, from, to, model_points, count, subset0, subset1); + if(!found) { + if(iter == 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + break; + } + + nmodels = kernel.run( subset0, subset1, M, model_points ); + if(nmodels <= 0) + continue; + + // TODO handle multimodel output + + numinliers = find_inliers(kernel, M, from, to, count, params.thresh, err, curr_mask.data); + + if( numinliers > Math.max(inliers_max, model_points-1) ) { + M.copy_to(model); + inliers_max = numinliers; + if(mask) curr_mask.copy_to(mask); + niters = params.update_iters((count - numinliers)/count, niters); + result = true; + } + } + + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + + return result; + }, + + lmeds: function(params, kernel, from, to, count, model, mask, max_iters) { + if (typeof max_iters === "undefined") { max_iters=1000; } + + if(count < params.size) return false; + + var model_points = params.size; + var niters = max_iters, iter=0; + var result = false; + + var subset0 = []; + var subset1 = []; + var found = false; + + var mc=model.cols,mr=model.rows; + var dt = model.type | jsfeat.C1_t; + + var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); + var ms_buff = jsfeat.cache.get_buffer(count); + var err_buff = jsfeat.cache.get_buffer(count<<2); + var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); + var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8_t|jsfeat.C1_t, ms_buff.data); + + var numinliers=0; + var nmodels = 0; + + var err = err_buff.f32; + var min_median = 1000000000.0, sigma=0.0, median=0.0; + + params.eps = 0.45; + niters = params.update_iters(params.eps, niters); + + // special case + if(count == model_points) { + if(kernel.run(from, to, M, count) <= 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + + M.copy_to(model); + if(mask) { + while(--count >= 0) { + mask.data[count] = 1; + } + } + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return true; + } + + for (; iter < niters; ++iter) { + // generate subset + found = get_subset(kernel, from, to, model_points, count, subset0, subset1); + if(!found) { + if(iter == 0) { + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + return false; + } + break; + } + + nmodels = kernel.run( subset0, subset1, M, model_points ); + if(nmodels <= 0) + continue; + + // TODO handle multimodel output + + kernel.error(from, to, M, err, count); + median = jsfeat.math.median(err, 0, count-1); + + if(median < min_median) { + min_median = median; + M.copy_to(model); + result = true; + } + } + + if(result) { + sigma = 2.5*1.4826*(1 + 5.0/(count - model_points))*Math.sqrt(min_median); + sigma = Math.max(sigma, 0.001); + + numinliers = find_inliers(kernel, model, from, to, count, sigma, err, curr_mask.data); + if(mask) curr_mask.copy_to(mask); + + result = numinliers >= model_points; + } + + jsfeat.cache.put_buffer(m_buff); + jsfeat.cache.put_buffer(ms_buff); + jsfeat.cache.put_buffer(err_buff); + + return result; + } + + }; + + })(); + + global.ransac_params_t = ransac_params_t; + global.motion_model = motion_model; + global.motion_estimator = motion_estimator; + +})(jsfeat); diff --git a/src/jsfeat_optical_flow_lk.js b/src/jsfeat_optical_flow_lk.js new file mode 100755 index 0000000..85730d2 --- /dev/null +++ b/src/jsfeat_optical_flow_lk.js @@ -0,0 +1,246 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * this code is a rewrite from OpenCV's Lucas-Kanade optical flow implementation + */ + +(function(global) { + "use strict"; + // + var optical_flow_lk = (function() { + + // short link to shar deriv + var scharr_deriv = jsfeat.imgproc.scharr_derivatives; + + return { + track: function(prev_pyr, curr_pyr, prev_xy, curr_xy, count, win_size, max_iter, status, eps, min_eigen_threshold) { + if (typeof max_iter === "undefined") { max_iter = 30; } + if (typeof status === "undefined") { status = new Uint8Array(count); } + if (typeof eps === "undefined") { eps = 0.01; } + if (typeof min_eigen_threshold === "undefined") { min_eigen_threshold = 0.0001; } + + var half_win = (win_size-1)*0.5; + var win_area = (win_size*win_size)|0; + var win_area2 = win_area << 1; + var prev_imgs = prev_pyr.data, next_imgs = curr_pyr.data; + var img_prev=prev_imgs[0].data,img_next=next_imgs[0].data; + var w0 = prev_imgs[0].cols, h0 = prev_imgs[0].rows,lw=0,lh=0; + + var iwin_node = jsfeat.cache.get_buffer(win_area<<2); + var deriv_iwin_node = jsfeat.cache.get_buffer(win_area2<<2); + var deriv_lev_node = jsfeat.cache.get_buffer((h0*(w0<<1))<<2); + + var deriv_m = new jsfeat.matrix_t(w0, h0, jsfeat.S32C2_t, deriv_lev_node.data); + + var iwin_buf = iwin_node.i32; + var deriv_iwin = deriv_iwin_node.i32; + var deriv_lev = deriv_lev_node.i32; + + var dstep=0,src=0,dsrc=0,iptr=0,diptr=0,jptr=0; + var lev_sc=0.0,prev_x=0.0,prev_y=0.0,next_x=0.0,next_y=0.0; + var prev_delta_x=0.0,prev_delta_y=0.0,delta_x=0.0,delta_y=0.0; + var iprev_x=0,iprev_y=0,inext_x=0,inext_y=0; + var i=0,j=0,x=0,y=0,level=0,ptid=0,iter=0; + var brd_tl=0,brd_r=0,brd_b=0; + var a=0.0,b=0.0,b1=0.0,b2=0.0; + + // fixed point math + var W_BITS14 = 14; + var W_BITS4 = 14; + var W_BITS1m5 = W_BITS4 - 5; + var W_BITS1m51 = (1 << ((W_BITS1m5) - 1)); + var W_BITS14_ = (1 << W_BITS14); + var W_BITS41 = (1 << ((W_BITS4) - 1)); + var FLT_SCALE = 1.0/(1 << 20); + var iw00=0,iw01=0,iw10=0,iw11=0,ival=0,ixval=0,iyval=0; + var A11=0.0,A12=0.0,A22=0.0,D=0.0,min_eig=0.0; + + var FLT_EPSILON = 0.00000011920929; + eps *= eps; + + // reset status + for(; i < count; ++i) { + status[i] = 1; + } + + var max_level = (prev_pyr.levels - 1)|0; + level = max_level; + + for(; level >= 0; --level) { + lev_sc = (1.0/(1 << level)); + lw = w0 >> level; + lh = h0 >> level; + dstep = lw << 1; + img_prev = prev_imgs[level].data; + img_next = next_imgs[level].data; + + brd_r = (lw - win_size)|0; + brd_b = (lh - win_size)|0; + + // calculate level derivatives + scharr_deriv(prev_imgs[level], deriv_m); + + // iterate through points + for(ptid = 0; ptid < count; ++ptid) { + i = ptid << 1; + j = i + 1; + prev_x = prev_xy[i]*lev_sc; + prev_y = prev_xy[j]*lev_sc; + + if( level == max_level ) { + next_x = prev_x; + next_y = prev_y; + } else { + next_x = curr_xy[i]*2.0; + next_y = curr_xy[j]*2.0; + } + curr_xy[i] = next_x; + curr_xy[j] = next_y; + + prev_x -= half_win; + prev_y -= half_win; + iprev_x = prev_x|0; + iprev_y = prev_y|0; + + // border check + x = (iprev_x <= brd_tl)|(iprev_x >= brd_r)|(iprev_y <= brd_tl)|(iprev_y >= brd_b); + if( x != 0 ) { + if( level == 0 ) { + status[ptid] = 0; + } + continue; + } + + a = prev_x - iprev_x; + b = prev_y - iprev_y; + iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; + iw11 = (W_BITS14_ - iw00 - iw01 - iw10); + + A11 = 0.0, A12 = 0.0, A22 = 0.0; + + // extract the patch from the first image, compute covariation matrix of derivatives + for( y = 0; y < win_size; ++y ) { + src = ( (y + iprev_y)*lw + iprev_x )|0; + dsrc = src << 1; + + iptr = (y*win_size)|0; + diptr = iptr << 1; + for(x = 0 ; x < win_size; ++x, ++src, ++iptr, dsrc += 2) { + ival = ( (img_prev[src])*iw00 + (img_prev[src+1])*iw01 + + (img_prev[src+lw])*iw10 + (img_prev[src+lw+1])*iw11 ); + ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); + + ixval = ( deriv_lev[dsrc]*iw00 + deriv_lev[dsrc+2]*iw01 + + deriv_lev[dsrc+dstep]*iw10 + deriv_lev[dsrc+dstep+2]*iw11 ); + ixval = (((ixval) + W_BITS41) >> (W_BITS4)); + + iyval = ( deriv_lev[dsrc+1]*iw00 + deriv_lev[dsrc+3]*iw01 + deriv_lev[dsrc+dstep+1]*iw10 + + deriv_lev[dsrc+dstep+3]*iw11 ); + iyval = (((iyval) + W_BITS41) >> (W_BITS4)); + + iwin_buf[iptr] = ival; + deriv_iwin[diptr++] = ixval; + deriv_iwin[diptr++] = iyval; + + A11 += ixval*ixval; + A12 += ixval*iyval; + A22 += iyval*iyval; + } + } + + A11 *= FLT_SCALE; A12 *= FLT_SCALE; A22 *= FLT_SCALE; + + D = A11*A22 - A12*A12; + min_eig = (A22 + A11 - Math.sqrt((A11-A22)*(A11-A22) + 4.0*A12*A12)) / win_area2; + + if( min_eig < min_eigen_threshold || D < FLT_EPSILON ) + { + if( level == 0 ) { + status[ptid] = 0; + } + continue; + } + + D = 1.0/D; + + next_x -= half_win; + next_y -= half_win; + prev_delta_x = 0.0; + prev_delta_y = 0.0; + + for( iter = 0; iter < max_iter; ++iter ) { + inext_x = next_x|0; + inext_y = next_y|0; + + x = (inext_x <= brd_tl)|(inext_x >= brd_r)|(inext_y <= brd_tl)|(inext_y >= brd_b); + if( x != 0 ) { + if( level == 0 ) { + status[ptid] = 0; + } + break; + } + + a = next_x - inext_x; + b = next_y - inext_y; + iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; + iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; + iw11 = (W_BITS14_ - iw00 - iw01 - iw10); + b1 = 0.0, b2 = 0.0; + + for( y = 0; y < win_size; ++y ) { + jptr = ( (y + inext_y)*lw + inext_x )|0; + + iptr = (y*win_size)|0; + diptr = iptr << 1; + for( x = 0 ; x < win_size; ++x, ++jptr, ++iptr ) { + ival = ( (img_next[jptr])*iw00 + (img_next[jptr+1])*iw01 + + (img_next[jptr+lw])*iw10 + (img_next[jptr+lw+1])*iw11 ); + ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); + ival = (ival - iwin_buf[iptr]); + + b1 += ival * deriv_iwin[diptr++]; + b2 += ival * deriv_iwin[diptr++]; + } + } + + b1 *= FLT_SCALE; + b2 *= FLT_SCALE; + + delta_x = ((A12*b2 - A22*b1) * D); + delta_y = ((A12*b1 - A11*b2) * D); + + next_x += delta_x; + next_y += delta_y; + curr_xy[i] = next_x + half_win; + curr_xy[j] = next_y + half_win; + + if( delta_x*delta_x + delta_y*delta_y <= eps ) { + break; + } + + if( iter > 0 && Math.abs(delta_x + prev_delta_x) < 0.01 && + Math.abs(delta_y + prev_delta_y) < 0.01 ) { + curr_xy[i] -= delta_x*0.5; + curr_xy[j] -= delta_y*0.5; + break; + } + + prev_delta_x = delta_x; + prev_delta_y = delta_y; + } + } // points loop + } // levels loop + + jsfeat.cache.put_buffer(iwin_node); + jsfeat.cache.put_buffer(deriv_iwin_node); + jsfeat.cache.put_buffer(deriv_lev_node); + } + }; + })(); + + global.optical_flow_lk = optical_flow_lk; + +})(jsfeat); diff --git a/src/jsfeat_orb.js b/src/jsfeat_orb.js new file mode 100644 index 0000000..4b09186 --- /dev/null +++ b/src/jsfeat_orb.js @@ -0,0 +1,364 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Original implementation derived from OpenCV, + * @authors Ethan Rublee, Vincent Rabaud, Gary Bradski + */ + +(function(global) { + "use strict"; + // + + var orb = (function() { + + var bit_pattern_31_ = new Int32Array([ + 8,-3, 9,5/*mean (0), correlation (0)*/, + 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/, + -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/, + 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/, + 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/, + 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/, + -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/, + -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/, + -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/, + 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/, + -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/, + -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/, + 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/, + -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/, + -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/, + -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/, + 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/, + -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/, + -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/, + 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/, + 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/, + 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/, + 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/, + -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/, + -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/, + -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/, + -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/, + -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/, + -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/, + 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/, + 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/, + 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/, + 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/, + 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/, + 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/, + -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/, + -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/, + 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/, + 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/, + -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/, + -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/, + -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/, + 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/, + 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/, + 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/, + -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/, + 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/, + -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/, + 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/, + -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/, + -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/, + 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/, + 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/, + -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/, + 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/, + 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/, + -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/, + -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/, + -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/, + -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/, + 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/, + -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/, + -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/, + -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/, + 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/, + -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/, + -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/, + 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/, + -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/, + -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/, + 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/, + -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/, + -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/, + -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/, + 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/, + 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/, + -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/, + -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/, + 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/, + -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/, + -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/, + -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/, + 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/, + -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/, + 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/, + 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/, + -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/, + -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/, + 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/, + 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/, + 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/, + -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/, + -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/, + 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/, + 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/, + 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/, + 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/, + 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/, + 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/, + 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/, + 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/, + -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/, + -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/, + 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/, + 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/, + 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/, + 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/, + 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/, + 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/, + -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/, + -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/, + -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/, + -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/, + 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/, + 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/, + -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/, + 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/, + -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/, + -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/, + 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/, + -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/, + -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/, + 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/, + -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/, + 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/, + 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/, + -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/, + 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/, + -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/, + 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/, + 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/, + 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/, + -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/, + 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/, + -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/, + 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/, + 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/, + -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/, + -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/, + -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/, + 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/, + -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/, + -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/, + 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/, + 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/, + -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/, + 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/, + -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/, + 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/, + -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/, + -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/, + 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/, + 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/, + -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/, + 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/, + 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/, + -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/, + -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/, + -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/, + 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/, + 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/, + -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/, + 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/, + 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/, + -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/, + -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/, + -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/, + 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/, + -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/, + -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/, + -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, + -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, + -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, + 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, + -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/, + -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/, + -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/, + -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, + 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, + -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, + 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, + 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, + -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, + -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, + -7,1, -6,7/*mean (0.175), correlation (0.500024)*/, + -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/, + -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/, + -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/, + -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/, + -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/, + 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/, + 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/, + 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/, + 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/, + -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/, + -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/, + -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/, + -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/, + 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/, + 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/, + 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/, + -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/, + -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, + 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/, + 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/, + -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/, + 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/, + 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/, + -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/, + 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/, + -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/, + 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/, + -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/, + 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/, + -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/, + 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/, + 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/, + 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/, + -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/, + -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/, + -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/, + -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/, + -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/, + 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/, + 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/, + 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/, + 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/, + 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/, + -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/, + 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/, + 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/, + -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/, + -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/, + -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/, + 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/, + 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/, + -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/, + -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/, + -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/, + 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/, + 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/, + -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/, + 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/, + 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/, + 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/, + -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/, + 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/, + -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/, + 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/, + 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/, + 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/, + -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/, + 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/, + 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/, + 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/, + -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/ + ]); + + var H = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); + var patch_img = new jsfeat.matrix_t(32, 32, jsfeat.U8_t|jsfeat.C1_t); + + var rectify_patch = function(src, dst, angle, px, py, psize) { + var cosine = Math.cos(angle); + var sine = Math.sin(angle); + + H.data[0] = cosine, H.data[1] = -sine, H.data[2] = (-cosine + sine ) * psize*0.5 + px, + H.data[3] = sine, H.data[4] = cosine, H.data[5] = (-sine - cosine) * psize*0.5 + py; + + jsfeat.imgproc.warp_affine(src, dst, H, 128); + } + + return { + + describe: function(src, corners, count, descriptors) { + var DESCR_SIZE = 32; // bytes; + var i=0,b=0,px=0.0,py=0.0,angle=0.0; + var t0=0, t1=0, val=0; + var img = src.data, w = src.cols, h = src.rows; + var patch_d = patch_img.data; + var patch_off = 16*32 + 16; // center of patch + var patt=0; + + if(!(descriptors.type&jsfeat.U8_t)) { + // relocate to U8 type + descriptors.type = jsfeat.U8_t; + descriptors.cols = DESCR_SIZE; + descriptors.rows = count; + descriptors.channel = 1; + descriptors.allocate(); + } else { + descriptors.resize(DESCR_SIZE, count, 1); + } + + var descr_d = descriptors.data; + var descr_off = 0; + + for(i = 0; i < count; ++i) { + px = corners[i].x; + py = corners[i].y; + angle = corners[i].angle; + + rectify_patch(src, patch_img, angle, px, py, 32); + + // describe the patch + patt = 0; + for (b = 0; b < DESCR_SIZE; ++b) { + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val = (t0 < t1)|0; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 1; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 2; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 3; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 4; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 5; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 6; + + t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 + val |= (t0 < t1) << 7; + + descr_d[descr_off+b] = val; + } + descr_off += DESCR_SIZE; + } + } + }; + })(); + + global.orb = orb; + +})(jsfeat); diff --git a/src/jsfeat_struct.js b/src/jsfeat_struct.js new file mode 100755 index 0000000..c7cbbcc --- /dev/null +++ b/src/jsfeat_struct.js @@ -0,0 +1,232 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + // CONSTANTS + var EPSILON = 0.0000001192092896; + var FLT_MIN = 1E-37; + + // implementation from CCV project + // currently working only with u8,s32,f32 + var U8_t = 0x0100, + S32_t = 0x0200, + F32_t = 0x0400, + S64_t = 0x0800, + F64_t = 0x1000; + + var C1_t = 0x01, + C2_t = 0x02, + C3_t = 0x03, + C4_t = 0x04; + + var _data_type_size = new Int32Array([ -1, 1, 4, -1, 4, -1, -1, -1, 8, -1, -1, -1, -1, -1, -1, -1, 8 ]); + + var get_data_type = (function () { + return function(type) { + return (type & 0xFF00); + } + })(); + + var get_channel = (function () { + return function(type) { + return (type & 0xFF); + } + })(); + + var get_data_type_size = (function () { + return function(type) { + return _data_type_size[(type & 0xFF00) >> 8]; + } + })(); + + // color conversion + var COLOR_RGBA2GRAY = 0; + var COLOR_RGB2GRAY = 1; + var COLOR_BGRA2GRAY = 2; + var COLOR_BGR2GRAY = 3; + + // box blur option + var BOX_BLUR_NOSCALE = 0x01; + // svd options + var SVD_U_T = 0x01; + var SVD_V_T = 0x02; + + var data_t = (function () { + function data_t(size_in_bytes, buffer) { + // we need align size to multiple of 8 + this.size = ((size_in_bytes + 7) | 0) & -8; + if (typeof buffer === "undefined") { + this.buffer = new ArrayBuffer(this.size); + } else { + this.buffer = buffer; + this.size = buffer.length; + } + this.u8 = new Uint8Array(this.buffer); + this.i32 = new Int32Array(this.buffer); + this.f32 = new Float32Array(this.buffer); + this.f64 = new Float64Array(this.buffer); + } + return data_t; + })(); + + var matrix_t = (function () { + // columns, rows, data_type + function matrix_t(c, r, data_type, data_buffer) { + this.type = get_data_type(data_type)|0; + this.channel = get_channel(data_type)|0; + this.cols = c|0; + this.rows = r|0; + if (typeof data_buffer === "undefined") { + this.allocate(); + } else { + this.buffer = data_buffer; + // data user asked for + this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); + } + } + matrix_t.prototype.allocate = function() { + // clear references + delete this.data; + delete this.buffer; + // + this.buffer = new data_t((this.cols * get_data_type_size(this.type) * this.channel) * this.rows); + this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); + } + matrix_t.prototype.copy_to = function(other) { + var od = other.data, td = this.data; + var i = 0, n = (this.cols*this.rows*this.channel)|0; + for(; i < n-4; i+=4) { + od[i] = td[i]; + od[i+1] = td[i+1]; + od[i+2] = td[i+2]; + od[i+3] = td[i+3]; + } + for(; i < n; ++i) { + od[i] = td[i]; + } + } + matrix_t.prototype.resize = function(c, r, ch) { + if (typeof ch === "undefined") { ch = this.channel; } + // relocate buffer only if new size doesnt fit + var new_size = (c * get_data_type_size(this.type) * ch) * r; + if(new_size > this.buffer.size) { + this.cols = c; + this.rows = r; + this.channel = ch; + this.allocate(); + } else { + this.cols = c; + this.rows = r; + this.channel = ch; + } + } + + return matrix_t; + })(); + + var pyramid_t = (function () { + + function pyramid_t(levels) { + this.levels = levels|0; + this.data = new Array(levels); + this.pyrdown = jsfeat.imgproc.pyrdown; + } + + pyramid_t.prototype.allocate = function(start_w, start_h, data_type) { + var i = this.levels; + while(--i >= 0) { + this.data[i] = new matrix_t(start_w >> i, start_h >> i, data_type); + } + } + + pyramid_t.prototype.build = function(input, skip_first_level) { + if (typeof skip_first_level === "undefined") { skip_first_level = true; } + // just copy data to first level + var i = 2, a = input, b = this.data[0]; + if(!skip_first_level) { + var j=input.cols*input.rows; + while(--j >= 0) { + b.data[j] = input.data[j]; + } + } + b = this.data[1]; + this.pyrdown(a, b); + for(; i < this.levels; ++i) { + a = b; + b = this.data[i]; + this.pyrdown(a, b); + } + } + + return pyramid_t; + })(); + + var keypoint_t = (function () { + function keypoint_t(x,y,score,level,angle) { + if (typeof x === "undefined") { x=0; } + if (typeof y === "undefined") { y=0; } + if (typeof score === "undefined") { score=0; } + if (typeof level === "undefined") { level=0; } + if (typeof angle === "undefined") { angle=-1.0; } + + this.x = x; + this.y = y; + this.score = score; + this.level = level; + this.angle = angle; + } + return keypoint_t; + })(); + + + // data types + global.U8_t = U8_t; + global.S32_t = S32_t; + global.F32_t = F32_t; + global.S64_t = S64_t; + global.F64_t = F64_t; + // data channels + global.C1_t = C1_t; + global.C2_t = C2_t; + global.C3_t = C3_t; + global.C4_t = C4_t; + + // popular formats + global.U8C1_t = U8_t | C1_t; + global.U8C3_t = U8_t | C3_t; + global.U8C4_t = U8_t | C4_t; + + global.F32C1_t = F32_t | C1_t; + global.F32C2_t = F32_t | C2_t; + global.S32C1_t = S32_t | C1_t; + global.S32C2_t = S32_t | C2_t; + + // constants + global.EPSILON = EPSILON; + global.FLT_MIN = FLT_MIN; + + // color convert + global.COLOR_RGBA2GRAY = COLOR_RGBA2GRAY; + global.COLOR_RGB2GRAY = COLOR_RGB2GRAY; + global.COLOR_BGRA2GRAY = COLOR_BGRA2GRAY; + global.COLOR_BGR2GRAY = COLOR_BGR2GRAY; + + // options + global.BOX_BLUR_NOSCALE = BOX_BLUR_NOSCALE; + global.SVD_U_T = SVD_U_T; + global.SVD_V_T = SVD_V_T; + + global.get_data_type = get_data_type; + global.get_channel = get_channel; + global.get_data_type_size = get_data_type_size; + + global.data_t = data_t; + global.matrix_t = matrix_t; + global.pyramid_t = pyramid_t; + global.keypoint_t = keypoint_t; + +})(jsfeat); diff --git a/src/jsfeat_transform.js b/src/jsfeat_transform.js new file mode 100644 index 0000000..9684426 --- /dev/null +++ b/src/jsfeat_transform.js @@ -0,0 +1,172 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + */ + +(function(global) { + "use strict"; + // + + var transform = (function() { + // + return { + affine_3point_transform: function(mat, src_x0, src_y0, dst_x0, dst_y0, + src_x1, src_y1, dst_x1, dst_y1, + src_x2, src_y2, dst_x2, dst_y2) { + // we need linear algebra module first + }, + + perspective_4point_transform: function(mat, src_x0, src_y0, dst_x0, dst_y0, + src_x1, src_y1, dst_x1, dst_y1, + src_x2, src_y2, dst_x2, dst_y2, + src_x3, src_y3, dst_x3, dst_y3) { + var t1 = src_x0; + var t2 = src_x2; + var t4 = src_y1; + var t5 = t1 * t2 * t4; + var t6 = src_y3; + var t7 = t1 * t6; + var t8 = t2 * t7; + var t9 = src_y2; + var t10 = t1 * t9; + var t11 = src_x1; + var t14 = src_y0; + var t15 = src_x3; + var t16 = t14 * t15; + var t18 = t16 * t11; + var t20 = t15 * t11 * t9; + var t21 = t15 * t4; + var t24 = t15 * t9; + var t25 = t2 * t4; + var t26 = t6 * t2; + var t27 = t6 * t11; + var t28 = t9 * t11; + var t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + var t32 = t1 * t15; + var t35 = t14 * t11; + var t41 = t4 * t1; + var t42 = t6 * t41; + var t43 = t14 * t2; + var t46 = t16 * t9; + var t48 = t14 * t9 * t11; + var t51 = t4 * t6 * t2; + var t55 = t6 * t14; + var Hr0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hr1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hr2 = t1; + var Hr3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hr4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hr5 = t14; + var Hr6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hr7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + t1 = dst_x0; + t2 = dst_x2; + t4 = dst_y1; + t5 = t1 * t2 * t4; + t6 = dst_y3; + t7 = t1 * t6; + t8 = t2 * t7; + t9 = dst_y2; + t10 = t1 * t9; + t11 = dst_x1; + t14 = dst_y0; + t15 = dst_x3; + t16 = t14 * t15; + t18 = t16 * t11; + t20 = t15 * t11 * t9; + t21 = t15 * t4; + t24 = t15 * t9; + t25 = t2 * t4; + t26 = t6 * t2; + t27 = t6 * t11; + t28 = t9 * t11; + t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); + t32 = t1 * t15; + t35 = t14 * t11; + t41 = t4 * t1; + t42 = t6 * t41; + t43 = t14 * t2; + t46 = t16 * t9; + t48 = t14 * t9 * t11; + t51 = t4 * t6 * t2; + t55 = t6 * t14; + var Hl0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; + var Hl1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; + var Hl2 = t1; + var Hl3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; + var Hl4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; + var Hl5 = t14; + var Hl6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; + var Hl7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; + + // the following code computes R = Hl * inverse Hr + t2 = Hr4-Hr7*Hr5; + t4 = Hr0*Hr4; + t5 = Hr0*Hr5; + t7 = Hr3*Hr1; + t8 = Hr2*Hr3; + t10 = Hr1*Hr6; + var t12 = Hr2*Hr6; + t15 = 1.0 / (t4-t5*Hr7-t7+t8*Hr7+t10*Hr5-t12*Hr4); + t18 = -Hr3+Hr5*Hr6; + var t23 = -Hr3*Hr7+Hr4*Hr6; + t28 = -Hr1+Hr2*Hr7; + var t31 = Hr0-t12; + t35 = Hr0*Hr7-t10; + t41 = -Hr1*Hr5+Hr2*Hr4; + var t44 = t5-t8; + var t47 = t4-t7; + t48 = t2*t15; + var t49 = t28*t15; + var t50 = t41*t15; + mat[0] = Hl0*t48+Hl1*(t18*t15)-Hl2*(t23*t15); + mat[1] = Hl0*t49+Hl1*(t31*t15)-Hl2*(t35*t15); + mat[2] = -Hl0*t50-Hl1*(t44*t15)+Hl2*(t47*t15); + mat[3] = Hl3*t48+Hl4*(t18*t15)-Hl5*(t23*t15); + mat[4] = Hl3*t49+Hl4*(t31*t15)-Hl5*(t35*t15); + mat[5] = -Hl3*t50-Hl4*(t44*t15)+Hl5*(t47*t15); + mat[6] = Hl6*t48+Hl7*(t18*t15)-t23*t15; + mat[7] = Hl6*t49+Hl7*(t31*t15)-t35*t15; + mat[8] = -Hl6*t50-Hl7*(t44*t15)+t47*t15; + }, + + invert_affine_transform: function(src, dst) { + var m11 = src[0], m12 = src[1], m13 = src[2]; + var m21 = src[3], m22 = src[4], m23 = src[5]; + + var det = 1.0 / (m11 * m22 - m12 * m21); + + dst[0] = det * m22; + dst[1] = det * -m12; + dst[2] = det * (m12*m23 - m13*m22); + + dst[3] = det * -m21; + dst[4] = det * m11; + dst[5] = det * (m13*m21 - m11*m23); + }, + + invert_perspective_transform: function(src, dst) { + var m11 = src[0], m12 = src[1], m13 = src[2]; + var m21 = src[3], m22 = src[4], m23 = src[5]; + var m31 = src[6], m32 = src[7], m33 = src[8]; + + var det = 1.0 / (m11 * (m22*m33 - m23*m32) - m12 * (m21*m33 - m23*m31) + m13 * (m21*m32 - m22*m31)); + + dst[0] = det * (m22*m33 - m23*m32); + dst[1] = det * (m13*m32 - m12*m33); + dst[2] = det * (m12*m23 - m13*m22); + + dst[3] = det * (m23*m31 - m21*m33); + dst[4] = det * (m11*m33 - m13*m31); + dst[5] = det * (m13*m21 - m11*m23); + + dst[6] = det * (m21*m32 - m22*m31); + dst[7] = det * (m12*m31 - m11*m32); + dst[8] = det * (m11*m22 - m12*m21); + } + }; + })(); + + global.transform = transform; + +})(jsfeat); \ No newline at end of file diff --git a/src/jsfeat_yape.js b/src/jsfeat_yape.js new file mode 100755 index 0000000..46616df --- /dev/null +++ b/src/jsfeat_yape.js @@ -0,0 +1,410 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Copyright 2007 Computer Vision Lab, + * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. + */ + +(function(global) { + "use strict"; + // + + var yape = (function() { + + var precompute_directions = function(step, dirs, R) { + var i = 0; + var x, y; + + x = R; + for(y = 0; y < x; y++, i++) + { + x = (Math.sqrt((R * R - y * y)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for(x-- ; x < y && x >= 0; x--, i++) + { + y = (Math.sqrt((R * R - x * x)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for( ; -x < y; x--, i++) + { + y = (Math.sqrt((R * R - x * x)) + 0.5)|0; + dirs[i] = (x + step * y); + } + for(y-- ; y >= 0; y--, i++) + { + x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(; y > x; y--, i++) + { + x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(x++ ; x <= 0; x++, i++) + { + y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for( ; x < -y; x++, i++) + { + y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; + dirs[i] = (x + step * y); + } + for(y++ ; y < 0; y++, i++) + { + x = (Math.sqrt((R * R - y * y)) + 0.5)|0; + dirs[i] = (x + step * y); + } + + dirs[i] = dirs[0]; + dirs[i + 1] = dirs[1]; + return i; + } + + var third_check = function (Sb, off, step) { + var n = 0; + if(Sb[off+1] != 0) n++; + if(Sb[off-1] != 0) n++; + if(Sb[off+step] != 0) n++; + if(Sb[off+step+1] != 0) n++; + if(Sb[off+step-1] != 0) n++; + if(Sb[off-step] != 0) n++; + if(Sb[off-step+1] != 0) n++; + if(Sb[off-step-1] != 0) n++; + + return n; + } + + var is_local_maxima = function(p, off, v, step, neighborhood) { + var x, y; + + if (v > 0) { + off -= step*neighborhood; + for (y= -neighborhood; y<=neighborhood; ++y) { + for (x= -neighborhood; x<=neighborhood; ++x) { + if (p[off+x] > v) return false; + } + off += step; + } + } else { + off -= step*neighborhood; + for (y= -neighborhood; y<=neighborhood; ++y) { + for (x= -neighborhood; x<=neighborhood; ++x) { + if (p[off+x] < v) return false; + } + off += step; + } + } + return true; + } + + var perform_one_point = function(I, x, Scores, Im, Ip, dirs, opposite, dirs_nb) { + var score = 0; + var a = 0, b = (opposite - 1)|0; + var A=0, B0=0, B1=0, B2=0; + var state=0; + + // WE KNOW THAT NOT(A ~ I0 & B1 ~ I0): + A = I[x+dirs[a]]; + if ((A <= Ip)) { + if ((A >= Im)) { // A ~ I0 + B0 = I[x+dirs[b]]; + if ((B0 <= Ip)) { + if ((B0 >= Im)) { Scores[x] = 0; return; } + else { + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 3; + else if ((B2 < Im)) state = 6; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else/* if ((B1 < Im))*/ { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 7; + else if ((B2 < Im)) state = 2; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + //else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 + } + } + else { // B0 < I0 + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 3; + else if ((B2 < Im)) state = 6; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else if ((B1 < Im)) { + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) state = 7; + else if ((B2 < Im)) state = 2; + else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + } + else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 + } + } + else { // A > I0 + B0 = I[x+dirs[b]]; + if ((B0 > Ip)) { Scores[x] = 0; return; } + b++; B1 = I[x+dirs[b]]; + if ((B1 > Ip)) { Scores[x] = 0; return; } + b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + state = 1; + } + } + else // A < I0 + { + B0 = I[x+dirs[b]]; + if ((B0 < Im)) { Scores[x] = 0; return; } + b++; B1 = I[x+dirs[b]]; + if ((B1 < Im)) { Scores[x] = 0; return; } + b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + state = 0; + } + + for(a = 1; a <= opposite; a++) + { + A = I[x+dirs[a]]; + + switch(state) + { + case 0: + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + if ((A < Im)) { + if ((B1 > Ip)) { Scores[x] = 0; return; } + if ((B2 > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 8; break; }; + } + // A ~ I0 + if ((B1 <= Ip)) { Scores[x] = 0; return; } + if ((B2 <= Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 1: + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + if ((A > Ip)) { + if ((B1 < Im)) { Scores[x] = 0; return; } + if ((B2 < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 9; break; }; + } + // A ~ I0 + if ((B1 >= Im)) { Scores[x] = 0; return; } + if ((B2 >= Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } + + case 2: + if ((A > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((A < Im)) + { + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 4; break; }; + } + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 3: + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((A > Ip)) { + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 5; break; }; + } + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 4: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + if ((B2 >= Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } + + case 5: + if ((A < Im)) { Scores[x] = 0; return; } + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + // A ~ I0 + if ((B2 <= Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } + + case 7: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + // A ~ I0 + if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; + if ((B2 < Im)) { score -= A + B1; state = 6; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 6: + if ((A > Ip)) { Scores[x] = 0; return; } + if ((A < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + // A ~ I0 + if ((B2 < Im)) { score -= A + B1; state = 2; break; }; + if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; + { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 + + case 8: + if ((A > Ip)) { + if ((B2 < Im)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 9; break; }; + } + if ((A < Im)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 1; break; }; + } + { Scores[x] = 0; return; } + + case 9: + if ((A < Im)) { + if ((B2 > Ip)) { Scores[x] = 0; return; } + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 > Ip)) { Scores[x] = 0; return; } + { score -= A + B1; state = 8; break; }; + } + if ((A > Ip)) { + B1 = B2; b++; B2 = I[x+dirs[b]]; + if ((B2 < Im)) { Scores[x] = 0; return; } + { score -= A + B1; state = 0; break; }; + } + { Scores[x] = 0; return; } + + default: + //"PB default"; + break; + } // switch(state) + } // for(a...) + + Scores[x] = (score + dirs_nb * I[x]); + } + + var lev_table_t = (function () { + function lev_table_t(w, h, r) { + this.dirs = new Int32Array(1024); + this.dirs_count = precompute_directions(w, this.dirs, r)|0; + this.scores = new Int32Array(w*h); + this.radius = r|0; + } + return lev_table_t; + })(); + + return { + + level_tables: [], + tau: 7, + + init: function(width, height, radius, pyramid_levels) { + if (typeof pyramid_levels === "undefined") { pyramid_levels = 1; } + var i; + radius = Math.min(radius, 7); + radius = Math.max(radius, 3); + for(i = 0; i < pyramid_levels; ++i) { + this.level_tables[i] = new lev_table_t(width>>i, height>>i, radius); + } + }, + + detect: function(src, points, border) { + if (typeof border === "undefined") { border = 4; } + var t = this.level_tables[0]; + var R = t.radius|0, Rm1 = (R-1)|0; + var dirs = t.dirs; + var dirs_count = t.dirs_count|0; + var opposite = dirs_count >> 1; + var img = src.data, w=src.cols|0, h=src.rows|0,hw=w>>1; + var scores = t.scores; + var x=0,y=0,row=0,rowx=0,ip=0,im=0,abs_score=0, score=0; + var tau = this.tau|0; + var number_of_points = 0, pt; + + var sx = Math.max(R+1, border)|0; + var sy = Math.max(R+1, border)|0; + var ex = Math.min(w-R-2, w-border)|0; + var ey = Math.min(h-R-2, h-border)|0; + + row = (sy*w+sx)|0; + for(y = sy; y < ey; ++y, row+=w) { + for(x = sx, rowx = row; x < ex; ++x, ++rowx) { + ip = img[rowx] + tau, im = img[rowx] - tau; + + if (im= 3 && is_local_maxima(scores, rowx, score, hw, R)) { + pt = points[number_of_points]; + pt.x = x, pt.y = y, pt.score = abs_score; + ++number_of_points; + + x += Rm1, rowx += Rm1; + } + } + } + } + + return number_of_points; + } + }; + + })(); + + global.yape = yape; + +})(jsfeat); \ No newline at end of file diff --git a/src/jsfeat_yape06.js b/src/jsfeat_yape06.js new file mode 100755 index 0000000..205266d --- /dev/null +++ b/src/jsfeat_yape06.js @@ -0,0 +1,100 @@ +/** + * @author Eugene Zatepyakin / http://inspirit.ru/ + * + * Copyright 2007 Computer Vision Lab, + * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. + * @author Vincent Lepetit (http://cvlab.epfl.ch/~lepetit) + */ + +(function(global) { + "use strict"; + // + + var yape06 = (function() { + + var compute_laplacian = function(src, dst, w, h, Dxx, Dyy, sx,sy, ex,ey) { + var y=0,x=0,yrow=(sy*w+sx)|0,row=yrow; + + for(y = sy; y < ey; ++y, yrow+=w, row = yrow) { + for(x = sx; x < ex; ++x, ++row) { + dst[row] = -4 * src[row] + src[row+Dxx] + src[row-Dxx] + src[row+Dyy] + src[row-Dyy]; + } + } + } + + var hessian_min_eigen_value = function(src, off, tr, Dxx, Dyy, Dxy, Dyx) { + var Ixx = -2 * src[off] + src[off + Dxx] + src[off - Dxx]; + var Iyy = -2 * src[off] + src[off + Dyy] + src[off - Dyy]; + var Ixy = src[off + Dxy] + src[off - Dxy] - src[off + Dyx] - src[off - Dyx]; + var sqrt_delta = ( Math.sqrt(((Ixx - Iyy) * (Ixx - Iyy) + 4 * Ixy * Ixy) ) )|0; + + return Math.min(Math.abs(tr - sqrt_delta), Math.abs(-(tr + sqrt_delta))); + } + + return { + + laplacian_threshold: 30, + min_eigen_value_threshold: 25, + + detect: function(src, points, border) { + if (typeof border === "undefined") { border = 5; } + var x=0,y=0; + var w=src.cols, h=src.rows, srd_d=src.data; + var Dxx = 5, Dyy = (5 * w)|0; + var Dxy = (3 + 3 * w)|0, Dyx = (3 - 3 * w)|0; + var lap_buf = jsfeat.cache.get_buffer((w*h)<<2); + var laplacian = lap_buf.i32; + var lv=0, row=0,rowx=0,min_eigen_value=0,pt; + var number_of_points = 0; + var lap_thresh = this.laplacian_threshold; + var eigen_thresh = this.min_eigen_value_threshold; + + var sx = Math.max(5, border)|0; + var sy = Math.max(3, border)|0; + var ex = Math.min(w-5, w-border)|0; + var ey = Math.min(h-3, h-border)|0; + + x = w*h; + while(--x>=0) {laplacian[x]=0;} + compute_laplacian(srd_d, laplacian, w, h, Dxx, Dyy, sx,sy, ex,ey); + + row = (sy*w+sx)|0; + for(y = sy; y < ey; ++y, row += w) { + for(x = sx, rowx=row; x < ex; ++x, ++rowx) { + + lv = laplacian[rowx]; + if ((lv < -lap_thresh && + lv < laplacian[rowx - 1] && lv < laplacian[rowx + 1] && + lv < laplacian[rowx - w] && lv < laplacian[rowx + w] && + lv < laplacian[rowx - w - 1] && lv < laplacian[rowx + w - 1] && + lv < laplacian[rowx - w + 1] && lv < laplacian[rowx + w + 1]) + || + (lv > lap_thresh && + lv > laplacian[rowx - 1] && lv > laplacian[rowx + 1] && + lv > laplacian[rowx - w] && lv > laplacian[rowx + w] && + lv > laplacian[rowx - w - 1] && lv > laplacian[rowx + w - 1] && + lv > laplacian[rowx - w + 1] && lv > laplacian[rowx + w + 1]) + ) { + + min_eigen_value = hessian_min_eigen_value(srd_d, rowx, lv, Dxx, Dyy, Dxy, Dyx); + if (min_eigen_value > eigen_thresh) { + pt = points[number_of_points]; + pt.x = x, pt.y = y, pt.score = min_eigen_value; + ++number_of_points; + ++x, ++rowx; // skip next pixel since this is maxima in 3x3 + } + } + } + } + + jsfeat.cache.put_buffer(lap_buf); + + return number_of_points; + } + + }; + })(); + + global.yape06 = yape06; + +})(jsfeat);