From 6bd3f6a450149e6fa8a5bdac1768b02fc8962892 Mon Sep 17 00:00:00 2001 From: Sanket Sharma Date: Sat, 13 May 2023 03:45:52 +0530 Subject: [PATCH 1/8] non_gps: added feature matching and offset calculation --- .../Ubuntu/ap_camera_non_gps/feature_match.py | 182 ++++++++++++++++++ .../Ubuntu/ap_camera_non_gps/requirements.txt | 5 + Common/Ubuntu/ap_camera_non_gps/test1.png | Bin 0 -> 1235958 bytes Common/Ubuntu/ap_camera_non_gps/test2.png | Bin 0 -> 1190965 bytes 4 files changed, 187 insertions(+) create mode 100644 Common/Ubuntu/ap_camera_non_gps/feature_match.py create mode 100644 Common/Ubuntu/ap_camera_non_gps/requirements.txt create mode 100644 Common/Ubuntu/ap_camera_non_gps/test1.png create mode 100644 Common/Ubuntu/ap_camera_non_gps/test2.png diff --git a/Common/Ubuntu/ap_camera_non_gps/feature_match.py b/Common/Ubuntu/ap_camera_non_gps/feature_match.py new file mode 100644 index 00000000..cd2cb918 --- /dev/null +++ b/Common/Ubuntu/ap_camera_non_gps/feature_match.py @@ -0,0 +1,182 @@ +import numpy as np +import cv2 as cv +from matplotlib import pyplot as plt +import subprocess +import pyexiv2, json +import math + +x_offset = 0 +y_offset = 0 + +MIN_MATCH_COUNT = 30 + +LOCATION_SCALING_FACTOR_INV = 89.83204953368922 + +inCMX = 0 +inCMY = 0 + +focal = 476.7030836014194 + + + +def readExif(image): + with open(image, 'rb') as f: + with pyexiv2.ImageData(f.read()) as img: + data = img.read_exif() + print(data['Exif.Photo.UserComment']) + f.close() + +def modifyExif(image,data=None): + with open(image, 'rb+') as f: + with pyexiv2.ImageData(f.read()) as img: + matadata = data + dict1 = {'Exif.Photo.UserComment': json.dumps(metadata)} + img.modify_exif(dict1) + + f.seek(0) + f.truncate() + f.write(img.get_bytes()) + f.seek(0) + with pyexiv2.ImageData(f.read()) as img: + result = img.read_exif() + #print(result) + f.close() + + +def compParam(img1,img2): + global inCMY, inCMX + global x_offset, y_offset + img1 = cv.imread(img1,0) + img2 = cv.imread(img2,0) + #img2 = cv.resize(img2, (1778,866)) + sift = cv.SIFT_create(nfeatures = 10000) + + kp1, des1 = sift.detectAndCompute(img1,None) + kp2, des2 = sift.detectAndCompute(img2,None) + FLANN_INDEX_KDTREE = 1 + index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) + search_params = dict(checks = 50) + flann = cv.FlannBasedMatcher(index_params, search_params) + matches = flann.knnMatch(des1,des2,k=2) + + good = [] + for m,n in matches: + if m.distance < 0.7*n.distance: + good.append(m) + + + if len(good)>MIN_MATCH_COUNT: + src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) + dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) + M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0) + matchesMask = mask.ravel().tolist() + h,w = img1.shape + pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2) + dst = cv.perspectiveTransform(pts,M) + img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA) + + + + #Orientation + + diff = np.abs(dst - pts) + orientation_diff = np.arctan2(np.abs(dst[1][0][1] - dst[0][0][1]), np.abs(dst[1][0][0] - dst[0][0][0])) + print("Orientation difference: {:.2f} degrees".format(90 - np.degrees(orientation_diff))) + + + #x, y offset + + h1, w1 = img1.shape[:2] + h, w = img2.shape + + sr_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) + ds_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) + Mr, maskr = cv.findHomography(ds_pts, sr_pts, cv.RANSAC, 5.0) + + corners = np.array([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]], dtype=np.float32).reshape(-1, 1, 2) + transformed_corners = cv.perspectiveTransform(corners, Mr) + + x, y = np.mean(transformed_corners, axis=0).astype(int)[0] + + print("Offset x, y: ",x-w1/2, y-h1/2) + + inCMX = (x-w1/2) * (10) / focal + inCMY = (y-h1/2) * (10) / focal + + + #Zoom percentage -- Not complete + + width = abs(transformed_corners[2][0][0]) - abs(transformed_corners[0][0][0]) + height = abs(transformed_corners[2][0][1]) - abs(transformed_corners[0][0][1]) + + im2 = width * height + im1 = img1.shape[0] * img1.shape[1] + + #print(transformed_corners) + + print(f"Zoom percent: {im2/im1*100} %") + + else: + print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) ) + matchesMask = None + + + draw_params = dict(matchColor = (0,255,0), + singlePointColor = (255,0,0), + matchesMask = matchesMask, + flags = 2) + + + binToBool= [True if n == 1 else False for n in matchesMask] + res = list(filter(lambda i: binToBool[i], range(len(binToBool)))) + res_listQ = [src_pts[i].tolist() for i in res] + res_listT = [dst_pts[i].tolist() for i in res] + + + cv.namedWindow("img3", cv.WINDOW_NORMAL) + img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params) + + cv.imshow('img3', img3) + + cv.waitKey(0) + + + +def offset_latlng(lat,lng,ofs_north,ofs_east): + dlat = ofs_north * LOCATION_SCALING_FACTOR_INV + dlng = (ofs_east * LOCATION_SCALING_FACTOR_INV) / longitude_scale(lat+dlat/2) + lat += dlat + lat = limit_lattitude(lat) + lng = wrap_longitude(dlng+lng) + print("New Lat & Lng: ",lat,lng) + +def MAX(one, two): + return one if one > two else two + +def longitude_scale(lat): + scale = math.cos(lat * (1.0e-7 * (math.pi/180))) + return MAX(scale, 0.01) + + +def limit_lattitude(lat): + if lat > 900000000: + lat = 1800000000 - lat + elif lat < -900000000: + lat = -(1800000000 + lat) + return lat + + +def wrap_longitude(lon): + if lon > 1800000000: + lon = int(lon-3600000000) + elif lon < -1800000000: + lon = int(lon+3600000000) + return int(lon) + + +if __name__ == '__main__': + + compParam("test1.png",'test2.png') #To find the features and get orientation, offset and zoom + + offset_latlng(-353632621,1491652378,-inCMX,inCMY) #Extract new lat and lng based upon pixel offset + diff --git a/Common/Ubuntu/ap_camera_non_gps/requirements.txt b/Common/Ubuntu/ap_camera_non_gps/requirements.txt new file mode 100644 index 00000000..1f371f55 --- 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Common/Ubuntu/ap_camera_non_gps/feature_match.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/Common/Ubuntu/ap_camera_non_gps/feature_match.py b/Common/Ubuntu/ap_camera_non_gps/feature_match.py index cd2cb918..dd8dd0be 100644 --- a/Common/Ubuntu/ap_camera_non_gps/feature_match.py +++ b/Common/Ubuntu/ap_camera_non_gps/feature_match.py @@ -150,12 +150,10 @@ def offset_latlng(lat,lng,ofs_north,ofs_east): lng = wrap_longitude(dlng+lng) print("New Lat & Lng: ",lat,lng) -def MAX(one, two): - return one if one > two else two def longitude_scale(lat): scale = math.cos(lat * (1.0e-7 * (math.pi/180))) - return MAX(scale, 0.01) + return max(scale, 0.01) def limit_lattitude(lat): From 9f6f64b7ceab9b10429000e497c2d1a647ca214d Mon Sep 17 00:00:00 2001 From: Sanket Sharma Date: Tue, 11 Jul 2023 01:34:38 +0530 Subject: [PATCH 3/8] Corrected and updated zoom scale Made code modular and corrected offset and zoom scaling --- .../Ubuntu/ap_camera_non_gps/feature_match.py | 95 +++++++++++-------- 1 file changed, 55 insertions(+), 40 deletions(-) diff --git a/Common/Ubuntu/ap_camera_non_gps/feature_match.py b/Common/Ubuntu/ap_camera_non_gps/feature_match.py index dd8dd0be..55ceb87b 100644 --- a/Common/Ubuntu/ap_camera_non_gps/feature_match.py +++ b/Common/Ubuntu/ap_camera_non_gps/feature_match.py @@ -1,21 +1,27 @@ import numpy as np import cv2 as cv from matplotlib import pyplot as plt -import subprocess import pyexiv2, json import math x_offset = 0 y_offset = 0 +inCMX = 0 +inCMY = 0 MIN_MATCH_COUNT = 30 - LOCATION_SCALING_FACTOR_INV = 89.83204953368922 -inCMX = 0 -inCMY = 0 - +#user input ______________ focal = 476.7030836014194 +latBase = -353632621 +lonbase = 1491652371 + +baseImg = "test1.jpeg" +testImg = "test2.jpeg" + +#__________________________ + @@ -39,35 +45,48 @@ def modifyExif(image,data=None): f.seek(0) with pyexiv2.ImageData(f.read()) as img: result = img.read_exif() - #print(result) f.close() +def calc_dist(x1, y1, x2, y2): + distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2) + return distance + def compParam(img1,img2): global inCMY, inCMX global x_offset, y_offset + + print('comparing ' + img1 + ' vs ' + img2 + "\n") + img1 = cv.imread(img1,0) img2 = cv.imread(img2,0) #img2 = cv.resize(img2, (1778,866)) - sift = cv.SIFT_create(nfeatures = 10000) + # extract key features from each image + sift = cv.SIFT_create(nfeatures = 10000) kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) + + # find matching features from each image FLANN_INDEX_KDTREE = 1 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict(checks = 50) flann = cv.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1,des2,k=2) + # create array of best matches good = [] for m,n in matches: if m.distance < 0.7*n.distance: good.append(m) + # ensure we have at at least 30 matching points if len(good)>MIN_MATCH_COUNT: - src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2) - dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2) + + # display image showing matching points + src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2) + dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2) M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0) matchesMask = mask.ravel().tolist() h,w = img1.shape @@ -75,49 +94,42 @@ def compParam(img1,img2): dst = cv.perspectiveTransform(pts,M) img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA) - - - #Orientation - + # calculate rotation difference diff = np.abs(dst - pts) orientation_diff = np.arctan2(np.abs(dst[1][0][1] - dst[0][0][1]), np.abs(dst[1][0][0] - dst[0][0][0])) print("Orientation difference: {:.2f} degrees".format(90 - np.degrees(orientation_diff))) - - #x, y offset - + # calculate x, y offset h1, w1 = img1.shape[:2] h, w = img2.shape - sr_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2) - ds_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2) - Mr, maskr = cv.findHomography(ds_pts, sr_pts, cv.RANSAC, 5.0) + Mr, maskr = cv.findHomography(dst_pts, src_pts, cv.RANSAC, 5.0) corners = np.array([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]], dtype=np.float32).reshape(-1, 1, 2) transformed_corners = cv.perspectiveTransform(corners, Mr) x, y = np.mean(transformed_corners, axis=0).astype(int)[0] - print("Offset x, y: ",x-w1/2, y-h1/2) + print("Offset x, y(in cms): ",x-w1/2, y-h1/2) inCMX = (x-w1/2) * (10) / focal inCMY = (y-h1/2) * (10) / focal - #Zoom percentage -- Not complete - - width = abs(transformed_corners[2][0][0]) - abs(transformed_corners[0][0][0]) - height = abs(transformed_corners[2][0][1]) - abs(transformed_corners[0][0][1]) + # calculate zoom percentage + binToBool= [True if n == 1 else False for n in matchesMask] + res = list(filter(lambda i: binToBool[i], range(len(binToBool)))) + res_listQ = [src_pts[i].tolist() for i in res] + res_listT = [dst_pts[i].tolist() for i in res] - im2 = width * height - im1 = img1.shape[0] * img1.shape[1] + mainDist = calc_dist(res_listQ[0][0][0],res_listQ[0][0][1],res_listQ[-1][0][0],res_listQ[-1][0][1]) + desDist = calc_dist(res_listT[0][0][0],res_listT[0][0][1],res_listT[-1][0][0],res_listT[-1][0][1]) - #print(transformed_corners) - - print(f"Zoom percent: {im2/im1*100} %") + scaleRatio = mainDist/desDist + print("Scaling Ratio:",scaleRatio) # multiplied by base altitude generates current altitude else: - print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) ) + print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) ) matchesMask = None @@ -127,17 +139,18 @@ def compParam(img1,img2): flags = 2) - binToBool= [True if n == 1 else False for n in matchesMask] - res = list(filter(lambda i: binToBool[i], range(len(binToBool)))) - res_listQ = [src_pts[i].tolist() for i in res] - res_listT = [dst_pts[i].tolist() for i in res] + cv.namedWindow("img3", cv.WINDOW_NORMAL) img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params) - cv.imshow('img3', img3) + + # calculate new lat and lng based upon pixel offset + # To-Do: get lat,lon,alt from image1 or user + offset_latlng(latBase,lonbase,-inCMX,inCMY) + print("Waiting for key press") cv.waitKey(0) @@ -148,7 +161,7 @@ def offset_latlng(lat,lng,ofs_north,ofs_east): lat += dlat lat = limit_lattitude(lat) lng = wrap_longitude(dlng+lng) - print("New Lat & Lng: ",lat,lng) + print("Updated Lat:",lat,"\n","Updated Lon:",lng) def longitude_scale(lat): @@ -170,11 +183,13 @@ def wrap_longitude(lon): elif lon < -1800000000: lon = int(lon+3600000000) return int(lon) - - + if __name__ == '__main__': - compParam("test1.png",'test2.png') #To find the features and get orientation, offset and zoom + compParam(baseImg, testImg) - offset_latlng(-353632621,1491652378,-inCMX,inCMY) #Extract new lat and lng based upon pixel offset + #offset_latlng(latBase,lonbase,-inCMX,inCMY) + # metadata = {"Latitude": "192.11.123", "Longitude": "172.11.12", "AMSL": "10", "Terr_Alt": "5"} + # modifyExif('test1.jpeg', metadata) + # readExif('test1.jpeg') From 96de755d2bc423e5bbe97c13b695e82e3f0277ea Mon Sep 17 00:00:00 2001 From: Sanket Sharma Date: Tue, 11 Jul 2023 01:35:26 +0530 Subject: [PATCH 4/8] added test images --- Common/Ubuntu/ap_camera_non_gps/test1.jpeg | Bin 0 -> 423407 bytes Common/Ubuntu/ap_camera_non_gps/test2.jpeg | Bin 0 -> 373390 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Common/Ubuntu/ap_camera_non_gps/test1.jpeg create mode 100644 Common/Ubuntu/ap_camera_non_gps/test2.jpeg diff --git a/Common/Ubuntu/ap_camera_non_gps/test1.jpeg b/Common/Ubuntu/ap_camera_non_gps/test1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..e36c9e7b17045e8baff763e00381a99f1c7719a9 GIT binary patch literal 423407 zcmb4qby!;ts`Kic5jwTA&1Xcfyyx&%Mw6 z``bJ@IoVm6oijW0+u1#@ORt-NcXCp)QUEwO000j50lfYN#7lbHSONgDvP=MEz`typ zaD;y(zXDi#VV 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