Added support for python3 (and python2)

README.md

@@ -27,7 +27,7 @@ imshow(image)
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabcc7ab390>
+    <matplotlib.image.AxesImage at 0x7f511c1b0588>
 
 
 
@@ -62,18 +62,18 @@ ocrodeg.random_transform()
 
 
 
-    {'angle': -0.016783842893063807,
-     'aniso': 0.805280370671964,
-     'scale': 0.9709145529604223,
-     'translation': (0.014319657859164045, 0.03676897986267606)}
+    {'angle': -0.012292452096776464,
+     'scale': 1.0082362389124033,
+     'aniso': 1.1871904039346834,
+     'translation': (-0.015090714955534455, -0.011666614466062153)}
 
 
 
 Here are four samples generated by random transforms.
 
 
 ```python
-for i in xrange(4):
+for i in range(4):
     subplot(2, 2, i+1)
     imshow(ocrodeg.transform_image(image, **ocrodeg.random_transform()))
 ```
@@ -198,7 +198,7 @@ imshow(patch)
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabc88c7e10>
+    <matplotlib.image.AxesImage at 0x7f5118190710>
 
 
 
@@ -233,7 +233,6 @@ for i, s in enumerate([0, 1, 2, 4]):
 
 
 ```python
-reload(ocrodeg)
 for i, s in enumerate([0.0, 1.0, 2.0, 4.0]):
     subplot(2, 2, i+1)
     blurred = ocrodeg.binary_blur(patch, s)
@@ -261,7 +260,6 @@ for i, s in enumerate([0.0, 0.1, 0.2, 0.3]):
 
 
 ```python
-reload(ocrodeg)
 for i in range(4):
     noisy = ocrodeg.make_multiscale_noise_uniform((512, 512))
     subplot(2, 2, i+1); imshow(noisy, vmin=0, vmax=1)
@@ -286,7 +284,6 @@ for i, s in enumerate([2, 5, 10, 20]):
 
 
 ```python
-reload(ocrodeg)
 blotched = ocrodeg.random_blotches(patch, 3e-4, 1e-4)
 #blotched = minimum(maximum(patch, ocrodeg.random_blobs(patch.shape, 30, 10)), 1-ocrodeg.random_blobs(patch.shape, 15, 8))
 subplot(121); imshow(patch); subplot(122); imshow(blotched)
@@ -295,7 +292,7 @@ subplot(121); imshow(patch); subplot(122); imshow(blotched)
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabc8a35490>
+    <matplotlib.image.AxesImage at 0x7f51187a3438>
 
 
 
@@ -313,7 +310,7 @@ imshow(ocrodeg.make_fibrous_image((256, 256), 700, 300, 0.01))
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabc8852450>
+    <matplotlib.image.AxesImage at 0x7f51185f4160>
 
 
 
@@ -331,7 +328,7 @@ subplot(121); imshow(patch); subplot(122); imshow(ocrodeg.printlike_multiscale(p
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabc8676d90>
+    <matplotlib.image.AxesImage at 0x7f511858f0f0>
 
 
 
@@ -347,7 +344,7 @@ subplot(121); imshow(patch); subplot(122); imshow(ocrodeg.printlike_fibrous(patc
 
 
 
-    <matplotlib.image.AxesImage at 0x7fabc8d1b250>
+    <matplotlib.image.AxesImage at 0x7f51185bd8d0>
 
 
 

ocrodeg/__init__.py

@@ -1 +1,2 @@
-from degrade import *  # noqa
+from __future__ import absolute_import
+from ocrodeg.degrade import *  # noqa

ocrodeg/degrade.py

@@ -103,19 +103,19 @@ def make_noise_at_scale(shape, scale):
         result = ndi.zoom(data, scale)
     return result[:h, :w]
 
-def make_multiscale_noise(shape, scales, weights=None, range=(0.0, 1.0)):
+def make_multiscale_noise(shape, scales, weights=None, span=(0.0, 1.0)):
     if weights is None: weights = [1.0] * len(scales)
     result = make_noise_at_scale(shape, scales[0]) * weights[0]
     for s, w in zip(scales, weights):
         result += make_noise_at_scale(shape, s) * w
-    lo, hi = range
+    lo, hi = span
     result -= amin(result)
     result /= amax(result)
     result *= (hi-lo)
     result += lo
     return result
 
-def make_multiscale_noise_uniform(shape, srange=(1.0, 100.0), nscales=4, range=(0.0, 1.0)):
+def make_multiscale_noise_uniform(shape, srange=(1.0, 100.0), nscales=4, span=(0.0, 1.0)):
     lo, hi = log10(srange[0]), log10(srange[1])
     scales = np.random.uniform(size=nscales)
     scales = add.accumulate(scales)
@@ -125,18 +125,19 @@ def make_multiscale_noise_uniform(shape, srange=(1.0, 100.0), nscales=4, range=(
     scales += lo
     scales = 10**scales
     weights = 2.0 * np.random.uniform(size=nscales)
-    return make_multiscale_noise(shape, scales, weights=weights, range=range)
+    return make_multiscale_noise(shape, scales, weights=weights, span=span)
 
 #
 # random blobs
 #
 
 def random_blobs(shape, blobdensity, size, roughness=2.0):
     from random import randint
+    from builtins import range  # python2 compatible
     h, w = shape
     numblobs = int(blobdensity * w * h)
     mask = np.zeros((h, w), 'i')
-    for i in xrange(numblobs):
+    for i in range(numblobs):
         mask[randint(0, h-1), randint(0, w-1)] = 1
     dt = ndi.distance_transform_edt(1-mask)
     mask =  np.array(dt < size, 'f')
@@ -166,11 +167,12 @@ def make_fiber(l, a, stepsize=0.5):
     sins = add.accumulate(sin(angles)*stepsize)
     return array([coss, sins]).transpose(1, 0)
 
-def make_fibrous_image(shape, nfibers=300, l=300, a=0.2, stepsize=0.5, range=(0.1, 1.0), blur=1.0):
+def make_fibrous_image(shape, nfibers=300, l=300, a=0.2, stepsize=0.5, span=(0.1, 1.0), blur=1.0):
+    from builtins import range  # python2 compatible
     h, w = shape
-    lo, hi = range
+    lo, hi = span
     result = zeros(shape)
-    for i in xrange(nfibers):
+    for i in range(nfibers):
         v = pylab.rand() * (hi-lo) + lo
         fiber = make_fiber(l, a, stepsize=stepsize)
         y, x = randint(0, h-1), randint(0, w-1)
@@ -195,8 +197,8 @@ def make_fibrous_image(shape, nfibers=300, l=300, a=0.2, stepsize=0.5, range=(0.
 def printlike_multiscale(image, blur=1.0, blotches=5e-5):
     selector = autoinvert(image)
     selector = random_blotches(selector, 3*blotches, blotches)
-    paper = make_multiscale_noise_uniform(image.shape, range=(0.5, 1.0))
-    ink = make_multiscale_noise_uniform(image.shape, range=(0.0, 0.5))
+    paper = make_multiscale_noise_uniform(image.shape, span=(0.5, 1.0))
+    ink = make_multiscale_noise_uniform(image.shape, span=(0.0, 0.5))
     blurred = ndi.gaussian_filter(selector, blur)
     printed = blurred * ink + (1-blurred) * paper
     return printed
@@ -205,9 +207,9 @@ def printlike_multiscale(image, blur=1.0, blotches=5e-5):
 def printlike_fibrous(image, blur=1.0, blotches=5e-5):
     selector = autoinvert(image)
     selector = random_blotches(selector, 3*blotches, blotches)
-    paper = make_multiscale_noise(image.shape, [1.0, 5.0, 10.0, 50.0], weights=[1.0, 0.3, 0.5, 0.3], range=(0.7, 1.0))
-    paper -= make_fibrous_image(image.shape, 300, 500, 0.01, range=(0.0, 0.25), blur=0.5)
-    ink = make_multiscale_noise(image.shape, [1.0, 5.0, 10.0, 50.0], range=(0.0, 0.5))
+    paper = make_multiscale_noise(image.shape, [1.0, 5.0, 10.0, 50.0], weights=[1.0, 0.3, 0.5, 0.3], span=(0.7, 1.0))
+    paper -= make_fibrous_image(image.shape, 300, 500, 0.01, span=(0.0, 0.25), blur=0.5)
+    ink = make_multiscale_noise(image.shape, [1.0, 5.0, 10.0, 50.0], span=(0.0, 0.5))
     blurred = ndi.gaussian_filter(selector, blur)
     printed = blurred * ink + (1-blurred) * paper
     return printed

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+matplotlib
+scipy

run-tests

@@ -1,5 +1,5 @@
 #!/bin/bash
 set -e
 for nbtest in test-*.ipynb; do
-    jupyter nbconvert --execute --to markdown $nbtest
+    jupyter nbconvert --execute --to markdown $nbtest --ExecutePreprocessor.kernel_name=python
 done

test-degrade.ipynb

@@ -2,30 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python2.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['angle']\n",
-      "`%matplotlib` prevents importing * from pylab and numpy\n",
-      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pylab inline"
    ]
@@ -366,7 +349,6 @@
    },
    "outputs": [],
    "source": [
-    "reload(ocrodeg)\n",
     "for i, s in enumerate([0.0, 1.0, 2.0, 4.0]):\n",
     "    subplot(2, 2, i+1)\n",
     "    blurred = ocrodeg.binary_blur(patch, s)\n",
@@ -411,7 +393,6 @@
    },
    "outputs": [],
    "source": [
-    "reload(ocrodeg)\n",
     "for i in range(4):\n",
     "    noisy = ocrodeg.make_multiscale_noise_uniform((512, 512))\n",
     "    subplot(2, 2, i+1); imshow(noisy, vmin=0, vmax=1)"
@@ -453,7 +434,6 @@
    },
    "outputs": [],
    "source": [
-    "reload(ocrodeg)\n",
     "blotched = ocrodeg.random_blotches(patch, 3e-4, 1e-4)\n",
     "#blotched = minimum(maximum(patch, ocrodeg.random_blobs(patch.shape, 30, 10)), 1-ocrodeg.random_blobs(patch.shape, 15, 8))\n",
     "subplot(121); imshow(patch); subplot(122); imshow(blotched)"
@@ -535,21 +515,21 @@
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,