Mercurial Hosting > traffic-intelligence
annotate python/cvutils.py @ 411:31604ef1cad4
added hog functions and the display of road user types if known
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Wed, 21 Aug 2013 00:00:23 -0400 |
| parents | 91954c76d12c |
| children | 8fdbc13dad8b |
| rev | line source |
|---|---|
| 28 | 1 #! /usr/bin/env python |
| 2 '''Image/Video utilities''' | |
| 3 | |
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4 try: |
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5 import cv2 |
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6 opencvAvailable = True |
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7 except ImportError: |
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8 print('OpenCV library could not be loaded (video replay functions will not be available)') |
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9 opencvAvailable = False |
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10 try: |
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11 import skimage |
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12 skimageAvailable = True |
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13 except ImportError: |
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14 print('Scikit-image library could not be loaded (HoG-based classification methods will not be available)') |
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15 skimageAvailable = False |
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16 |
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17 from sys import stdout |
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18 |
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19 import utils |
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20 |
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44
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21 #import aggdraw # agg on top of PIL (antialiased drawing) |
| 28 | 22 #import utils |
| 23 | |
| 24 __metaclass__ = type | |
| 25 | |
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26 cvRed = (0,0,255) |
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27 cvGreen = (0,255,0) |
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28 cvBlue = (255,0,0) |
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29 cvColors = utils.PlottingPropertyValues([cvRed, |
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30 cvGreen, |
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31 cvBlue]) |
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32 |
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33 def quitKey(key): |
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34 return chr(key&255)== 'q' or chr(key&255) == 'Q' |
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35 |
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36 def saveKey(key): |
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37 return chr(key&255) == 's' |
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38 |
| 28 | 39 def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'): |
| 40 '''Draws lines over the image ''' | |
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41 import Image, ImageDraw # PIL |
| 28 | 42 |
| 43 img = Image.open(filename) | |
| 44 | |
| 45 draw = ImageDraw.Draw(img) | |
| 46 #draw = aggdraw.Draw(img) | |
| 47 #pen = aggdraw.Pen("red", width) | |
| 48 for p1, p2 in zip(origins, destinations): | |
| 49 draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0)) | |
| 50 #draw.line([p1.x, p1.y, p2.x, p2.y], pen) | |
| 51 del draw | |
| 52 | |
| 53 #out = utils.openCheck(resultFilename) | |
| 54 img.save(resultFilename) | |
| 55 | |
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56 def matlab2PointCorrespondences(filename): |
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57 '''Loads and converts the point correspondences saved |
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58 by the matlab camera calibration tool''' |
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59 from numpy.lib.io import loadtxt, savetxt |
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60 from numpy.lib.function_base import append |
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61 points = loadtxt(filename, delimiter=',') |
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62 savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0)) |
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63 |
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64 def loadPointCorrespondences(filename): |
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65 '''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)''' |
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66 from numpy.lib.npyio import loadtxt |
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67 from numpy import float32 |
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68 points = loadtxt(filename, dtype=float32) |
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69 return (points[:2,:].T, points[2:,:].T) # (world points, image points) |
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70 |
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71 def cvMatToArray(cvmat): |
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72 '''Converts an OpenCV CvMat to numpy array.''' |
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73 print('Deprecated, use new interface') |
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74 from numpy.core.multiarray import zeros |
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75 a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height) |
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76 for i in xrange(cvmat.rows): |
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77 for j in xrange(cvmat.cols): |
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78 a[i,j] = cvmat[i,j] |
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79 return a |
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80 |
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81 if opencvAvailable: |
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82 def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=3.0): |
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83 '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)''' |
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84 H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold) |
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85 return H |
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86 |
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87 def arrayToCvMat(a, t = cv2.CV_64FC1): |
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88 '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.''' |
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89 print('Deprecated, use new interface') |
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90 cvmat = cv2.cv.CreateMat(a.shape[0], a.shape[1], t) |
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91 for i in range(cvmat.rows): |
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92 for j in range(cvmat.cols): |
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93 cvmat[i,j] = a[i,j] |
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94 return cvmat |
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95 |
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96 def draw(img, positions, color, lastCoordinate = None): |
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97 last = lastCoordinate+1 |
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98 if lastCoordinate != None and lastCoordinate >=0: |
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99 last = min(positions.length()-1, lastCoordinate) |
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100 for i in range(0, last-1): |
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101 cv2.line(img, positions[i].asint().astuple(), positions[i+1].asint().astuple(), color) |
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102 |
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103 def cvImshow(windowName, img, rescale = 1.0): |
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104 'Rescales the image (in particular if too large)' |
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105 from cv2 import resize |
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106 if rescale != 1.: |
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107 size = (int(round(img.shape[1]*rescale)), int(round(img.shape[0]*rescale))) |
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108 resizedImg = resize(img, size) |
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109 cv2.imshow(windowName, resizedImg) |
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110 else: |
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111 cv2.imshow(windowName, img) |
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112 |
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113 def playVideo(filename, firstFrameNum = 0, frameRate = -1, interactive = False, printFrames = True, text = None, rescale = 1.): |
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114 '''Plays the video''' |
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115 wait = 5 |
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116 if frameRate > 0: |
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117 wait = int(round(1000./frameRate)) |
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118 if interactive: |
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119 wait = 0 |
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120 capture = cv2.VideoCapture(filename) |
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121 if capture.isOpened(): |
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122 key = -1 |
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123 ret = True |
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124 frameNum = firstFrameNum |
|
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|
125 capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) |
|
305
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|
126 while ret and not quitKey(key): |
|
149
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diff
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|
127 ret, img = capture.read() |
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128 if ret: |
|
381
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129 if printFrames: |
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130 print('frame {0}'.format(frameNum)) |
|
226
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|
131 frameNum+=1 |
|
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132 if text != None: |
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133 cv2.putText(img, text, (10,50), cv2.FONT_HERSHEY_PLAIN, 1, cvRed) |
|
385
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|
134 cvImshow('frame', img, rescale) |
|
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135 key = cv2.waitKey(wait) |
|
346
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|
136 cv2.destroyAllWindows() |
|
149
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137 |
|
396
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138 def getImagesFromVideo(videoFilename, firstFrameNum = 0, nFrames = 1, saveImage = False, outputPrefix = 'image'): |
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139 '''Returns nFrames images from the video sequence''' |
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140 from math import floor, log10 |
|
150
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141 images = [] |
|
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142 capture = cv2.VideoCapture(videoFilename) |
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143 if capture.isOpened(): |
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144 nDigits = int(floor(log10(capture.get(cv2.CAP_PROP_FRAME_COUNT))))+1 |
|
150
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145 ret = False |
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146 capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) |
|
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147 imgNum = 0 |
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148 while imgNum<nFrames: |
|
171
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149 ret, img = capture.read() |
|
216
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150 i = 0 |
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151 while not ret and i<10: |
|
150
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152 ret, img = capture.read() |
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153 i += 1 |
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171
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154 if img.size>0: |
|
216
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155 if saveImage: |
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156 imgNumStr = format(firstFrameNum+imgNum, '0{}d'.format(nDigits)) |
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157 cv2.imwrite(outputPrefix+imgNumStr+'.png', img) |
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216
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158 else: |
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159 images.append(img) |
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160 imgNum +=1 |
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161 capture.release() |
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162 else: |
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163 print('Video capture failed') |
|
150
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164 return images |
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165 |
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166 def getFPS(videoFilename): |
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167 capture = cv2.VideoCapture(videoFilename) |
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168 if capture.isOpened(): |
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169 fps = capture.get(cv2.CAP_PROP_FPS) |
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170 capture.release() |
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171 return fps |
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172 else: |
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173 print 'Cannot load file ' + videoFilename |
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174 return None |
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175 |
|
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|
176 def imageBox(img, obj, frameNum, homography, width, height, px=.2, py=.2, PixelThreshold=800): |
|
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changeset
|
177 x = [] |
|
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changeset
|
178 y = [] |
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|
179 for f in obj.features: |
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diff
changeset
|
180 if f.existsAtInstant(frameNum): |
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diff
changeset
|
181 x.append(f.getPositionAtInstant(frameNum).project(homography).x) |
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changeset
|
182 y.append(f.getPositionAtInstant(frameNum).project(homography).y) |
|
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diff
changeset
|
183 Xmin = min(x) |
|
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diff
changeset
|
184 Xmax = max(x) |
|
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diff
changeset
|
185 Ymin = min(y) |
|
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diff
changeset
|
186 Ymax = max(y) |
|
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diff
changeset
|
187 XMm = px * (Xmax - Xmin) |
|
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diff
changeset
|
188 YMm = py * (Ymax - Ymin) |
|
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diff
changeset
|
189 a = max(Ymax - Ymin + (2 * YMm), Xmax - (Xmin + 2 * XMm)) |
|
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410
diff
changeset
|
190 Y_Crop_min = int(max(0, .5 * (Ymin + Ymax - a))) |
|
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added hog functions and the display of road user types if known
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diff
changeset
|
191 Y_Crop_Max = int(min(height - 1, .5 * (Ymin + Ymax + a))) |
|
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diff
changeset
|
192 X_Crop_min = int(max(0, .5 * (Xmin + Xmax - a))) |
|
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diff
changeset
|
193 X_Crop_Max = int(min(width - 1, .5 * (Xmin + Xmax + a))) |
|
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diff
changeset
|
194 if Y_Crop_Max != Y_Crop_min and X_Crop_Max != X_Crop_min and (Y_Crop_Max - Y_Crop_min) * (X_Crop_Max - X_Crop_min) > PixelThreshold: |
|
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diff
changeset
|
195 img_crop = img[Y_Crop_min : Y_Crop_Max, X_Crop_min : X_Crop_Max] |
|
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diff
changeset
|
196 else: |
|
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diff
changeset
|
197 img_crop = [] |
|
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diff
changeset
|
198 return img_crop, Y_Crop_min, Y_Crop_Max, X_Crop_min, X_Crop_Max |
|
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diff
changeset
|
199 |
|
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diff
changeset
|
200 |
|
393
eaf7765221d9
added code to create bounding boxes and display them (inc scripts)
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parents:
385
diff
changeset
|
201 def displayTrajectories(videoFilename, objects, boundingBoxes, homography = None, firstFrameNum = 0, lastFrameNumArg = None, printFrames = True, rescale = 1.): |
|
203
e2f31813ade6
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171
diff
changeset
|
202 '''Displays the objects overlaid frame by frame over the video ''' |
|
411
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diff
changeset
|
203 from moving import userTypeNames |
|
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diff
changeset
|
204 |
|
203
e2f31813ade6
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diff
changeset
|
205 capture = cv2.VideoCapture(videoFilename) |
|
411
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diff
changeset
|
206 width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) |
|
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diff
changeset
|
207 height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
|
203
e2f31813ade6
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diff
changeset
|
208 if capture.isOpened(): |
|
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diff
changeset
|
209 key = -1 |
|
227
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226
diff
changeset
|
210 ret = True |
|
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parents:
226
diff
changeset
|
211 frameNum = firstFrameNum |
|
406
37c7b46f6e21
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parents:
402
diff
changeset
|
212 capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) |
|
248
571ba5ed22e2
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parents:
243
diff
changeset
|
213 if not lastFrameNumArg: |
|
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diff
changeset
|
214 from sys import maxint |
|
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diff
changeset
|
215 lastFrameNum = maxint |
|
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243
diff
changeset
|
216 else: |
|
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243
diff
changeset
|
217 lastFrameNum = lastFrameNumArg |
|
305
ca9131968bce
added sample to replay video
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parents:
303
diff
changeset
|
218 while ret and not quitKey(key) and frameNum < lastFrameNum: |
|
203
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diff
changeset
|
219 ret, img = capture.read() |
|
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diff
changeset
|
220 if ret: |
|
381
387cc0142211
script to replay event annotations
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365
diff
changeset
|
221 if printFrames: |
|
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changeset
|
222 print('frame {0}'.format(frameNum)) |
|
203
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diff
changeset
|
223 for obj in objects: |
|
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diff
changeset
|
224 if obj.existsAtInstant(frameNum): |
|
236
eb4525853030
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235
diff
changeset
|
225 if not hasattr(obj, 'projectedPositions'): |
|
218
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parents:
216
diff
changeset
|
226 if homography != None: |
|
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216
diff
changeset
|
227 obj.projectedPositions = obj.positions.project(homography) |
|
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228 else: |
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229 obj.projectedPositions = obj.positions |
|
203
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230 draw(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) |
|
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231 if frameNum in boundingBoxes.keys(): |
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232 for rect in boundingBoxes[frameNum]: |
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233 cv2.rectangle(img, rect[0].asint().astuple(), rect[1].asint().astuple(), cvRed) |
|
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234 objDescription = '{} '.format(obj.num) |
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235 if userTypeNames[obj.userType] != 'unknown': |
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236 objDescription += userTypeNames[obj.userType][0].upper() |
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237 cv2.putText(img, objDescription, obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvRed) |
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238 if obj.features != None: |
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239 img_crop, Y_Crop_min, Y_Crop_Max, X_Crop_min, X_Crop_Max = imageBox(img, obj, frameNum, homography, width, height) |
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240 cv2.rectangle(img, (X_Crop_min, Y_Crop_min), (X_Crop_Max, Y_Crop_Max), cvBlue, 1) |
|
385
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241 cvImshow('frame', img, rescale) |
|
266
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242 key = cv2.waitKey() |
|
305
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243 if saveKey(key): |
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266
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244 cv2.imwrite('image.png', img) |
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245 frameNum += 1 |
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346
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246 cv2.destroyAllWindows() |
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402
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247 |
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248 def computeHomographyFromPDTV(cameraFilename, method=0, ransacReprojThreshold=3.0): |
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249 '''Returns the homography matrix at ground level from PDTV format |
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250 https://bitbucket.org/hakanardo/pdtv''' |
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251 import pdtv |
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252 from numpy import array |
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253 camera = pdtv.load(cameraFilename) |
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254 srcPoints = [[x,y] for x, y in zip([1.,2.,2.,1.],[1.,1.,2.,2.])] # need floats!! |
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255 dstPoints = [] |
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256 for srcPoint in srcPoints: |
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257 projected = camera.image_to_world(tuple(srcPoint)) |
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258 dstPoints.append([projected[0], projected[1]]) |
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259 H, mask = cv2.findHomography(array(srcPoints), array(dstPoints), method, ransacReprojThreshold) |
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260 return H |
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261 |
|
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262 |
|
99
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263 def printCvMat(cvmat, out = stdout): |
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264 '''Prints the cvmat to out''' |
|
384
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265 print('Deprecated, use new interface') |
|
99
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266 for i in xrange(cvmat.rows): |
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267 for j in xrange(cvmat.cols): |
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268 out.write('{0} '.format(cvmat[i,j])) |
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269 out.write('\n') |
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270 |
|
98
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271 def projectArray(homography, points): |
|
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272 '''Returns the coordinates of the projected points through homography |
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273 (format: array 2xN points)''' |
|
315
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274 from numpy.core import dot |
| 28 | 275 from numpy.core.multiarray import array |
|
98
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276 from numpy.lib.function_base import append |
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277 |
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278 if points.shape[0] != 2: |
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279 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) |
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280 |
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281 if (homography!=None) and homography.size>0: |
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282 augmentedPoints = append(points,[[1]*points.shape[1]], 0) |
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283 prod = dot(homography, augmentedPoints) |
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284 return prod[0:2]/prod[2] |
| 28 | 285 else: |
| 286 return p | |
| 287 | |
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288 def project(homography, p): |
| 319 | 289 '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] |
|
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290 through homography''' |
|
243
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291 from numpy import array |
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292 return projectArray(homography, array([[p[0]],[p[1]]])) |
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293 |
| 28 | 294 def projectTrajectory(homography, trajectory): |
| 295 '''Projects a series of points in the format | |
| 296 [[x1, x2, ...], | |
|
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297 [y1, y2, ...]]''' |
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298 from numpy.core.multiarray import array |
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299 return projectArray(homography, array(trajectory)) |
| 28 | 300 |
|
48
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301 def invertHomography(homography): |
|
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302 '''Returns an inverted homography |
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303 Unnecessary for reprojection over camera image''' |
|
48
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304 from numpy.linalg.linalg import inv |
|
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305 invH = inv(homography) |
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306 invH /= invH[2,2] |
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307 return invH |
|
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308 |
|
365
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309 if opencvAvailable: |
|
154
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310 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): |
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311 '''Computes the translation of img2 with respect to img1 |
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312 (loaded using OpenCV as numpy arrays) |
|
112
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313 img1Points are used to compute the translation |
|
100
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314 |
|
154
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315 TODO add diagnostic if data is all over the place, and it most likely is not a translation (eg zoom, other non linear distortion)''' |
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316 from numpy.core.multiarray import array |
|
112
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317 from numpy.lib.function_base import median |
|
154
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318 from numpy.core.fromnumeric import sum |
|
100
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319 |
|
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320 nextPoints = array([]) |
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321 (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria) |
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322 # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err |
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323 delta = [] |
|
112
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324 for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)): |
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325 if status[k] == 1: |
|
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326 dp = p2-p1 |
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327 d = sum(dp**2) |
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328 if d < maxTranslation2: |
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329 delta.append(dp) |
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330 if len(delta) >= minNMatches: |
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331 return median(delta, axis=0) |
|
112
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332 else: |
|
156
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333 print(dp) |
|
112
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334 return None |
|
365
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335 |
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336 if skimageAvailable: |
|
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337 def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): |
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338 from skimage.feature import hog |
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339 from skimage import color, transform |
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340 |
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341 bwImg = color.rgb2gray(image) |
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342 inputImg = transform.resize(bwImg, rescaleSize) |
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343 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) |
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344 if visualize: |
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345 from matplotlib.pyplot import imshow, figure, subplot |
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346 hogViz = features[1] |
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347 features = features[0] |
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348 figure() |
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349 subplot(1,2,1) |
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350 imshow(img) |
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351 subplot(1,2,2) |
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352 imshow(hogViz) |
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353 return features |
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354 |
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355 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): |
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356 from os import listdir |
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357 from numpy import array, float32 |
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358 from matplotlib.pyplot import imread |
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359 |
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360 inputData = [] |
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361 for filename in listdir(imageDirectory): |
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362 img = imread(imageDirectory+filename) |
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363 features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) |
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364 inputData.append(features) |
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365 |
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366 nImages = len(inputData) |
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367 return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32) |
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368 |
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369 |