Mercurial > hg > nsaunier > traffic-intelligence
annotate python/cvutils.py @ 407:5eeb3b9fb568
commented problem code (opencv 2.4.6)
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Mon, 12 Aug 2013 22:50:42 -0400 |
| parents | 37c7b46f6e21 |
| children | 91954c76d12c |
| rev | line source |
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| 28 | 1 #! /usr/bin/env python |
| 2 '''Image/Video utilities''' | |
| 3 | |
| 4 import Image, ImageDraw # PIL | |
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5 try: |
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6 import cv2 |
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7 opencvAvailable = True |
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8 except ImportError: |
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9 print('OpenCV library could not be loaded') |
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10 opencvAvailable = False |
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12 import skimage |
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13 skimageAvailable = True |
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14 except ImportError: |
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15 print('Scikit-image library could not be loaded') |
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16 skimageAvailable = False |
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17 |
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18 from sys import stdout |
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19 |
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20 import utils |
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21 |
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22 #import aggdraw # agg on top of PIL (antialiased drawing) |
| 28 | 23 #import utils |
| 24 | |
| 25 __metaclass__ = type | |
| 26 | |
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27 cvRed = (0,0,255) |
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28 cvGreen = (0,255,0) |
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29 cvBlue = (255,0,0) |
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30 cvColors = utils.PlottingPropertyValues([cvRed, |
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31 cvGreen, |
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32 cvBlue]) |
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33 |
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34 def quitKey(key): |
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35 return chr(key&255)== 'q' or chr(key&255) == 'Q' |
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36 |
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37 def saveKey(key): |
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38 return chr(key&255) == 's' |
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39 |
| 28 | 40 def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'): |
| 41 '''Draws lines over the image ''' | |
| 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) |
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126 while ret and not quitKey(key): |
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127 ret, img = capture.read() |
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128 if ret: |
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129 if printFrames: |
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130 print('frame {0}'.format(frameNum)) |
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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) |
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134 cvImshow('frame', img, rescale) |
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135 key = cv2.waitKey(wait) |
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136 cv2.destroyAllWindows() |
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137 |
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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 |
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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 |
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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: |
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171
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149 ret, img = capture.read() |
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216
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150 i = 0 |
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151 while not ret and i<10: |
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152 ret, img = capture.read() |
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153 i += 1 |
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154 if img.size>0: |
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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') |
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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 displayTrajectories(videoFilename, objects, boundingBoxes, homography = None, firstFrameNum = 0, lastFrameNumArg = None, printFrames = True, rescale = 1.): |
|
203
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177 '''Displays the objects overlaid frame by frame over the video ''' |
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178 capture = cv2.VideoCapture(videoFilename) |
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179 if capture.isOpened(): |
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180 key = -1 |
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227
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181 ret = True |
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182 frameNum = firstFrameNum |
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183 capture.set(cv2.CAP_PROP_POS_FRAMES, firstFrameNum) |
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248
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184 if not lastFrameNumArg: |
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185 from sys import maxint |
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186 lastFrameNum = maxint |
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187 else: |
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188 lastFrameNum = lastFrameNumArg |
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189 while ret and not quitKey(key) and frameNum < lastFrameNum: |
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203
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190 ret, img = capture.read() |
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191 if ret: |
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192 if printFrames: |
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193 print('frame {0}'.format(frameNum)) |
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203
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194 for obj in objects: |
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195 if obj.existsAtInstant(frameNum): |
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236
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196 if not hasattr(obj, 'projectedPositions'): |
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218
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197 if homography != None: |
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198 obj.projectedPositions = obj.positions.project(homography) |
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199 else: |
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200 obj.projectedPositions = obj.positions |
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203
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201 draw(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) |
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202 if frameNum in boundingBoxes.keys(): |
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203 for rect in boundingBoxes[frameNum]: |
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204 cv2.rectangle(img, rect[0].asint().astuple(), rect[1].asint().astuple(), cvRed) |
|
223
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diff
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205 cv2.putText(img, '{0}'.format(obj.num), obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.FONT_HERSHEY_PLAIN, 1, cvRed) |
|
385
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206 cvImshow('frame', img, rescale) |
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266
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207 key = cv2.waitKey() |
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305
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208 if saveKey(key): |
|
266
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209 cv2.imwrite('image.png', img) |
|
203
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210 frameNum += 1 |
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346
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211 cv2.destroyAllWindows() |
|
402
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212 |
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213 def computeHomographyFromPDTV(cameraFilename, method=0, ransacReprojThreshold=3.0): |
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214 '''Returns the homography matrix at ground level from PDTV format |
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215 https://bitbucket.org/hakanardo/pdtv''' |
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216 import pdtv |
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217 from numpy import array |
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218 camera = pdtv.load(cameraFilename) |
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219 srcPoints = [[x,y] for x, y in zip([1.,2.,2.,1.],[1.,1.,2.,2.])] # need floats!! |
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220 dstPoints = [] |
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221 for srcPoint in srcPoints: |
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222 projected = camera.image_to_world(tuple(srcPoint)) |
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223 dstPoints.append([projected[0], projected[1]]) |
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224 H, mask = cv2.findHomography(array(srcPoints), array(dstPoints), method, ransacReprojThreshold) |
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225 return H |
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226 |
|
203
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227 |
|
99
e7dc5a780f09
added conversion functions and homography computation
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228 def printCvMat(cvmat, out = stdout): |
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229 '''Prints the cvmat to out''' |
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230 print('Deprecated, use new interface') |
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231 for i in xrange(cvmat.rows): |
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232 for j in xrange(cvmat.cols): |
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233 out.write('{0} '.format(cvmat[i,j])) |
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234 out.write('\n') |
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235 |
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236 def projectArray(homography, points): |
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237 '''Returns the coordinates of the projected points through homography |
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238 (format: array 2xN points)''' |
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315
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239 from numpy.core import dot |
| 28 | 240 from numpy.core.multiarray import array |
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241 from numpy.lib.function_base import append |
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242 |
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243 if points.shape[0] != 2: |
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244 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) |
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245 |
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246 if (homography!=None) and homography.size>0: |
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247 augmentedPoints = append(points,[[1]*points.shape[1]], 0) |
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248 prod = dot(homography, augmentedPoints) |
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249 return prod[0:2]/prod[2] |
| 28 | 250 else: |
| 251 return p | |
| 252 | |
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253 def project(homography, p): |
| 319 | 254 '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] |
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255 through homography''' |
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243
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256 from numpy import array |
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257 return projectArray(homography, array([[p[0]],[p[1]]])) |
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258 |
| 28 | 259 def projectTrajectory(homography, trajectory): |
| 260 '''Projects a series of points in the format | |
| 261 [[x1, x2, ...], | |
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262 [y1, y2, ...]]''' |
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263 from numpy.core.multiarray import array |
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264 return projectArray(homography, array(trajectory)) |
| 28 | 265 |
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266 def invertHomography(homography): |
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267 '''Returns an inverted homography |
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268 Unnecessary for reprojection over camera image''' |
|
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269 from numpy.linalg.linalg import inv |
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270 invH = inv(homography) |
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271 invH /= invH[2,2] |
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272 return invH |
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273 |
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274 if opencvAvailable: |
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275 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): |
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276 '''Computes the translation of img2 with respect to img1 |
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277 (loaded using OpenCV as numpy arrays) |
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278 img1Points are used to compute the translation |
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279 |
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280 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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281 from numpy.core.multiarray import array |
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282 from numpy.lib.function_base import median |
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283 from numpy.core.fromnumeric import sum |
|
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284 |
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285 nextPoints = array([]) |
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286 (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria) |
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287 # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err |
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288 delta = [] |
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289 for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)): |
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290 if status[k] == 1: |
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291 dp = p2-p1 |
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292 d = sum(dp**2) |
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293 if d < maxTranslation2: |
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294 delta.append(dp) |
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295 if len(delta) >= minNMatches: |
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296 return median(delta, axis=0) |
|
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297 else: |
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298 print(dp) |
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299 return None |
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300 |
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301 if skimageAvailable: |
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302 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(3, 3), visualize=False, normalize=False): |
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303 from os import listdir |
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304 from matplotlib.pyplot import imread, imshow, figure, subplot |
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305 from skimage.feature import hog |
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306 from skimage import color, transform |
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307 from numpy import array, float32 |
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308 |
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309 inputData = [] |
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310 for filename in listdir(imageDirectory): |
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311 img = imread(imageDirectory+filename) |
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312 bwImg = color.rgb2gray(img) |
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313 inputImg = transform.resize(bwImg, rescaleSize) |
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314 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) |
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315 if visualize: |
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316 hogViz = features[1] |
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317 features = features[0] |
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318 figure() |
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319 subplot(1,2,1) |
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320 imshow(img) |
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321 subplot(1,2,2) |
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322 imshow(hogViz) |
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323 inputData.append(features) |
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324 |
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325 nImages = len(inputData) |
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326 return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32) |
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327 |
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328 |