Mercurial Hosting > traffic-intelligence
annotate python/cvutils.py @ 649:df9ddeaee4a6
added ability to select lanes to count collisions or stationary vehicles in VISSIM files
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 16 Apr 2015 11:34:51 +0200 |
parents | 4e7925cb4f8f |
children | 51269511229b |
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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)') # TODO change to logging module |
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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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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 |
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39 def plotLines(filename, origins, destinations, w = 1, resultFilename='image.png'): |
28 | 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) | |
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51 del draw |
28 | 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 cvPlot(img, positions, color, lastCoordinate = None): |
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97 last = lastCoordinate+1 |
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98 if lastCoordinate is not 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 computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients): |
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114 from copy import deepcopy |
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115 from numpy import identity, array |
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116 newImgSize = (int(round(width*undistortedImageMultiplication)), int(round(height*undistortedImageMultiplication))) |
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117 newCameraMatrix = deepcopy(intrinsicCameraMatrix) |
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118 newCameraMatrix[0,2] = newImgSize[0]/2. |
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119 newCameraMatrix[1,2] = newImgSize[1]/2. |
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120 return cv2.initUndistortRectifyMap(intrinsicCameraMatrix, array(distortionCoefficients), identity(3), newCameraMatrix, newImgSize, cv2.CV_32FC1) |
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121 |
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122 def playVideo(filename, firstFrameNum = 0, frameRate = -1, interactive = False, printFrames = True, text = None, rescale = 1.): |
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123 '''Plays the video''' |
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124 windowName = 'frame' |
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125 cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) |
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126 wait = 5 |
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127 if frameRate > 0: |
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128 wait = int(round(1000./frameRate)) |
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129 if interactive: |
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130 wait = 0 |
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131 capture = cv2.VideoCapture(filename) |
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132 if capture.isOpened(): |
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133 key = -1 |
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134 ret = True |
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135 frameNum = firstFrameNum |
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136 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) |
305
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137 while ret and not quitKey(key): |
149
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138 ret, img = capture.read() |
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139 if ret: |
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140 if printFrames: |
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141 print('frame {0}'.format(frameNum)) |
226
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142 frameNum+=1 |
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143 if text is not None: |
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144 cv2.putText(img, text, (10,50), cv2.cv.CV_FONT_HERSHEY_PLAIN, 1, cvRed) |
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145 cvImshow(windowName, img, rescale) |
305
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146 key = cv2.waitKey(wait) |
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147 if saveKey(key): |
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148 cv2.imwrite('image-{}.png'.format(frameNum), img) |
346
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149 cv2.destroyAllWindows() |
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150 else: |
538
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151 print('Video capture for {} failed'.format(filename)) |
149
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152 |
396
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153 def getImagesFromVideo(videoFilename, firstFrameNum = 0, nFrames = 1, saveImage = False, outputPrefix = 'image'): |
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154 '''Returns nFrames images from the video sequence''' |
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155 from math import floor, log10 |
150
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156 images = [] |
396
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157 capture = cv2.VideoCapture(videoFilename) |
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158 if capture.isOpened(): |
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159 nDigits = int(floor(log10(capture.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))))+1 |
150
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160 ret = False |
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161 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) |
396
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162 imgNum = 0 |
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163 while imgNum<nFrames: |
171
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164 ret, img = capture.read() |
216
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165 i = 0 |
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166 while not ret and i<10: |
150
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167 ret, img = capture.read() |
216
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168 i += 1 |
171
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169 if img.size>0: |
216
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170 if saveImage: |
396
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171 imgNumStr = format(firstFrameNum+imgNum, '0{}d'.format(nDigits)) |
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172 cv2.imwrite(outputPrefix+imgNumStr+'.png', img) |
216
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173 else: |
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174 images.append(img) |
396
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175 imgNum +=1 |
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176 capture.release() |
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177 else: |
435
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178 print('Video capture for {} failed'.format(videoFilename)) |
150
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179 return images |
398
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180 |
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181 def getFPS(videoFilename): |
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182 capture = cv2.VideoCapture(videoFilename) |
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183 if capture.isOpened(): |
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184 fps = capture.get(cv2.cv.CV_CAP_PROP_FPS) |
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185 capture.release() |
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186 return fps |
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187 else: |
435
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188 print('Video capture for {} failed'.format(videoFilename)) |
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189 return None |
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190 |
416
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diff
changeset
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191 def imageBox(img, obj, frameNum, homography, width, height, px = 0.2, py = 0.2, pixelThreshold = 800): |
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diff
changeset
|
192 'Computes the bounding box of object at frameNum' |
411
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diff
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193 x = [] |
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194 y = [] |
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195 for f in obj.features: |
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|
196 if f.existsAtInstant(frameNum): |
416
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diff
changeset
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197 projectedPosition = f.getPositionAtInstant(frameNum).project(homography) |
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198 x.append(projectedPosition.x) |
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199 y.append(projectedPosition.y) |
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diff
changeset
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200 xmin = min(x) |
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diff
changeset
|
201 xmax = max(x) |
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diff
changeset
|
202 ymin = min(y) |
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diff
changeset
|
203 ymax = max(y) |
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diff
changeset
|
204 xMm = px * (xmax - xmin) |
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diff
changeset
|
205 yMm = py * (ymax - ymin) |
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diff
changeset
|
206 a = max(ymax - ymin + (2 * yMm), xmax - (xmin + 2 * xMm)) |
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diff
changeset
|
207 yCropMin = int(max(0, .5 * (ymin + ymax - a))) |
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diff
changeset
|
208 yCropMax = int(min(height - 1, .5 * (ymin + ymax + a))) |
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diff
changeset
|
209 xCropMin = int(max(0, .5 * (xmin + xmax - a))) |
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changeset
|
210 xCropMax = int(min(width - 1, .5 * (xmin + xmax + a))) |
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diff
changeset
|
211 if yCropMax != yCropMin and xCropMax != xCropMin and (yCropMax - yCropMin) * (xCropMax - xCropMin) > pixelThreshold: |
520
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diff
changeset
|
212 croppedImg = img[yCropMin : yCropMax, xCropMin : xCropMax] |
411
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213 else: |
520
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changeset
|
214 croppedImg = [] |
548 | 215 return croppedImg, yCropMin, yCropMax, xCropMin, xCropMax |
411
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changeset
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216 |
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changeset
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217 |
509
935430b1d408
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diff
changeset
|
218 def displayTrajectories(videoFilename, objects, boundingBoxes = {}, homography = None, firstFrameNum = 0, lastFrameNumArg = None, printFrames = True, rescale = 1., nFramesStep = 1, saveAllImages = False, undistort = False, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1.): |
203
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219 '''Displays the objects overlaid frame by frame over the video ''' |
411
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|
220 from moving import userTypeNames |
482
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adding functionalities (save images directly to display trajectories to create movies
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478
diff
changeset
|
221 from math import ceil, log10 |
509
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changeset
|
222 |
203
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diff
changeset
|
223 capture = cv2.VideoCapture(videoFilename) |
429
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reverted to stable OpenCV release 2.4.6
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diff
changeset
|
224 width = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)) |
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225 height = int(capture.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) |
509
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226 |
544
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|
227 windowName = 'frame' |
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changeset
|
228 #cv2.namedWindow(windowName, cv2.WINDOW_NORMAL) |
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229 |
509
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changeset
|
230 if undistort: # setup undistortion |
510
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parents:
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diff
changeset
|
231 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) |
203
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232 if capture.isOpened(): |
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|
233 key = -1 |
227
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234 ret = True |
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235 frameNum = firstFrameNum |
429
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reverted to stable OpenCV release 2.4.6
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changeset
|
236 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, firstFrameNum) |
636
3058e00887bc
removed all issues because of tests with None, using is instead of == or !=
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|
237 if lastFrameNumArg is None: |
248
571ba5ed22e2
added utils for bus processing
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238 from sys import maxint |
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239 lastFrameNum = maxint |
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240 else: |
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241 lastFrameNum = lastFrameNumArg |
482
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242 nZerosFilename = int(ceil(log10(lastFrameNum))) |
628
977407c9f815
corrected bugs in loading interactions (index shifted) and added functionalities to play/plot interactions
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243 while ret and not quitKey(key) and frameNum <= lastFrameNum: |
203
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244 ret, img = capture.read() |
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245 if ret: |
509
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corrected mask bug in feature tracking, updated display-trajectories to display on undistorted image
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246 if undistort: |
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247 img = cv2.remap(img, map1, map2, interpolation=cv2.INTER_LINEAR) |
381
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248 if printFrames: |
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249 print('frame {0}'.format(frameNum)) |
203
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250 for obj in objects: |
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251 if obj.existsAtInstant(frameNum): |
236
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252 if not hasattr(obj, 'projectedPositions'): |
636
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253 if homography is not None: |
218
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254 obj.projectedPositions = obj.positions.project(homography) |
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255 else: |
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256 obj.projectedPositions = obj.positions |
515
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renamed all draw functions to plot for consistency
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257 cvPlot(img, obj.projectedPositions, cvRed, frameNum-obj.getFirstInstant()) |
393
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258 if frameNum in boundingBoxes.keys(): |
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259 for rect in boundingBoxes[frameNum]: |
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260 cv2.rectangle(img, rect[0].asint().astuple(), rect[1].asint().astuple(), cvRed) |
528
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261 elif len(obj.features) != 0: |
416
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262 imgcrop, yCropMin, yCropMax, xCropMin, xCropMax = imageBox(img, obj, frameNum, homography, width, height) |
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263 cv2.rectangle(img, (xCropMin, yCropMin), (xCropMax, yCropMax), cvBlue, 1) |
411
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added hog functions and the display of road user types if known
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264 objDescription = '{} '.format(obj.num) |
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265 if userTypeNames[obj.userType] != 'unknown': |
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266 objDescription += userTypeNames[obj.userType][0].upper() |
429
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267 cv2.putText(img, objDescription, obj.projectedPositions[frameNum-obj.getFirstInstant()].asint().astuple(), cv2.cv.CV_FONT_HERSHEY_PLAIN, 1, cvRed) |
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268 if not saveAllImages: |
544
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269 cvImshow(windowName, img, rescale) |
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270 key = cv2.waitKey() |
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271 if saveAllImages or saveKey(key): |
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272 cv2.imwrite('image-{{:0{}}}.png'.format(nZerosFilename).format(frameNum), img) |
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273 frameNum += nFramesStep |
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274 if nFramesStep > 1: |
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275 capture.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, frameNum) |
346
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276 cv2.destroyAllWindows() |
435
17185fe77316
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277 else: |
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278 print 'Cannot load file ' + videoFilename |
402
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279 |
639
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280 def computeHomographyFromPDTV(camera): |
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281 '''Returns the homography matrix at ground level from PDTV camera |
402
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282 https://bitbucket.org/hakanardo/pdtv''' |
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283 from numpy import array |
639
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284 # camera = pdtv.load(cameraFilename) |
402
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285 srcPoints = [[x,y] for x, y in zip([1.,2.,2.,1.],[1.,1.,2.,2.])] # need floats!! |
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286 dstPoints = [] |
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287 for srcPoint in srcPoints: |
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288 projected = camera.image_to_world(tuple(srcPoint)) |
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289 dstPoints.append([projected[0], projected[1]]) |
638
852f5de42d01
added functionality to read Aliaksei Tsai camera model data
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290 H, mask = cv2.findHomography(array(srcPoints), array(dstPoints), method = 0) # No need for different methods for finding homography |
402
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291 return H |
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292 |
474
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293 def undistortedCoordinates(map1, map2, x, y, maxDistance = 1.): |
468
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294 '''Returns the coordinates of a point in undistorted image |
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295 map1 and map2 are the mapping functions from undistorted image |
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296 to distorted (original image) |
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297 map1(x,y) = originalx, originaly''' |
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298 from numpy import abs, logical_and, unravel_index, dot, sum |
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299 from matplotlib.mlab import find |
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300 distx = abs(map1-x) |
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301 disty = abs(map2-y) |
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302 indices = logical_and(distx<maxDistance, disty<maxDistance) |
468
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diff
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303 closeCoordinates = unravel_index(find(indices), distx.shape) # returns i,j, ie y,x |
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304 xWeights = 1-distx[indices] |
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diff
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305 yWeights = 1-disty[indices] |
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306 return dot(xWeights, closeCoordinates[1])/sum(xWeights), dot(yWeights, closeCoordinates[0])/sum(yWeights) |
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307 |
474
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308 def undistortTrajectoryFromCVMapping(map1, map2, t): |
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309 '''test 'perfect' inversion''' |
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310 from moving import Trajectory |
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311 from numpy import isnan |
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312 undistortedTrajectory = Trajectory() |
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313 for i,p in enumerate(t): |
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314 res = undistortedCoordinates(map1, map2, p.x,p.y) |
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315 if not isnan(res).any(): |
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316 undistortedTrajectory.addPositionXY(res[0], res[1]) |
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317 else: |
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318 print i,p,res |
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319 return undistortedTrajectory |
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320 |
472
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321 def computeInverseMapping(originalImageSize, map1, map2): |
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322 'Computes inverse mapping from maps provided by cv2.initUndistortRectifyMap' |
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323 from numpy import ones, isnan |
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324 invMap1 = -ones(originalImageSize) |
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325 invMap2 = -ones(originalImageSize) |
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326 for x in range(0,originalImageSize[1]): |
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327 for y in range(0,originalImageSize[0]): |
474
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328 res = undistortedCoordinates(x,y, map1, map2) |
472
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329 if not isnan(res).any(): |
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330 invMap1[y,x] = res[0] |
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331 invMap2[y,x] = res[1] |
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332 return invMap1, invMap2 |
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333 |
544
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changeset
|
334 def cameraIntrinsicCalibration(path, checkerBoardSize=[6,7], secondPassSearch=False, display=False): |
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335 ''' Camera calibration searches through all the images (jpg or png) located |
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336 in _path_ for matches to a checkerboard pattern of size checkboardSize. |
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337 These images should all be of the same camera with the same resolution. |
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|
338 |
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339 For best results, use an asymetric board and ensure that the image has |
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340 very high contrast, including the background. Suitable checkerboard: |
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341 http://ftp.isr.ist.utl.pt/pub/roswiki/attachments/camera_calibration(2f)Tutorials(2f)StereoCalibration/check-108.png |
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342 |
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343 The code below is based off of: |
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344 https://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html |
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345 Modified by Paul St-Aubin |
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346 ''' |
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347 from numpy import zeros, mgrid, float32, savetxt |
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348 import glob, os |
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349 |
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350 # termination criteria |
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351 criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) |
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352 |
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353 # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0) |
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354 objp = zeros((checkerBoardSize[0]*checkerBoardSize[1],3), float32) |
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355 objp[:,:2] = mgrid[0:checkerBoardSize[1],0:checkerBoardSize[0]].T.reshape(-1,2) |
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356 |
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357 # Arrays to store object points and image points from all the images. |
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358 objpoints = [] # 3d point in real world space |
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359 imgpoints = [] # 2d points in image plane. |
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360 |
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361 ## Loop throuhg all images in _path_ |
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362 images = glob.glob(os.path.join(path,'*.[jJ][pP][gG]'))+glob.glob(os.path.join(path,'*.[jJ][pP][eE][gG]'))+glob.glob(os.path.join(path,'*.[pP][nN][gG]')) |
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363 for fname in images: |
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364 img = cv2.imread(fname) |
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365 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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366 |
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367 # Find the chess board corners |
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368 ret, corners = cv2.findChessboardCorners(gray, (checkerBoardSize[1],checkerBoardSize[0]), None) |
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369 |
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370 # If found, add object points, image points (after refining them) |
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371 if ret: |
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372 print 'Found pattern in '+fname |
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373 |
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374 if(secondPassSearch): |
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375 corners = cv2.cornerSubPix(gray, corners, (11,11), (-1,-1), criteria) |
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376 |
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377 objpoints.append(objp) |
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378 imgpoints.append(corners) |
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379 |
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380 # Draw and display the corners |
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381 if(display): |
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382 img = cv2.drawChessboardCorners(img, (checkerBoardSize[1],checkerBoardSize[0]), corners, ret) |
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383 if(img): |
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384 cv2.imshow('img',img) |
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385 cv2.waitKey(0) |
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386 |
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387 ## Close up image loading and calibrate |
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388 cv2.destroyAllWindows() |
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389 if len(objpoints) == 0 or len(imgpoints) == 0: |
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390 return False |
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391 try: |
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392 ret, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None) |
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393 except NameError: |
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394 return False |
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395 savetxt('intrinsic-camera.txt', camera_matrix) |
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396 return camera_matrix, dist_coeffs |
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397 |
545
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398 def undistortImage(img, intrinsicCameraMatrix = None, distortionCoefficients = None, undistortedImageMultiplication = 1., interpolation=cv2.INTER_LINEAR): |
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399 '''Undistorts the image passed in argument''' |
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400 width = img.shape[1] |
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401 height = img.shape[0] |
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402 [map1, map2] = computeUndistortMaps(width, height, undistortedImageMultiplication, intrinsicCameraMatrix, distortionCoefficients) |
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403 return cv2.remap(img, map1, map2, interpolation=interpolation) |
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404 |
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405 |
99
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406 def printCvMat(cvmat, out = stdout): |
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407 '''Prints the cvmat to out''' |
384
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408 print('Deprecated, use new interface') |
99
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409 for i in xrange(cvmat.rows): |
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410 for j in xrange(cvmat.cols): |
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411 out.write('{0} '.format(cvmat[i,j])) |
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412 out.write('\n') |
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413 |
98
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414 def projectArray(homography, points): |
384
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415 '''Returns the coordinates of the projected points through homography |
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416 (format: array 2xN points)''' |
315
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417 from numpy.core import dot |
28 | 418 from numpy.core.multiarray import array |
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419 from numpy.lib.function_base import append |
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420 |
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421 if points.shape[0] != 2: |
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422 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) |
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423 |
636
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424 if (homography is not None) and homography.size>0: |
639
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425 #alternatively, on could use cv2.convertpointstohomogeneous and other conversion to/from homogeneous coordinates |
98
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426 augmentedPoints = append(points,[[1]*points.shape[1]], 0) |
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427 prod = dot(homography, augmentedPoints) |
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428 return prod[0:2]/prod[2] |
28 | 429 else: |
547 | 430 return points |
28 | 431 |
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432 def project(homography, p): |
319 | 433 '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] |
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434 through homography''' |
243
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435 from numpy import array |
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436 return projectArray(homography, array([[p[0]],[p[1]]])) |
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437 |
28 | 438 def projectTrajectory(homography, trajectory): |
439 '''Projects a series of points in the format | |
440 [[x1, x2, ...], | |
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441 [y1, y2, ...]]''' |
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442 from numpy.core.multiarray import array |
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443 return projectArray(homography, array(trajectory)) |
28 | 444 |
48
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445 def invertHomography(homography): |
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446 '''Returns an inverted homography |
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447 Unnecessary for reprojection over camera image''' |
48
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448 from numpy.linalg.linalg import inv |
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449 invH = inv(homography) |
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450 invH /= invH[2,2] |
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451 return invH |
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452 |
474
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453 def undistortTrajectory(invMap1, invMap2, positions): |
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454 from numpy import floor, ceil |
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455 floorPositions = floor(positions) |
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456 #ceilPositions = ceil(positions) |
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457 undistortedTrajectory = [[],[]] |
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458 for i in xrange(len(positions[0])): |
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459 x,y = None, None |
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460 if positions[0][i]+1 < invMap1.shape[1] and positions[1][i]+1 < invMap1.shape[0]: |
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461 floorX = invMap1[floorPositions[1][i], floorPositions[0][i]] |
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462 floorY = invMap2[floorPositions[1][i], floorPositions[0][i]] |
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463 ceilX = invMap1[floorPositions[1][i]+1, floorPositions[0][i]+1] |
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464 ceilY = invMap2[floorPositions[1][i]+1, floorPositions[0][i]+1] |
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465 #ceilX = invMap1[ceilPositions[1][i], ceilPositions[0][i]] |
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466 #ceilY = invMap2[ceilPositions[1][i], ceilPositions[0][i]] |
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467 if floorX >=0 and floorY >=0 and ceilX >=0 and ceilY >=0: |
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468 x = floorX+(positions[0][i]-floorPositions[0][i])*(ceilX-floorX) |
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469 y = floorY+(positions[1][i]-floorPositions[1][i])*(ceilY-floorY) |
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470 undistortedTrajectory[0].append(x) |
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471 undistortedTrajectory[1].append(y) |
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472 return undistortedTrajectory |
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473 |
491
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474 def projectGInputPoints(homography, points): |
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475 from numpy import array |
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476 return projectTrajectory(homography, array(points+[points[0]]).T) |
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477 |
365
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478 if opencvAvailable: |
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479 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)): |
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480 '''Computes the translation of img2 with respect to img1 |
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481 (loaded using OpenCV as numpy arrays) |
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482 img1Points are used to compute the translation |
100
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483 |
154
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484 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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485 from numpy.core.multiarray import array |
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486 from numpy.lib.function_base import median |
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487 from numpy.core.fromnumeric import sum |
100
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488 |
154
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489 nextPoints = array([]) |
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490 (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria) |
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491 # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err |
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492 delta = [] |
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493 for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)): |
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494 if status[k] == 1: |
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495 dp = p2-p1 |
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496 d = sum(dp**2) |
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497 if d < maxTranslation2: |
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498 delta.append(dp) |
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499 if len(delta) >= minNMatches: |
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500 return median(delta, axis=0) |
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501 else: |
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502 print(dp) |
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503 return None |
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504 |
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505 if skimageAvailable: |
411
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506 def HOG(image, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): |
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507 from skimage.feature import hog |
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508 from skimage import color, transform |
411
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509 |
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510 bwImg = color.rgb2gray(image) |
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511 inputImg = transform.resize(bwImg, rescaleSize) |
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512 features = hog(inputImg, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) |
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513 if visualize: |
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514 from matplotlib.pyplot import imshow, figure, subplot |
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515 hogViz = features[1] |
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516 features = features[0] |
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517 figure() |
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518 subplot(1,2,1) |
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519 imshow(img) |
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520 subplot(1,2,2) |
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521 imshow(hogViz) |
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522 return features |
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523 |
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524 def createHOGTrainingSet(imageDirectory, classLabel, rescaleSize = (64, 64), orientations=9, pixelsPerCell=(8, 8), cellsPerBlock=(2, 2), visualize=False, normalize=False): |
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525 from os import listdir |
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526 from numpy import array, float32 |
411
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527 from matplotlib.pyplot import imread |
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528 |
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529 inputData = [] |
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530 for filename in listdir(imageDirectory): |
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531 img = imread(imageDirectory+filename) |
411
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532 features = HOG(img, rescaleSize, orientations, pixelsPerCell, cellsPerBlock, visualize, normalize) |
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533 inputData.append(features) |
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534 |
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535 nImages = len(inputData) |
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536 return array(inputData, dtype = float32), array([classLabel]*nImages, dtype = float32) |
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537 |
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538 |