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annotate python/cvutils.py @ 171:8e7b354666ec

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corrected bug in to get images from video
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 17 Oct 2011 14:16:50 -0400
parents b0719b3ad3db
children e2f31813ade6
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1 #! /usr/bin/env python
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2 '''Image/Video utilities'''
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3
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4 import Image, ImageDraw # PIL
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5 try:
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6 import cv2
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7 opencvExists = 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 opencvExists = False
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11 from sys import stdout
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13 import utils
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15 #import aggdraw # agg on top of PIL (antialiased drawing)
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16 #import utils
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17
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18 __metaclass__ = type
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19
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20 cvRed = (0,0,255)
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21 cvGreen = (0,255,0)
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22 cvBlue = (255,0,0)
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23 cvColors = utils.PlottingPropertyValues([cvRed,
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24 cvGreen,
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25 cvBlue])
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27 cvKeyNumbers = {'a':1048673,
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28 'n': 1048686,
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29 'y': 1048697}
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31 def drawLines(filename, origins, destinations, w = 1, resultFilename='image.png'):
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32 '''Draws lines over the image '''
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33
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34 img = Image.open(filename)
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35
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36 draw = ImageDraw.Draw(img)
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37 #draw = aggdraw.Draw(img)
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38 #pen = aggdraw.Pen("red", width)
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39 for p1, p2 in zip(origins, destinations):
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40 draw.line([p1.x, p1.y, p2.x, p2.y], width = w, fill = (256,0,0))
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41 #draw.line([p1.x, p1.y, p2.x, p2.y], pen)
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42 del draw
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43
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44 #out = utils.openCheck(resultFilename)
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45 img.save(resultFilename)
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46
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47 def matlab2PointCorrespondences(filename):
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48 '''Loads and converts the point correspondences saved
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49 by the matlab camera calibration tool'''
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50 from numpy.lib.io import loadtxt, savetxt
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51 from numpy.lib.function_base import append
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52 points = loadtxt(filename, delimiter=',')
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53 savetxt(utils.removeExtension(filename)+'-point-correspondences.txt',append(points[:,:2].T, points[:,3:].T, axis=0))
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54
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55 def loadPointCorrespondences(filename):
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56 '''Loads and returns the corresponding points in world (first 2 lines) and image spaces (last 2 lines)'''
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57 from numpy.lib.io import loadtxt
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58 from numpy import float32
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59 points = loadtxt(filename, dtype=float32)
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60 return (points[:2,:].T, points[2:,:].T) # (world points, image points)
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62 def computeHomography(srcPoints, dstPoints, method=0, ransacReprojThreshold=0.0):
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63 '''Returns the homography matrix mapping from srcPoints to dstPoints (dimension Nx2)'''
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64 #cvSrcPoints = arrayToCvMat(srcPoints);
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65 #cvDstPoints = arrayToCvMat(dstPoints);
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66 #H = cv.CreateMat(3, 3, cv.CV_64FC1)
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67 H, mask = cv2.findHomography(srcPoints, dstPoints, method, ransacReprojThreshold)
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68 return H
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69
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70 def cvMatToArray(cvmat):
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71 '''Converts an OpenCV CvMat to numpy array.'''
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72 from numpy.core.multiarray import zeros
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73 a = zeros((cvmat.rows, cvmat.cols))#array([[0.0]*cvmat.width]*cvmat.height)
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74 for i in xrange(cvmat.rows):
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75 for j in xrange(cvmat.cols):
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76 a[i,j] = cvmat[i,j]
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77 return a
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79 if opencvExists:
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80 def arrayToCvMat(a, t = cv2.cv.CV_64FC1):
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81 '''Converts a numpy array to an OpenCV CvMat, with default type CV_64FC1.'''
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82 cvmat = cv2.cv.CreateMat(a.shape[0], a.shape[1], t)
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83 for i in range(cvmat.rows):
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84 for j in range(cvmat.cols):
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85 cvmat[i,j] = a[i,j]
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86 return cvmat
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87
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88 def playVideo(filename):
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89 '''Plays the video'''
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90 capture = cv2.VideoCapture(filename)
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91 if capture.isOpened():
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92 key = -1
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93 while key!= 1048689: # 'q'
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94 ret, img = capture.read()
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95 if ret:
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96 cv2.imshow('frame', img)
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97 key = cv2.waitKey(5)
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98
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99 def getImagesFromVideo(filename, nImages = 1):
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100 '''Returns nImages images from the video sequence'''
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101 images = []
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102 capture = cv2.VideoCapture(filename)
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103 if capture.isOpened():
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104 ret = False
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105 while len(images)<nImages:
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106 ret, img = capture.read()
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107 while not ret:
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108 ret, img = capture.read()
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109 if img.size>0:
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110 images.append(img)
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111 return images
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113 def printCvMat(cvmat, out = stdout):
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114 '''Prints the cvmat to out'''
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115 for i in xrange(cvmat.rows):
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116 for j in xrange(cvmat.cols):
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117 out.write('{0} '.format(cvmat[i,j]))
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118 out.write('\n')
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119
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120 def projectArray(homography, points):
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121 '''Returns the coordinates of the projected points (format 2xN points)
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122 through homography'''
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123 from numpy.core._dotblas import dot
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124 from numpy.core.multiarray import array
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125 from numpy.lib.function_base import append
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126
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127 if points.shape[0] != 2:
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128 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1]))
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129
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130 if (homography!=None) and homography.size>0:
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131 augmentedPoints = append(points,[[1]*points.shape[1]], 0)
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132 prod = dot(homography, augmentedPoints)
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133 return prod[0:2]/prod[2]
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134 else:
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135 return p
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136
98
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137 def project(homography, p):
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138 '''Returns the coordinates of the projection of the point p
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139 through homography'''
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140 from numpy.core.multiarray import array
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141 return projectArray(homography, array([[p[0]],p[1]]))
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142
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143 def projectTrajectory(homography, trajectory):
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144 '''Projects a series of points in the format
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145 [[x1, x2, ...],
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146 [y1, y2, ...]]'''
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147 from numpy.core.multiarray import array
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148 return projectArray(homography, array(trajectory))
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149
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150 def invertHomography(homography):
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151 'Returns an inverted homography'
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152 from numpy.linalg.linalg import inv
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153 invH = inv(homography)
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154 invH /= invH[2,2]
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155 return invH
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156
112
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157 if opencvExists:
154
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158 def computeTranslation(img1, img2, img1Points, maxTranslation2, minNMatches, windowSize = (5,5), level = 5, criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)):
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159 '''Computes the translation of img2 with respect to img1
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160 (loaded using OpenCV as numpy arrays)
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161 img1Points are used to compute the translation
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162
154
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163 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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164 from numpy.core.multiarray import array
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165 from numpy.lib.function_base import median
154
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166 from numpy.core.fromnumeric import sum
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167
154
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168 nextPoints = array([])
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169 (img2Points, status, track_error) = cv2.calcOpticalFlowPyrLK(img1, img2, img1Points, nextPoints, winSize=windowSize, maxLevel=level, criteria=criteria)
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170 # calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, derivLambda[, flags]]]]]]]]) -> nextPts, status, err
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171 delta = []
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172 for (k, (p1,p2)) in enumerate(zip(img1Points, img2Points)):
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173 if status[k] == 1:
154
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174 dp = p2-p1
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175 d = sum(dp**2)
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176 if d < maxTranslation2:
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177 delta.append(dp)
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178 if len(delta) >= minNMatches:
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179 return median(delta, axis=0)
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180 else:
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181 print(dp)
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182 return None