Mercurial Hosting > traffic-intelligence
annotate python/calibration-translation.py @ 251:4978b5baf8f1
Win32 version for traffic intelligence. Use the README-Win32.txt to know how to
fetch the dependencies. A batch file is included to automate most of the process
author | Jeep-Tour@Jeep-Tour-PC |
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date | Mon, 23 Jul 2012 12:27:50 -0400 |
parents | b0719b3ad3db |
children | 514f6b98cd8c |
rev | line source |
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1 #!/usr/bin/env python |
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2 |
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3 import sys |
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4 import os |
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5 |
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6 import matplotlib.mlab as pylab |
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7 import matplotlib.pyplot as plt |
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8 import numpy as np |
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9 |
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10 import cv2 |
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11 import utils |
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12 import cvutils |
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13 |
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14 # development for the data collected and stabilized by Paul in Summer 2011 |
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15 # todo write help, add options to control the parameters for matching (n points and distance) |
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16 |
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17 options = utils.parseCLIOptions('Program to re-calibrate an initial calibration based on point correspondences by adjusting the points to slightly different viewpoints, where all the points are still visible\n\nUsage: ', ['ref_video=', 'ref_points='], sys.argv, ['mask_img=']) |
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18 |
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19 referenceVideoFilename=options['--ref_video'] |
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20 wldPts, imgPts = cvutils.loadPointCorrespondences(options['--ref_points']) |
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21 |
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22 def translatePoints(points, t): |
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23 'points is Nx2, t is [x,y]' |
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24 translated = points.copy() |
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25 for i in xrange(2): |
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26 translated[i] += t[i] |
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27 return translated |
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28 |
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29 filenames = [f for f in utils.listfiles('.','avi')] # directory to examine should be current directory |
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30 |
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31 referenceVideoIndex = filenames.index(referenceVideoFilename) |
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32 indices = set(range(len(filenames))) |
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33 indices.discard(referenceVideoIndex) |
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34 |
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35 images = {} |
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36 captures = {} |
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37 |
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38 captures[referenceVideoFilename] = cv2.VideoCapture(referenceVideoFilename) |
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39 (ret, img) = captures[referenceVideoFilename].read() |
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40 images[referenceVideoFilename] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) |
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41 |
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42 # load a mask image to compute the translation |
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43 if '--mask_img' in options.keys(): |
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44 maskImg = cv2.imread('mask.png', cv2.CV_LOAD_IMAGE_GRAYSCALE) # todo add possibility to look in the whole image if not providing mask |
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45 else: |
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46 maskImg = np.ones(images[referenceVideoFilename].shape, dtype=np.uint8) |
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47 |
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48 referenceFeatures = cv2.goodFeaturesToTrack(images[referenceVideoFilename], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) |
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49 displayRef = cv2.cvtColor(images[referenceVideoFilename], cv2.COLOR_GRAY2RGB) |
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50 for j,p in enumerate(imgPts): |
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51 cv2.circle(displayRef, tuple(p), 3, (255,0,0)) |
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52 cv2.putText(displayRef, str(j+1), tuple(p), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0)) |
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53 cv2.imshow('Reference',displayRef) |
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54 |
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55 # get suitable image references for each video |
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56 for f in filenames: |
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57 captures[f] = cv2.VideoCapture(f) |
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58 frameFilename = utils.removeExtension(f)+'-frame.png' # TODO if frame image already exists, no need to search for it again |
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59 if not os.path.exists(frameFilename): |
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60 key = -1 |
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61 while key != cvutils.cvKeyNumbers['y']: |
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62 (ret, img) = captures[f].read() |
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63 cv2.imshow('Image',img) |
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64 print('Can one see the reference points in the image? (y/n)') |
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65 key = cv2.waitKey(0) |
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66 |
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67 images[f] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) |
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68 cv2.imwrite(frameFilename, img) |
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69 else: |
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70 images[f] = cv2.imread(frameFilename, cv2.CV_LOAD_IMAGE_GRAYSCALE) |
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71 #features[f] = cv2.goodFeaturesToTrack(images[f], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) # todo put parameters on the command line ? |
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72 # goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) |
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73 # display features |
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74 # if False: |
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75 # display = img.copy()#cv2.cvtColor(images[f], cv2.COLOR_GRAY2RGB) #.copy() |
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76 # for p in features[f]: |
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77 # cv2.circle(display, tuple(p[0]), 3, (255,0,0)) |
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78 # cv2.imshow('Reference',display) |
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79 # cv2.waitKey() |
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80 |
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81 plt.close('all') |
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82 |
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83 # validate or input point correspondences and compute homography |
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84 for i in indices: |
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85 t = cvutils.computeTranslation(images[filenames[referenceVideoIndex]], images[filenames[i]], referenceFeatures, 100, 10) |
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86 print filenames[i],t |
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87 key = -1 |
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88 if t != None: # show translated points and ask if ok |
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89 displayImg = cv2.cvtColor(images[filenames[i]], cv2.COLOR_GRAY2RGB) #.copy() |
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90 for p in imgPts: |
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91 cv2.circle(displayImg, tuple(p+t[0]), 3, (255,0,0)) |
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92 cv2.imshow('Image',displayImg) |
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93 |
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94 while not(key == cvutils.cvKeyNumbers['y'] or key == cvutils.cvKeyNumbers['n']): |
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95 print('Are the translated points rightly located (y/n)?') |
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96 key = cv2.waitKey(0) |
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97 if key == cvutils.cvKeyNumbers['y']: # compute homography with translated numbers |
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98 newImgPts = np.array([p+t[0] for p in imgPts]) |
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99 else: |
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100 print('No translation could be found automatically. You will have to manually input world reference points.') |
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101 |
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102 if t==None or key != cvutils.cvKeyNumbers['y']:# if no translation could computed or it is not satisfactory |
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103 print('Select the corresponding points in the same order as in the reference image') |
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104 plt.figure(1) |
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105 plt.imshow(displayRef) |
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106 plt.figure(2) |
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107 plt.imshow(img) |
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108 plt.show() |
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109 newImgPts = np.array([list(p) for p in plt.ginput(n=wldPts.shape[0], timeout=-1)], dtype = np.float32) |
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110 |
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111 homography, mask = cv2.findHomography(newImgPts, wldPts) # method=0, ransacReprojThreshold=3 |
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112 print homography |
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113 np.savetxt(utils.removeExtension(filenames[i])+'-homography.txt',homography) |
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114 np.savetxt(utils.removeExtension(filenames[i])+'-point-correspondences.txt', np.append(wldPts.T, newImgPts.T, axis=0)) |
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115 |
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116 cv2.destroyAllWindows() |