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annotate python/calibration-translation.py @ 157:3aab19947a34

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added utility to recalibrate images with similar viewpoints
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Thu, 08 Sep 2011 19:25:02 -0400
parents
children 2d7c6d767a39
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157
3aab19947a34 added utility to recalibrate images with similar viewpoints
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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
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5 import matplotlib.mlab as pylab
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6 import matplotlib.pyplot as plt
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7 import numpy as np
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8
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9 import cv2
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10 import utils
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11 import cvutils
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12
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13 # development for the data collected and stabilized by Paul in Summer 2011
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14 # todo test other features
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15
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16 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_homography=', 'ref_points='], sys.argv, ['mask_img='])
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17
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18 referenceVideoFilename=options['--ref_video']#'1440-1459_Mercalli.avi'
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19 referenceHomographyFilename=options['--ref_homography']#'1440-1459_Mercalli-homography.txt'
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20 points = np.loadtxt(options['--ref_points'], dtype=np.float32) # '1440-1459_Mercalli-point-correspondences.txt'
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21 wldPts = points[:2,:].T
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22 imgPts = points[2:,:].T
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23
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24 def translatePoints(points, t):
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25 'points is Nx2, t is [x,y]'
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26 translated = points.copy()
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27 for i in xrange(2):
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28 translated[i] += t[i]
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29 return translated
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30
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31 filenames = [f for f in utils.listfiles('.','avi')] # directory to examine should be current directory
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32
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33 referenceHomography = np.loadtxt(referenceHomographyFilename)
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34 referenceVideoIndex = filenames.index(referenceVideoFilename)
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35 indices = set(range(len(filenames)))
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36 indices.discard(referenceVideoIndex)
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37
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38 images = {}
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39 #features = {}
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40 captures = {}
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41
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42 captures[referenceVideoFilename] = cv2.VideoCapture(referenceVideoFilename)
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43 (ret, img) = captures[referenceVideoFilename].read()
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44 images[referenceVideoFilename] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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45
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46 if '--mask_img' in options.keys():
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47 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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48 else:
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49 maskImg = np.ones(images[referenceVideoFilename].shape, dtype=np.uint8)
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50
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51 referenceFeatures = cv2.goodFeaturesToTrack(images[referenceVideoFilename], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg)
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52 displayRef = cv2.cvtColor(images[referenceVideoFilename], cv2.COLOR_GRAY2RGB)
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53 for j,p in enumerate(imgPts):
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54 cv2.circle(displayRef, tuple(p), 3, (255,0,0))
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55 cv2.putText(displayRef, str(j+1), tuple(p), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0))
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56 cv2.imshow('Reference',displayRef)
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57
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58 key = -1
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59 for f in filenames: # get suitable image references for each video
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60 captures[f] = cv2.VideoCapture(f)
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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(utils.removeExtension(filenames[i])+'-frame.png')
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69 #images[f] = cv2.imread(f, cv2.CV_LOAD_IMAGE_GRAYSCALE)
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70 #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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71 # goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]])
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72 # display features
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73 if False:
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74 display = img.copy()#cv2.cvtColor(images[f], cv2.COLOR_GRAY2RGB) #.copy()
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75 for p in features[f]:
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76 cv2.circle(display, tuple(p[0]), 3, (255,0,0))
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77 cv2.imshow('Reference',display)
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78 cv2.waitKey()
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79
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80 plt.close('all')
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81
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82 for i in indices:
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83 t = cvutils.computeTranslation(images[filenames[referenceVideoIndex]], images[filenames[i]], referenceFeatures, 100, 10)
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84 print filenames[i],t
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85 key = -1
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86 if t != None: # show translated points and ask if ok
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87 displayImg = cv2.cvtColor(images[filenames[i]], cv2.COLOR_GRAY2RGB) #.copy()
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88 for p in imgPts:
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89 cv2.circle(displayImg, tuple(p+t[0]), 3, (255,0,0))
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90 cv2.imshow('Image',displayImg)
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91
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92 while key != cvutils.cvKeyNumbers['y'] and key != cvutils.cvKeyNumbers['n']:
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93 print('Are the translated points rightly located (y/n)?')
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94 key = cv2.waitKey(0)
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95 if key == cvutils.cvKeyNumbers['y']: # compute homography with translated numbers
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96 newImgPts = [p+t[0] for p in imgPts]
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97 else:
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98 print('No translation could be found automatically. You will have to manually input world reference points.')
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99
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100 if t==None or key != cvutils.cvKeyNumbers['y']:# if no translation could computed or it is not satisfactory
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101 # image should be right to get points
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102 # todo save image
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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', append(wldPts.T, newImgPts.T, axis=0))
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115
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116 cv2.destroyAllWindows()