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annotate python/calibration-translation.py @ 160:b0719b3ad3db

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created function to load point correspondences and updates scripts that use it
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 19 Sep 2011 16:43:28 -0400
parents 115f7f90286d
children 514f6b98cd8c
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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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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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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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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()