Mercurial Hosting > traffic-intelligence
comparison python/calibration-translation.py @ 157:3aab19947a34
added utility to recalibrate images with similar viewpoints
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Thu, 08 Sep 2011 19:25:02 -0400 |
parents | |
children | 2d7c6d767a39 |
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156:2eef5620c0b3 | 157:3aab19947a34 |
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1 #!/usr/bin/env python | |
2 | |
3 import sys | |
4 | |
5 import matplotlib.mlab as pylab | |
6 import matplotlib.pyplot as plt | |
7 import numpy as np | |
8 | |
9 import cv2 | |
10 import utils | |
11 import cvutils | |
12 | |
13 # development for the data collected and stabilized by Paul in Summer 2011 | |
14 # todo test other features | |
15 | |
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=']) | |
17 | |
18 referenceVideoFilename=options['--ref_video']#'1440-1459_Mercalli.avi' | |
19 referenceHomographyFilename=options['--ref_homography']#'1440-1459_Mercalli-homography.txt' | |
20 points = np.loadtxt(options['--ref_points'], dtype=np.float32) # '1440-1459_Mercalli-point-correspondences.txt' | |
21 wldPts = points[:2,:].T | |
22 imgPts = points[2:,:].T | |
23 | |
24 def translatePoints(points, t): | |
25 'points is Nx2, t is [x,y]' | |
26 translated = points.copy() | |
27 for i in xrange(2): | |
28 translated[i] += t[i] | |
29 return translated | |
30 | |
31 filenames = [f for f in utils.listfiles('.','avi')] # directory to examine should be current directory | |
32 | |
33 referenceHomography = np.loadtxt(referenceHomographyFilename) | |
34 referenceVideoIndex = filenames.index(referenceVideoFilename) | |
35 indices = set(range(len(filenames))) | |
36 indices.discard(referenceVideoIndex) | |
37 | |
38 images = {} | |
39 #features = {} | |
40 captures = {} | |
41 | |
42 captures[referenceVideoFilename] = cv2.VideoCapture(referenceVideoFilename) | |
43 (ret, img) = captures[referenceVideoFilename].read() | |
44 images[referenceVideoFilename] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
45 | |
46 if '--mask_img' in options.keys(): | |
47 maskImg = cv2.imread('mask.png', cv2.CV_LOAD_IMAGE_GRAYSCALE) # todo add possibility to look in the whole image if not providing mask | |
48 else: | |
49 maskImg = np.ones(images[referenceVideoFilename].shape, dtype=np.uint8) | |
50 | |
51 referenceFeatures = cv2.goodFeaturesToTrack(images[referenceVideoFilename], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) | |
52 displayRef = cv2.cvtColor(images[referenceVideoFilename], cv2.COLOR_GRAY2RGB) | |
53 for j,p in enumerate(imgPts): | |
54 cv2.circle(displayRef, tuple(p), 3, (255,0,0)) | |
55 cv2.putText(displayRef, str(j+1), tuple(p), cv2.FONT_HERSHEY_PLAIN, 1, (255,0,0)) | |
56 cv2.imshow('Reference',displayRef) | |
57 | |
58 key = -1 | |
59 for f in filenames: # get suitable image references for each video | |
60 captures[f] = cv2.VideoCapture(f) | |
61 while key != cvutils.cvKeyNumbers['y']: | |
62 (ret, img) = captures[f].read() | |
63 cv2.imshow('Image',img) | |
64 print('Can one see the reference points in the image? (y/n)') | |
65 key = cv2.waitKey(0) | |
66 | |
67 images[f] = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
68 cv2.imwrite(utils.removeExtension(filenames[i])+'-frame.png') | |
69 #images[f] = cv2.imread(f, cv2.CV_LOAD_IMAGE_GRAYSCALE) | |
70 #features[f] = cv2.goodFeaturesToTrack(images[f], 1000, 0.02, 2, useHarrisDetector = True, mask=maskImg) # todo put parameters on the command line ? | |
71 # goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) | |
72 # display features | |
73 if False: | |
74 display = img.copy()#cv2.cvtColor(images[f], cv2.COLOR_GRAY2RGB) #.copy() | |
75 for p in features[f]: | |
76 cv2.circle(display, tuple(p[0]), 3, (255,0,0)) | |
77 cv2.imshow('Reference',display) | |
78 cv2.waitKey() | |
79 | |
80 plt.close('all') | |
81 | |
82 for i in indices: | |
83 t = cvutils.computeTranslation(images[filenames[referenceVideoIndex]], images[filenames[i]], referenceFeatures, 100, 10) | |
84 print filenames[i],t | |
85 key = -1 | |
86 if t != None: # show translated points and ask if ok | |
87 displayImg = cv2.cvtColor(images[filenames[i]], cv2.COLOR_GRAY2RGB) #.copy() | |
88 for p in imgPts: | |
89 cv2.circle(displayImg, tuple(p+t[0]), 3, (255,0,0)) | |
90 cv2.imshow('Image',displayImg) | |
91 | |
92 while key != cvutils.cvKeyNumbers['y'] and key != cvutils.cvKeyNumbers['n']: | |
93 print('Are the translated points rightly located (y/n)?') | |
94 key = cv2.waitKey(0) | |
95 if key == cvutils.cvKeyNumbers['y']: # compute homography with translated numbers | |
96 newImgPts = [p+t[0] for p in imgPts] | |
97 else: | |
98 print('No translation could be found automatically. You will have to manually input world reference points.') | |
99 | |
100 if t==None or key != cvutils.cvKeyNumbers['y']:# if no translation could computed or it is not satisfactory | |
101 # image should be right to get points | |
102 # todo save image | |
103 print('Select the corresponding points in the same order as in the reference image') | |
104 plt.figure(1) | |
105 plt.imshow(displayRef) | |
106 plt.figure(2) | |
107 plt.imshow(img) | |
108 plt.show() | |
109 newImgPts = np.array([list(p) for p in plt.ginput(n=wldPts.shape[0], timeout=-1)], dtype = np.float32) | |
110 | |
111 homography, mask = cv2.findHomography(newImgPts, wldPts) # method=0, ransacReprojThreshold=3 | |
112 print homography | |
113 np.savetxt(utils.removeExtension(filenames[i])+'-homography.txt',homography) | |
114 np.savetxt(utils.removeExtension(filenames[i])+'-point-correspondences.txt', append(wldPts.T, newImgPts.T, axis=0)) | |
115 | |
116 cv2.destroyAllWindows() |