Mercurial Hosting > traffic-intelligence
comparison scripts/compute-homography.py @ 334:1d90e9080cb2
moved python scripts to the scripts directory
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Fri, 14 Jun 2013 10:34:11 -0400 |
parents | python/compute-homography.py@9d88a4d97473 |
children | 5f75d6c23ed5 |
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333:c9201f6b143a | 334:1d90e9080cb2 |
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1 #! /usr/bin/env python | |
2 | |
3 import sys,getopt | |
4 | |
5 import matplotlib.pyplot as plt | |
6 import numpy as np | |
7 import cv2 | |
8 | |
9 import cvutils | |
10 import utils | |
11 | |
12 options, args = getopt.getopt(sys.argv[1:], 'hp:i:w:n:u:',['help']) | |
13 options = dict(options) | |
14 | |
15 # TODO process camera intrinsic and extrinsic parameters to obtain image to world homography, taking example from Work/src/python/generate-homography.py script | |
16 # cameraMat = load(videoFilenamePrefix+'-camera.txt'); | |
17 # T1 = cameraMat[3:6,:].copy(); | |
18 # A = cameraMat[0:3,0:3].copy(); | |
19 | |
20 # # pay attention, rotation may be the transpose | |
21 # # R = T1[:,0:3].T; | |
22 # R = T1[:,0:3]; | |
23 # rT = dot(R, T1[:,3]/1000); | |
24 # T = zeros((3,4),'f'); | |
25 # T[:,0:3] = R[:]; | |
26 # T[:,3] = rT; | |
27 | |
28 # AT = dot(A,T); | |
29 | |
30 # nPoints = 4; | |
31 # worldPoints = cvCreateMat(nPoints, 3, CV_64FC1); | |
32 # imagePoints = cvCreateMat(nPoints, 3, CV_64FC1); | |
33 | |
34 # # extract homography from the camera calibration | |
35 # worldPoints = cvCreateMat(4, 3, CV_64FC1); | |
36 # imagePoints = cvCreateMat(4, 3, CV_64FC1); | |
37 | |
38 # worldPoints[0,:] = [[1, 1, 0]]; | |
39 # worldPoints[1,:] = [[1, 2, 0]]; | |
40 # worldPoints[2,:] = [[2, 1, 0]]; | |
41 # worldPoints[3,:] = [[2, 2, 0]]; | |
42 | |
43 # wPoints = [[1,1,2,2], | |
44 # [1,2,1,2], | |
45 # [0,0,0,0]]; | |
46 # iPoints = utils.worldToImage(AT, wPoints); | |
47 | |
48 # for i in range(nPoints): | |
49 # imagePoints[i,:] = [iPoints[:,i].tolist()]; | |
50 | |
51 # H = cvCreateMat(3, 3, CV_64FC1); | |
52 | |
53 # cvFindHomography(imagePoints, worldPoints, H); | |
54 | |
55 if '--help' in options.keys() or '-h' in options.keys() or len(options) == 0: | |
56 print('Usage: {0} --help|-h [-p point-correspondences.txt] [ -i video-frame] [ -w world-frame] [n number-points] [-u unit-per-pixel=1]'.format(sys.argv[0])) | |
57 print('''The input data can be provided either as point correspondences already saved | |
58 in a text file or inputed by clicking a certain number of points (>=4) | |
59 in a video frame and a world image. | |
60 | |
61 The point correspondence file contains at least 4 non-colinear point coordinates | |
62 with the following format: | |
63 - the first two lines are the x and y coordinates in the projected space (usually world space) | |
64 - the last two lines are the x and y coordinates in the origin space (usually image space) | |
65 | |
66 If providing video and world images, with a number of points to input | |
67 and a ration to convert pixels to world distance unit (eg meters per pixel), | |
68 the images will be shown in turn and the user should click | |
69 in the same order the corresponding points in world and image spaces. ''') | |
70 sys.exit() | |
71 | |
72 homography = np.array([]) | |
73 if '-p' in options.keys(): | |
74 worldPts, videoPts = cvutils.loadPointCorrespondences(options['-p']) | |
75 homography, mask = cv2.findHomography(videoPts, worldPts) # method=0, ransacReprojThreshold=3 | |
76 elif '-i' in options.keys() and '-w' in options.keys(): | |
77 nPoints = 4 | |
78 if '-n' in options.keys(): | |
79 nPoints = int(options['-n']) | |
80 unitsPerPixel = 1 | |
81 if '-u' in options.keys(): | |
82 unitsPerPixel = float(options['-u']) | |
83 worldImg = plt.imread(options['-w']) | |
84 videoImg = plt.imread(options['-i']) | |
85 print('Click on {0} points in the video frame'.format(nPoints)) | |
86 plt.figure() | |
87 plt.imshow(videoImg) | |
88 videoPts = np.array(plt.ginput(nPoints)) | |
89 print('Click on {0} points in the world image'.format(nPoints)) | |
90 plt.figure() | |
91 plt.imshow(worldImg) | |
92 worldPts = unitsPerPixel*np.array(plt.ginput(nPoints)) | |
93 plt.close('all') | |
94 homography, mask = cv2.findHomography(videoPts, worldPts) | |
95 # save the points in file | |
96 f = open('point-correspondences.txt', 'a') | |
97 np.savetxt(f, worldPts.T) | |
98 np.savetxt(f, videoPts.T) | |
99 f.close() | |
100 | |
101 if homography.size>0: | |
102 np.savetxt('homography.txt',homography) | |
103 | |
104 if '-i' in options.keys() and homography.size>0: | |
105 videoImg = cv2.imread(options['-i']) | |
106 worldImg = cv2.imread(options['-w']) | |
107 invHomography = np.linalg.inv(homography) | |
108 projectedWorldPts = cvutils.projectArray(invHomography, worldPts.T).T | |
109 if '-u' in options.keys(): | |
110 unitsPerPixel = float(options['-u']) | |
111 projectedVideoPts = cvutils.projectArray(invHomography, videoPts.T).T | |
112 for i in range(worldPts.shape[0]): | |
113 cv2.circle(videoImg,tuple(np.int32(np.round(videoPts[i]))),2,cvutils.cvRed) | |
114 cv2.circle(videoImg,tuple(np.int32(np.round(projectedWorldPts[i]))),2,cvutils.cvBlue) | |
115 if '-u' in options.keys(): | |
116 cv2.circle(worldImg,tuple(np.int32(np.round(worldPts[i]/unitsPerPixel))),2,cvutils.cvRed) | |
117 cv2.circle(worldImg,tuple(np.int32(np.round(projectedVideoPts[i]/unitsPerPixel))),2,cvutils.cvRed) | |
118 #print('img: {0} / projected: {1}'.format(videoPts[i], p)) | |
119 cv2.imshow('video frame',videoImg) | |
120 if '-u' in options.keys(): | |
121 cv2.imshow('world image',worldImg) | |
122 cv2.waitKey() |