Mercurial > hg > nsaunier > traffic-intelligence

comparison scripts/compute-homography.py @ 334:1d90e9080cb2

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moved python scripts to the scripts directory
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Fri, 14 Jun 2013 10:34:11 -0400
parents python/compute-homography.py@9d88a4d97473
children 5f75d6c23ed5
comparison
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333:c9201f6b143a 334:1d90e9080cb2
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()