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view python/compute-homography.py @ 159:115f7f90286d
updated calibration-translation and added function to convert point correspondences
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 12 Sep 2011 16:38:47 -0400 |
parents | 4af774bb186d |
children | b0719b3ad3db |
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#! /usr/bin/env python import sys,getopt import numpy as np import cv2 import cvutils import utils options, args = getopt.getopt(sys.argv[1:], 'h',['help','video_frame=']) options = dict(options) if '--help' in options.keys() or '-h' in options.keys(): print('''The argument should be the name of a file containing at least 4 non-colinear point coordinates: - the first two lines are the x and y coordinates in the projected space (usually world space) - the last two lines are the x and y coordinates in the origin space (usually image space)''') sys.exit() if len(args) == 0: print('Usage: {0} --help|-h [--video_frame <video frame filename>] [<point_correspondences.txt>]'.format(sys.argv[0])) sys.exit() points = np.loadtxt(args[0], dtype=np.float32) srcPts = points[2:,:].T dstPts = points[:2,:].T homography, mask = cv2.findHomography(srcPts, dstPts) # method=0, ransacReprojThreshold=3 np.savetxt(utils.removeExtension(sys.argv[1])+'-homography.txt',homography) if '--video_frame' in options.keys() and homography.size>0: img = cv2.imread(options['--video_frame']) for p in srcPts: cv2.circle(img,tuple(p),2,cvutils.cvRed) invHomography = np.linalg.inv(homography) projectedDstPts = cvutils.projectArray(invHomography, dstPts.T).T for i,p in enumerate(projectedDstPts): cv2.circle(img,tuple(np.int32(np.round(p))),2,cvutils.cvBlue) print('img: {0} / projected: {1}'.format(srcPts[i], p)) cv2.imshow('video frame',img) cv2.waitKey()