Mercurial > hg > nsaunier > traffic-intelligence

comparison python/cvutils.py @ 929:be28a3538dc9

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work in progress on projection
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Wed, 12 Jul 2017 18:00:53 -0400
parents 063d1267585d
children 7db0f2853bfd
comparison
equal deleted inserted replaced
928:063d1267585d 929:be28a3538dc9
262 x = [] 262 x = []
263 y = [] 263 y = []
264 if obj.hasFeatures(): 264 if obj.hasFeatures():
265 for f in obj.getFeatures(): 265 for f in obj.getFeatures():
266 if f.existsAtInstant(frameNum): 266 if f.existsAtInstant(frameNum):
267 projectedPosition = f.projectPositions[:, frameNum-f.getFirstInstant()] 267 p = f.getPositionAtInstant(frameNum)
268 x.append(projectedPosition[0]) 268 x.append(p.x)
269 y.append(projectedPosition[1]) 269 y.append(p.y)
270 xmin = min(x) 270 xmin = min(x)
271 xmax = max(x) 271 xmax = max(x)
272 ymin = min(y) 272 ymin = min(y)
273 ymax = max(y) 273 ymax = max(y)
274 xMm = px * (xmax - xmin) 274 xMm = px * (xmax - xmin)
519 out.write('\n') 519 out.write('\n')
520 520
521 def projectArray(homography, points, intrinsicCameraMatrix = None, distortionCoefficients = None): 521 def projectArray(homography, points, intrinsicCameraMatrix = None, distortionCoefficients = None):
522 '''Returns the coordinates of the projected points through homography 522 '''Returns the coordinates of the projected points through homography
523 (format: array 2xN points)''' 523 (format: array 2xN points)'''
524 if points.shape[0] not in [2, 3]: 524 if points.shape[0] != 2:
525 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1])) 525 raise Exception('points of dimension {0} {1}'.format(points.shape[0], points.shape[1]))
526 526
527 augmentedPoints = append(points,[[1]*points.shape[1]], 0) 527 augmentedPoints = append(points,[[1]*points.shape[1]], 0) # 3xN
528 if homography is not None and homography.size>0: 528 if homography is not None and homography.size>0:
529 prod = dot(homography, augmentedPoints) 529 prod = dot(homography, augmentedPoints)
530 projected = prod/prod[2] 530 projected = prod/prod[2]
531 projected[3,:] = 0
532 else: 531 else:
533 projected = augmentedPoints 532 projected = augmentedPoints
534 533
535 if intrinsicCameraMatrix is not None and distortionCoefficients is not None: 534 if intrinsicCameraMatrix is not None and distortionCoefficients is not None:
536 projected = cv2.projectPoints(projected, None, None, intrinsicCameraMatrix, distortionCoefficients) 535 #projected[2,:] = 0
537 return projected 536 projected, jacobian = cv2.projectPoints(projected.T, (0,0,0), (0,0,0), intrinsicCameraMatrix, distortionCoefficients) # in: 3xN, out: 2x1xN
538 537 projected = projected.reshape(-1,2).T
539 def project(homography, p): 538 return projected[:2,:]
539
540 def project(homography, p, intrinsicCameraMatrix = None, distortionCoefficients = None):
540 '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1] 541 '''Returns the coordinates of the projection of the point p with coordinates p[0], p[1]
541 through homography''' 542 through homography'''
542 return projectArray(homography, array([[p[0]],[p[1]]])) 543 return projectArray(homography, array([[p[0]],[p[1]]]), intrinsicCameraMatrix, distortionCoefficients)
543 544
544 def projectTrajectory(homography, trajectory): 545 def projectTrajectory(homography, trajectory, intrinsicCameraMatrix = None, distortionCoefficients = None):
545 '''Projects a series of points in the format 546 '''Projects a series of points in the format
546 [[x1, x2, ...], 547 [[x1, x2, ...],
547 [y1, y2, ...]]''' 548 [y1, y2, ...]]'''
548 return projectArray(homography, array(trajectory)) 549 return projectArray(homography, array(trajectory), intrinsicCameraMatrix, distortionCoefficients)
549 550
550 def invertHomography(homography): 551 def invertHomography(homography):
551 '''Returns an inverted homography 552 '''Returns an inverted homography
552 Unnecessary for reprojection over camera image''' 553 Unnecessary for reprojection over camera image'''
553 from numpy.linalg import inv 554 from numpy.linalg import inv