def main():
parser = argparse.ArgumentParser(description="Calibrate using keypoint matches");
parser.add_argument('pattern_key_fname', type=str, help='reference pattern keypoint file')
parser.add_argument('num_frames', type=int, help='Number of scene frames')
parser.add_argument('scene_key_fmt', type=str, help='Printf-formatted string representing keypoint files from N scene frames')
parser.add_argument('match_fmt', type=str, help='Printf-formatted string representing keypoint match files')
parser.add_argument('example_image', type=str, help='Example image to get dimensions from')
parser.add_argument('out_fname', type=str, help='filename for intrinsic calibrated camera')
parser.add_argument('--min_matches', default=20, type=str, help='omit frames with fewer than N matches')
args = parser.parse_args()
pattern_keys = feat.read_features(args.pattern_key_fname)[0];
fnames = [(args.scene_key_fmt % x, args.match_fmt % x) for x in range(1,args.num_frames+1)]
fnames = [x for x in fnames if os.path.isfile(x[0])]
if len(fnames) == 0:
print "No matching keypoint files"
exit(1)
missing = next((x[1] for x in fnames if not os.path.isfile(x[1])), None)
if missing is not None:
print "File not found: %s" % missing
exit(1)
[frame_fnames, match_fnames] = zip(*fnames)
print "reading keypoint from %d frames" % len(frame_fnames)
frames_keys = [feat.read_features(x)[0] for x in frame_fnames]
print "reading matches from %d frames" % len(match_fnames)
frames_matches = [feat.read_matches(x) for x in match_fnames]
img = cv2.imread(args.example_image)
img_size = (img.shape[1], img.shape[0]);
[obj_pts, image_pts] = to_calibration_data(frames_matches, pattern_keys, frames_keys, args.min_matches)
cam = calibrate_intrinsic(obj_pts, image_pts, img_size)
calib.write_intrinsic_camera(args.out_fname, cam);
def main():
parser = argparse.ArgumentParser(description="Match keypoints and calibrate camera");
parser.add_argument('pattern_key_fname', type=str, help='reference pattern keypoint file')
parser.add_argument('num_frames', type=int, help='Number of scene frames')
parser.add_argument('scene_key_fmt', type=str, help='Printf-formatted string representing keypoint files from N scene frames')
parser.add_argument('example_image', type=str, help='Example image (used to get dimensions)')
parser.add_argument('out_matches_fmt', type=str, help='printf string for output matches filenames')
parser.add_argument('out_camera_fname', type=str, help='output camera intrinsic calibration filename')
parser.add_argument('--num_iterations', nargs='?', type=int, default=0.6, help='number of iterations of match & homography re-estimation (2-3 is usually good)')
parser.add_argument('--lowe_threshold', nargs='?', type=float, default=0.6, help='Use David Lowe\'s ratio criterion for pruning bad matches')
parser.add_argument('--homography_threshold', nargs='?', type=float, default=0, help='fit a homography to matches and prune by this reprojection error threshold (zero ignores)')
parser.add_argument('--H_det_threshold', nargs='?', type=float, default=0, help='ignore frame if fitted homography has too small of a determinant.')
parser.add_argument('--f_threshold', nargs='?', type=float, default=2, help='fit a fundamental matrix to matches and prune by this error threshold (zero ignores)')
parser.add_argument('--min_matches', nargs='?', type=int, default=25, help='Minimum number of matches in a frame to be accepted into calibration set')
args = parser.parse_args()
[pattern_keys, pattern_descs] = feat.read_features(args.pattern_key_fname);
if args.num_frames == 1:
frame_fnames = [args.scene_key_fmt]
out_match_fnames = [args.out_matches_fmt]
else:
I = range(1, args.num_frames+1)
frame_fnames = [args.scene_key_fmt % x for x in I]
out_match_fnames = [args.out_matches_fmt % x for x in I]
tmp = [(a,b) for (a,b) in zip(frame_fnames, out_match_fnames) if os.path.isfile(a)]
[frame_fnames, out_match_fnames] = zip(*tmp)
if len(frame_fnames) == 0:
print "No files found matching %s" % args.scene_key_fmt
exit(1)
print "reading keypoint from %d frames" % len(frame_fnames)
frames_features = [feat.read_features(x) for x in frame_fnames]
[frames_keys, frames_descs] = zip(*frames_features)
img = cv2.imread(args.example_image)
img_size = (img.shape[1], img.shape[0]);
distortion = np.zeros((1,5))
pattern_kp_mat = np.array([x.pt for x in pattern_keys], np.float32)
frame_kp_mats = [np.array([x.pt for x in keys], np.float32) for keys in frames_keys]
fixed_kp_mats = [x.copy() for x in frame_kp_mats]
frames_matches = [feat.match_features(pattern_descs, descs, args.lowe_threshold) for descs in frames_descs]
for i in range(0, args.num_iterations):
print "Iteration %d" % i
homo_matches = []
homo_keys = []
for [kp_mat, fixed_kp_mat, matches] in zip(frame_kp_mats, fixed_kp_mats, frames_matches):
if len(matches) < args.min_matches:
homo_matches.append([])
homo_keys.append([])
continue
filtered_matches = matches;
if args.homography_threshold > 0:
[filtered_matches, H] = feat.filter_matches_by_homography(
pattern_kp_mat,
fixed_kp_mat,
filtered_matches,
args.homography_threshold)
det_H = np.linalg.det(H)
if args.H_det_threshold > 0 and det_H < args.H_det_threshold:
filtered_matches = []
print "%d matches after homography filter" % len(filtered_matches)
if args.f_threshold > 0 and len(filtered_matches) > 4:
filtered_matches = feat.filter_matches_by_fundamental_matrix(
pattern_kp_mat,
fixed_kp_mat,
filtered_matches,
args.f_threshold)
print "%d matches after fundamental matrix filter" % len(filtered_matches)
homo_matches.append(filtered_matches)
homo_keys.append(kp_mat)
if len(homo_matches) == 0:
print "Error: No frames with sufficient matches found"
exit(1)
# gather calibration data
tmp = [(x,y) for (x,y) in zip(homo_matches, homo_keys) if len(x) >= args.min_matches]
print "using %d frames for calibration after pruning bad frames" % len(tmp)
tmp = [calib.keypoint_matches_to_calibration_data(m, pattern_kp_mat, k) \
for (m,k) in tmp]
[obj_pts, img_pts] = zip(*tmp)
# perform calibration
[error, K, distortion, rvecs, tvecs] = cv2.calibrateCamera(obj_pts, img_pts, img_size)
if i < args.num_iterations-1:
# undistort image points, which will improve
# homography filtering next iteration
fixed_kp_mats = []
for kp_mat in frame_kp_mats:
tmp = np.reshape(kp_mat, (-1, 1, 2))
if kp_mat.shape[0] == 0:
tmp = kp_mat
else:
tmp = cv2.undistortPoints(tmp, K, distortion, P=K)
fixed_kp_mats.append(np.reshape(tmp, (-1, 2)))
out_matchset = [x if len(x) >= args.min_matches else [] for x in homo_matches]
assert(len(out_match_fnames) == len(out_matchset))
map(lambda (fname, m): feat.write_matches(fname, m), zip(out_match_fnames, out_matchset))
cam = calib.Intrinsic_camera()
cam.K = K
cam.distortion = distortion
cam.image_size = img_size
calib.write_intrinsic_camera(args.out_camera_fname, cam)
def main():
parser = argparse.ArgumentParser(
description="Match keypoints and calibrate camera")
parser.add_argument('pattern_key_fname',
type=str,
help='reference pattern keypoint file')
parser.add_argument('num_frames', type=int, help='Number of scene frames')
parser.add_argument(
'scene_key_fmt',
type=str,
help=
'Printf-formatted string representing keypoint files from N scene frames'
)
parser.add_argument('example_image',
type=str,
help='Example image (used to get dimensions)')
parser.add_argument('out_matches_fmt',
type=str,
help='printf string for output matches filenames')
parser.add_argument('out_camera_fname',
type=str,
help='output camera intrinsic calibration filename')
parser.add_argument(
'--num_iterations',
nargs='?',
type=int,
default=0.6,
help=
'number of iterations of match & homography re-estimation (2-3 is usually good)'
)
parser.add_argument(
'--lowe_threshold',
nargs='?',
type=float,
default=0.6,
help='Use David Lowe\'s ratio criterion for pruning bad matches')
parser.add_argument(
'--homography_threshold',
nargs='?',
type=float,
default=0,
help=
'fit a homography to matches and prune by this reprojection error threshold (zero ignores)'
)
parser.add_argument(
'--H_det_threshold',
nargs='?',
type=float,
default=0,
help='ignore frame if fitted homography has too small of a determinant.'
)
parser.add_argument(
'--f_threshold',
nargs='?',
type=float,
default=2,
help=
'fit a fundamental matrix to matches and prune by this error threshold (zero ignores)'
)
parser.add_argument(
'--min_matches',
nargs='?',
type=int,
default=25,
help=
'Minimum number of matches in a frame to be accepted into calibration set'
)
args = parser.parse_args()
[pattern_keys, pattern_descs] = feat.read_features(args.pattern_key_fname)
if args.num_frames == 1:
frame_fnames = [args.scene_key_fmt]
out_match_fnames = [args.out_matches_fmt]
else:
I = range(1, args.num_frames + 1)
frame_fnames = [args.scene_key_fmt % x for x in I]
out_match_fnames = [args.out_matches_fmt % x for x in I]
tmp = [(a, b) for (a, b) in zip(frame_fnames, out_match_fnames)
if os.path.isfile(a)]
[frame_fnames, out_match_fnames] = zip(*tmp)
if len(frame_fnames) == 0:
print "No files found matching %s" % args.scene_key_fmt
exit(1)
print "reading keypoint from %d frames" % len(frame_fnames)
frames_features = [feat.read_features(x) for x in frame_fnames]
[frames_keys, frames_descs] = zip(*frames_features)
img = cv2.imread(args.example_image)
img_size = (img.shape[1], img.shape[0])
distortion = np.zeros((1, 5))
pattern_kp_mat = np.array([x.pt for x in pattern_keys], np.float32)
frame_kp_mats = [
np.array([x.pt for x in keys], np.float32) for keys in frames_keys
]
fixed_kp_mats = [x.copy() for x in frame_kp_mats]
frames_matches = [
feat.match_features(pattern_descs, descs, args.lowe_threshold)
for descs in frames_descs
]
for i in range(0, args.num_iterations):
print "Iteration %d" % i
homo_matches = []
homo_keys = []
for [kp_mat, fixed_kp_mat,
matches] in zip(frame_kp_mats, fixed_kp_mats, frames_matches):
if len(matches) < args.min_matches:
homo_matches.append([])
homo_keys.append([])
continue
filtered_matches = matches
if args.homography_threshold > 0:
[filtered_matches, H] = feat.filter_matches_by_homography(
pattern_kp_mat, fixed_kp_mat, filtered_matches,
args.homography_threshold)
det_H = np.linalg.det(H)
if args.H_det_threshold > 0 and det_H < args.H_det_threshold:
filtered_matches = []
print "%d matches after homography filter" % len(
filtered_matches)
if args.f_threshold > 0 and len(filtered_matches) > 4:
filtered_matches = feat.filter_matches_by_fundamental_matrix(
pattern_kp_mat, fixed_kp_mat, filtered_matches,
args.f_threshold)
print "%d matches after fundamental matrix filter" % len(
filtered_matches)
homo_matches.append(filtered_matches)
homo_keys.append(kp_mat)
if len(homo_matches) == 0:
print "Error: No frames with sufficient matches found"
exit(1)
# gather calibration data
tmp = [(x, y) for (x, y) in zip(homo_matches, homo_keys)
if len(x) >= args.min_matches]
print "using %d frames for calibration after pruning bad frames" % len(
tmp)
tmp = [calib.keypoint_matches_to_calibration_data(m, pattern_kp_mat, k) \
for (m,k) in tmp]
[obj_pts, img_pts] = zip(*tmp)
# perform calibration
[error, K, distortion, rvecs,
tvecs] = cv2.calibrateCamera(obj_pts, img_pts, img_size)
if i < args.num_iterations - 1:
# undistort image points, which will improve
# homography filtering next iteration
fixed_kp_mats = []
for kp_mat in frame_kp_mats:
tmp = np.reshape(kp_mat, (-1, 1, 2))
if kp_mat.shape[0] == 0:
tmp = kp_mat
else:
tmp = cv2.undistortPoints(tmp, K, distortion, P=K)
fixed_kp_mats.append(np.reshape(tmp, (-1, 2)))
out_matchset = [
x if len(x) >= args.min_matches else [] for x in homo_matches
]
assert (len(out_match_fnames) == len(out_matchset))
map(lambda (fname, m): feat.write_matches(fname, m),
zip(out_match_fnames, out_matchset))
cam = calib.Intrinsic_camera()
cam.K = K
cam.distortion = distortion
cam.image_size = img_size
calib.write_intrinsic_camera(args.out_camera_fname, cam)
def main():
parser = argparse.ArgumentParser(
description="Calibrate using keypoint matches")
parser.add_argument('pattern_key_fname',
type=str,
help='reference pattern keypoint file')
parser.add_argument('num_frames', type=int, help='Number of scene frames')
parser.add_argument(
'scene_key_fmt',
type=str,
help=
'Printf-formatted string representing keypoint files from N scene frames'
)
parser.add_argument(
'match_fmt',
type=str,
help='Printf-formatted string representing keypoint match files')
parser.add_argument('example_image',
type=str,
help='Example image to get dimensions from')
parser.add_argument('out_fname',
type=str,
help='filename for intrinsic calibrated camera')
parser.add_argument('--min_matches',
default=20,
type=str,
help='omit frames with fewer than N matches')
args = parser.parse_args()
pattern_keys = feat.read_features(args.pattern_key_fname)[0]
fnames = [(args.scene_key_fmt % x, args.match_fmt % x)
for x in range(1, args.num_frames + 1)]
fnames = [x for x in fnames if os.path.isfile(x[0])]
if len(fnames) == 0:
print "No matching keypoint files"
exit(1)
missing = next((x[1] for x in fnames if not os.path.isfile(x[1])), None)
if missing is not None:
print "File not found: %s" % missing
exit(1)
[frame_fnames, match_fnames] = zip(*fnames)
print "reading keypoint from %d frames" % len(frame_fnames)
frames_keys = [feat.read_features(x)[0] for x in frame_fnames]
print "reading matches from %d frames" % len(match_fnames)
frames_matches = [feat.read_matches(x) for x in match_fnames]
img = cv2.imread(args.example_image)
img_size = (img.shape[1], img.shape[0])
[obj_pts, image_pts] = to_calibration_data(frames_matches, pattern_keys,
frames_keys, args.min_matches)
cam = calibrate_intrinsic(obj_pts, image_pts, img_size)
calib.write_intrinsic_camera(args.out_fname, cam)