def process_peter(calibration_path,
board,
cam_regex,
fisheye=False,
video_ext='avi'):
videos = glob(os.path.join(calibration_path,'*.' +video_ext))
videos = sorted(videos)
cam_videos = defaultdict(list)
cam_names = set()
for vid in videos:
name = tools.camname_from_regex(cam_regex, vid)
cam_videos[name].append(vid)
cam_names.add(name)
cam_names = sorted(cam_names)
cgroup = CameraGroup.from_names(cam_names, fisheye)
video_list = [sorted(cam_videos[cname]) for cname in cam_names]
if len(videos) == 0:
print('no videos or calibration file found, continuing...')
return
rows_fname = os.path.join(calibration_path, 'detections.pickle')
if os.path.exists(rows_fname):
with open(rows_fname, 'rb') as f:
all_rows = pickle.load(f)
else:
all_rows = cgroup.get_rows_videos(video_list, board)
with open(rows_fname, 'wb') as f:
pickle.dump(all_rows, f)
cgroup.set_camera_sizes_videos(video_list)
cgroup.calibrate_rows(all_rows, board,
init_extrinsics=True,
init_intrinsics=True,
max_nfev=100, n_iters=2,
n_samp_iter=100, n_samp_full=300,
verbose=True)
error = cgroup.calibrate_rows(all_rows, board,
init_intrinsics=False, init_extrinsics=False,
max_nfev=100, n_iters=10,
n_samp_iter=100, n_samp_full=1000,
verbose=True)
cgroup.metadata['adjusted'] = False
if error is not None:
cgroup.metadata['error'] = float(error)
outname = os.path.join(calibration_path, 'calibration.toml')
cgroup.dump(outname)
print(outname)
def process_session(config, session_path):
pipeline_calibration_videos = config['pipeline']['calibration_videos']
pipeline_calibration_results = config['pipeline']['calibration_results']
video_ext = config['video_extension']
print(session_path)
calibration_path = find_calibration_folder(config, session_path)
if calibration_path is None:
return
videos = glob(
os.path.join(calibration_path, pipeline_calibration_videos,
'*.' + video_ext))
videos = sorted(videos)
cam_videos = defaultdict(list)
cam_names = set()
for vid in videos:
name = get_cam_name(config, vid)
cam_videos[name].append(vid)
cam_names.add(name)
cam_names = sorted(cam_names)
video_list = [sorted(cam_videos[cname]) for cname in cam_names]
outname_base = 'calibration.toml'
outdir = os.path.join(calibration_path, pipeline_calibration_results)
outname = os.path.join(outdir, outname_base)
print(outname)
skip_calib = False
init_stuff = True
error = None
if os.path.exists(outname):
cgroup = CameraGroup.load(outname)
if (not config['calibration']['animal_calibration']) or \
('adjusted' in cgroup.metadata and cgroup.metadata['adjusted']):
return
else:
skip_calib = True
if 'error' in cgroup.metadata:
error = cgroup.metadata['error']
else:
error = None
init_stuff = False
elif config['calibration']['calibration_init'] is not None:
calib_path = os.path.join(config['path'],
config['calibration']['calibration_init'])
print('loading calibration from: {}'.format(calib_path))
cgroup = CameraGroup.load(calib_path)
init_stuff = False
skip_calib = len(videos) == 0
else:
if len(videos) == 0:
print('no videos or calibration file found, continuing...')
return
cgroup = CameraGroup.from_names(cam_names,
config['calibration']['fisheye'])
board = get_calibration_board(config)
if not skip_calib:
rows_fname = os.path.join(outdir, 'detections.pickle')
if os.path.exists(rows_fname):
with open(rows_fname, 'rb') as f:
all_rows = pickle.load(f)
else:
all_rows = cgroup.get_rows_videos(video_list, board)
with open(rows_fname, 'wb') as f:
pickle.dump(all_rows, f)
cgroup.set_camera_sizes_videos(video_list)
cgroup.calibrate_rows(all_rows,
board,
init_extrinsics=init_stuff,
init_intrinsics=init_stuff,
max_nfev=100,
n_iters=2,
n_samp_iter=100,
n_samp_full=300,
verbose=True)
error = cgroup.calibrate_rows(all_rows,
board,
init_intrinsics=False,
init_extrinsics=False,
max_nfev=100,
n_iters=10,
n_samp_iter=100,
n_samp_full=1000,
verbose=True)
cgroup.metadata['adjusted'] = False
if error is not None:
cgroup.metadata['error'] = float(error)
cgroup.dump(outname)
if config['calibration']['animal_calibration']:
all_points, all_scores, all_cam_names = load_2d_data(
config, calibration_path)
imgp = process_points_for_calibration(all_points, all_scores)
# error = cgroup.bundle_adjust(imgp, threshold=10, ftol=1e-4, loss='huber')
cgroup = cgroup.subset_cameras_names(all_cam_names)
error = cgroup.bundle_adjust_iter(imgp,
ftol=1e-4,
n_iters=10,
n_samp_iter=300,
n_samp_full=1000,
max_nfev=500,
verbose=True)
cgroup.metadata['adjusted'] = True
cgroup.metadata['error'] = float(error)
cgroup.dump(outname)