arch_path = '{:s}/{:s}.zip'.format(arch_root_dir,
arch_name)
print 'Reading data for tracker {:d} from zip archive: {:s}'.format(
tracker_id, arch_path)
if arch_fid:
arch_fid.close()
arch_fid = zipfile.ZipFile(arch_path, 'r')
tracking_data_fname = '{:s}/{:s}/{:s}/{:s}_{:s}_{:s}_{:d}.txt'.format(
in_arch_path, actor, seq_name, tracker['sm'],
tracker['am'], tracker['ssm'], tracker['iiw'])
else:
tracking_data_fname = '{:s}/tracking_data/{:s}/{:s}/{:s}_{:s}_{:s}_{:d}.txt'.format(
tracking_data_root_dir, actor, seq_name, tracker['sm'],
tracker['am'], tracker['ssm'], tracker['iiw'])
print 'Reading tracking data from:: ', tracking_data_fname
tracking_data = readTrackingData(tracking_data_fname, arch_fid)
tracking_data_list.append(tracking_data)
no_of_frames = tracking_data_list[0].shape[0]
print 'no_of_frames: ', no_of_frames
tracking_data_win_name = seq_name
cv2.namedWindow(tracking_data_win_name)
for frame_id in xrange(no_of_frames):
ret, curr_img = cap.read()
if not ret:
print 'End of sequence reached unexpectedly'
break
if convert_to_gs:
if len(sys.argv) > arg_id:
seq_id = int(sys.argv[arg_id])
arg_id += 1
if actor is None:
actor = actors[actor_id]
if seq_name is None:
sequences = sequences[actor]
if seq_id >= len(sequences):
print 'Invalid dataset_id: ', seq_id
sys.exit()
seq_name = sequences[seq_id]
src_fname = db_root_dir + '/' + actor + '/' + seq_name + '/' + img_name_fmt
ground_truth_fname = db_root_dir + '/' + actor + '/' + seq_name + '.txt'
ground_truth = readTrackingData(ground_truth_fname)
print 'actor: ', actor
print 'seq_id:', seq_id, 'seq_name:', seq_name
no_of_frames = ground_truth.shape[0]
print 'no_of_frames: ', no_of_frames
split_frames.append(no_of_frames)
n_split_seq = len(split_frames)
print 'Splitting sequence: {:s} into {:d} parts ending at the following frames:'.format(
seq_name, n_split_seq)
print split_frames
start_frame_id = 0
corrected_corners_fname = gt_corners_fname
else:
corrected_corners_fname = db_root_dir + '/' + actor + '/' + seq_name + '_corr.txt'
if not os.path.isfile(gt_corners_fname) or manual_init:
print 'ground truth file not found. Using manual object initialization instead'.format(gt_corners_fname)
sel_pts = getTrackingObject2(init_img)
init_corners = np.asarray(sel_pts).astype(np.float64).T
from_last_frame = True
print 'init_corners:', init_corners
pause_seq = True
gt_frames = no_of_frames
ground_truth = np.empty([1, 8])
ground_truth[0] = init_corners.flatten(order='F')
manual_init = True
else:
ground_truth = readTrackingData(gt_corners_fname)
gt_frames = ground_truth.shape[0]
print 'no_of_frames in ground truth: ', gt_frames
if gt_frames != no_of_frames:
print 'Mismatch between the no. of frames in the source directory and the ground truth'
init_corners = np.asarray([ground_truth[init_frame_id, 0:2].tolist(),
ground_truth[init_frame_id, 2:4].tolist(),
ground_truth[init_frame_id, 4:6].tolist(),
ground_truth[init_frame_id, 6:8].tolist()]).T
if overwrite_gt and os.path.isfile(gt_corners_fname):
backup_gt_fname = gt_corners_fname.replace('.txt', '.back_ptf')
print 'Backing up existing GT to {:s}'.format(backup_gt_fname)
shutil.move(gt_corners_fname, backup_gt_fname)
templ_corners = np.asarray([ground_truth[0, 0:2].tolist(),
ground_truth[0, 2:4].tolist(),
ground_truth[0, 4:6].tolist(),
print 'fname: ', fname
init_img = cv2.imread(fname)
if len(init_img.shape) == 2:
init_img = cv2.cvtColor(init_img, cv2.COLOR_GRAY2RGB)
if use_opt_gt:
gt_corners_fname = '{:s}/{:s}/OptGT/{:s}_{:s}.txt'.format(db_root_dir, actor, seq_name, opt_gt_ssm)
sel_corners_fname = '{:s}/{:s}/OptGT/{:s}_{:s}_{:s}.txt'.format(
db_root_dir, actor, seq_name, opt_gt_ssm, out_suffix)
else:
gt_corners_fname = '{:s}/{:s}/{:s}.txt'.format(db_root_dir, actor, seq_name)
sel_corners_fname = '{:s}/{:s}/{:s}_{:s}.txt'.format(db_root_dir, actor, seq_name, out_suffix)
print 'Reading ground truth from:: ', gt_corners_fname
print 'Writing extended ground truth to:: ', sel_corners_fname
ground_truth = readTrackingData(gt_corners_fname)
if ground_truth is None:
raise StandardError('Ground truth could not be read')
no_of_frames = ground_truth.shape[0]
file_list = [each for each in os.listdir(src_dir) if each.endswith('.jpg')]
n_files = len(file_list)
if n_files != no_of_frames:
print 'No. of frames in the ground truth: {:d} does not match the no. of images in the sequence: {:d}'.format(
no_of_frames, n_files)
print 'no_of_frames: ', no_of_frames
gt_corners_window_name = '{:d} : {:s}'.format(seq_id, seq_name)