def track(self, img_files, box, visualize=False, use_confidences=False):
frame_num = len(img_files)
boxes = np.zeros((frame_num, 4))
confs = np.zeros((frame_num))
boxes[0] = box
times = np.zeros(frame_num)
for f, img_file in enumerate(img_files):
image = Image.open(img_file)
if not image.mode == 'RGB':
image = image.convert('RGB')
image = np.asarray(image)[..., ::-1]
start_time = time.time()
if f == 0:
self.init(image, box)
else:
boxes[f, :] = self.track_img(image)['bbox']
confs[f] = self.track_img(image)['best_score']
times[f] = time.time() - start_time
if visualize:
show_frame(image, boxes[f, :])
return boxes, times, confs
def track(self,
img_files: List[str],
box: np.ndarray,
visualize: bool = False) -> Tuple[np.ndarray, np.ndarray]:
"""
Method `track` for GOT-10k toolkit trackers (MIT licence).
:param img_files: Image files.
:param box: Initial boxes.
:param visualize: Visualise tracking.
"""
from got10k.utils.viz import show_frame
from PIL import Image
frame_num = len(img_files)
boxes = np.zeros((frame_num, 4))
boxes[0] = box
times = np.zeros(frame_num)
for i_f, img_file in enumerate(img_files):
image = Image.open(img_file)
if not image.mode == "RGB":
image = image.convert("RGB")
start_time = time.time()
if i_f == 0:
self.init(image, box)
else:
boxes[i_f, :] = self.update(image)
times[i_f] = time.time() - start_time
if visualize:
show_frame(image, boxes[i_f, :])
return boxes, times
def example_loop_dataset():
# setup dataset
dataset = GOT10k(ROOT_DIR, subset='val')
# loop over the complete dataset
for s, (img_files, anno) in enumerate(dataset):
seq_name = dataset.seq_names[s]
print('Sequence:', seq_name)
for f, img_file in enumerate(img_files):
image = Image.open(img_file)
box = anno[f, :] # (left, top, width, height)
show_frame(image, box, colors='w')
def show(self, tracker_names, seq_names=None, play_speed=1):
if seq_names is None:
seq_names = self.dataset.seq_names
elif isinstance(seq_names, str):
seq_names = [seq_names]
assert isinstance(tracker_names, (list, tuple))
assert isinstance(seq_names, (list, tuple))
play_speed = int(round(play_speed))
assert play_speed > 0
self.dataset.return_meta = False
for s, seq_name in enumerate(seq_names):
print('[%d/%d] Showing results on %s...' %
(s + 1, len(seq_names), seq_name))
# load all tracking results
records = {}
for name in tracker_names:
record_file = os.path.join(self.result_dir, name, seq_name,
'%s_001.txt' % seq_name)
records[name] = np.loadtxt(record_file, delimiter=',')
# loop over the sequence and display results
img_files, anno = self.dataset[seq_name]
for f, img_file in enumerate(img_files):
if not f % play_speed == 0:
continue
image = Image.open(img_file)
boxes = [anno[f]
] + [records[name][f] for name in tracker_names]
show_frame(image,
boxes,
legends=['GroundTruth'] + tracker_names,
colors=[
'w', 'r', 'g', 'b', 'c', 'm', 'y', 'orange',
'purple', 'brown', 'pink'
])