def test_restore_result():
from mmtrack.core import restore_result
num_classes = 3
num_objects = [2, 0, 2]
result = [np.random.randn(num_objects[i], 5) for i in range(num_classes)]
bboxes, labels = restore_result(result, return_ids=False)
assert bboxes.shape == (4, 5)
assert (labels == np.array([0, 0, 2, 2])).all()
result = [np.random.randn(num_objects[i], 6) for i in range(num_classes)]
bboxes, labels, ids = restore_result(result, return_ids=True)
assert bboxes.shape == (4, 5)
assert (labels == np.array([0, 0, 2, 2])).all()
assert len(ids) == 4
def __call__(self, results):
detections = results['detections']
bboxes, labels = restore_result(detections)
results['public_bboxes'] = bboxes[:, :4]
if bboxes.shape[1] > 4:
results['public_scores'] = bboxes[:, -1]
results['public_labels'] = labels
results['bbox_fields'].append('public_bboxes')
return results
def format_track_results(self, results, infos, resfile):
"""Format tracking results."""
with open(resfile, 'wt') as f:
for res, info in zip(results, infos):
if 'mot_frame_id' in info:
frame = info['mot_frame_id']
else:
frame = info['frame_id'] + 1
bboxes, labels, ids = restore_result(res, return_ids=True)
for bbox, label, id in zip(bboxes, labels, ids):
x1, y1, x2, y2, conf = bbox
f.writelines(
f'{frame},{id},{x1:.3f},{y1:.3f},{(x2-x1):.3f},' +
f'{(y2-y1):.3f},{conf:.3f},-1,-1,-1\n')
def format_bbox_results(self, results, infos, resfile):
"""Format detection results."""
with open(resfile, 'wt') as f:
for res, info in zip(results, infos):
if 'mot_frame_id' in info:
frame = info['mot_frame_id']
else:
frame = info['frame_id'] + 1
bboxes, labels = restore_result(res)
for bbox, label in zip(bboxes, labels):
x1, y1, x2, y2, conf = bbox
f.writelines(
f'{frame},-1,{x1:.3f},{y1:.3f},{(x2-x1):.3f},' +
f'{(y2-y1):.3f},{conf:.3f}\n')
f.close()
def show_result(self,
img,
result,
thickness=1,
font_scale=0.5,
show=False,
out_file=None,
wait_time=0,
backend='cv2',
**kwargs):
"""Visualize tracking results.
Args:
img (str | ndarray): Filename of loaded image.
result (dict): Tracking result.
The value of key 'track_results' is ndarray with shape (n, 6)
in [id, tl_x, tl_y, br_x, br_y, score] format.
The value of key 'bbox_results' is ndarray with shape (n, 5)
in [tl_x, tl_y, br_x, br_y, score] format.
thickness (int, optional): Thickness of lines. Defaults to 1.
font_scale (float, optional): Font scales of texts. Defaults
to 0.5.
show (bool, optional): Whether show the visualizations on the
fly. Defaults to False.
out_file (str | None, optional): Output filename. Defaults to None.
backend (str, optional): Backend to draw the bounding boxes,
options are `cv2` and `plt`. Defaults to 'cv2'.
Returns:
ndarray: Visualized image.
"""
assert isinstance(result, dict)
track_result = result.get('track_results', None)
bboxes, labels, ids = restore_result(track_result, return_ids=True)
img = imshow_tracks(
img,
bboxes,
labels,
ids,
classes=self.CLASSES,
thickness=thickness,
font_scale=font_scale,
show=show,
out_file=out_file,
wait_time=wait_time,
backend=backend)
return img
def show_result(self,
img,
result,
thickness=1,
font_scale=0.5,
show=False,
out_file=None,
wait_time=0,
backend='cv2'):
"""Visualize tracking results.
Args:
img (str | ndarray): Filename of loaded image.
result (list[ndarray]): Tracking results.
thickness (int, optional): Thickness of lines. Defaults to 1.
font_scale (float, optional): Font scales of texts. Defaults
to 0.5.
show (bool, optional): Whether show the visualizations on the
fly. Defaults to False.
out_file (str | None, optional): Output filename. Defaults to None.
backend (str, optional): Backend to draw the bounding boxes,
options are `cv2` and `plt`. Defaults to 'cv2'.
Returns:
ndarray: Visualized image.
"""
bboxes, labels, ids = restore_result(result, return_ids=True)
img = imshow_tracks(img,
bboxes,
labels,
ids,
classes=self.CLASSES,
thickness=thickness,
font_scale=font_scale,
show=show,
out_file=out_file,
wait_time=wait_time,
backend=backend)
return img