def simple_test_ope(self, img, frame_id, gt_bboxes):
"""Test using OPE test mode.
Args:
img (Tensor): of shape (1, C, H, W) encoding input image.
frame_id (int): the id of current frame in the video.
gt_bboxes (list[Tensor]): list of ground truth bboxes for each
image with shape (1, 4) in [tl_x, tl_y, br_x, br_y] format or
shape (1, 8) in [x1, y1, x2, y2, x3, y3, x4, y4].
Returns:
bbox_pred (Tensor): in [tl_x, tl_y, br_x, br_y] format.
best_score (Tensor): the tracking bbox confidence in range [0,1],
and the score of initial frame is -1.
"""
if frame_id == 0:
gt_bboxes = gt_bboxes[0][0]
self.memo = Dict()
self.memo.bbox = quad2bbox(gt_bboxes)
self.memo.z_feat, self.memo.avg_channel = self.init(
img, self.memo.bbox)
best_score = -1.
else:
best_score, self.memo.bbox = self.track(img, self.memo.bbox,
self.memo.z_feat,
self.memo.avg_channel)
bbox_pred = bbox_cxcywh_to_xyxy(self.memo.bbox)
return bbox_pred, best_score
def test_quad2bbox():
quad = torch.zeros((5, 8), dtype=torch.float)
low_coord_index = torch.tensor([0, 1, 3, 6], dtype=torch.long)
high_coord_index = torch.tensor([2, 4, 5, 7], dtype=torch.long)
quad[:, low_coord_index] = torch.randint(1, 10, (5, 4), dtype=torch.float)
quad[:, high_coord_index] = torch.randint(10,
20, (5, 4),
dtype=torch.float)
bbox = quad2bbox(quad)
assert (bbox > 0).all()
def simple_test_vot(self, img, frame_id, gt_bboxes, img_metas=None):
"""Test using VOT test mode.
Args:
img (Tensor): of shape (1, C, H, W) encoding input image.
frame_id (int): the id of current frame in the video.
gt_bboxes (list[Tensor]): list of ground truth bboxes for each
image with shape (1, 4) in [tl_x, tl_y, br_x, br_y] format or
shape (1, 8) in [x1, y1, x2, y2, x3, y3, x4, y4].
img_metas (list[dict]): list of image information dict where each
dict has: 'img_shape', 'scale_factor', 'flip', and may also
contain 'filename', 'ori_shape', 'pad_shape', and
'img_norm_cfg'. For details on the values of these keys see
`mmtrack/datasets/pipelines/formatting.py:VideoCollect`.
Returns:
bbox_pred (Tensor): in [tl_x, tl_y, br_x, br_y] format.
best_score (Tensor): the tracking bbox confidence in range [0,1],
and the score of initial frame is -1.
"""
if frame_id == 0:
self.init_frame_id = 0
if self.init_frame_id == frame_id:
# initialization
gt_bboxes = gt_bboxes[0][0]
self.memo = Dict()
self.memo.bbox = quad2bbox(gt_bboxes)
self.memo.z_feat, self.memo.avg_channel = self.init(
img, self.memo.bbox)
# 1 denotes the initialization state
bbox_pred = img.new_tensor([1.])
best_score = -1.
elif self.init_frame_id > frame_id:
# 0 denotes unknown state, namely the skipping frame after failure
bbox_pred = img.new_tensor([0.])
best_score = -1.
else:
# normal tracking state
best_score, self.memo.bbox = self.track(img, self.memo.bbox,
self.memo.z_feat,
self.memo.avg_channel)
# convert bbox to region
track_bbox = bbox_cxcywh_to_x1y1wh(self.memo.bbox).cpu().numpy()
track_region = bbox2region(track_bbox)
gt_bbox = gt_bboxes[0][0]
if len(gt_bbox) == 4:
gt_bbox = bbox_xyxy_to_x1y1wh(gt_bbox)
gt_region = bbox2region(gt_bbox.cpu().numpy())
if img_metas is not None and 'img_shape' in img_metas[0]:
image_shape = img_metas[0]['img_shape']
image_wh = (image_shape[1], image_shape[0])
else:
image_wh = None
Warning('image shape are need when calculating bbox overlap')
overlap = calculate_region_overlap(track_region,
gt_region,
bounds=image_wh)
if overlap <= 0:
# tracking failure
self.init_frame_id = frame_id + 5
# 2 denotes the failure state
bbox_pred = img.new_tensor([2.])
else:
bbox_pred = bbox_cxcywh_to_xyxy(self.memo.bbox)
return bbox_pred, best_score