def main():
args = parse_args()
cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
dataset = build_dataset(cfg.data.train)
progress_bar = mmcv.ProgressBar(len(dataset))
for item in dataset:
filename = os.path.join(args.output_dir,
Path(item['filename']).name
) if args.output_dir is not None else None
gt_masks = item.get('gt_masks', None)
if gt_masks is not None:
gt_masks = mask2ndarray(gt_masks)
imshow_det_bboxes(item['img'],
item['gt_bboxes'],
item['gt_labels'],
gt_masks,
class_names=dataset.CLASSES,
show=not args.not_show,
wait_time=args.show_interval,
out_file=filename,
bbox_color=(255, 102, 61),
text_color=(255, 102, 61))
progress_bar.update()
def main():
args = parse_args()
cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
if 'gt_semantic_seg' in cfg.train_pipeline[-1]['keys']:
cfg.data.train.pipeline = [
p for p in cfg.data.train.pipeline if p['type'] != 'SegRescale'
]
dataset = build_dataset(cfg.data.train)
progress_bar = mmcv.ProgressBar(len(dataset))
for item in dataset:
filename = os.path.join(args.output_dir,
Path(item['filename']).name
) if args.output_dir is not None else None
gt_bboxes = item['gt_bboxes']
gt_labels = item['gt_labels']
gt_masks = item.get('gt_masks', None)
if gt_masks is not None:
gt_masks = mask2ndarray(gt_masks)
gt_seg = item.get('gt_semantic_seg', None)
if gt_seg is not None:
pad_value = 255 # the padding value of gt_seg
sem_labels = np.unique(gt_seg)
all_labels = np.concatenate((gt_labels, sem_labels), axis=0)
all_labels, counts = np.unique(all_labels, return_counts=True)
stuff_labels = all_labels[np.logical_and(counts < 2,
all_labels != pad_value)]
stuff_masks = gt_seg[None] == stuff_labels[:, None, None]
gt_labels = np.concatenate((gt_labels, stuff_labels), axis=0)
gt_masks = np.concatenate((gt_masks, stuff_masks.astype(np.uint8)),
axis=0)
# If you need to show the bounding boxes,
# please comment the following line
gt_bboxes = None
imshow_det_bboxes(item['img'],
gt_bboxes,
gt_labels,
gt_masks,
class_names=dataset.CLASSES,
show=not args.not_show,
wait_time=args.show_interval,
out_file=filename,
bbox_color=dataset.PALETTE,
text_color=(200, 200, 200),
mask_color=dataset.PALETTE)
progress_bar.update()
def test_mask2ndarray():
raw_masks = np.ones((3, 28, 28))
bitmap_mask = BitmapMasks(raw_masks, 28, 28)
output_mask = mask2ndarray(bitmap_mask)
assert np.allclose(raw_masks, output_mask)
raw_masks = dummy_raw_polygon_masks((3, 28, 28))
polygon_masks = PolygonMasks(raw_masks, 28, 28)
output_mask = mask2ndarray(polygon_masks)
assert output_mask.shape == (3, 28, 28)
raw_masks = np.ones((3, 28, 28))
output_mask = mask2ndarray(raw_masks)
assert np.allclose(raw_masks, output_mask)
raw_masks = torch.ones((3, 28, 28))
output_mask = mask2ndarray(raw_masks)
assert np.allclose(raw_masks, output_mask)
# test unsupported type
raw_masks = []
with pytest.raises(TypeError):
output_mask = mask2ndarray(raw_masks)
def solov2_target_single(self, gt_bboxes_raw, gt_labels_raw, gt_masks_raw,
mask_feat_size):
device = gt_labels_raw[0].device
# ins
gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
(gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
ins_label_list = []
cate_label_list = []
ins_ind_label_list = []
grid_order_list = []
for (lower_bound, upper_bound), stride, num_grid \
in zip(self.scale_ranges, self.strides, self.seg_num_grids):
hit_indices = ((gt_areas >= lower_bound) &
(gt_areas <= upper_bound)).nonzero(
as_tuple=False).flatten()
num_ins = len(hit_indices)
ins_label = []
grid_order = []
cate_label = torch.zeros([num_grid, num_grid],
dtype=torch.int64,
device=device)
ins_ind_label = torch.zeros([num_grid**2],
dtype=torch.bool,
device=device)
if num_ins == 0:
ins_label = torch.zeros(
[0, mask_feat_size[0], mask_feat_size[1]],
dtype=torch.uint8,
device=device)
ins_label_list.append(ins_label)
cate_label_list.append(cate_label)
ins_ind_label_list.append(ins_ind_label)
grid_order_list.append([])
continue
gt_bboxes = gt_bboxes_raw[hit_indices]
gt_labels = gt_labels_raw[hit_indices]
gt_masks = gt_masks_raw[hit_indices.cpu().numpy(), ...]
half_ws = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * self.sigma
half_hs = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1]) * self.sigma
gt_masks = mask2ndarray(gt_masks)
# mass center
gt_masks_pt = torch.from_numpy(gt_masks).to(device=device)
center_ws, center_hs = center_of_mass(gt_masks_pt)
valid_mask_flags = gt_masks_pt.sum(dim=-1).sum(dim=-1) > 0
output_stride = 4
for seg_mask, gt_label, half_h, half_w, center_h, center_w, valid_mask_flag in zip(
gt_masks, gt_labels, half_hs, half_ws, center_hs,
center_ws, valid_mask_flags):
if not valid_mask_flag:
continue
upsampled_size = (mask_feat_size[0] * 4, mask_feat_size[1] * 4)
coord_w = int(
(center_w / upsampled_size[1]) // (1. / num_grid))
coord_h = int(
(center_h / upsampled_size[0]) // (1. / num_grid))
# left, top, right, down
top_box = max(
0,
int(((center_h - half_h) / upsampled_size[0]) //
(1. / num_grid)))
down_box = min(
num_grid - 1,
int(((center_h + half_h) / upsampled_size[0]) //
(1. / num_grid)))
left_box = max(
0,
int(((center_w - half_w) / upsampled_size[1]) //
(1. / num_grid)))
right_box = min(
num_grid - 1,
int(((center_w + half_w) / upsampled_size[1]) //
(1. / num_grid)))
top = max(top_box, coord_h - 1)
down = min(down_box, coord_h + 1)
left = max(coord_w - 1, left_box)
right = min(right_box, coord_w + 1)
cate_label[top:(down + 1), left:(right + 1)] = gt_label
seg_mask = mmcv.imrescale(seg_mask, scale=1. / output_stride)
seg_mask = torch.from_numpy(seg_mask).to(device=device)
for i in range(top, down + 1):
for j in range(left, right + 1):
label = int(i * num_grid + j)
cur_ins_label = torch.zeros(
[mask_feat_size[0], mask_feat_size[1]],
dtype=torch.uint8,
device=device)
cur_ins_label[:seg_mask.shape[0], :seg_mask.
shape[1]] = seg_mask
ins_label.append(cur_ins_label)
ins_ind_label[label] = True
grid_order.append(label)
if len(ins_label) == 0:
ins_label = torch.zeros(
[0, mask_feat_size[0], mask_feat_size[1]],
dtype=torch.uint8,
device=device)
else:
ins_label = torch.stack(ins_label, 0)
ins_label_list.append(ins_label)
cate_label_list.append(cate_label)
ins_ind_label_list.append(ins_ind_label)
grid_order_list.append(grid_order)
return ins_label_list, cate_label_list, ins_ind_label_list, grid_order_list
