def match_targets_to_proposals(self, proposal, target):
masks_field = "masks"
rrects_field = "rrects"
# anchor_tensor = anchor.get_field(rrects_field)
proposal_tensor = get_boxlist_rotated_rect_tensor(
proposal, masks_field, rrects_field)
target_tensor = get_boxlist_rotated_rect_tensor(
target, masks_field, rrects_field)
if not target.has_field(
rrects_field): # add rrects to gt if not already added
target.add_field(rrects_field, target_tensor)
match_quality_matrix = rotate_iou(target_tensor, proposal_tensor)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# Mask RCNN needs "labels" and "masks "fields for creating the targets
target = target.copy_with_fields(["labels", "masks"])
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
matched_targets = target[matched_idxs.clamp(min=0)]
matched_targets.add_field("matched_idxs", matched_idxs)
return matched_targets
def add_gt_proposals(self, proposals, targets):
"""
Arguments:
proposals: list[BoxList]
targets: list[BoxList]
"""
# Get the device we're operating on
device = proposals[0].bbox.device
masks_field = "masks"
rrects_field = "rrects"
gt_boxes = [target.copy_with_fields([]) for target in targets]
# later cat of bbox requires all fields to be present for all bbox
# so we need to add a dummy for objectness that's missing
for ix, gt_box in enumerate(gt_boxes):
target = targets[ix]
gt_rrect = get_boxlist_rotated_rect_tensor(target, masks_field,
rrects_field)
aug_rrect = gt_rrect.clone()
N = len(gt_rrect)
multiplier = int(4)
for m in range(multiplier - 1):
aug_rrect[:, :2] += torch.randint(-7,
7, (N, 2),
dtype=torch.float32,
device=device)
aug_rrect[:, 2:4] *= torch.randint(
94, 106, (N, 2), dtype=torch.float32, device=device) / 100
aug_rrect[:, -1] += torch.randint(-5,
5, (N, ),
dtype=torch.float32,
device=device)
gt_rrect = torch.cat((gt_rrect, aug_rrect))
# convert anchor rects to bboxes
bboxes = convert_rects_to_bboxes(gt_rrect, torch)
boxlist = BoxList(bboxes, gt_box.size, mode="xyxy")
boxlist.add_field("rrects", gt_rrect)
boxlist.add_field("objectness",
torch.ones(len(gt_rrect), device=device))
gt_boxes[ix] = boxlist
proposals = [
cat_boxlist((proposal, gt_box))
for proposal, gt_box in zip(proposals, gt_boxes)
]
return proposals
def match_targets_to_anchors(self, anchor, target, copied_fields=[]):
"""
:param anchor: BoxList
:param target: BoxList
:param copied_fields: list
:return:
"""
masks_field = "masks"
rrects_field = "rrects"
# anchor_tensor = anchor.get_field(rrects_field)
anchor_tensor = get_boxlist_rotated_rect_tensor(
anchor, masks_field, rrects_field)
target_tensor = get_boxlist_rotated_rect_tensor(
target, masks_field, rrects_field)
if not target.has_field(
rrects_field): # add rrects to gt if not already added
target.add_field(rrects_field, target_tensor)
match_quality_matrix = rotate_iou(target_tensor, anchor_tensor)
matched_idxs = self.proposal_matcher(match_quality_matrix)
# RPN doesn't need any fields from target
# for creating the labels, so clear them all
target = target.copy_with_fields(copied_fields + [rrects_field])
# get the targets corresponding GT for each anchor
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
matched_idxs_clamped = matched_idxs.clamp(min=0)
matched_targets = target[matched_idxs_clamped]
# matched_ious = match_quality_matrix[matched_idxs_clamped,
# torch.arange(len(anchor_tensor), device=anchor_tensor.device)]
matched_targets.add_field("matched_idxs", matched_idxs)
# matched_targets.add_field("matched_ious", matched_ious)
return matched_targets
#
# imtt = im1.unsqueeze(0)
# grid = F.affine_grid(theta.unsqueeze(0), imtt.size())
# im1_warped = F.grid_sample(imtt, grid)
# im1_warped = im1_warped.squeeze().numpy().astype(np.uint8)
# im1_warped = np.transpose(im1_warped, [1,2,0]) # CHW to HWC
#
# cv2.imshow("im_warped", im1_warped)
# cv2.waitKey(0)
m_field = t1.get_field("masks")
labels = t1.get_field('labels')
bboxes = t1.bbox
if is_rotated:
rrects = get_boxlist_rotated_rect_tensor(t1, "masks", "rrects")
for ix, label in enumerate(labels):
seg_mask = m_field[ix]
sm = seg_mask.get_mask_tensor().numpy()
p = m_field.instances.polygons[ix]
m = p.convert_to_binarymask()
# visualize_mask(m.numpy())
m = m.numpy() # uint8 format, 0-1
m *= 255
m = cv2.cvtColor(m, cv2.COLOR_GRAY2BGR)
if is_rotated:
rr = rrects[ix]
proposal = rr.numpy().copy()
# proposal[:2] += 15
# # top_iou_ids = torch.sort(pos_matched_gt_ious[nz], descending=True)[1][:MAX_GT_NUM]
# # inds = nz[top_iou_ids]
# label_weights[inds] = total_pos / MAX_GT_NUM
# = total_pos / label_cnts.to(dtype=torch.float32)
for ix, cnt in enumerate(anchors_cnt):
gt = targets[ix]
gt_bbox = np.round(gt.bbox.cpu().numpy())
# gt_mask_instance = gt.get_field("masks")
# gt_polygons = [p.polygons for p in gt_mask_instance.instances.polygons]
# for gx,gtp in enumerate(gt_polygons):
# gtp = [p.view(-1, 2).cpu().numpy() for p in gtp]
# gt_polygons[gx] = gtp
gt_rrects = get_boxlist_rotated_rect_tensor(gt,
"masks").cpu().numpy()
img_t = img_tensors[ix]
inds = sampled_pos_inds[cumu_cnt < sampled_pos_inds]
inds = inds[inds < (cumu_cnt + cnt)]
pos_anchors = anchors[ix][inds - cumu_cnt]
reg_targets = regression_targets[inds]
reg_target_gt_inds = matched_gt_ids[inds]
# reg_target_gt_ious = matched_gt_ious[inds]
cumu_cnt += cnt
anchor_rrects = pos_anchors.get_field("rrects")
rr = anchor_rrects.cpu().numpy()
# print(rr)