def forward_for_single_feature_map(self, anchors, objectness, box_regression):
"""
Arguments:
anchors: list[BoxList]
objectness: tensor of size N, A, H, W
box_regression: tensor of size N, A * 5, H, W
"""
device = objectness.device
N, A, H, W = objectness.shape
# put in the same format as anchors
objectness = objectness.permute(0, 2, 3, 1).reshape(N, -1)
objectness = objectness.sigmoid()
box_regression = box_regression.view(N, -1, 5, H, W).permute(0, 3, 4, 1, 2)
box_regression = box_regression.reshape(N, -1, 5)
num_anchors = A * H * W
pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
batch_idx = torch.arange(N, device=device)[:, None]
box_regression = box_regression[batch_idx, topk_idx]
image_shapes = [box.size for box in anchors]
concat_anchors = torch.cat([a.bbox for a in anchors], dim=0)
concat_anchors = concat_anchors.reshape(N, -1, 5)[batch_idx, topk_idx]
# print('concat_anchors:', concat_anchors.size(), concat_anchors[:, 2:4])
proposals = self.box_coder.decode(
box_regression.view(-1, 5), concat_anchors.view(-1, 5)
)
proposals = proposals.view(N, -1, 5)
# print('outsider:', concat_anchors[:10, ], proposals.size(), proposals[:10, ], 'box_regression:', box_regression[:10, ])
#-------
result = []
for proposal, score, im_shape in zip(proposals, objectness, image_shapes):
boxlist = RBoxList(proposal, im_shape, mode="xywha")
# print('before nms:', boxlist.bbox.size(), boxlist.bbox[:, 2:4])
boxlist.add_field("objectness", score)
# boxlist = boxlist.clip_to_image(remove_empty=False)
boxlist = remove_small_boxes(boxlist, self.min_size)
boxlist = boxlist_nms(
boxlist,
self.nms_thresh,
max_proposals=self.post_nms_top_n,
score_field="objectness",
)
# print('rrpn after nms:', boxlist.bbox.size(), boxlist.bbox[:10, ])
result.append(boxlist)
return result
def shrink_proposals(boxes, cfg):
assert isinstance(boxes, (list, tuple))
assert isinstance(boxes[0], RBoxList)
new_boxes = []
for boxes_per_image in boxes:
proposals = boxes_per_image.bbox
im_info = boxes_per_image.size
proposals[:, 2:4] /= cfg.MODEL.RRPN.GT_BOX_MARGIN
new_boxes_per_image = RBoxList(proposals, im_info, mode="xywha")
new_boxes_per_image._copy_extra_fields(boxes_per_image)
new_boxes.append(new_boxes_per_image)
return new_boxes
def __getitem__(self, idx):
img_name = self.id_to_img_map[idx]
img = utils.pil_load_img(os.path.join(self.root, img_name))
anno = utils.read_anno(self.annotations, img_name)
# filter illegal
anno = [obj for obj in anno if not obj['illegibility']]
# bounding boxes
boxes = [utils.generate_rbox(obj["points"], np.array(img).shape[:2]) for obj in anno]
boxes = torch.as_tensor(boxes).reshape(-1, 5) # guard against no boxes
target = RBoxList(boxes, img.size, mode="xywha")
# classes
classes = [1] * len(anno)
classes = torch.tensor(classes)
target.add_field("labels", classes)
target.add_field("difficult", torch.tensor([0 for i in range(len(classes))]))
# masks
masks = [obj["points"].reshape((1, -1)).tolist() for obj in anno]
masks = SegmentationMask(masks, img.size)
target.add_field("masks", masks)
# target = target.clip_to_image(remove_empty=True)
if self.transforms is not None:
img, target = self.transforms(img, target)
assert target is not None, "{} target is None.".format(img_name)
return img, target, idx
def __getitem__(self, index):
# if _DEBUG:
# index = 0
# img_id = self.ids[index]
im_path = self.annobase[index]['image']# os.path.join(self.root, img_id + '.jpg')
img = Image.open(im_path).convert("RGB")
# im = cv2.imread(im_path)
anno = self.annobase[index]
target = RBoxList(torch.from_numpy(anno["boxes"]), (anno['width'], anno['height']), mode="xywha")
target.add_field("labels", torch.from_numpy(anno["gt_classes"]))
target.add_field("difficult", torch.Tensor([0 for i in range(len(anno["gt_classes"]))]))
target = target.clip_to_image(remove_empty=True)
# print('target:', target, im_path)
if self.transforms is not None:
# off = int(self.num_samples * np.random.rand())
# mix_index = (off + index) % self.num_samples
# img_mix = Image.open(self.annobase[mix_index]['image']).convert("RGB")
# img, target = self.mixup(img, img_mix, target)
img, target = self.transforms(img, target)
if _DEBUG:
if not target is None:
self.show_boxes(img, target)
return img, target, index
def prepare_boxlist(self, boxes, scores, image_shape):
"""
Returns BoxList from `boxes` and adds probability scores information
as an extra field
`boxes` has shape (#detections, 4 * #classes), where each row represents
a list of predicted bounding boxes for each of the object classes in the
dataset (including the background class). The detections in each row
originate from the same object proposal.
`scores` has shape (#detection, #classes), where each row represents a list
of object detection confidence scores for each of the object classes in the
dataset (including the background class). `scores[i, j]`` corresponds to the
box at `boxes[i, j * 5:(j + 1) * 5]`.
"""
boxes = boxes.reshape(-1, 5)
scores = scores.reshape(-1)
boxlist = RBoxList(boxes, image_shape, mode="xywha")
boxlist.add_field("scores", scores)
return boxlist
def forward(self, x, boxes):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
reference, one for ech image
Returns:
results (list[BoxList]): one BoxList for each image, containing
the extra field mask
"""
mask_prob = x.sigmoid()
# select masks coresponding to the predicted classes
num_masks = x.shape[0]
labels = [bbox.get_field("labels") for bbox in boxes]
labels = torch.cat(labels)
index = torch.arange(num_masks, device=labels.device)
mask_prob = mask_prob[index, labels][:, None]
boxes_per_image = [len(box) for box in boxes]
mask_prob = mask_prob.split(boxes_per_image, dim=0)
if self.masker:
mask_prob = self.masker(mask_prob, boxes)
results = []
for prob, box in zip(mask_prob, boxes):
bbox = RBoxList(box.bbox, box.size, mode="xywha")
for field in box.fields():
bbox.add_field(field, box.get_field(field))
bbox.add_field("mask", prob)
results.append(bbox)
return results
def filter_results(self, boxlist, num_classes, num_of_fwd_left):
"""Returns bounding-box detection results by thresholding on scores and
applying non-maximum suppression (NMS).
"""
# unwrap the boxlist to avoid additional overhead.
# if we had multi-class NMS, we could perform this directly on the boxlist
boxes = boxlist.bbox.reshape(-1, num_classes * 5)
scores = boxlist.get_field("scores").reshape(-1, num_classes)
device = scores.device
result = []
# Apply threshold on detection probabilities and apply NMS
# Skip j = 0, because it's the background class
inds_all = scores > self.score_thresh
for j in range(1, num_classes):
inds = inds_all[:, j].nonzero().squeeze(1)
scores_j = scores[inds, j]
# print("scores_j:", np.unique(scores_j.data.cpu().numpy())[-10:])
boxes_j = boxes[inds, j * 5 : (j + 1) * 5]
boxlist_for_class = RBoxList(boxes_j, boxlist.size, mode="xywha")
boxlist_for_class.add_field("scores", scores_j)
if num_of_fwd_left == 0:
boxlist_for_class.rescale(1. / self.shrink_margin)
boxlist_for_class = self.nms_fn(
boxlist_for_class, self.nms, score_field="scores"
)
num_labels = len(boxlist_for_class)
boxlist_for_class.add_field(
"labels", torch.full((num_labels,), j, dtype=torch.int64, device=device)
)
result.append(boxlist_for_class)
result = cat_boxlist(result)
number_of_detections = len(result)
# Limit to max_per_image detections **over all classes**
if number_of_detections > self.detections_per_img > 0:
cls_scores = result.get_field("scores")
image_thresh, _ = torch.kthvalue(
cls_scores.cpu(), number_of_detections - self.detections_per_img + 1
)
keep = cls_scores >= image_thresh.item()
keep = torch.nonzero(keep).squeeze(1)
result = result[keep]
return result
def forward(self, image_list, feature_maps):
grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps]
anchors_over_all_feature_maps = self.grid_anchors(grid_sizes)
anchors = []
for i, (image_height, image_width) in enumerate(image_list.image_sizes):
anchors_in_image = []
for anchors_per_feature_map in anchors_over_all_feature_maps:
boxlist = RBoxList(
anchors_per_feature_map, (image_width, image_height), mode="xywha"
)
self.add_visibility_to(boxlist)
anchors_in_image.append(boxlist)
anchors.append(anchors_in_image)
return anchors
def forward(self, boxes, pred_maskiou, labels):
num_masks = pred_maskiou.shape[0]
index = torch.arange(num_masks, device=labels.device)
maskious = pred_maskiou[index, labels]
maskious = [maskious]
results = []
for maskiou, box in zip(maskious, boxes):
bbox = RBoxList(box.bbox, box.size, mode="xywha")
for field in box.fields():
bbox.add_field(field, box.get_field(field))
bbox_scores = bbox.get_field("scores")
mask_scores = bbox_scores * maskiou
bbox.add_field("mask_scores", mask_scores)
results.append(bbox)
return results
def forward(self, x, boxes, transformer):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
reference, one for ech image
Returns:
results (list[BoxList]): one BoxList for each image, containing
the extra field mask
"""
# mask_prob = x.sigmoid()
# [B, T, C]
# word_probs = x.permute(1, 0, 2).softmax(2)
# select masks coresponding to the predicted classes
# num_words = word_probs.shape[0]
# labels = [bbox.get_field("labels") for bbox in boxes]
# labels = torch.cat(labels)
# index = torch.arange(num_words, device=word_probs.device)
# word_probs = word_probs[index][:, None]
boxes_per_image = [len(box) for box in boxes]
word_probs = x.split(boxes_per_image, dim=0)
results = []
for x_feature, box in zip(word_probs, boxes):
bbox = RBoxList(box.bbox, box.size, mode="xywha")
predict_prob = greedy_decode(transformer, x_feature, self.src_mask, self.max_step)
for field in box.fields():
bbox.add_field(field, box.get_field(field))
bbox.add_field("word_probs", predict_prob)
results.append(bbox)
return results
def forward(self, x, boxes):
"""
Arguments:
x (Tensor): the mask logits
boxes (list[BoxList]): bounding boxes that are used as
reference, one for ech image
Returns:
results (list[BoxList]): one BoxList for each image, containing
the extra field mask
"""
# mask_prob = x.sigmoid()
# [T, B, C] -> [B, T, C]
word_probs = x.permute(1, 0, 2).softmax(2)
# print('word_probs:', np.unique(word_probs.data.cpu().numpy()))
# select masks coresponding to the predicted classes
num_words = word_probs.shape[0]
labels = [bbox.get_field("labels") for bbox in boxes]
labels = torch.cat(labels)
index = torch.arange(num_words, device=labels.device)
word_probs = word_probs[index][:, None]
boxes_per_image = [len(box) for box in boxes]
word_probs = word_probs.split(boxes_per_image, dim=0)
results = []
for prob, box in zip(word_probs, boxes):
bbox = RBoxList(box.bbox, box.size, mode="xywha")
# print('prob:', prob)
for field in box.fields():
bbox.add_field(field, box.get_field(field))
bbox.add_field("word_probs", prob)
results.append(bbox)
return results
def __getitem__(self, index):
if _DEBUG:
index = 0
anno = self.annobase[index % self.database_num][
int(index / self.database_num) %
len(self.annobase[index % self.database_num])]
im_path = anno['image']
img = Image.open(im_path).convert("RGB")
# print('im_path:', im_path)
text, text_len = self.wk_converter.encode(anno['gt_words'])
text_label_split = []
off_cnt = 0
mx_len = np.max(text_len)
word_num = len(text_len)
for i in range(len(text_len)):
text_label_split.append(text[off_cnt:off_cnt + text_len[i]])
off_cnt += text_len[i]
padding_words = np.zeros((word_num, mx_len))
for i in range(word_num):
padding_words[i][:text_len[i]] = text_label_split[i]
if anno["boxes"].shape[0] > 0:
target = RBoxList(torch.from_numpy(anno["boxes"]),
(anno['width'], anno['height']),
mode="xywha")
target.add_field("labels", torch.from_numpy(anno["gt_classes"]))
target.add_field(
"difficult",
torch.tensor([0 for i in range(len(anno["gt_classes"]))]))
target.add_field("words", torch.from_numpy(padding_words))
target.add_field("word_length", torch.tensor(text_len))
target = target.clip_to_image(remove_empty=True)
else:
target = torch.from_numpy(padding_words)
if self.transforms is not None:
img, target = self.transforms(img, target)
if _DEBUG:
self.show_boxes(img, target)
return img, target, index
def rotate_boxes(self, target, angle):
# def rotate_gt_bbox(iminfo, gt_boxes, gt_classes, angle):
gt_boxes = target.bbox
if isinstance(target.bbox, torch.Tensor):
gt_boxes = target.bbox.data.cpu().numpy()
gt_labels = target.get_field("labels")
gt_masks = [
gt_polygon.polygons[0].numpy().reshape(-1, 2)
for gt_polygon in target.get_field("masks")
]
rotated_gt_boxes = np.empty((len(gt_boxes), 5), dtype=np.float32)
iminfo = target.size
im_height = iminfo[1]
im_width = iminfo[0]
origin_gt_boxes = gt_boxes
# anti-clockwise to clockwise arc
cos_cita = np.cos(np.pi / 180 * angle)
sin_cita = np.sin(np.pi / 180 * angle)
# clockwise matrix
rotation_matrix = np.array([[cos_cita, sin_cita],
[-sin_cita, cos_cita]])
# rotate rbox
pts_ctr = origin_gt_boxes[:, 0:2]
pts_ctr = pts_ctr - np.tile((im_width / 2, im_height / 2),
(gt_boxes.shape[0], 1))
pts_ctr = np.array(np.dot(pts_ctr, rotation_matrix), dtype=np.int16)
pts_ctr = np.squeeze(pts_ctr, axis=-1) + np.tile(
(im_width / 2, im_height / 2), (gt_boxes.shape[0], 1))
# rotate masks
rotated_gt_masks = []
for polygon in gt_masks:
polygon = polygon - np.tile((im_width / 2, im_height / 2),
(polygon.shape[0], 1))
polygon = np.array(np.dot(polygon, rotation_matrix),
dtype=np.int16)
polygon = np.squeeze(polygon, axis=-1) + np.tile(
(im_width / 2, im_height / 2), (polygon.shape[0], 1))
rotated_gt_masks.append(polygon.astype(np.int32))
# print('pts_ctr:', pts_ctr, np.tile((im_width / 2, im_height / 2), (gt_boxes.shape[0], 1)).shape)
origin_gt_boxes[:, 0:2] = pts_ctr
# print origin_gt_boxes[:, 0:2]
len_of_gt = len(origin_gt_boxes)
# rectificate the angle in the range of [-45, 45]
for idx in range(len_of_gt):
ori_angle = origin_gt_boxes[idx, 4]
height = origin_gt_boxes[idx, 3]
width = origin_gt_boxes[idx, 2]
# step 1: normalize gt (-45,135)
if width < height:
ori_angle += 90
width, height = height, width
# step 2: rotate (-45,495)
rotated_angle = ori_angle + angle
# step 3: normalize rotated_angle (-45,135)
while rotated_angle > 135:
rotated_angle = rotated_angle - 180
rotated_gt_boxes[idx, 0] = origin_gt_boxes[idx, 0]
rotated_gt_boxes[idx, 1] = origin_gt_boxes[idx, 1]
# rotated_gt_boxes[idx, 3] = height * self.gt_margin
# rotated_gt_boxes[idx, 2] = width * self.gt_margin
rotated_gt_boxes[idx, 3] = height
rotated_gt_boxes[idx, 2] = width
rotated_gt_boxes[idx, 4] = rotated_angle
x_inbound = np.logical_and(rotated_gt_boxes[:, 0] >= 0,
rotated_gt_boxes[:, 0] < im_width)
y_inbound = np.logical_and(rotated_gt_boxes[:, 1] >= 0,
rotated_gt_boxes[:, 1] < im_height)
inbound = np.logical_and(x_inbound, y_inbound)
inbound_th = torch.tensor(np.where(inbound)).long().view(-1)
rotated_gt_boxes_th = torch.tensor(rotated_gt_boxes[inbound]).to(
target.bbox.device)
# print('gt_labels before:', gt_labels.size(), inbound_th.size())
gt_labels = gt_labels[inbound_th]
# print('gt_labels after:', gt_labels.size())
difficulty = target.get_field("difficult")
difficulty = difficulty[inbound_th]
target_cpy = RBoxList(rotated_gt_boxes_th, iminfo, mode='xywha')
target_cpy.add_field('difficult', difficulty)
target_cpy.add_field('labels', gt_labels)
# add mask filed
masks = [
polygon.reshape((1, -1)).tolist() for polygon in rotated_gt_masks
]
masks = SegmentationMask(masks, iminfo)
target_cpy.add_field("masks", masks)
# print('has word:', target.has_field("words"), target.get_field("words"))
if target.has_field("words"):
words = target.get_field("words")[inbound_th]
target_cpy.add_field('words', words)
if target.has_field("word_length"):
word_length = target.get_field("word_length")[inbound_th]
target_cpy.add_field('word_length', word_length)
# print('rotated_gt_boxes_th:', origin_gt_boxes[0], target_cpy.bbox[0])
# print('rotated_gt_boxes_th:', target.bbox.size(), gt_boxes.shape)
if target_cpy.bbox.size()[0] <= 0:
print(target_cpy.bbox.size()[0])
return None
return target_cpy
def forward_for_single_feature_map(self, anchors, objectness_,
box_regression_, scale):
"""
Arguments:
anchors: list[BoxList]
objectness: tensor of size N, A, H, W
box_regression: tensor of size N, A * 5, H, W
"""
device = objectness_.device
N, A, H, W = objectness_.shape
width, height = anchors[0].size
# scale = width / W
# put in the same format as anchors
objectness = objectness_.permute(0, 2, 3, 1)
objectness = objectness.reshape(N, -1)
# get the first 5 channels
box_regression = box_regression_[:, :5].view(N, -1, 5, H,
W).permute(0, 3, 4, 1, 2)
box_regression = box_regression.reshape(N, -1, 5)
all_proposals = eastbox2rbox(box_regression, self.base_size, (H, W),
scale)
num_anchors = A * H * W
pre_nms_top_n = min(self.pre_nms_top_n, num_anchors)
objectness, topk_idx = objectness.topk(pre_nms_top_n,
dim=1,
sorted=True)
batch_idx = torch.arange(N, device=device)[:, None]
proposals = all_proposals.view(N, -1, 5)[batch_idx, topk_idx]
image_shapes = [box.size for box in anchors]
result = []
for proposal, score, im_shape in zip(proposals, objectness,
image_shapes):
if not self.training:
# print("score:", score.shape)
# print("proposal:", proposal.shape)
proposal = proposal[score > self.score_thresh]
score = score[score > self.score_thresh]
# print("score:", score.shape, score)
# print("proposal:", proposal.shape)
# print("score:", score)
boxlist = RBoxList(proposal, im_shape, mode="xywha")
boxlist.add_field("objectness", score)
boxlist = boxlist.clip_to_image(remove_empty=False)
boxlist = remove_small_boxes(boxlist, self.min_size)
boxlist = self.nms_fn(
boxlist,
self.nms_thresh,
max_proposals=self.post_nms_top_n,
score_field="objectness",
)
result.append(boxlist)
return result
def gt_crop(self, target, crop_portion, x_factor, y_factor):
gt_boxes = target.bbox
if isinstance(target.bbox, torch.Tensor):
gt_boxes = target.bbox.data.cpu().numpy()
gt_classes = target.get_field("labels")
ow, oh = target.size
dh = int(oh * crop_portion)
dw = int(ow * crop_portion)
th = int(oh * (1 - crop_portion))
tw = int(ow * (1 - crop_portion))
y0 = int((dh - 1) * y_factor)
x0 = int((dw - 1) * x_factor)
gt_boxes[:, 0] -= x0
gt_boxes[:, 1] -= y0
#####################
outer_bound = 0.2
polys = rbox2poly(gt_boxes).reshape(-1, 4, 2)
# (b, 4)
x_poly = polys[..., 0]
y_poly = polys[..., 1]
# bounding box with outer border on their heights and widths
outer_bound_x = np.tile(outer_bound * gt_boxes[:, 2:3],
(1, x_poly.shape[-1]))
outer_bound_y = np.tile(outer_bound * gt_boxes[:, 3:4],
(1, x_poly.shape[-1]))
# (b, 4)
x_check = np.logical_and(x_poly >= 0 - outer_bound_x,
x_poly < tw + outer_bound_x)
y_check = np.logical_and(y_poly >= 0 - outer_bound_y,
y_poly < th + outer_bound_y)
x_sum = np.sum(x_check.astype(np.int32), axis=-1)
y_sum = np.sum(y_check.astype(np.int32), axis=-1)
inbound = (x_sum + y_sum) > 7.
#####################
# x_inbound = np.logical_and(gt_boxes[:, 0] >= 0, gt_boxes[:, 0] < tw)
# y_inbound = np.logical_and(gt_boxes[:, 1] >= 0, gt_boxes[:, 1] < th)
#####################
iminfo = (tw, th)
# inbound = np.logical_and(x_inbound, y_inbound)
inbound_th = torch.tensor(np.where(inbound)).long().view(-1)
crop_gt_boxes_th = torch.tensor(gt_boxes[inbound]).to(
target.bbox.device)
# print('gt_labels before:', gt_labels.size(), inbound_th.size())
gt_labels = gt_classes[inbound_th].to(target.bbox.device)
# print('gt_labels after:', gt_labels.size())
difficulty = target.get_field("difficult")
difficulty = difficulty[inbound_th].to(target.bbox.device)
target_cpy = RBoxList(crop_gt_boxes_th, iminfo, mode='xywha')
target_cpy.add_field('difficult', difficulty)
target_cpy.add_field('labels', gt_labels)
# print('has word:', target.has_field("words"), target.get_field("words"))
if target.has_field("words"):
words = target.get_field("words")[inbound_th]
target_cpy.add_field('words', words)
if target.has_field("word_length"):
word_length = target.get_field("word_length")[inbound_th]
target_cpy.add_field('word_length', word_length)
if target.has_field("masks"):
seg_masks = target.get_field("masks")[inbound_th]
# print('seg_masks:', seg_masks)
target_cpy.add_field('masks', seg_masks.shift(-x0, -y0, iminfo))
# print('rotated_gt_boxes_th:', origin_gt_boxes[0], target_cpy.bbox[0])
# print('rotated_gt_boxes_th:', target.bbox.size(), gt_boxes.shape)
if target_cpy.bbox.size()[0] <= 0:
# print("target has no boxes...")
return None
return target_cpy
