def __getitem__(self, index):
img_id = self.images[index]
img_info = self.coco.loadImgs(ids=[img_id])[0]
img_path = os.path.join(self.img_dir, img_info['file_name'])
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.])
s = max(img.shape[0], img.shape[1]) * 1.0
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
c[0] += img.shape[1] * np.clip(np.random.randn() * cf, -2 * cf,
2 * cf)
c[1] += img.shape[0] * np.clip(np.random.randn() * cf, -2 * cf,
2 * cf)
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
trans_input = get_affine_transform(
c, s, 0, [self.opt.input_res, self.opt.input_res])
inp = cv2.warpAffine(img,
trans_input,
(self.opt.input_res, self.opt.input_res),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_res = self.opt.output_res
num_classes = self.opt.num_classes
trans_output = get_affine_transform(c, s, 0, [output_res, output_res])
num_hm = 1 if self.opt.agnostic_ex else num_classes
hm_t = np.zeros((num_hm, output_res, output_res), dtype=np.float32)
hm_l = np.zeros((num_hm, output_res, output_res), dtype=np.float32)
hm_b = np.zeros((num_hm, output_res, output_res), dtype=np.float32)
hm_r = np.zeros((num_hm, output_res, output_res), dtype=np.float32)
hm_c = np.zeros((num_classes, output_res, output_res),
dtype=np.float32)
reg_t = np.zeros((self.max_objs, 2), dtype=np.float32)
reg_l = np.zeros((self.max_objs, 2), dtype=np.float32)
reg_b = np.zeros((self.max_objs, 2), dtype=np.float32)
reg_r = np.zeros((self.max_objs, 2), dtype=np.float32)
ind_t = np.zeros((self.max_objs), dtype=np.int64)
ind_l = np.zeros((self.max_objs), dtype=np.int64)
ind_b = np.zeros((self.max_objs), dtype=np.int64)
ind_r = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
for k in range(num_objs):
ann = anns[k]
# bbox = self._coco_box_to_bbox(ann['bbox'])
# tlbr
pts = np.array(ann['extreme_points'],
dtype=np.float32).reshape(4, 2)
# cls_id = int(self.cat_ids[ann['category_id']] - 1) # bug
cls_id = int(self.cat_ids[ann['category_id']])
hm_id = 0 if self.opt.agnostic_ex else cls_id
if flipped:
pts[:, 0] = width - pts[:, 0] - 1
pts[1], pts[3] = pts[3].copy(), pts[1].copy()
for j in range(4):
pts[j] = affine_transform(pts[j], trans_output)
pts = np.clip(pts, 0, self.opt.output_res - 1)
h, w = pts[2, 1] - pts[0, 1], pts[3, 0] - pts[1, 0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
pt_int = pts.astype(np.int32)
draw_gaussian(hm_t[hm_id], pt_int[0], radius)
draw_gaussian(hm_l[hm_id], pt_int[1], radius)
draw_gaussian(hm_b[hm_id], pt_int[2], radius)
draw_gaussian(hm_r[hm_id], pt_int[3], radius)
reg_t[k] = pts[0] - pt_int[0]
reg_l[k] = pts[1] - pt_int[1]
reg_b[k] = pts[2] - pt_int[2]
reg_r[k] = pts[3] - pt_int[3]
ind_t[k] = pt_int[0, 1] * output_res + pt_int[0, 0]
ind_l[k] = pt_int[1, 1] * output_res + pt_int[1, 0]
ind_b[k] = pt_int[2, 1] * output_res + pt_int[2, 0]
ind_r[k] = pt_int[3, 1] * output_res + pt_int[3, 0]
ct = [
int((pts[3, 0] + pts[1, 0]) / 2),
int((pts[0, 1] + pts[2, 1]) / 2)
]
draw_gaussian(hm_c[cls_id], ct, radius)
reg_mask[k] = 1
ret = {
'input': inp,
'hm_t': hm_t,
'hm_l': hm_l,
'hm_b': hm_b,
'hm_r': hm_r,
'hm_c': hm_c
}
if self.opt.reg_offset:
ret.update({
'reg_mask': reg_mask,
'reg_t': reg_t,
'reg_l': reg_l,
'reg_b': reg_b,
'reg_r': reg_r,
'ind_t': ind_t,
'ind_l': ind_l,
'ind_b': ind_b,
'ind_r': ind_r
})
return ret
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
c[1] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(
c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img, trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2), dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2), dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
ct_int = ct.astype(np.int32)
lt = np.array(
[bbox[0], bbox[1]], dtype=np.float32)
lt_int = lt.astype(np.int32)
rb = np.array(
[bbox[2], bbox[3]], dtype=np.float32)
rb_int = rb.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
draw_gaussian(hm[cls_id], lt_int, radius)
draw_gaussian(hm[cls_id], rb_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
cat_spec_wh[k, cls_id * 2: cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2: cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k], radius)
gt_det.append([ct[0] - w / 2, ct[1] - h / 2,
ct[0] + w / 2, ct[1] + h / 2, 1, cls_id])
ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(
self.img_dir,
self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
img = self.coco.loadImgs(ids=[img_id])[0]
w_img = int(img['width'])
h_img = int(img['height'])
labels = []
bboxes = []
shapes = []
for anno in annotations:
if anno['iscrowd'] == 1 or type(
anno['segmentation']) != list: # Excludes crowd objects
continue
if len(anno['segmentation']) > 1:
obj_contours = [
np.array(s).reshape((-1, 2)).astype(np.int32)
for s in anno['segmentation']
]
obj_contours = sorted(obj_contours, key=cv2.contourArea)
polygons = obj_contours[-1]
else:
polygons = anno['segmentation'][0]
gt_x1, gt_y1, gt_w, gt_h = anno['bbox']
contour = np.array(polygons).reshape((-1, 2))
# Downsample the contour to fix number of vertices
if cv2.contourArea(contour.astype(np.int32)) < 6:
continue
fixed_contour = uniformsample(contour, self.n_vertices)
fixed_contour[:, 0] = np.clip(fixed_contour[:, 0], gt_x1,
gt_x1 + gt_w)
fixed_contour[:, 1] = np.clip(fixed_contour[:, 1], gt_y1,
gt_y1 + gt_h)
contour_std = np.sqrt(np.sum(np.std(fixed_contour, axis=0)**2))
if contour_std < 1e-6 or contour_std == np.inf or contour_std == np.nan: # invalid shapes
continue
updated_bbox = [
np.min(fixed_contour[:, 0]),
np.min(fixed_contour[:, 1]),
np.max(fixed_contour[:, 0]),
np.max(fixed_contour[:, 1])
]
shapes.append(np.ndarray.flatten(fixed_contour).tolist())
labels.append(self.cat_ids[anno['category_id']])
bboxes.append(updated_bbox)
labels = np.array(labels)
bboxes = np.array(bboxes, dtype=np.float32)
shapes = np.array(shapes, dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
shapes = np.zeros((1, self.n_vertices * 2), dtype=np.float32)
# bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.],
dtype=np.float32) # center of image
scale = max(height, width) * 1.0
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(160, width)
h_border = get_border(160, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(
center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img,
(self.img_size['w'], self.img_size['h']))
img = img.astype(np.float32) / 255.
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(
center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
hmap = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap
w_h_ = np.zeros((self.max_objs, 2),
dtype=np.float32) # width and height of bboxes
shapes_ = np.zeros((self.max_objs, self.n_vertices * 2),
dtype=np.float32) # gt amodal segmentation polygons
center_offsets = np.zeros(
(self.max_objs, 2),
dtype=np.float32) # gt mass centers to bbox center
codes_ = np.zeros((self.max_objs, self.n_codes), dtype=np.float32)
activated_codes = np.zeros(
(self.max_objs, self.n_codes),
dtype=np.int64) # keep track of codes that is activated
regs = np.zeros(
(self.max_objs, 2),
dtype=np.float32) # regression for offsets of shape center
inds = np.zeros((self.max_objs, ), dtype=np.int64)
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
for k, (bbox, label, shape) in enumerate(zip(bboxes, labels, shapes)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# Flip the contour x-axis
for m in range(self.n_vertices):
shape[2 * m] = width - shape[2 * m] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
# generate gt shape mean and std from contours
for m in range(self.n_vertices
): # apply scale and crop transform to shapes
shape[2 * m:2 * m + 2] = affine_transform(
shape[2 * m:2 * m + 2], trans_fmap)
shape_clipped = np.reshape(shape, (self.n_vertices, 2))
shape_clipped[:, 0] = np.clip(shape_clipped[:, 0], 0,
self.fmap_size['w'] - 1)
shape_clipped[:, 1] = np.clip(shape_clipped[:, 1], 0,
self.fmap_size['h'] - 1)
clockwise_flag = check_clockwise_polygon(shape_clipped)
if not clockwise_flag:
fixed_contour = np.flip(shape_clipped, axis=0)
else:
fixed_contour = shape_clipped.copy()
# Indexing from the left-most vertex, argmin x-axis
idx = np.argmin(fixed_contour[:, 0])
indexed_shape = np.concatenate(
(fixed_contour[idx:, :], fixed_contour[:idx, :]), axis=0)
mass_center = np.mean(indexed_shape, axis=0)
if h < 1e-6 or w < 1e-6: # remove small bboxes
continue
norm_shape = (indexed_shape - mass_center) / np.array(
[w / 2., h / 2.])
if h > 0 and w > 0:
obj_c = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
obj_c_int = obj_c.astype(np.int32)
radius = max(
0,
int(
gaussian_radius((math.ceil(h), math.ceil(w)),
self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
shapes_[k] = norm_shape.reshape((1, -1))
center_offsets[k] = mass_center - obj_c
codes_[k], _ = fast_ista(norm_shape.reshape((1, -1)),
self.dictionary,
lmbda=self.sparse_alpha,
max_iter=60)
activated_codes[k] = (np.abs(codes_[k]) >
1e-4) * 1 # active codes annotated 1
w_h_[k] = 1. * w, 1. * h
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
return {
'image': img,
'shapes': shapes_,
'codes': codes_,
'offsets': center_offsets,
'active': activated_codes,
'hmap': hmap,
'w_h_': w_h_,
'regs': regs,
'inds': inds,
'ind_masks': ind_masks,
'c': center,
's': scale,
'img_id': img_id
}
def __get_default_coco(self, img, anns, num_objs):
boxes = []
if self.num_keypoints > 0:
kpts = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
boxes.append(BoundingBox(*bbox))
if self.num_keypoints > 0:
if 'keypoints' not in ann:
ann['keypoints'] = np.zeros((3 * self.num_keypoints, ))
kpt = [
Keypoint(*x)
for x in np.array(ann['keypoints']).reshape(-1, 3)[:, :2]
]
kpts.extend(kpt)
bbs = BoundingBoxesOnImage(boxes, shape=img.shape)
if self.num_keypoints > 0:
kpts = KeypointsOnImage(kpts, shape=img.shape)
if self.augmentation is not None:
if self.num_keypoints > 0:
img_aug, bbs_aug, kpts_aug = self.augmentation(
image=img, bounding_boxes=bbs, keypoints=kpts)
else:
img_aug, bbs_aug = self.augmentation(image=img,
bounding_boxes=bbs)
else:
if self.num_keypoints > 0:
kpts_aug = kpts.copy()
img_aug, bbs_aug = np.copy(img), bbs.copy()
if self.num_keypoints > 0:
img_aug, bbs_aug, kpts_aug = self.resize(image=img_aug,
bounding_boxes=bbs_aug,
keypoints=kpts_aug)
else:
img_aug, bbs_aug = self.resize(image=img_aug,
bounding_boxes=bbs_aug)
img = (img_aug.astype(np.float32) / 255.)
inp = (img - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = self.input_size[1] // self.down_ratio
output_w = self.input_size[0] // self.down_ratio
num_classes = self.num_classes
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_detections, 2), dtype=np.float32)
reg = np.zeros((self.max_detections, 2), dtype=np.float32)
ind = np.zeros((self.max_detections), dtype=np.int64)
reg_mask = np.zeros((self.max_detections), dtype=np.uint8)
gt_det = np.zeros((self.max_detections, 6), dtype=np.float32)
gt_areas = np.zeros((self.max_detections), dtype=np.float32)
if self.num_keypoints > 0:
kp = np.zeros((self.max_detections, self.num_keypoints * 2),
dtype=np.float32)
gt_kp = np.zeros((self.max_detections, self.num_keypoints, 2),
dtype=np.float32)
kp_reg_mask = np.zeros(
(self.max_detections, self.num_keypoints * 2), dtype=np.uint8)
bbs_aug, kpts_aug = self.resize_out(bounding_boxes=bbs_aug,
keypoints=kpts_aug)
else:
bbs_aug = self.resize_out(bounding_boxes=bbs_aug)
for k in range(num_objs):
ann = anns[k]
bbox_aug = bbs_aug[k].clip_out_of_image((output_w, output_h))
bbox = np.array(
[bbox_aug.x1, bbox_aug.y1, bbox_aug.x2, bbox_aug.y2])
cls_id = int(self.cat_mapping[ann['category_id']])
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((np.ceil(h), np.ceil(w)))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
gt_det[k] = ([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
if self.num_keypoints > 0:
valid = np.array(ann["keypoints"]).reshape(-1, 3)[:, -1]
for i, p in enumerate(
kpts_aug[k * self.num_keypoints:k *
self.num_keypoints + self.num_keypoints]):
kp[k][i * 2] = p.x - ct_int[0]
kp[k][i * 2 + 1] = p.y - ct_int[1]
is_valid = valid[i] == 2 and not p.is_out_of_image(
(output_w, output_w))
kp_reg_mask[k, i * 2] = int(is_valid)
kp_reg_mask[k, i * 2 + 1] = int(is_valid)
gt_kp[k][i] = p.x, p.y
if "area" not in ann:
gt_areas[k] = w * h
else:
gt_areas[k] = ann["area"]
del bbs
del bbs_aug
del img_aug
gt_det = np.array(gt_det,
dtype=np.float32) if len(gt_det) > 0 else np.zeros(
(1, 6), dtype=np.float32)
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'reg': reg,
'gt_dets': gt_det,
'gt_areas': gt_areas,
}
if self.num_keypoints > 0:
ret['kps'] = kp
ret['gt_kps'] = gt_kp
ret['kp_reg_mask'] = kp_reg_mask
del kpts_aug
return ret
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
s = max(img.shape[0], img.shape[1]) * 1.0
rot = 0
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
#TODO这里是更改多尺度训练的地方。
s = s#* np.random.choice(np.arange(0.8, 1.5, 0.1))#change 0.6 1.4
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
c[1] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.aug_rot:
rf = self.opt.rotate
rot = np.clip(np.random.randn()*rf, -rf*2, rf*2)
if self.opt.angle_norm and self.split == 'train':
#首先是读取标注文件，获得中心点和头部点获得所有角度的集合
angle_list=[]
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
pts = np.array(ann['keypoints'][0:3], np.float32).reshape( self.num_joints, 3)#tmjx
ct = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
angle= math.atan2((pts[0, 0] - ct[0]), (pts[0, 1] - ct[1]))
angle_list.append(angle)
#下面这段代码求旋转的角度
angle_list=np.array(angle_list)%np.pi #首先归一化到np.pi
angle_int=(angle_list// (np.pi/9)).astype('int')
angle_b=np.bincount(angle_int)
index_rot=np.argmax(angle_b)
ind_rot=(angle_list>(index_rot)*np.pi/9) * (angle_list<=(index_rot+1)*np.pi/9)
angle_rot=np.average(angle_list[ind_rot])
rot=angle_rot*(-180)/np.pi
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(
c, s, rot, [self.opt.input_res, self.opt.input_res])
inp = cv2.warpAffine(img, trans_input,
(self.opt.input_res, self.opt.input_res),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_res = self.opt.output_res
num_joints = self.num_joints
trans_output_rot = get_affine_transform(c, s, rot, [output_res, output_res])
trans_output = get_affine_transform(c, s, 0, [output_res, output_res])
hm = np.zeros((self.num_classes, output_res, output_res), dtype=np.float32)
hm_hp = np.zeros((num_joints, output_res, output_res), dtype=np.float32)
dense_kps = np.zeros((num_joints, 2, output_res, output_res),
dtype=np.float32)
dense_kps_mask = np.zeros((num_joints, output_res, output_res),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
kps = np.zeros((self.max_objs, num_joints * 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
kps_mask = np.zeros((self.max_objs, self.num_joints * 2), dtype=np.uint8)
hp_offset = np.zeros((self.max_objs * num_joints, 2), dtype=np.float32)
hp_ind = np.zeros((self.max_objs * num_joints), dtype=np.int64)
hp_mask = np.zeros((self.max_objs * num_joints), dtype=np.int64)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
#TODO change wwlekeuihx
cls_id = int(ann['category_id']) - 1
pts = np.array(ann['keypoints'][0:3], np.float32).reshape(num_joints, 3)#tmjx
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
pts[:, 0] = width - pts[:, 0] - 1
#for e in self.flip_idx:
#pts[e[0]], pts[e[1]] = pts[e[1]].copy(), pts[e[0]].copy()
# bbox[:2] = affine_transform(bbox[:2], trans_output)
# bbox[2:] = affine_transform(bbox[2:], trans_output)
#bbox = np.clip(bbox, 0, output_res - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
center_obj=[(bbox[2] + bbox[0])/2,(bbox[3] + bbox[1])/2]
center_obj=affine_transform(center_obj, trans_output_rot)
scale_trans= self.opt.output_res/s
h *= scale_trans
w *= scale_trans
h = np.clip(h , 0, output_res - 1)
w = np.clip(w , 0, output_res - 1)
if (h > 0 and w > 0) or (rot != 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w))) *1.2
sqrt_wh = np.sqrt(np.sqrt(h*w))
radius_w = radius * np.sqrt(w) / sqrt_wh
radius_h = radius * np.sqrt(h) / sqrt_wh
radius_w = self.opt.hm_gauss if self.opt.mse_loss else max(0, np.ceil(radius_w))
radius_h = self.opt.hm_gauss if self.opt.mse_loss else max(0, np.ceil(radius_h))
# radius = self.opt.hm_gauss if self.opt.mse_loss else max(0, int(radius))
ct = np.array( center_obj, dtype=np.float32)
# if self.opt.Rguass:
if ct[0]<0 or ct[0]>output_res - 1 or ct[1]<0 or ct[1]>output_res - 1: #
continue
ct[0] = np.clip(ct[0], 0, output_res - 1)
ct[1] = np.clip(ct[1], 0, output_res - 1)
ct_int = ct.astype(np.int32)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_res + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
num_kpts = pts[:, 2].sum()
if num_kpts == 0:
hm[cls_id, ct_int[1], ct_int[0]] = 0.9999
reg_mask[k] = 0
hp_radius = gaussian_radius((math.ceil(h), math.ceil(w)))
hp_radius = self.opt.hm_gauss \
if self.opt.mse_loss else max(0, int(hp_radius))
for j in range(num_joints):
if pts[j, 2] > 0:
pts[j, :2] = affine_transform(pts[j, :2], trans_output_rot)
if pts[j, 0] >= 0 and pts[j, 0] < output_res and \
pts[j, 1] >= 0 and pts[j, 1] < output_res:
kps[k, j * 2: j * 2 + 2] = pts[j, :2] - ct_int
kps_mask[k, j * 2: j * 2 + 2] = 1
pt_int = pts[j, :2].astype(np.int32)
hp_offset[k * num_joints + j] = pts[j, :2] - pt_int
hp_ind[k * num_joints + j] = pt_int[1] * output_res + pt_int[0]
hp_mask[k * num_joints + j] = 1
if self.opt.dense_hp:
# must be before draw center hm gaussian
draw_dense_reg(dense_kps[j], hm[cls_id], ct_int,
pts[j, :2] - ct_int, radius, is_offset=True)
draw_gaussian(dense_kps_mask[j], ct_int, radius)
draw_gaussian(hm_hp[j], pt_int, hp_radius)
#TODO change
angle= math.atan2((pts[0, 0] - ct[0]), (pts[0, 1] - ct[1]))
if self.opt.Rguass:
draw_gaussian(hm[cls_id], ct_int, [radius_w,radius_h,angle])
else:
radius = self.opt.hm_gauss if self.opt.mse_loss else max(0, int(radius))
draw_gaussian(hm[cls_id], ct_int, radius)
gt_det.append([ct[0] - w / 2, ct[1] - h / 2,
ct[0] + w / 2, ct[1] + h / 2, 1] +
pts[:, :2].reshape(num_joints * 2).tolist() + [cls_id])
# if rot != 0:
# hm = hm * 0 + 0.9999
# reg_mask *= 0
# kps_mask *= 0
ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh,
'hps': kps, 'hps_mask': kps_mask}
if self.opt.dense_hp:
dense_kps = dense_kps.reshape(num_joints * 2, output_res, output_res)
dense_kps_mask = dense_kps_mask.reshape(
num_joints, 1, output_res, output_res)
dense_kps_mask = np.concatenate([dense_kps_mask, dense_kps_mask], axis=1)
dense_kps_mask = dense_kps_mask.reshape(
num_joints * 2, output_res, output_res)
ret.update({'dense_hps': dense_kps, 'dense_hps_mask': dense_kps_mask})
del ret['hps'], ret['hps_mask']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.hm_hp:
ret.update({'hm_hp': hm_hp})
if self.opt.reg_hp_offset:
ret.update({'hp_offset': hp_offset, 'hp_ind': hp_ind, 'hp_mask': hp_mask})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 40), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
#这里是调试可视化生成的特征图的程序
# debugger = Debugger(dataset=self.opt.dataset, ipynb=(self.opt.debug==3),
# theme=self.opt.debugger_theme)
# inp1 = inp.transpose(1,2,0)
# inp1=(inp1*self.std + self.mean)*255.
# self.debug(debugger, inp1, ret)
return ret
def __getitem__(self, index):
img_id = self.ids[index]
file_name = self.hoi_annotations[img_id]['file_name']
img_path = os.path.join(self.root, self.image_dir, file_name)
anns = self.hoi_annotations[img_id]['annotations']
hoi_anns = self.hoi_annotations[img_id]['hoi_annotation']
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.7, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
hm_human = np.zeros((1, output_h, output_w), dtype=np.float32)
hm_rel = np.zeros((self.num_classes_verb, output_h, output_w),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
sub_offset = np.zeros((self.max_rels, 2), dtype=np.float32)
obj_offset = np.zeros((self.max_rels, 2), dtype=np.float32)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
bbox_ct = []
num_rels = min(len(hoi_anns), self.max_rels)
for k in range(num_objs):
ann = anns[k]
bbox = np.asarray(ann['bbox'])
if isinstance(ann['category_id'], str):
ann['category_id'] = int(ann['category_id'].replace('\n', ''))
cls_id = int(self.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
ct = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
bbox_ct.append(ct_int.tolist())
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
if cls_id == 0:
draw_gaussian(hm_human[cls_id], ct_int, radius)
else:
draw_gaussian(hm[cls_id], ct_int, radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
offset_mask = np.zeros((self.max_rels), dtype=np.uint8)
rel_ind = np.zeros((self.max_rels), dtype=np.int64)
for k in range(num_rels):
hoi = hoi_anns[k]
if isinstance(hoi['category_id'], str):
hoi['category_id'] = int(hoi['category_id'].replace('\n', ''))
hoi_cate = int(self.cat_ids_verb[hoi['category_id']])
sub_ct = bbox_ct[hoi['subject_id']]
obj_ct = bbox_ct[hoi['object_id']]
offset_mask[k] = 1
rel_ct = np.array([(sub_ct[0] + obj_ct[0]) / 2,
(sub_ct[1] + obj_ct[1]) / 2],
dtype=np.float32)
radius = gaussian_radius((math.ceil(abs(sub_ct[0] - obj_ct[0])),
math.ceil(abs(sub_ct[1] - obj_ct[1]))))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
rel_ct_int = rel_ct.astype(np.int32)
draw_gaussian(hm_rel[hoi_cate], rel_ct_int, radius)
rel_sub_offset = np.array(
[rel_ct_int[0] - sub_ct[0], rel_ct_int[1] - sub_ct[1]],
dtype=np.float32)
rel_obj_offset = np.array(
[rel_ct_int[0] - obj_ct[0], rel_ct_int[1] - obj_ct[1]],
dtype=np.float32)
sub_offset[k] = 1. * rel_sub_offset[0], 1. * rel_sub_offset[1]
obj_offset[k] = 1. * rel_obj_offset[0], 1. * rel_obj_offset[1]
rel_ind[k] = rel_ct_int[1] * output_w + rel_ct_int[0]
ret = {
'input': inp,
'hm': hm,
'hm_human': hm_human,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'hm_rel': hm_rel,
'sub_offset': sub_offset,
'obj_offset': obj_offset,
'offset_mask': offset_mask,
'rel_ind': rel_ind
}
if self.opt.reg_offset:
ret.update({'reg': reg})
return ret
def __getitem__(self, index):
img_id = self.images[index]
video_info = self.coco.loadImgs(ids=[img_id])[0]
file_name = video_info['file_name']
image_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
input_h, input_w = self.opt.input_h, self.opt.input_w
centers = np.array([ann['keypoints_2d'] for ann in anns])[:, 0::9, :2]
centers = centers.reshape(-1, 2)
keep = np.where(np.all((0 < centers) & (1 > centers), axis=1) == True)
centers = centers[keep]
anns = [anns[i] for i in keep[0]]
img = cv2.imread(image_path)
# resize, pad, and color augs
centers[:, 0], centers[:, 1] = centers[:, 0]*img.shape[1], centers[:, 1]*img.shape[0]
augmented = self.augs(image=img, keypoints=centers)
inp, centers = augmented['image'], np.array(augmented['keypoints'])
num_objs = min(len(centers), self.max_objs)
wh_ratio = img.shape[1] / img.shape[0]
c = np.array([inp.shape[1] / 2., inp.shape[0] / 2.], dtype=np.float32)
s = max(inp.shape[0], inp.shape[1]) * 1.0
aug = False
if self.split == 'train' and np.random.random() < self.opt.aug_ddd and num_objs > 0:
aug = True
sf = self.opt.scale
# cf = self.opt.shift
scale_rand = np.random.random()
s = s * np.clip(scale_rand * sf + 1, 1 - sf, 1 + sf)
trans_input = get_affine_transform(
c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(inp, trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
centers = np.concatenate([centers, np.ones((centers.shape[0], 1))], axis=1)
centers = np.matmul(trans_input, centers.T).T
if num_objs > 0:
centers[:, 0], centers[:, 1] = centers[:, 0] / inp.shape[1], centers[:, 1] / inp.shape[0]
inp = (inp.astype(np.float32) / 255.)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
# empty input
heat_map = np.zeros([self.num_classes, output_h, output_w], dtype=np.float32)
scales = np.zeros([self.max_objs, 3], dtype=np.float32)
translations = np.zeros([self.max_objs, 3], dtype=np.float32)
rotvecs = np.zeros([self.max_objs, 3], dtype=np.float32)
reg_mask = np.zeros([self.max_objs], dtype=np.uint8)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
for k in range(num_objs):
ann = anns[k]
bbox = np.array(ann['bbox'])
scale = np.array(ann['scale'])
rot_angles = np.array(ann['rot'])
translation = np.array(ann['translation'])
if aug:
translation[2] *= np.clip(scale_rand * sf + 1, 1 - sf, 1 + sf)
# translation[0] += translation[0] * y_shift * cf
# translation[1] -= (x_shift * cf) * 0.3
ct = centers[k][:2]
ct[0], ct[1] = ct[0] * output_h, ct[1] * output_w
ct[0], ct[1] = np.clip(ct[0], 0, output_w - 1), np.clip(ct[1], 0, output_w - 1)
cls_id = int(self.cat_ids[ann['category_id']])
bbox[[0, 2]] *= output_w
bbox[[1, 3]] *= output_h
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius/2))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct_int = ct.astype(np.int32)
draw_umich_gaussian(heat_map[cls_id], ct_int, radius)
scales[k] = scale
translations[k] = translation
rotvecs[k] = rot_angles
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
if DEBUG:
lines = (
[1, 5], [2, 6], [3, 7], [4, 8], # lines along x-axis
[1, 3], [5, 7], [2, 4], [6, 8], # lines along y-axis
[1, 2], [3, 4], [5, 6], [7, 8] # lines along z-axis
)
plt.scatter(ct_int[0], ct_int[1])
r = R.from_euler('zyx', rot_angles).as_matrix()
box_3d = Box.from_transformation(r, translation, scale).vertices
points_2d = project_points(box_3d, np.array(video_info['projection_matrix']))
points_2d[:, 0] = points_2d[:, 0] * (128*wh_ratio) + 128*(1-wh_ratio)/2
points_2d[:, 1] *= 128
points_2d = points_2d.astype(int)
for ids in lines:
plt.plot(
(points_2d[ids[0]][0], points_2d[ids[1]][0]),
(points_2d[ids[0]][1], points_2d[ids[1]][1]),
color='r',
)
# points_2d = np.array(ann['keypoints_2d'])
# points_2d[:, 0] *= 128
# points_2d[:, 1] *= 128
#
# points_2d = points_2d.astype(int)
# for ids in lines:
# plt.plot(
# (points_2d[ids[0]][0], points_2d[ids[1]][0]),
# (points_2d[ids[0]][1], points_2d[ids[1]][1]),
# color='b',
# )
ret = {
'input': inp,
'hm': heat_map,
'reg_mask': reg_mask,
'ind': ind,
'dim': scales,
'rot': rotvecs,
'loc': translations
}
if self.opt.reg_offset:
ret.update({'reg': reg})
if DEBUG:
if inp.shape[0] == 3:
plot_img = inp.transpose(1, 2, 0)
plot_img = (plot_img * self.std) + self.mean
else:
plot_img = inp.copy()
plot_img = cv2.resize(plot_img, (output_w, output_h))
plot_img = cv2.cvtColor(plot_img, cv2.COLOR_BGR2RGB)
plt.imshow(plot_img)
plt.show()
plt.imshow(heat_map[0])
plt.show()
return ret
def __getitem__(self, index):
img_id = self.images[index]
#loadImgs(ids=[img_id]) return a list, whose length = 1
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
cropped = False
if self.split == 'train':
if np.random.random() < 1:
cropped = True
file_name = file_name.split('.')[0] + 'crop.jpg'
img_path = os.path.join(self.img_dir, file_name)
if self.split == 'val':
cropped = True
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
s = max(img.shape[0], img.shape[1]) * 1.0
rot = 0
flipped = False
rotted = False
if self.split == 'train':
if not self.opts.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self.get_border(128, img.shape[1])
h_border = self.get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opts.scale
cf = self.opts.shift
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opts.aug_rot:
rotted = True
rf = self.opts.rotate
rot = np.clip(np.random.randn() * rf, -rf * 2, rf * 2)
if np.random.random() < self.opts.flip:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
# input_res is max(input_h, input_w), input is the size of original img
if np.random.random() < self.opts.keep_inp_res_prob and max(
(height | 127) + 1, (width | 127) + 1) < 1024:
self.opts.input_h = (height | 127) + 1
self.opts.input_w = (width | 127) + 1
self.opts.output_h = self.opts.input_h // self.opts.down_ratio
self.opts.output_w = self.opts.input_w // self.opts.down_ratio
self.opts.input_res = max(self.opts.input_h, self.opts.input_w)
self.opts.output_res = max(self.opts.output_h, self.opts.output_w)
trans_input = get_affine_transform(
c, s, rot, [self.opts.input_res, self.opts.input_res])
inp = cv2.warpAffine(img,
trans_input,
(self.opts.input_res, self.opts.input_res),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opts.no_color_aug:
color_aug(self.data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
#change data shape to [3, input_size, input_size]
inp = inp.transpose(2, 0, 1)
#output_res is max(output_h, output_w), output is the size after down sampling
output_res = self.opts.output_res
num_joints = self.num_joints
trans_output_rot = get_affine_transform(c, s, rot,
[output_res, output_res])
trans_output = get_affine_transform(c, s, 0, [output_res, output_res])
hm = np.zeros((self.num_classes, output_res, output_res),
dtype=np.float32)
hm_hp = np.zeros((num_joints, output_res, output_res),
dtype=np.float32)
dense_kps = np.zeros((num_joints, 2, output_res, output_res),
dtype=np.float32)
dense_kps_mask = np.zeros((num_joints, output_res, output_res),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
kps = np.zeros((self.max_objs, 2 * num_joints), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
kps_mask = np.zeros((self.max_objs, self.num_joints * 2),
dtype=np.uint8)
hp_offset = np.zeros((self.max_objs * num_joints, 2), dtype=np.float32)
hp_ind = np.zeros((self.max_objs * num_joints), dtype=np.int64)
hp_mask = np.zeros((self.max_objs * num_joints), dtype=np.int64)
draw_gaussian = draw_msra_gaussian if self.opts.mse_loss else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
if cropped:
bbox = np.array(ann['bbox'])
else:
bbox = np.array(ann['org_bbox'])
cls_id = int(ann['category_id']) - 1
if cropped:
pts = np.array(ann['keypoints'],
np.float32).reshape(num_joints, 3)
else:
pts = np.array(ann['org_keypoints'],
np.float32).reshape(num_joints, 3)
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
pts[:, 0] = width - pts[:, 0] - 1
for joint_idx in self.flip_idx:
pts[joint_idx[0]], pts[joint_idx[1]] = pts[
joint_idx[1]].copy(), pts[
joint_idx[0]].copy() #don't forget copy first
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
if rotted:
pts_rot = np.zeros((num_joints, 2))
for j in range(num_joints):
if pts[j, 2] > 0:
pts_rot[j, :2] = affine_transform(
pts[j, :2], trans_output_rot)
bbox[:2] = np.min(pts_rot, axis=0)
bbox[2:] = np.max(pts_rot, axis=0)
bbox = np.clip(bbox, 0, output_res - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if (h > 0 and w > 0) or (rot != 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = self.opts.hm_gauss if self.opts.mse_loss else max(
0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_res + ct_int[0]
reg[k] = ct - ct_int # the error of center[x, y]
reg_mask[k] = 1
num_kpts = pts[:, 2].sum() #whether joint can be seen or not
if num_kpts == 0:
hm[cls_id, ct_int[1], ct_int[0]] = 0.9999
reg_mask[k] = 0 #means this obj can'e be seen
hp_radius = gaussian_radius((math.ceil(h), math.ceil(w)),
min_overlap=1)
hp_radius = self.opts.hm_gauss if self.opts.mse_loss else max(
0, int(hp_radius))
for j in range(num_joints):
if pts[j, 2] > 0: #means this joint can be seen
pts[j, :2] = affine_transform(pts[j, :2],
trans_output_rot)
if pts[j, 0] >= 0 and pts[j, 0] < output_res and pts[
j, 1] >= 0 and pts[j, 1] < output_res:
kps[k, j * 2:j * 2 + 2] = pts[j, :2] - ct_int
kps_mask[k, j * 2:j * 2 + 2] = 1
pt_int = pts[j, :2].astype(np.int32)
hp_offset[k * num_joints + j] = pts[j, :2] - pt_int
hp_ind[k * num_joints +
j] = pt_int[1] * output_res + pt_int[0]
hp_mask[k * num_joints + j] = 1
if self.opts.dense_hp:
# must be before draw center hm gaussian
draw_dense_reg(dense_kps[j],
hm[cls_id],
ct_int,
pts[j, :2] - ct_int,
radius,
is_offset=True)
draw_gaussian(dense_kps_mask[j], ct_int,
radius)
hp1 = draw_gaussian(hm_hp[j], pt_int, hp_radius)
# plt.imsave('/home/mry/Desktop/testimg/hp_{}_{}.jpg'.format(k, j), hp1)
draw_gaussian(hm[cls_id], ct_int, radius)
##ge_det:x0, y0, x1, y1, joint1_x, joint1_y,...,joint17_x, joint17_y, cls_id
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] +
h / 2, 1
] + pts[:, :2].reshape(num_joints * 2).tolist() + [cls_id])
#if rot != 0:
# hm = hm * 0 + 0.9999
# reg_mask *= 0
# kps_mask *= 0
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'hps': kps,
'hps_mask': kps_mask
}
if self.opts.dense_hp:
dense_kps = dense_kps.reshape(num_joints * 2, output_res,
output_res)
dense_kps_mask = dense_kps_mask.reshape(num_joints, 1, output_res,
output_res)
dense_kps_mask = np.concatenate([dense_kps_mask, dense_kps_mask],
axis=1)
dense_kps_mask = dense_kps_mask.reshape(num_joints * 2, output_res,
output_res)
ret.update({
'dense_hps': dense_kps,
'dense_hps_mask': dense_kps_mask
})
del ret['hps'], ret['hps_mask']
if self.opts.reg_offset:
ret.update({'reg': reg})
if self.opts.hm_hp:
ret.update({'hm_hp': hm_hp})
if self.opts.reg_hp_offset:
ret.update({
'hp_offset': hp_offset,
'hp_ind': hp_ind,
'hp_mask': hp_mask
})
if self.opts.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 40), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
img_show = copy.deepcopy(img)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
# flipped = False # remove flip process
############## remove image preprocess
# if self.split == 'train':
# if not self.opt.not_rand_crop:
# s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
# w_border = self._get_border(128, img.shape[1])
# h_border = self._get_border(128, img.shape[0])
# c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border)
# c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border)
# else:
# sf = self.opt.scale
# cf = self.opt.shift
# c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
# c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
# s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
#
# if np.random.random() < self.opt.flip:
# flipped = True
# img = img[:, ::-1, :]
# c[0] = width - c[0] - 1
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
################## plot
# cv2.imwrite('/Workspace/CenterNet/in_{}'.format(file_name), inp)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
################# plot
# inp_out = cv2.warpAffine(img_show, trans_output,
# (output_w, output_h),
# flags=cv2.INTER_LINEAR)
# for k in range(num_objs):
# ann = anns[k]
# bbox_show = copy.deepcopy(ann['bbox'])
# bbox_show[:2] = affine_transform(bbox_show[:2], trans_output)
# cv2.circle(inp_out, tuple(list(map(int, bbox_show[:2]))), 2, (0, 0, 255), -1)
# print('file {} num {}'.format(file_name, num_objs))
# cv2.imwrite('/Workspace/CenterNet/out_{}'.format(file_name), inp_out)
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
### add angle regression
reg_angle = np.zeros((self.max_objs, 1), dtype=np.float32)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2),
dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2),
dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
########### show gt
# for k in range(num_objs):
# ann = anns[k]
# bbox = ann['bbox']
# bbox[:2] = affine_transform(bbox[:2], trans_output)
# bbox[2:4] = affine_transform(bbox[2:4], trans_output)
# bbox[0] = np.clip(bbox[0], 0, output_w - 1)
# bbox[1] = np.clip(bbox[1], 0, output_h - 1)
# self.getfourpoints(bbox, inp_out)
# cv2.imwrite('/Workspace/CenterNet/gt_{}'.format(file_name), inp_out)
gt_det = []
for k in range(num_objs):
ann = anns[k]
# bbox = self._coco_box_to_bbox(ann['bbox'])
bbox = ann['bbox']
cls_id = int(self.cat_ids[ann['category_id']])
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:4] = affine_transform(bbox[2:4], trans_output)
bbox[0] = np.clip(bbox[0], 0, output_w - 1)
bbox[1] = np.clip(bbox[1], 0, output_h - 1)
h, w = bbox[3], bbox[2]
if h > 0 and w > 0:
ct = np.array([bbox[0], bbox[1]], dtype=np.float32)
ct_int = ct.astype(np.int32)
reg_angle[k] = bbox[4]
if not self.opt.ellipse:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
draw_gaussian(hm[cls_id], ct_int, radius)
else:
draw_ellipse_gaussian(hm[cls_id], ct_int, w, h,
reg_angle[k])
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
cat_spec_wh[k, cls_id * 2:cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2:cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k],
radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
if self.opt.ellipse:
hm = np.where(hm > 1e-2, hm, 0)
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'angle': reg_angle
}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({
'cat_spec_wh': cat_spec_wh,
'cat_spec_mask': cat_spec_mask
})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def __getitem__(self, index):
image_fn = self.flist[index]
image = cv2.imread(image_fn)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
box_fn = str(Path(self.box_root)/(Path(image_fn).stem + '.txt'))
if osp.exists(box_fn):
xywh = np.loadtxt(box_fn)
xx,yy,ww,hh = xywh
x1,y1,x2,y2 = xx-ww/2,yy-hh/2,xx+ww/2,yy+hh/2
boxes = np.array([[x1,y1,x2,y2]]).astype('float32')
else:
boxes = np.array([[0.0,0.0,1.0,1.0]]).astype('float32')
if self.transform:
image, boxes = self.transform(image, boxes)
#generate box_gt for loss
#box x1,y1,x2,y2, [0,1]
output_h,output_w,grid_wh = self.configs.hh,self.configs.ww,self.configs.grid_wh
hin,win = self.configs.image_size
hm = np.zeros((self.configs.num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.configs.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
dense_xy = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.configs.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.configs.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.configs.max_objs), dtype=np.uint8)
num_objs = min(boxes.shape[0], self.configs.max_objs)
# gt_det = []
for k in range(num_objs):
bbox = boxes[k]
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h*grid_wh), math.ceil(w*grid_wh)))
radius = max(0, int(radius))
#radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array(
[(bbox[0] + bbox[2]) / 2.0 * grid_wh, (bbox[1] + bbox[3]) / 2.0* grid_wh], dtype=np.float32)
ct_int = ct.astype(np.int32)
ct_int = np.clip(ct_int, 0, grid_wh-1)
draw_umich_gaussian(hm[k], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k], radius)
draw_dense_reg(dense_xy, hm.max(axis=0), ct_int, reg[k], radius)
# gt_det.append([ct[0] - w / 2, ct[1] - h / 2,
# ct[0] + w / 2, ct[1] + h / 2, 1, cls_id])
#
#ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh}
#if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_mask = np.concatenate([hm_a, hm_a], axis=0)
ret = {'hm': hm, 'wh': wh, 'xy': reg, 'ind': ind,'dense_xy': dense_xy,'dense_wh': dense_wh,'dense_mask':dense_mask, 'boxes': boxes}
#ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
#del ret['wh']
#elif self.opt.cat_spec_wh:
#ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask})
#del ret['wh']
#if self.opt.reg_offset:
#ret.update({'reg': reg})
# if self.opt.debug > 0 or not self.split == 'train':
# gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
# np.zeros((1, 6), dtype=np.float32)
# meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
# ret['meta'] = meta
# return ret
#
return image, ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(self.img_dir, self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
img = self.coco.loadImgs(ids=[img_id])[0]
w_img = int(img['width'])
h_img = int(img['height'])
labels = []
bboxes = []
shapes = []
for anno in annotations:
if anno['iscrowd'] == 1: # Excludes crowd objects
continue
polygons = get_connected_polygon_using_mask(anno['segmentation'], (h_img, w_img),
n_vertices=self.n_vertices, closing_max_kernel=50)
gt_x1, gt_y1, gt_w, gt_h = anno['bbox']
contour = np.array(polygons).reshape((-1, 2))
# Downsample the contour to fix number of vertices
if len(contour) > self.n_vertices:
fixed_contour = resample(contour, num=self.n_vertices)
else:
fixed_contour = turning_angle_resample(contour, self.n_vertices)
fixed_contour[:, 0] = np.clip(fixed_contour[:, 0], gt_x1, gt_x1 + gt_w)
fixed_contour[:, 1] = np.clip(fixed_contour[:, 1], gt_y1, gt_y1 + gt_h)
# contour_mean = np.mean(fixed_contour, axis=0)
contour_std = np.sqrt(np.sum(np.std(fixed_contour, axis=0) ** 2))
if contour_std < 1e-6 or contour_std == np.inf or contour_std == np.nan: # invalid shapes
continue
updated_bbox = [np.min(fixed_contour[:, 0]), np.min(fixed_contour[:, 1]),
np.max(fixed_contour[:, 0]), np.max(fixed_contour[:, 1])]
shapes.append(np.ndarray.flatten(fixed_contour).tolist())
labels.append(self.cat_ids[anno['category_id']])
bboxes.append(updated_bbox)
labels = np.array(labels)
bboxes = np.array(bboxes, dtype=np.float32)
shapes = np.array(shapes, dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
shapes = np.zeros((1, self.n_vertices * 2), dtype=np.float32) # in xyxy format
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.], dtype=np.float32) # center of image
scale = max(height, width) * 1.0
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(128, width)
h_border = get_border(128, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img, (self.img_size['w'], self.img_size['h']))
# -----------------------------------debug---------------------------------
# image_show = img.copy()
# for bbox, label in zip(bboxes, labels):
# if flipped:
# bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# bbox[:2] = affine_transform(bbox[:2], trans_img)
# bbox[2:] = affine_transform(bbox[2:], trans_img)
# bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.img_size['w'] - 1)
# bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.img_size['h'] - 1)
# cv2.rectangle(image_show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
# cv2.putText(image_show, self.class_name[label + 1], (int(bbox[0]), int(bbox[1])),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# cv2.imshow('img', image_show)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
img = img.astype(np.float32) / 255.
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
hmap = np.zeros((self.num_classes, self.fmap_size['h'], self.fmap_size['w']), dtype=np.float32) # heatmap
w_h_ = np.zeros((self.max_objs, 4), dtype=np.float32) # width and height of the shape
real_ = torch.FloatTensor(np.zeros((self.max_objs, self.n_vertices), dtype=np.float32))
imaginary_ = torch.FloatTensor(np.zeros((self.max_objs, self.n_vertices), dtype=np.float32))
regs = np.zeros((self.max_objs, 2), dtype=np.float32) # regression for 4 offsets of center of mass to the bbox
inds = np.zeros((self.max_objs,), dtype=np.int64)
ind_masks = np.zeros((self.max_objs,), dtype=np.uint8)
# detections = []
for k, (bbox, label, shape) in enumerate(zip(bboxes, labels, shapes)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# Flip the contour
for m in range(self.n_vertices):
shape[2 * m] = width - shape[2 * m] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
# generate gt shape mean and std from contours
for m in range(self.n_vertices): # apply scale and crop transform to shapes
shape[2 * m:2 * m + 2] = affine_transform(shape[2 * m:2 * m + 2], trans_fmap)
shape_clipped = np.reshape(shape, (self.n_vertices, 2))
shape_clipped[:, 0] = np.clip(shape_clipped[:, 0], 0, self.fmap_size['w'] - 1)
shape_clipped[:, 1] = np.clip(shape_clipped[:, 1], 0, self.fmap_size['h'] - 1)
clockwise_flag = check_clockwise_polygon(shape_clipped)
if not clockwise_flag:
fixed_contour = np.flip(shape_clipped, axis=0)
else:
fixed_contour = shape_clipped.copy()
# Indexing from the left-most vertex, argmin x-axis
idx = np.argmin(fixed_contour[:, 0])
indexed_shape = np.concatenate((fixed_contour[idx:, :], fixed_contour[:idx, :]), axis=0)
# box_center = np.array([(bbox[0] + bbox[2]) / 2., (bbox[1] + bbox[3]) / 2.], dtype=np.float32)
mass_center = np.mean(indexed_shape, axis=0)
if h < 1e-6 or w < 1e-6: # remove small bboxes
continue
centered_shape = indexed_shape - mass_center
if h > 0 and w > 0:
obj_c = mass_center
obj_c_int = obj_c.astype(np.int32)
radius = max(0, int(gaussian_radius((math.ceil(h), math.ceil(w)), self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
fourier_results = torch.fft(torch.FloatTensor(centered_shape), signal_ndim=1) / 32.
real_[k] = fourier_results[:, 0]
imaginary_[k] = fourier_results[:, 1]
w_h_[k] = mass_center[1] - bbox[1], bbox[3] - mass_center[1], \
mass_center[0] - bbox[0], bbox[2] - mass_center[0] # [top, bottom, left, right] distance
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
# detections = np.array(detections, dtype=np.float32) \
# if len(detections) > 0 else np.zeros((1, 6), dtype=np.float32)
# -----------------------------------debug---------------------------------
# canvas = np.zeros((self.fmap_size['h'] * 2, self.fmap_size['w'] * 2, 3), dtype=np.float32)
# canvas[0:self.fmap_size['h'], 0:self.fmap_size['w'], :] = np.tile(np.expand_dims(hmap[0], 2), (1, 1, 3))
# canvas[0:self.fmap_size['h'], self.fmap_size['w']:, :] = np.tile(np.expand_dims(hmap[1], 2), (1, 1, 3))
# canvas[self.fmap_size['h']:, 0:self.fmap_size['w'], :] = np.tile(np.expand_dims(hmap[2], 2), (1, 1, 3))
# canvas[self.fmap_size['h']:, self.fmap_size['w']:, :] = np.tile(np.expand_dims(hmap[3], 2), (1, 1, 3))
# print(w_h_[0], regs[0])
# cv2.imshow('hmap', canvas)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
return {'image': img, 'real': real_.numpy(), 'imaginary': imaginary_.numpy(),
'hmap': hmap, 'w_h_': w_h_, 'regs': regs, 'inds': inds, 'ind_masks': ind_masks,
'c': center, 's': scale, 'img_id': img_id}
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
channel_counter = 1 # len(self.coco.getCatIds())
# target
# target_img = cv2.imread(img_path)
N_FRAMES = self.opt.nbr_frames
middle = int(N_FRAMES/2)
index = os.path.basename(img_path).replace('.jpg', '').replace('img', '').replace('.JPEG', '')
rest = img_path.replace(index + '.jpg', '').replace(os.path.dirname(img_path), '')
length = len(index)
modulo = '1'
for i in range(length):
modulo += '0'
img_paths = []
for i in range(N_FRAMES):
new_img_path = os.path.dirname(img_path) \
+ rest \
+ str((int(index) - (i-middle)) % int(modulo)).zfill(length) + '.jpg'
if not os.path.exists(new_img_path):
new_img_path = img_path
img_paths.append(new_img_path)
imgs = []
for path in img_paths:
imgs.append(cv2.imread(path))
img = np.concatenate(imgs, -1)
bboxes = {}
for ann in anns:
if str(ann['category_id']) in bboxes:
bboxes[str(ann['category_id'])].append([int(ann['bbox'][0]),
int(ann['bbox'][1]),
int(ann['bbox'][0] + ann['bbox'][2]),
int(ann['bbox'][1] + ann['bbox'][3])])
else:
bboxes[str(ann['category_id'])] = [[int(ann['bbox'][0]),
int(ann['bbox'][1]),
int(ann['bbox'][0] + ann['bbox'][2]),
int(ann['bbox'][1] + ann['bbox'][3])]]
# """
PYFLOW = True
if PYFLOW:
if 'uav' in self.opt.dataset:
seg_path = os.path.join('/store/datasets/UAV/bgsubs',
os.path.dirname(file_name).split('/')[-1],
os.path.basename(file_name).replace('jpg', 'png'))
else:
seg_path = os.path.join('/store/datasets/OlderUA-Detrac/pyflow-bgsubs',
os.path.dirname(file_name).split('/')[-1],
os.path.basename(file_name).replace('jpg', 'png'))
# """
if PYFLOW:
seg_img = cv2.imread(seg_path, 0) # hughes
else:
seg_img = np.zeros([channel_counter, img.shape[0], img.shape[1]])
for label in range(1, channel_counter+1):
if str(label) in bboxes:
for bbox in bboxes[str(label)]:
seg_img[label-1, bbox[1]:bbox[3], bbox[0]:bbox[2]] = 255
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
c[1] += s * np.clip(np.random.randn()*cf, -2*cf, 2*cf)
s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
# target
# target_img = target_img[:, ::-1, :]
seg_img = seg_img[:, ::-1]
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(
c, s, 0, [input_w, input_h])
seg_inp = cv2.warpAffine(seg_img, trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
# print('pre: ', img.shape)
# target
# target_inp = cv2.warpAffine(target_img, trans_input,(input_w, input_h),flags=cv2.INTER_LINEAR)
inp = np.zeros((input_w, input_h, N_FRAMES*3))
if inp.shape[2] == N_FRAMES*3:
for i in range(N_FRAMES):
inp[:, :, i*3:i*3+3] = cv2.warpAffine(img[:, :, i*3:i*3+3], trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
# print('post: ', inp.shape)
# target
# target_inp = (target_inp.astype(np.float32) / 255.)
inp = (inp.astype(np.float32) / 255.)
seg_inp = (seg_inp.astype(np.float32) / 255.) # hughes
# print('np.mean(inp), PRE: ', np.mean(inp))
if inp.shape[2] == N_FRAMES*3:
for i in range(N_FRAMES):
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp[:, :, i*3:i*3+3], self._eig_val, self._eig_vec)
else:
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
# target
# color_aug(self._data_rng, target_inp, self._eig_val, self._eig_vec)
# print('np.mean(inp), POST: ', np.mean(inp))
if inp.shape[2] == N_FRAMES*3:
for i in range(N_FRAMES):
inp[:, :, i*3:i*3+3] = (inp[:, :, i*3:i*3+3] - self.mean) / self.std
else:
inp = (inp - self.mean) / self.std
# target
# target_inp = (target_inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
# target
# target_inp = target_inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2), dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2), dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
cat_spec_wh[k, cls_id * 2: cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2: cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k], radius)
gt_det.append([ct[0] - w / 2, ct[1] - h / 2,
ct[0] + w / 2, ct[1] + h / 2, 1, cls_id])
# write_hm = cv2.resize(((hm-np.min(hm)/np.max(hm))*255).astype(np.uint8).squeeze(0), (512, 512))
# cv2.imwrite('/store/datasets/UA-Detrac/test_sample/VID_HM/' + 'inp_' + os.path.basename(file_name) + '_' + 'HM.jpg', write_hm)
# ReScale 1/4
# scale_percent = 25 # percent of original size
# width = int(seg_inp.shape[1] * scale_percent / 100)
# height = int(seg_inp.shape[0] * scale_percent / 100)
# dim = (width, height)
# seg_inp = cv2.resize(seg_inp, dim, interpolation=cv2.INTER_AREA)
seg_inp = np.expand_dims(seg_inp, 0)
ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh, 'seg': seg_inp} # 'seg': np.expand_dims(seg_inp, 0)}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
# if inp.shape[0] == N_FRAMES*3:
# for i in range(N_FRAMES):
# img_test = (inp[i*3:i*3+3, :, :].transpose(1, 2, 0) * 255).astype(np.uint8)
# cv2.imwrite(os.path.join("/store/datasets/UA-Detrac/test_sample/VID_HM/", 'inp_' + os.path.basename(file_name) + '_' + str(i)), img_test)
#img_test = (target_inp.transpose(1, 2, 0) * 255).astype(np.uint8)
# cv2.imwrite(os.path.join("/store/datasets/UA-Detrac/exp/tensors/VID_HM/", os.path.basename(file_name) + '_target'), img_test)
# cv2.imwrite(os.path.join("/store/datasets/UA-Detrac/COCO-format/img_tests/", "inp_" + os.path.basename(file_name)).replace('.jpg', '_seg.jpg'), (seg_inp.transpose(1, 2, 0) * 255).astype(np.uint8))
# exit()
return ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(
self.img_dir,
self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
labels = np.array(
[self.cat_ids[anno['category_id']] for anno in annotations])
bboxes = np.array([anno['bbox'] for anno in annotations],
dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.],
dtype=np.float32) # center of image
scale = max(height, width) * 1.0
'''
imgs = coco_a.getImgIds() [391895, 522418, 184613, 318219, ...]图片id列表
bboxes 图片标记的所有框的列表
[[359.17 146.17 112.45 213.57]
[339.88 22.16 153.88 300.73]
[471.64 172.82 35.92 48.1 ]
[486.01 183.31 30.63 34.98]]
'''
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(128, width)
h_border = get_border(128, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(
center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img,
(self.img_size['w'], self.img_size['h']))
# -----------------------------------debug---------------------------------
# for bbox, label in zip(bboxes, labels):
# if flipped:
# bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# bbox[:2] = affine_transform(bbox[:2], trans_img)
# bbox[2:] = affine_transform(bbox[2:], trans_img)
# bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.img_size['w'] - 1)
# bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.img_size['h'] - 1)
# cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
# cv2.putText(img, self.class_name[label + 1], (int(bbox[0]), int(bbox[1])),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# cv2.imshow('img', img)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
img = img.astype(np.float32) / 255.
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(
center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
hmap = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap
w_h_ = np.zeros((self.max_objs, 2),
dtype=np.float32) # width and height
regs = np.zeros((self.max_objs, 2), dtype=np.float32) # regression
inds = np.zeros((self.max_objs, ), dtype=np.int64)
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
# detections = []
'''
bbox = bboxes[0]
print(bbox[[0, 2]])
print(bbox)
print(bbox[0:2])
[359.17 112.45]
[359.17 146.17 112.45 213.57]
[359.17 146.17]
<class 'numpy.ndarray'>
'''
for k, (bbox, label) in enumerate(zip(bboxes, labels)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
obj_c = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
obj_c_int = obj_c.astype(np.int32)
radius = max(
0,
int(
gaussian_radius((math.ceil(h), math.ceil(w)),
self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
w_h_[k] = 1. * w, 1. * h
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
# groundtruth bounding box coordinate with class
# detections.append([obj_c[0] - w / 2, obj_c[1] - h / 2,
# obj_c[0] + w / 2, obj_c[1] + h / 2, 1, label])
# detections = np.array(detections, dtype=np.float32) \
# if len(detections) > 0 else np.zeros((1, 6), dtype=np.float32)
return {
'image': img,
'hmap': hmap,
'w_h_': w_h_,
'regs': regs,
'inds': inds,
'ind_masks': ind_masks,
'c': center,
's': scale,
'img_id': img_id
}
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
#根据图像文件名,读入对应的关键点标注文件
(filepath, tempfilename) = os.path.split(img_path)
(filename, extension) = os.path.splitext(tempfilename)
kps_path = os.path.join(
'/media/srt/dataset/L_Shelf_0114/Kps_Ann', filename + '_kps.npy'
) #/media/srt/resource/Halcon_Project/L_shelf_dataset/HG_Dataset/kps
kps_raw = np.load(kps_path)
c3 = np.ones(6)
kps_ann = np.column_stack((kps_raw, c3)) #将关键点维度变为[6,3]
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
s = max(img.shape[0], img.shape[1]) * 1.0 #对crop,shift进行赋值
input_h, input_w = self.opt.input_h, self.opt.input_w #在opt中定义的分辨率
rot = 0
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opt.scale #0
cf = self.opt.shift #0
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
#加上multi-pose中的随机旋转
if np.random.random() < self.opt.aug_rot:
rf = self.opt.rotate
rot = np.clip(np.random.randn() * rf, -rf * 2, rf * 2)
# if np.random.random() < self.opt.flip:
# flipped = True
# img = img[:, ::-1, :]
# c[0] = width - c[0] - 1
# 对输入执行仿射变换
trans_input = get_affine_transform(c, s, rot, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
test_image = inp[1] #用于与kps_hp可视化使用
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
num_kps = 6 #点数是否需要+1 ?
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
trans_output_rot = get_affine_transform(c, s, rot,
[output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w),
dtype=np.float32) #中心对应的hp
hm_hp = np.zeros((num_kps, output_h, output_w),
dtype=np.float32) #kps对应的hp
#此处只是初始化,未赋值
dense_kps = np.zeros((num_kps, 2, output_h, output_w),
dtype=np.float32)
dense_kps_mask = np.zeros((num_kps, output_h, output_w),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
kps = np.zeros((num_kps, num_kps * 2),
dtype=np.float32) #其他关键点指向某个关键点的向量
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
kps_mask = np.zeros((self.max_objs, self.num_kps * 2), dtype=np.uint8)
hp_offset = np.zeros((self.max_objs * num_kps, 2), dtype=np.float32)
hp_ind = np.zeros((self.max_objs * num_kps), dtype=np.int64)
hp_mask = np.zeros((self.max_objs * num_kps), dtype=np.int64)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2),
dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2),
dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
#获取标注各项数据的标志
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.cat_ids[ann['category_id']])
#pts的读入方式可以自行定义
pts = np.array(kps_ann,
np.float32).reshape(num_kps,
3) #原来的按照coco数据集json标注读入
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
num_kpts = pts[:, 2].sum()
if num_kpts == 0:
hm[cls_id, ct_int[1], ct_int[0]] = 0.9999
reg_mask[k] = 0
cat_spec_wh[k, cls_id * 2:cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2:cls_id * 2 + 2] = 1
hp_radius = gaussian_radius((math.ceil(h), math.ceil(w)))
hp_radius = self.opt.hm_gauss \
if self.opt.mse_loss else max(0, int(hp_radius))
for j in range(num_kps):
if pts[j, 2] > 0:
#如果关键点的第3位>0,则对关键点进行变换
pts[j, :2] = affine_transform(pts[j, :2],
trans_output) #对关键点进行变换
if pts[j, 0] >= 0 and pts[j, 0] < output_w and \
pts[j, 1] >= 0 and pts[j, 1] < output_h:
#计算其他点指向该点的向量
kps[j, j * 2:j * 2 + 2] = pts[:, :2] - pts[j, :2]
kps_mask[k, j * 2:j * 2 + 2] = 1
pt_int = pts[j, :2].astype(np.int32)
hp_offset[k * num_kps + j] = pts[j, :2] - pt_int
hp_mask[k * num_kps + j] = 1
if self.opt.dense_hp:
#必须在中心点hm gassian之前画
print('draw dense hp!!!')
draw_dense_reg(dense_kps[j],
hm[cls_id],
ct_int,
pts[j, :2] - ct_int,
radius,
is_offset=True)
draw_gaussian(dense_kps_mask[j], ct_int,
radius)
draw_gaussian(hm_hp[j], pt_int, hp_radius)
heatmap = np.squeeze(hm_hp[j]) #(1,160,240)
heatmap = cv2.resize(heatmap, (960, 640),
interpolation=cv2.INTER_CUBIC)
new_image = test_image + heatmap * 2
array_name = 'forbidden_s_c_kps_hp/visual_kps_' + str(
index) + '_' + str(j) + '.png'
# matplotlib.image.imsave(array_name, new_image)
#画中心点的高斯图
draw_gaussian(hm[cls_id], ct_int, radius)
heatmap = np.squeeze(hm[cls_id]) # (1,160,240)
heatmap = cv2.resize(heatmap, (960, 640),
interpolation=cv2.INTER_CUBIC)
new_image = test_image + heatmap * 2
array_name = 'visual_center_' + str(index) + '.png'
# matplotlib.image.imsave(array_name, new_image)
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k],
radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] +
h / 2, 1
] + pts[:, :2].reshape(num_kps * 2).tolist() + [cls_id])
# gt_det.append([ct[0] - w / 2, ct[1] - h / 2,
# ct[0] + w / 2, ct[1] + h / 2, 1, cls_id])
#在原来的基础上增加了 'hps','hps_mask'
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'hps': kps,
'hps_mask': kps_mask
}
if self.opt.dense_hp:
dense_kps = dense_kps.reshape(num_kps * 2, output_h, output_w)
dense_kps_mask = dense_kps_mask.reshape(num_kps, 1, output_h,
output_w)
dense_kps_mask = np.concatenate([dense_kps_mask, dense_kps_mask],
axis=1)
dense_kps_mask = dense_kps_mask.reshape(num_kps * 2, output_h,
output_w)
ret.update({
'dense_hps': dense_kps,
'dense_hps_mask': dense_kps_mask
})
del ret['hps'], ret['hps_mask']
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({
'cat_spec_wh': cat_spec_wh,
'cat_spec_mask': cat_spec_mask
})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.hm_hp:
ret.update({'hm_hp': hm_hp})
if self.opt.reg_hp_offset:
ret.update({
'hp_offset': hp_offset,
'hp_ind': hp_ind,
'hp_mask': hp_mask
})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def _get_data(self, position):
img_id = self.images[self._indexes[position]]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.params.max_objs)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
assert (
len(img.shape) == 3
), f"The dimensions of img should be 3. Filename: {img_path}, shape: {img.shape}"
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._rng, inp, self.params._eig_val,
self.params._eig_vec)
inp = (inp - self.params.mean) / self.params.std
if self.mixed_precision:
inp = fast_pad(inp)
# Transpose to NCHW if channel_last is not enabled
if not self.channel_last:
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.params.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
ind = np.zeros((self.params.max_objs), dtype=np.int32)
wh = np.zeros((self.params.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.params.max_objs, 2), dtype=np.float32)
reg_mask = np.zeros((self.params.max_objs, 1), dtype=np.float32)
cls = np.zeros((self.params.max_objs, 1), dtype=np.int32)
draw_gaussian = draw_umich_gaussian
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.params.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
cls[k] = cls_id
# Transpose heatmap to NHWC if channel last is enabled
if self.channel_last:
hm = np.transpose(hm, (1, 2, 0))
ret = (inp, hm, ind, wh, reg, reg_mask, cls)
return ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(
self.img_dir,
self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
img = self.coco.loadImgs(ids=[img_id])[0]
w_img = int(img['width'])
h_img = int(img['height'])
labels = []
bboxes = []
shapes = []
for anno in annotations:
if anno['iscrowd'] == 1: # Excludes crowd objects
continue
# polygons = anno['segmentation'][0]
polygons = anno['segmentation']
if len(polygons) > 1:
bg = np.zeros((h_img, w_img, 1), dtype=np.uint8)
for poly in polygons:
len_poly = len(poly)
vertices = np.zeros((1, len_poly // 2, 2), dtype=np.int32)
for i in range(len_poly // 2):
vertices[0, i, 0] = int(poly[2 * i])
vertices[0, i, 1] = int(poly[2 * i + 1])
# cv2.fillPoly(bg, vertices, color=(255))
cv2.drawContours(bg,
vertices,
color=(255),
contourIdx=-1,
thickness=-1)
pads = 5
while True:
kernel = np.ones((pads, pads), np.uint8)
bg_closed = cv2.morphologyEx(bg, cv2.MORPH_CLOSE, kernel)
obj_contours, _ = cv2.findContours(bg_closed,
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if len(obj_contours) > 1:
pads += 5
else:
polygons = obj_contours[0]
break
else:
# continue
polygons = anno['segmentation'][0]
gt_x1, gt_y1, gt_w, gt_h = anno['bbox']
contour = np.array(polygons).reshape((-1, 2))
# Downsample the contour to fix number of vertices
fixed_contour = resample(contour, num=self.n_vertices)
fixed_contour[:, 0] = np.clip(fixed_contour[:, 0], gt_x1,
gt_x1 + gt_w)
fixed_contour[:, 1] = np.clip(fixed_contour[:, 1], gt_y1,
gt_y1 + gt_h)
# contour_mean = np.mean(fixed_contour, axis=0)
contour_std = np.sqrt(np.sum(np.std(fixed_contour, axis=0)**2))
if contour_std < 1e-6 or contour_std == np.inf or contour_std == np.nan: # invalid shapes
continue
shapes.append(np.ndarray.flatten(fixed_contour).tolist())
labels.append(self.cat_ids[anno['category_id']])
bboxes.append(anno['bbox'])
labels = np.array(labels)
bboxes = np.array(bboxes, dtype=np.float32)
shapes = np.array(shapes, dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
shapes = np.zeros((1, self.n_vertices * 2), dtype=np.float32)
bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
# if img_id in self.all_annotations.keys():
# annotations = self.all_annotations[img_id]
# shape_annots = self.all_shapes[img_id]
# labels = annotations['cat_id']
# bboxes = annotations['bbox'] # xyxy format
# shapes = shape_annots['shape'] # polygonal vertices format xyxyxyxyxy...
# codes = annotations['codes']
# labels = np.array(labels)
# bboxes = np.array(bboxes, dtype=np.float32)
# codes = np.array(codes, dtype=np.float32)
# shapes = np.array(shapes, dtype=np.float32)
# else:
# bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
# labels = np.array([[0]])
# codes = np.zeros(shape=(1, self.n_codes), dtype=np.float32)
# shapes = np.zeros(shape=(1, self.n_vertices * 2), dtype=np.float32)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.],
dtype=np.float32) # center of image
scale = max(height, width) * 1.0
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(128, width)
h_border = get_border(128, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(
center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img,
(self.img_size['w'], self.img_size['h']))
# -----------------------------------debug---------------------------------
# image_show = img.copy()
# for bbox, label, shape in zip(bboxes, labels, shapes):
# if flipped:
# bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# # Flip the contour
# for m in range(self.n_vertices):
# shape[2 * m] = width - shape[2 * m] - 1
# bbox[:2] = affine_transform(bbox[:2], trans_img)
# bbox[2:] = affine_transform(bbox[2:], trans_img)
# bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.img_size['w'] - 1)
# bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.img_size['h'] - 1)
#
# # generate gt shape mean and std from contours
# for m in range(self.n_vertices): # apply scale and crop transform to shapes
# shape[2 * m:2 * m + 2] = affine_transform(shape[2 * m:2 * m + 2], trans_img)
#
# contour = np.reshape(shape, (self.n_vertices, 2))
# # Indexing from the left-most vertex, argmin x-axis
# idx = np.argmin(contour[:, 0])
# indexed_shape = np.concatenate((contour[idx:, :], contour[:idx, :]), axis=0)
#
# clockwise_flag = check_clockwise_polygon(indexed_shape)
# if not clockwise_flag:
# fixed_contour = np.flip(indexed_shape, axis=0)
# else:
# fixed_contour = indexed_shape
#
# contour[:, 0] = np.clip(fixed_contour[:, 0], 0, self.img_size['w'] - 1)
# contour[:, 1] = np.clip(fixed_contour[:, 1], 0, self.img_size['h'] - 1)
#
# # cv2.rectangle(image_show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
# # cv2.polylines(image_show, [contour.astype(np.int32)], True, (0, 0, 255), thickness=2)
# cv2.drawContours(image_show, [contour.astype(np.int32)],
# color=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)),
# contourIdx=-1, thickness=-1)
#
# cv2.imshow('img', image_show)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
img = img.astype(np.float32) / 255.
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(
center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
hmap = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap
# w_h_ = np.zeros((self.max_objs, 2), dtype=np.float32) # width and height of the shape
w_h_std = np.zeros((self.max_objs, 2),
dtype=np.float32) # width and height of the shape
codes_ = np.zeros((self.max_objs, self.n_codes),
dtype=np.float32) # gt coefficients/codes for shapes
regs = np.zeros(
(self.max_objs, 2),
dtype=np.float32) # regression for offsets of shape center
inds = np.zeros((self.max_objs, ), dtype=np.int64)
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
# detections = []
for k, (bbox, label, shape) in enumerate(zip(bboxes, labels, shapes)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# Flip the contour
for m in range(self.n_vertices):
shape[2 * m] = width - shape[2 * m] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
# generate gt shape mean and std from contours
for m in range(self.n_vertices
): # apply scale and crop transform to shapes
shape[2 * m:2 * m + 2] = affine_transform(
shape[2 * m:2 * m + 2], trans_fmap)
contour = np.reshape(shape, (self.n_vertices, 2))
# Indexing from the left-most vertex, argmin x-axis
idx = np.argmin(contour[:, 0])
indexed_shape = np.concatenate(
(contour[idx:, :], contour[:idx, :]), axis=0)
clockwise_flag = check_clockwise_polygon(indexed_shape)
if not clockwise_flag:
fixed_contour = np.flip(indexed_shape, axis=0)
else:
fixed_contour = indexed_shape.copy()
contour[:, 0] = np.clip(fixed_contour[:, 0], 0,
self.fmap_size['w'] - 1)
contour[:, 1] = np.clip(fixed_contour[:, 1], 0,
self.fmap_size['h'] - 1)
contour_mean = np.mean(contour, axis=0)
contour_std = np.std(contour, axis=0)
if np.sqrt(np.sum(contour_std**2)) <= 1e-6:
continue
else:
norm_shape = (contour - contour_mean) / np.sqrt(
np.sum(contour_std**2))
if h > 0 and w > 0 and np.sqrt(np.sum(contour_std**2)) > 1e-6:
obj_c = contour_mean
obj_c_int = obj_c.astype(np.int32)
radius = max(
0,
int(
gaussian_radius((math.ceil(h), math.ceil(w)),
self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
w_h_std[k] = contour_std
temp_codes, _ = fast_ista(norm_shape.reshape((1, -1)),
self.dictionary,
lmbda=self.sparse_alpha,
max_iter=80)
codes_[k] = np.exp(temp_codes)
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
# groundtruth bounding box coordinate with class
# detections.append([obj_c[0] - w / 2, obj_c[1] - h / 2,
# obj_c[0] + w / 2, obj_c[1] + h / 2, 1, label])
# detections = np.array(detections, dtype=np.float32) \
# if len(detections) > 0 else np.zeros((1, 6), dtype=np.float32)
# -----------------------------------debug---------------------------------
# canvas = np.zeros((self.fmap_size['h'] * 2, self.fmap_size['w'] * 2, 3), dtype=np.float32)
# canvas[0:self.fmap_size['h'], 0:self.fmap_size['w'], :] = np.tile(np.expand_dims(hmap[0], 2), (1, 1, 3))
# canvas[0:self.fmap_size['h'], self.fmap_size['w']:, :] = np.tile(np.expand_dims(hmap[1], 2), (1, 1, 3))
# canvas[self.fmap_size['h']:, 0:self.fmap_size['w'], :] = np.tile(np.expand_dims(hmap[2], 2), (1, 1, 3))
# canvas[self.fmap_size['h']:, self.fmap_size['w']:, :] = np.tile(np.expand_dims(hmap[3], 2), (1, 1, 3))
# print(w_h_[0], regs[0])
# cv2.imshow('hmap', canvas)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
# -----------------------------------debug---------------------------------
# image_show = img.copy()
# for bbox, label, shape in zip(bboxes, labels, shapes):
# cv2.rectangle(image_show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255, 0, 0), 2)
# cv2.polylines(image_show, [contour.astype(np.int32)], True, (0, 0, 255), thickness=2)
# cv2.imshow('img', image_show)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
return {
'image': img,
'codes': codes_,
'hmap': hmap,
'w_h_std': w_h_std,
'regs': regs,
'inds': inds,
'ind_masks': ind_masks,
'c': center,
's': scale,
'img_id': img_id
}
def __next__(self):
load_vid_time, img_transform_time, create_heatmap_time = 0, 0, 0
start = time.time()
if self.cap is None or self.count >= self.length:
if self.cap is not None and self.vid_i == self.num_videos and self.loop:
self.vid_i = 0
elif self.cap is not None and self.vid_i == self.num_videos:
raise StopIteration
if self.opt.vidstream == 'skvideo':
self.cap = skvideo.io.vread(self.video_paths[self.vid_i])
metadata = skvideo.io.ffprobe(self.video_paths[self.vid_i])
fr_lst = metadata['video']['@avg_frame_rate'].split('/')
self.rate = int(fr_lst[0]) / int(fr_lst[1])
self.length = int(metadata['video']['@nb_frames'])
else:
self.cap = cv2.VideoCapture(self.video_paths[self.vid_i])
width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.rate = self.cap.get(cv2.CAP_PROP_FPS)
self.length = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
self.frame_gen = self._frame_from_video(self.cap)
# self.detections = pickle.load(open(self.annotation_path[self.vid_i], 'rb'))
self.count = 0
self.vid_i += 1
end_load_vid = time.time()
load_vid_time = end_load_vid - start
# load image depending on stream
start_resize = time.time()
if self.opt.vidstream == 'skvideo':
img = self.cap[self.count]
else:
img = next(self.frame_gen)
# in_h = int(original_img.shape[0] / self.opt.downsample)
# in_w = int(original_img.shape[1] / self.opt.downsample)
# img = cv2.resize(original_img, (in_w, in_h))
# cv2.imwrite("/home/jl5/CenterNet/tmp.png", img)
start_img_transform = time.time()
anns = self.mmdetect_pred2inst(self.count)
num_objs = min(len(anns), self.max_objs)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
# send to gpu
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = torch.from_numpy(inp).cuda()
inp = (inp.float() / 255.)
# if self.split == 'train' and not self.opt.no_color_aug:
# color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - torch.from_numpy(self.mean).cuda()) / torch.from_numpy(
self.std).cuda()
inp = inp.permute(2, 0, 1)
end_img_transform = time.time()
img_transform_time = end_img_transform - start_img_transform
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2),
dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2),
dtype=np.uint8)
unconfident_hm = np.zeros((num_classes, output_h, output_w),
dtype=np.float32)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
def show_bbox(im):
fig, ax = plt.subplots(1)
ax.imshow(im)
for i in range(num_objs):
bbox = np.array(anns[i]['bbox'], dtype=np.int32)
bbox = bbox / self.opt.downsample
rect = patches.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
linewidth=1,
edgecolor='r',
facecolor='none')
ax.add_patch(rect)
plt.savefig('/home/jl5/CenterNet/tmp.png')
pdb.set_trace()
# detect = self.detections[self.count]
# if self.opt.task == 'ctdet_semseg':
# seg_mask, weight_mask = batch_segmentation_masks(1, (720, 1280), np.array([detect['boxes']]), np.array([detect['classes']]), detect['masks'],
# np.array([detect['scores']]), [len(detect['boxes'])], True, coco_class_groups, mask_threshold=0.5, box_threshold=self.opt.center_thresh, scale_boxes=False)
# unbatch_seg = seg_mask[0].astype(np.uint8)
# unbatch_weight = weight_mask[0].astype(np.uint8)
# seg_mask = np.expand_dims(cv2.resize(unbatch_seg, (1280, 736)), axis=0).astype(np.int32)
# weight_mask = np.expand_dims(cv2.resize(unbatch_weight, (1280, 736)), axis = 0).astype(bool)
start_detect = time.time()
for k in range(num_objs):
ann = anns[k]
bbox = np.array(
ann['bbox'],
dtype=np.float32) # self._coco_box_to_bbox(ann['bbox'])
# bbox = bbox / self.opt.downsample # if need to downsample
cls_id = int(self.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
if ann['score'] >= 0.3 and ann['score'] < 0.5:
draw_gaussian(unconfident_hm[cls_id], ct_int, radius)
reg_mask[k] = 0
else:
draw_gaussian(hm[cls_id], ct_int, radius)
reg_mask[k] = 1
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
cat_spec_wh[k, cls_id * 2:cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2:cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k],
radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
if self.opt.task == 'ctdet_semseg':
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'seg': seg_mask,
'weight_seg': weight_mask
}
else:
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'unconf_hm': unconfident_hm
}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({
'cat_spec_wh': cat_spec_wh,
'cat_spec_mask': cat_spec_mask
})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
# meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': self.count}
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': self.count}
ret['meta'] = meta
self.count += 1
end_detect_time = time.time()
create_heatmap_time = end_detect_time - start_detect
# print("load vid {:.4f} | img transform {:.4f} | create instance {:.4f} \n".format(load_vid_time, img_transform_time, create_heatmap_time))
return ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(self.img_dir, self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
img = self.coco.loadImgs(ids=[img_id])[0]
w_img = int(img['width'])
h_img = int(img['height'])
labels = []
bboxes = []
shapes = []
for anno in annotations:
# add some fields for evaluation
# anno['iscrowd'] = 0
# anno['segmentation'] = anno['a_segm'] # only evaluate amodal segmentation
# anno['bbox'] = anno['i_bbox'] # only evaluate inmodal detection
if anno['category_id'] not in KINS_IDS:
continue # excludes 3: person-sitting class for evaluation
polygons = get_connected_polygon_using_mask(anno['segmentation'], (h_img, w_img),
n_vertices=self.n_vertices, closing_max_kernel=50)
gt_x1, gt_y1, gt_w, gt_h = anno['a_bbox'] # this is used to clip resampled polygons
contour = np.array(polygons).reshape((-1, 2))
# Downsample the contour to fix number of vertices
if len(contour) > self.n_vertices:
fixed_contour = resample(contour, num=self.n_vertices)
else:
fixed_contour = turning_angle_resample(contour, self.n_vertices)
fixed_contour[:, 0] = np.clip(fixed_contour[:, 0], gt_x1, gt_x1 + gt_w)
fixed_contour[:, 1] = np.clip(fixed_contour[:, 1], gt_y1, gt_y1 + gt_h)
contour_std = np.sqrt(np.sum(np.std(fixed_contour, axis=0) ** 2))
if contour_std < 1e-6 or contour_std == np.inf or contour_std == np.nan: # invalid shapes
continue
shapes.append(np.ndarray.flatten(fixed_contour).tolist())
labels.append(self.cat_ids[anno['category_id']])
bboxes.append(anno['bbox'])
labels = np.array(labels)
bboxes = np.array(bboxes, dtype=np.float32)
shapes = np.array(shapes, dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
shapes = np.zeros((1, self.n_vertices * 2), dtype=np.float32)
bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.], dtype=np.float32) # center of image
scale = max(height, width) * 1.0
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(400, width)
h_border = get_border(180, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(center, scale, 0, [self.img_size['w'], self.img_size['h']])
# -----------------------------------debug---------------------------------
# image_show = img.copy()
img = cv2.warpAffine(img, trans_img, (self.img_size['w'], self.img_size['h']))
img = img.astype(np.float32) / 255.
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
# -----------------------------------debug---------------------------------
# image_show = cv2.warpAffine(image_show, trans_fmap, (self.fmap_size['w'], self.fmap_size['h']))
hmap = np.zeros((self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap of centers
occ_map = np.zeros((1, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # grayscale map for occlusion levels
w_h_ = np.zeros((self.max_objs, 2), dtype=np.float32) # width and height of inmodal bboxes
shapes_ = np.zeros((self.max_objs, self.n_vertices * 2), dtype=np.float32) # gt amodal segmentation polygons
center_offsets = np.zeros((self.max_objs, 2), dtype=np.float32) # gt amodal mass centers to inmodal bbox center
codes_ = np.zeros((self.max_objs, self.n_codes), dtype=np.float32) # gt amodal coefficients
regs = np.zeros((self.max_objs, 2), dtype=np.float32) # regression for quantization error
inds = np.zeros((self.max_objs,), dtype=np.int64)
ind_masks = np.zeros((self.max_objs,), dtype=np.uint8)
for k, (bbox, label, shape) in enumerate(zip(bboxes, labels, shapes)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# Flip the contour x-axis
for m in range(self.n_vertices):
shape[2 * m] = width - shape[2 * m] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
# generate gt shape mean and std from contours
for m in range(self.n_vertices): # apply scale and crop transform to shapes
shape[2 * m:2 * m + 2] = affine_transform(shape[2 * m:2 * m + 2], trans_fmap)
shape_clipped = np.reshape(shape, (self.n_vertices, 2))
shape_clipped[:, 0] = np.clip(shape_clipped[:, 0], 0, self.fmap_size['w'] - 1)
shape_clipped[:, 1] = np.clip(shape_clipped[:, 1], 0, self.fmap_size['h'] - 1)
clockwise_flag = check_clockwise_polygon(shape_clipped)
if not clockwise_flag:
fixed_contour = np.flip(shape_clipped, axis=0)
else:
fixed_contour = shape_clipped.copy()
# Indexing from the left-most vertex, argmin x-axis
idx = np.argmin(fixed_contour[:, 0])
indexed_shape = np.concatenate((fixed_contour[idx:, :], fixed_contour[:idx, :]), axis=0)
mass_center = np.mean(indexed_shape, axis=0)
if h < 1e-6 or w < 1e-6: # remove small bboxes
continue
centered_shape = indexed_shape - mass_center
if h > 0 and w > 0:
obj_c = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
# obj_c = mass_center
obj_c_int = obj_c.astype(np.int32)
radius = max(0, int(gaussian_radius((math.ceil(h), math.ceil(w)), self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
shapes_[k] = centered_shape.reshape((1, -1))
# shapes_[k] = indexed_shape.reshape((1, -1)) # only for debugging
center_offsets[k] = mass_center - obj_c
codes_[k], _ = fast_ista(centered_shape.reshape((1, -1)), self.dictionary,
lmbda=self.sparse_alpha, max_iter=60)
w_h_[k] = 1. * w, 1. * h
# w_h_[k] = mass_center[1] - bbox[1], bbox[3] - mass_center[1], \
# mass_center[0] - bbox[0], bbox[2] - mass_center[0] # [top, bottom, left, right] distance
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
# occlusion level map gt
occ_map[0] += self.polys_to_mask([np.ndarray.flatten(indexed_shape).tolist()], self.fmap_size['h'],
self.fmap_size['w']) * 1.
occ_map = np.clip(occ_map, 0, self.max_occ) / self.max_occ
# -----------------------------------debug---------------------------------
# for bbox, label, shape in zip(bboxes, labels, shapes_):
# # cv2.rectangle(image_show, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 0), 1)
# cv2.putText(image_show, str(self.reverse_labels[label]), (int(bbox[0]), int(bbox[1])), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# # print(shape, shape.shape)
# cv2.polylines(image_show, [shape.reshape(self.n_vertices, 2).astype(np.int32)], True, (0, 0, 255),
# thickness=1)
# # cv2.imshow('img', image_show)
# # cv2.imshow('occ', occ_map.astype(np.uint8).reshape(occ_map.shape[1], occ_map.shape[2]) * 255)
# m_img = cv2.cvtColor((occ_map * 255).astype(np.uint8).reshape(occ_map.shape[1], occ_map.shape[2]),
# code=cv2.COLOR_GRAY2BGR)
# cat_img = np.concatenate([m_img, image_show], axis=0)
# cv2.imshow('segm', cat_img)
# cv2.waitKey()
# -----------------------------------debug---------------------------------
return {'image': img, 'shapes': shapes_, 'codes': codes_, 'offsets': center_offsets, 'occ_map': occ_map,
'hmap': hmap, 'w_h_': w_h_, 'regs': regs, 'inds': inds, 'ind_masks': ind_masks,
'c': center, 's': scale, 'img_id': img_id}
def __getitem__(self, index):
img_id = self.img_paths[index]
img_set, img_vid, img_name = img_id.split("_", 2)
img_name = img_name.replace("txt", "jpg")
img_path = os.path.join(self.img_dir, img_set, img_vid)
img_rgb = cv2.imread(os.path.join(img_path, "visible", img_name),
cv2.IMREAD_COLOR)
img_ir = cv2.imread(os.path.join(img_path, "lwir", img_name),
cv2.IMREAD_GRAYSCALE)
with open(os.path.join(self.annot_path,
self.img_paths[index])) as annot_file:
annot_data = [line.rstrip('\n') for line in annot_file][1:]
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
if len(annot_data) != 0:
bboxes = bboxes.repeat(len(annot_data), axis=0)
for i in range(len(annot_data)):
line_data = annot_data[i].split()
label = line_data[0]
if self.split == "train":
if label not in ["person", "person?", "people"]:
continue
elif label != "person":
continue
bboxes[i, :] = list(map(int, line_data[1:5]))
bboxes[:, 2:] += bboxes[:, :2]
# resize image and bbox
height, width = img_rgb.shape[:2]
img_rgb = cv2.resize(img_rgb, (self.img_size['w'], self.img_size['h']))
img_ir = cv2.resize(img_ir, (self.img_size['w'], self.img_size['h']))
img_ir = np.expand_dims(img_ir, axis=2)
bboxes[:, 0::2] *= self.img_size['w'] / width
bboxes[:, 1::2] *= self.img_size['h'] / height
# discard non-valid bboxes
bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, self.img_size['w'] - 1)
bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, self.img_size['h'] - 1)
keep_inds = np.logical_and((bboxes[:, 2] - bboxes[:, 0]) > 0,
(bboxes[:, 3] - bboxes[:, 1]) > 0)
bboxes = bboxes[keep_inds]
# randomly flip image and bboxes
if self.split == 'train' and np.random.uniform() > 0.5:
img_rgb[:] = img_rgb[:, ::-1, :]
img_ir[:] = img_ir[:, ::-1, :]
bboxes[:, [0, 2]] = img_rgb.shape[1] - bboxes[:, [2, 0]] - 1
img_rgb = img_rgb.astype(np.float32) / 255.
img_ir = img_ir.astype(np.float32) / 255.
img_rgb -= self.mean[0, 0, :3]
img_rgb /= self.std[0, 0, :3]
img_ir -= self.mean[0, 0, 3]
img_ir /= self.std[0, 0, 3]
img_rgb = img_rgb.transpose((2, 0, 1)) # [H, W, C] to [C, H, W]
img_ir = img_ir.transpose((2, 0, 1))
hmap_tl = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32)
hmap_br = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32)
regs_tl = np.zeros((self.max_objs, 2), dtype=np.float32)
regs_br = np.zeros((self.max_objs, 2), dtype=np.float32)
inds_tl = np.zeros((self.max_objs, ), dtype=np.int64)
inds_br = np.zeros((self.max_objs, ), dtype=np.int64)
num_objs = np.array(min(bboxes.shape[0], self.max_objs))
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
ind_masks[:num_objs] = 1
for i, (xtl, ytl, xbr, ybr) in enumerate(bboxes):
fxtl = (xtl * self.fmap_size['w'] / self.img_size['w'])
fytl = (ytl * self.fmap_size['h'] / self.img_size['h'])
fxbr = (xbr * self.fmap_size['w'] / self.img_size['w'])
fybr = (ybr * self.fmap_size['h'] / self.img_size['h'])
ixtl = int(fxtl)
iytl = int(fytl)
ixbr = int(fxbr)
iybr = int(fybr)
if self.gaussian:
width = xbr - xtl
height = ybr - ytl
width = math.ceil(width * self.fmap_size['w'] /
self.img_size['w'])
height = math.ceil(height * self.fmap_size['h'] /
self.img_size['h'])
radius = max(
0, int(gaussian_radius((height, width),
self.gaussian_iou)))
draw_gaussian(hmap_tl[0], [ixtl, iytl], radius)
draw_gaussian(hmap_br[0], [ixbr, iybr], radius)
else:
hmap_tl[0, iytl, ixtl] = 1
hmap_br[0, iybr, ixbr] = 1
regs_tl[i, :] = [fxtl - ixtl, fytl - iytl]
regs_br[i, :] = [fxbr - ixbr, fybr - iybr]
inds_tl[i] = iytl * self.fmap_size['w'] + ixtl
inds_br[i] = iybr * self.fmap_size['w'] + ixbr
return {
'img_rgb': img_rgb,
'img_ir': img_ir,
'hmap_tl': hmap_tl,
'hmap_br': hmap_br,
'regs_tl': regs_tl,
'regs_br': regs_br,
'inds_tl': inds_tl,
'inds_br': inds_br,
'ind_masks': ind_masks
}
def __getitem__(self, index):
img_id = self.images[index]
inp, ann_list, output_w, output_h, meta = self.get_img_ann(index,
scale_lv=2)
# TBD: Mosaic augmentation requires large input image size
# Increase input image size from 512x512 to 800x800 or larger and
# adjust the scale level to avoid the mosaic boundary to become
# a significant boundary of objects
#inp, ann_list, output_w, output_h, meta = self.mosaic_mix( index )
if False: # Augmnetation visualization
img = inp.transpose(1, 2, 0)
img = (img * self.std + self.mean) * 255
for an in ann_list:
bbox, cls_id, bbox2 = an
bbox = bbox.astype(np.int32)
bbox2 = bbox2.astype(np.int32)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, img.shape[1])
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, img.shape[0])
if bbox[2] - bbox[0] > 0 and bbox[3] - bbox[1] > 0:
cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
(255, 0, 0), 3)
if bbox2.shape[0] > 0:
cv2.rectangle(img, (bbox2[0], bbox2[1]),
(bbox2[2], bbox2[3]), (0, 255, 0), 2)
cv2.imwrite('temp_%d.jpg' % (index), img)
num_objs = min(len(ann_list), self.max_objs)
num_classes = self.num_classes
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_reg = np.zeros((4, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
dense_wh_mask = np.zeros((4, output_h, output_w), dtype=np.float32)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2),
dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2),
dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
xs = np.random.randint(output_w, size=(self.max_objs, 1))
ys = np.random.randint(output_h, size=(self.max_objs, 1))
bgs = np.concatenate([xs, ys], axis=1)
for k in range(num_objs):
bbox, cls_id, bbox2 = ann_list[k]
bbox /= self.opt.down_ratio
bbox2 /= self.opt.down_ratio
oh, ow = bbox[3] - bbox[1], bbox[2] - bbox[0]
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if (h / (oh + 0.01) < 0.9 or w /
(ow + 0.01) < 0.9) and bbox2.shape[0] > 0:
bbox = bbox2
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
#get center of box
ct = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
if (h > 2 or h / (oh + 0.01) > 0.5) and (w > 2 or w /
(ow + 0.01) > 0.5):
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
draw_dense_reg(dense_reg, dense_wh_mask, ct_int, bbox, radius)
draw_gaussian(hm[cls_id], ct_int, radius)
cat_spec_wh[k, cls_id * 2:cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2:cls_id * 2 + 2] = 1
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
dense_wh = dense_reg[:2, :, :]
dense_off = dense_reg[2:, :, :]
ret = {
'input': inp,
'hm': hm,
'dense_wh': dense_wh,
'dense_off': dense_off,
'dense_wh_mask': dense_wh_mask[:2]
}
if self.opt.cat_spec_wh:
ret.update({
'cat_spec_wh': cat_spec_wh,
'cat_spec_mask': cat_spec_mask
})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {
'c': meta[0],
's': meta[1],
'gt_det': gt_det,
'img_id': img_id
}
ret['meta'] = meta
return ret
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(
self.img_dir,
self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
labels = np.array(
[self.cat_ids[anno['category_id']] for anno in annotations])
bboxes = np.array([anno['bbox'] for anno in annotations],
dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
center = np.array([width / 2., height / 2.],
dtype=np.float32) # center of image
scale = max(height, width) * 1.0
flipped = False
if self.split == 'train':
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(128, width)
h_border = get_border(128, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
trans_img = get_affine_transform(
center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img,
(self.img_size['w'], self.img_size['h']))
img = (img.astype(np.float32) / 255.)
if self.split == 'train':
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
trans_fmap = get_affine_transform(
center, scale, 0, [self.fmap_size['w'], self.fmap_size['h']])
hmap = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap
w_h_ = np.zeros((self.max_objs, 2),
dtype=np.float32) # width and height
regs = np.zeros((self.max_objs, 2), dtype=np.float32) # regression
inds = np.zeros((self.max_objs, ), dtype=np.int64)
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
for k, (bbox, label) in enumerate(zip(bboxes, labels)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
obj_c = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
obj_c_int = obj_c.astype(np.int32)
radius = max(
0,
int(
gaussian_radius((math.ceil(h), math.ceil(w)),
self.gaussian_iou)))
draw_umich_gaussian(hmap[label], obj_c_int, radius)
w_h_[k] = 1. * w, 1. * h
regs[k] = obj_c - obj_c_int # discretization error
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
ind_masks[k] = 1
return {
'image': img,
'hmap': hmap,
'w_h_': w_h_,
'regs': regs,
'inds': inds,
'ind_masks': ind_masks,
'c': center,
's': scale,
'img_id': img_id
}
def __getitem__(self, index):
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
anns = list(
filter(
lambda x: x['category_id'] in self._valid_ids and x['iscrowd']
!= 1, anns))
num_objs = min(len(anns), self.max_objs)
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
s = max(img.shape[0], img.shape[1]) * 1.0
rot = 0
flipped = False
if self.split == 'train':
if self.cfg.DATASET.RANDOM_CROP:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border,
high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border,
high=img.shape[0] - h_border)
else:
sf = self.cfg.DATASET.SCALE
cf = self.cfg.DATASET.SHIFT
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.cfg.DATASET.AUG_ROT:
rf = self.cfg.DATASET.ROTATE
rot = np.clip(np.random.randn() * rf, -rf * 2, rf * 2)
if np.random.random() < self.cfg.DATASET.FLIP:
flipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(
c, s, rot, [self.cfg.MODEL.INPUT_RES, self.cfg.MODEL.INPUT_RES])
inp = cv2.warpAffine(
img,
trans_input, (self.cfg.MODEL.INPUT_RES, self.cfg.MODEL.INPUT_RES),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.cfg.DATASET.NO_COLOR_AUG:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - np.array(self.cfg.DATASET.MEAN).astype(
np.float32)) / np.array(self.cfg.DATASET.STD).astype(np.float32)
inp = inp.transpose(2, 0, 1)
output_res = self.cfg.MODEL.OUTPUT_RES
num_joints = self.num_joints
trans_output_rot = get_affine_transform(c, s, rot,
[output_res, output_res])
trans_output = get_affine_transform(c, s, 0, [output_res, output_res])
trans_seg_output = get_affine_transform(c, s, 0,
[output_res, output_res])
hm = np.zeros((self.num_classes, output_res, output_res),
dtype=np.float32)
hm_hp = np.zeros((num_joints, output_res, output_res),
dtype=np.float32)
dense_kps = np.zeros((num_joints, 2, output_res, output_res),
dtype=np.float32)
dense_kps_mask = np.zeros((num_joints, output_res, output_res),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
kps = np.zeros((self.max_objs, num_joints * 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
seg = np.zeros((self.max_objs, output_res, output_res),
dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
kps_mask = np.zeros((self.max_objs, self.num_joints * 2),
dtype=np.uint8)
hp_offset = np.zeros((self.max_objs * num_joints, 2), dtype=np.float32)
hp_ind = np.zeros((self.max_objs * num_joints), dtype=np.int64)
hp_mask = np.zeros((self.max_objs * num_joints), dtype=np.int64)
draw_gaussian = draw_msra_gaussian if self.cfg.LOSS.MSE_LOSS else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(ann['category_id']) - 1
pts = np.array(ann['keypoints'], np.float32).reshape(num_joints, 3)
segment = self.coco.annToMask(ann)
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
pts[:, 0] = width - pts[:, 0] - 1
for e in self.flip_idx:
pts[e[0]], pts[e[1]] = pts[e[1]].copy(), pts[e[0]].copy()
segment = segment[:, ::-1]
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox = np.clip(bbox, 0, output_res - 1)
segment = cv2.warpAffine(segment,
trans_seg_output,
(output_res, output_res),
flags=cv2.INTER_LINEAR)
segment = segment.astype(np.float32)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if (h > 0 and w > 0) or (rot != 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = self.cfg.hm_gauss if self.cfg.LOSS.MSE_LOSS else max(
0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_res + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
#mask
pad_rate = 0.3
segment_mask = np.ones_like(segment)
x,y = (np.clip([ct[0] - (1 + pad_rate)*w/2 ,ct[0] + (1 + pad_rate)*w/2 ],0,output_res - 1)*2).astype(np.int), \
(np.clip([ct[1] - (1 + pad_rate)*h/2 , ct[1] + (1 + pad_rate)*h/2],0,output_res - 1)*2).astype(np.int)
segment_mask[y[0]:y[1], x[0]:x[1]] = 0
segment[segment_mask == 1] = 255
seg[k] = segment
#keypoint
num_kpts = pts[:, 2].sum()
if num_kpts == 0:
hm[cls_id, ct_int[1], ct_int[0]] = 0.9999
reg_mask[k] = 0
hp_radius = gaussian_radius((math.ceil(h), math.ceil(w)))
hp_radius = self.cfg.hm_gauss \
if self.cfg.LOSS.MSE_LOSS else max(0, int(hp_radius))
for j in range(num_joints):
if pts[j, 2] > 0:
pts[j, :2] = affine_transform(pts[j, :2],
trans_output_rot)
if pts[j, 0] >= 0 and pts[j, 0] < output_res and \
pts[j, 1] >= 0 and pts[j, 1] < output_res:
kps[k, j * 2:j * 2 + 2] = pts[j, :2] - ct_int
kps_mask[k, j * 2:j * 2 + 2] = 1
pt_int = pts[j, :2].astype(np.int32)
hp_offset[k * num_joints + j] = pts[j, :2] - pt_int
hp_ind[k * num_joints +
j] = pt_int[1] * output_res + pt_int[0]
hp_mask[k * num_joints + j] = 1
if self.cfg.LOSS.DENSE_HP:
# must be before draw center hm gaussian
draw_dense_reg(dense_kps[j],
hm[cls_id],
ct_int,
pts[j, :2] - ct_int,
radius,
is_offset=True)
draw_gaussian(dense_kps_mask[j], ct_int,
radius)
draw_gaussian(hm_hp[j], pt_int, hp_radius)
draw_gaussian(hm[cls_id], ct_int, radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] +
h / 2, 1
] + pts[:, :2].reshape(num_joints * 2).tolist() + [cls_id])
if rot != 0:
hm = hm * 0 + 0.9999
reg_mask *= 0
kps_mask *= 0
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'hps': kps,
'hps_mask': kps_mask,
'seg': seg
}
if self.cfg.LOSS.DENSE_HP:
dense_kps = dense_kps.reshape(num_joints * 2, output_res,
output_res)
dense_kps_mask = dense_kps_mask.reshape(num_joints, 1, output_res,
output_res)
dense_kps_mask = np.concatenate([dense_kps_mask, dense_kps_mask],
axis=1)
dense_kps_mask = dense_kps_mask.reshape(num_joints * 2, output_res,
output_res)
ret.update({
'dense_hps': dense_kps,
'dense_hps_mask': dense_kps_mask
})
del ret['hps'], ret['hps_mask']
if self.cfg.LOSS.REG_OFFSET:
ret.update({'reg': reg})
if self.cfg.LOSS.HM_HP:
ret.update({'hm_hp': hm_hp})
if self.cfg.LOSS.REG_HP_OFFSET:
ret.update({
'hp_offset': hp_offset,
'hp_ind': hp_ind,
'hp_mask': hp_mask
})
if self.cfg.DEBUG > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 40), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def __get_rotated_coco(self, img, anns, num_objs):
kpts = []
kpts_tmp = []
for k in range(num_objs):
ann = anns[k]
ann_rotated = get_annotation_with_angle(ann)
ann_rotated[4] = ann_rotated[4]
rot = rotate_bbox(*ann_rotated)
kpts.extend([Keypoint(*x) for x in rot])
if self.num_keypoints > 0:
if 'keypoints' not in ann:
ann['keypoints'] = np.zeros((3 * self.num_keypoints, ))
kpt = [
Keypoint(*x)
for x in np.array(ann['keypoints']).reshape(-1, 3)[:, :2]
]
kpts_tmp.extend(kpt)
idx_boxes = len(kpts)
if self.num_keypoints > 0:
kpts.extend(kpts_tmp)
kpts = KeypointsOnImage(kpts, shape=img.shape)
if self.augmentation is not None:
img_aug, kpts_aug = self.augmentation(image=img, keypoints=kpts)
else:
img_aug, kpts_aug = np.copy(img), kpts.copy()
img_aug, kpts_aug = self.resize(image=img_aug, keypoints=kpts_aug)
img = (img_aug.astype(np.float32) / 255.)
inp = (img - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = self.input_size[1] // self.down_ratio
output_w = self.input_size[0] // self.down_ratio
num_classes = self.num_classes
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_detections, 3), dtype=np.float32)
reg = np.zeros((self.max_detections, 2), dtype=np.float32)
ind = np.zeros((self.max_detections), dtype=np.int64)
reg_mask = np.zeros((self.max_detections), dtype=np.uint8)
gt_det = np.zeros(
(self.max_detections, 7 if self.use_rotated_boxes else 6),
dtype=np.float32)
gt_areas = np.zeros((self.max_detections), dtype=np.float32)
if self.num_keypoints > 0:
kp = np.zeros((self.max_detections, self.num_keypoints * 2),
dtype=np.float32)
gt_kp = np.zeros((self.max_detections, self.num_keypoints, 2),
dtype=np.float32)
kp_reg_mask = np.zeros(
(self.max_detections, self.num_keypoints * 2), dtype=np.uint8)
kpts_aug = self.resize_out(keypoints=kpts_aug)
box_kpts_aug, kpts_aug = kpts_aug[:idx_boxes], kpts_aug[idx_boxes:]
assert num_objs == len(box_kpts_aug) // 4
for k in range(num_objs):
ann = anns[k]
points = []
for p in box_kpts_aug[k * 4:k * 4 + 4]:
box_kp = list(
(np.clip(p.x, 0,
output_w - 1), np.clip(p.y, 0, output_h - 1)))
points.append(box_kp)
points = np.array(points).astype(np.float32)
cv_ct, cv_wh, cv_angle = cv2.minAreaRect(points)
if cv_wh[0] == 0 or cv_wh[1] == 0:
continue
cx, cy, w, h, angle = get_annotation_with_angle({
'rbbox':
np.array([cv_ct[0], cv_ct[1], cv_wh[0], cv_wh[1], cv_angle])
})
ct = np.array((cx, cy))
cls_id = int(self.cat_mapping[ann['category_id']])
if h > 0 and w > 0:
radius = gaussian_radius((np.ceil(h), np.ceil(w)))
radius = max(0, int(radius))
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = w, h, angle
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
gt_det[k] = ([ct[0], ct[1], w, h, angle, 1, cls_id])
if self.num_keypoints > 0:
valid = np.array(ann["keypoints"]).reshape(-1, 3)[:, -1]
for i, p in enumerate(
kpts_aug[k * self.num_keypoints:k *
self.num_keypoints + self.num_keypoints]):
kp[k][i * 2] = p.x - ct_int[0]
kp[k][i * 2 + 1] = p.y - ct_int[1]
is_valid = valid[i] == 2 and not p.is_out_of_image(
(output_w, output_w))
kp_reg_mask[k, i * 2] = int(is_valid)
kp_reg_mask[k, i * 2 + 1] = int(is_valid)
gt_kp[k][i] = p.x, p.y
if "area" not in ann:
gt_areas[k] = w * h
else:
gt_areas[k] = ann["area"]
del box_kpts_aug
del img_aug
gt_det = np.array(gt_det,
dtype=np.float32) if len(gt_det) > 0 else np.zeros(
(1, 7), dtype=np.float32)
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'reg': reg,
'gt_dets': gt_det,
'gt_areas': gt_areas,
}
if self.num_keypoints > 0:
ret['kps'] = kp
ret['gt_kps'] = gt_kp
ret['kp_reg_mask'] = kp_reg_mask
del kpts_aug
return ret
def __getitem__(self, index):
index = 45236
img_id = self.images[index]
file_name = self.coco.loadImgs(ids=[img_id])[0]['file_name']
img_path = os.path.join(self.img_dir, file_name)
assert os.path.exists(img_path), 'Image path does not exist: {}'.format(img_path)
# Target has {'segmentation', 'area', 'iscrowd', 'image_id', 'bbox', 'category_id'}
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
target = self.coco.loadAnns(ids=ann_ids)
# Separate out crowd annotations. These are annotations that signify a large crowd of
# objects of said class, where there is no annotation for each individual object.
target = [x for x in target if not ('iscrowd' in x and x['iscrowd'])]
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
if len(target) > 0:
# Pool all the masks for this image into one [num_objects,height,width] matrix
masks = [self.coco.annToMask(obj).reshape(-1) for obj in target]
masks = np.vstack(masks)
masks = masks.reshape(-1, height, width)
# if doesn't transpose, error will occur in augmentation (line 100)
masks = masks.transpose(1, 2, 0)
# labels = [int(self.cat_ids[obj['category_id']]) for obj in target]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.], dtype=np.float32)
if self.opt.keep_res:
input_h = (height | self.opt.pad) + 1
input_w = (width | self.opt.pad) + 1
s = np.array([input_w, input_h], dtype=np.float32)
else:
s = max(img.shape[0], img.shape[1]) * 1.0
input_h, input_w = self.opt.input_h, self.opt.input_w
flipped = False
if self.split == 'train':
if not self.opt.not_rand_crop:
s = s * np.random.choice(np.arange(0.6, 1.4, 0.1))
w_border = self._get_border(128, img.shape[1])
h_border = self._get_border(128, img.shape[0])
c[0] = np.random.randint(low=w_border, high=img.shape[1] - w_border)
c[1] = np.random.randint(low=h_border, high=img.shape[0] - h_border)
else:
sf = self.opt.scale
cf = self.opt.shift
c[0] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
c[1] += s * np.clip(np.random.randn() * cf, -2 * cf, 2 * cf)
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if np.random.random() < self.opt.flip:
flipped = True
img = img[:, ::-1, :]
masks = masks[:, ::-1, :]
c[0] = width - c[0] - 1
trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
inp = cv2.warpAffine(img, trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
if self.split == 'train' and not self.opt.no_color_aug:
color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
if self.rgb:
inp = inp[..., ::-1]
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
d1 = masks.shape[2]
masks = cv2.warpAffine(masks, trans_input,
(input_w, input_h),
flags=cv2.INTER_LINEAR)
masks = np.expand_dims(masks, 2) if masks.ndim != 3 else masks
d2 = masks.shape[2]
assert d1 == d2
masks = masks.transpose(2, 0, 1)
masks = (masks >= 0.5).astype(np.uint8)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, 0, [output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
# centers = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2), dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2), dtype=np.uint8)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else draw_umich_gaussian
segm_masks = []
gt_det = []
num_objs = min(len(target), self.max_objs)
for k in range(num_objs):
ann = target[k]
# convert bboxes to point_form (xmin, ymin, xmax, ymax)
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.cat_ids[ann['category_id']])
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
# After augmentation some masks will be empty.
if h > 0 and w > 0 and masks[k].sum() > 0.0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = self.opt.hm_gauss if self.opt.mse_loss else radius
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2], dtype=np.float32)
ct_int = ct.astype(np.int32)
# centers[k] = ct_int[0], ct_int[1]
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
cat_spec_wh[k, cls_id * 2: cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2: cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k], radius)
det = [ct[0] - w / 2, ct[1] - h / 2,
ct[0] + w / 2, ct[1] + h / 2, cls_id]
gt_det.append(det)
segm_masks.append(masks[k])
if len(segm_masks) > 0:
masks = np.stack(segm_masks)
gt_det = np.stack(gt_det)
ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind,
'wh': wh, 'masks': masks, 'gt_bbox_lbl': gt_det}
# ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh,
# 'masks': masks, 'centers': centers, 'gt_bbox_lbl': gt_det}
# ret = {'input': inp, 'hm': hm, 'reg_mask': reg_mask, 'ind': ind, 'wh': wh,
# 'masks': masks, 'labels': labels, 'crowd': crowd, 'centers': centers, 'gt_bbox': gt_det}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({'cat_spec_wh': cat_spec_wh, 'cat_spec_mask': cat_spec_mask})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
def __getitem__(self, files_index):
for i, c in enumerate(self.cds):
if files_index >= c:
ds = list(self.label_files.keys())[i]
start_index = c
img_path = self.img_files[ds][files_index - start_index]
label_path = self.label_files[ds][files_index - start_index]
imgs, labels, img_path, (input_h, input_w) = self.get_data(
img_path, label_path)
for i, _ in enumerate(labels):
if labels[i, 1] > -1:
labels[i, 1] += self.tid_start_index[ds]
output_h = imgs.shape[1] // self.opt.down_ratio
output_w = imgs.shape[2] // self.opt.down_ratio
num_classes = self.num_classes
num_objs = labels.shape[0]
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
if self.opt.ltrb:
wh = np.zeros((self.max_objs, 4), dtype=np.float32)
else:
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs, ), dtype=np.int64)
reg_mask = np.zeros((self.max_objs, ), dtype=np.uint8)
ids = np.zeros((self.max_objs, ), dtype=np.int64)
bbox_xys = np.zeros((self.max_objs, 4), dtype=np.float32)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else draw_umich_gaussian
for k in range(num_objs):
label = labels[k]
bbox = label[2:]
cls_id = int(label[0])
bbox[[0, 2]] = bbox[[0, 2]] * output_w
bbox[[1, 3]] = bbox[[1, 3]] * output_h
bbox_amodal = copy.deepcopy(bbox)
bbox_amodal[0] = bbox_amodal[0] - bbox_amodal[2] / 2.
bbox_amodal[1] = bbox_amodal[1] - bbox_amodal[3] / 2.
bbox_amodal[2] = bbox_amodal[0] + bbox_amodal[2]
bbox_amodal[3] = bbox_amodal[1] + bbox_amodal[3]
bbox[0] = np.clip(bbox[0], 0, output_w - 1)
bbox[1] = np.clip(bbox[1], 0, output_h - 1)
h = bbox[3]
w = bbox[2]
bbox_xy = copy.deepcopy(bbox)
bbox_xy[0] = bbox_xy[0] - bbox_xy[2] / 2
bbox_xy[1] = bbox_xy[1] - bbox_xy[3] / 2
bbox_xy[2] = bbox_xy[0] + bbox_xy[2]
bbox_xy[3] = bbox_xy[1] + bbox_xy[3]
if h > 0 and w > 0:
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = 6 if self.opt.mse_loss else radius
#radius = max(1, int(radius)) if self.opt.mse_loss else radius
ct = np.array([bbox[0], bbox[1]], dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
if self.opt.ltrb:
wh[k] = ct[0] - bbox_amodal[0], ct[1] - bbox_amodal[1], \
bbox_amodal[2] - ct[0], bbox_amodal[3] - ct[1]
else:
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
ids[k] = label[1]
bbox_xys[k] = bbox_xy
ret = {
'input': imgs,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh,
'reg': reg,
'ids': ids,
'bbox': bbox_xys
}
return ret
def _get_pre_dets(self, anns, trans_input, trans_output):
hm_h, hm_w = self.opt.input_h, self.opt.input_w
down_ratio = self.opt.down_ratio
trans = trans_input
reutrn_hm = self.opt.pre_hm
pre_hm = np.zeros((1, hm_h, hm_w), dtype=np.float32) if reutrn_hm else None
pre_cts, pre_whs, track_ids, pre_bboxes, pre_bbox_amodals= [], [], [],[],[]
ignore_regions =[]
for ann in anns:
cls_id = int(ann['category_id'])
if cls_id > self.opt.num_classes or cls_id <= -999 or cls_id <= 0 or (
'iscrowd' in ann and ann['iscrowd'] > 0):
bbox, _ = self._get_bbox_output(
ann['bbox'], trans_output, hm_h, hm_w)
ignore_regions.append(bbox)
for ann in anns:
cls_id = int(ann['category_id'])
if cls_id > self.opt.num_classes or cls_id <= -99 or \
('iscrowd' in ann and ann['iscrowd'] > 0):
continue
## bbox input
bbox = self._coco_box_to_bbox(ann['bbox'])
bbox[:2] = affine_transform(bbox[:2], trans)
bbox[2:] = affine_transform(bbox[2:], trans)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, hm_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, hm_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
max_rad = 1
ignored = False
if (h > 0 and w > 0):
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
max_rad = max(max_rad, radius)
ct = np.array(
[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2], dtype=np.float32)
ct0 = ct.copy()
conf = 1
for area in ignore_regions:
if (area[0] <= ct[0] and ct[0] <= area[2]) and (area[1] <= ct[1] and ct[1] <= area[3]):
ignored = True
break
if ignored:
continue
else:
ct[0] = ct[0] + np.random.randn() * self.opt.hm_disturb * w
ct[1] = ct[1] + np.random.randn() * self.opt.hm_disturb * h
conf = 1 if np.random.random() > self.opt.lost_disturb else 0
ct_int = ct.astype(np.int32)
if conf == 0:
pre_cts.append(ct / down_ratio) ### output ct
else:
pre_cts.append(ct0 / down_ratio)
track_ids.append(ann['track_id'] if 'track_id' in ann else -1)
if reutrn_hm:
draw_umich_gaussian(pre_hm[0], ct_int, radius, k=conf)
if np.random.random() < self.opt.fp_disturb and reutrn_hm:
ct2 = ct0.copy()
# Hard code heatmap disturb ratio, haven't tried other numbers.
ct2[0] = ct2[0] + np.random.randn() * 0.05 * w
ct2[1] = ct2[1] + np.random.randn() * 0.05 * h
ct2_int = ct2.astype(np.int32)
draw_umich_gaussian(pre_hm[0], ct2_int, radius, k=conf)
## get the bbox out
bbox_out, bbox_amodal = self._get_bbox_output(ann['bbox'], trans_output)
pre_bboxes.append(np.array(bbox_out))
pre_bbox_amodals.append(np.array(bbox_amodal))
h_out, w_out = bbox_out[3] - bbox_out[1], bbox_out[2] - bbox_out[0]
pre_wh = np.array(
[w_out, h_out], dtype=np.float32)
pre_whs.append(pre_wh)
return pre_hm, pre_cts, track_ids, pre_whs, pre_bboxes,pre_bbox_amodals
def _add_instance(self,
ret,
gt_det,
k,
cls_id,
bbox,
bbox_amodal,
ann,
trans_output,
aug_s,
calib,
pre_cts=None,
track_ids=None):
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h <= 0 or w <= 0:
return
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
ret['cat'][k] = cls_id - 1
ret['mask'][k] = 1
if 'wh' in ret:
ret['wh'][k] = 1. * w, 1. * h
ret['wh_mask'][k] = 1
ret['ind'][k] = ct_int[1] * self.opt.output_w + ct_int[0]
if 'reg' in ret:
ret['reg'][k] = ct - ct_int
ret['reg_mask'][k] = 1
draw_umich_gaussian(ret['hm'][cls_id - 1], ct_int, radius)
gt_det['bboxes'].append(
np.array(
[ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2],
dtype=np.float32))
gt_det['scores'].append(1)
gt_det['clses'].append(cls_id - 1)
gt_det['cts'].append(ct)
if 'tracking' in self.opt.heads:
if ann['track_id'] in track_ids and ann['track_id'] >= 0:
pre_ct = pre_cts[track_ids.index(ann['track_id'])]
ret['tracking_mask'][k] = 1
ret['tracking'][k] = pre_ct - ct_int
gt_det['tracking'].append(ret['tracking'][k])
else:
gt_det['tracking'].append(np.zeros(2, np.float32))
if 'ltrb' in self.opt.heads:
ret['ltrb'][k] = bbox[0] - ct_int[0], bbox[1] - ct_int[1], \
bbox[2] - ct_int[0], bbox[3] - ct_int[1]
ret['ltrb_mask'][k] = 1
if 'ltrb_amodal' in self.opt.heads:
ret['ltrb_amodal'][k] = \
bbox_amodal[0] - ct_int[0], bbox_amodal[1] - ct_int[1], \
bbox_amodal[2] - ct_int[0], bbox_amodal[3] - ct_int[1]
ret['ltrb_amodal_mask'][k] = 1
gt_det['ltrb_amodal'].append(bbox_amodal)
if 'nuscenes_att' in self.opt.heads:
if ('attributes' in ann) and ann['attributes'] > 0:
att = int(ann['attributes'] - 1)
ret['nuscenes_att'][k][att] = 1
ret['nuscenes_att_mask'][k][self.nuscenes_att_range[att]] = 1
gt_det['nuscenes_att'].append(ret['nuscenes_att'][k])
if 'velocity' in self.opt.heads:
if ('velocity' in ann) and min(ann['velocity']) > -1000:
ret['velocity'][k] = np.array(ann['velocity'], np.float32)[:3]
ret['velocity_mask'][k] = 1
gt_det['velocity'].append(ret['velocity'][k])
if 'hps' in self.opt.heads:
self._add_hps(ret, k, ann, gt_det, trans_output, ct_int, bbox, h,
w)
if 'rot' in self.opt.heads:
self._add_rot(ret, ann, k, gt_det)
if 'dep' in self.opt.heads:
if 'depth' in ann:
ret['dep_mask'][k] = 1
ret['dep'][k] = ann['depth'] * aug_s
gt_det['dep'].append(ret['dep'][k])
else:
gt_det['dep'].append(2)
if 'dim' in self.opt.heads:
if 'dim' in ann:
ret['dim_mask'][k] = 1
ret['dim'][k] = ann['dim']
gt_det['dim'].append(ret['dim'][k])
else:
gt_det['dim'].append([1, 1, 1])
if 'amodel_offset' in self.opt.heads:
if 'amodel_center' in ann:
amodel_center = affine_transform(ann['amodel_center'],
trans_output)
ret['amodel_offset_mask'][k] = 1
ret['amodel_offset'][k] = amodel_center - ct_int
gt_det['amodel_offset'].append(ret['amodel_offset'][k])
else:
gt_det['amodel_offset'].append([0, 0])
def __getitem__(self, index):
img_id = self.images[index]
img_path = os.path.join(
self.img_dir,
self.coco.loadImgs(ids=[img_id])[0]['file_name'])
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
annotations = self.coco.loadAnns(ids=ann_ids)
labels = np.array(
[self.cat_ids[anno['category_id']] for anno in annotations])
bboxes = np.array([anno['bbox'] for anno in annotations],
dtype=np.float32)
if len(bboxes) == 0:
bboxes = np.array([[0., 0., 0., 0.]], dtype=np.float32)
labels = np.array([[0]])
bboxes[:, 2:] += bboxes[:, :2] # xywh to xyxy
img = cv2.imread(img_path)
height, width = img.shape[0], img.shape[1]
# 获取中心坐标p
center = np.array([width / 2., height / 2.],
dtype=np.float32) # center of image
scale = max(height, width) * 1.0 # 仿射变换
flipped = False
if self.split == 'train':
# 随机选择一个尺寸来训练
scale = scale * np.random.choice(self.rand_scales)
w_border = get_border(128, width)
h_border = get_border(128, height)
center[0] = np.random.randint(low=w_border, high=width - w_border)
center[1] = np.random.randint(low=h_border, high=height - h_border)
if np.random.random() < 0.5:
flipped = True
img = img[:, ::-1, :]
center[0] = width - center[0] - 1
# 仿射变换
trans_img = get_affine_transform(
center, scale, 0, [self.img_size['w'], self.img_size['h']])
img = cv2.warpAffine(img, trans_img,
(self.img_size['w'], self.img_size['h']))
# 归一化
img = (img.astype(np.float32) / 255.)
if self.split == 'train':
# 对图片的亮度对比度等属性进行修改
color_aug(self.data_rng, img, self.eig_val, self.eig_vec)
img -= self.mean
img /= self.std
img = img.transpose(2, 0, 1) # from [H, W, C] to [C, H, W]
# 对Ground Truth heatmap进行仿射变换
trans_fmap = get_affine_transform(
center, scale, 0,
[self.fmap_size['w'], self.fmap_size['h']]) # 这时候已经是下采样为原来的四分之一了
# 3个最重要的变量
hmap = np.zeros(
(self.num_classes, self.fmap_size['h'], self.fmap_size['w']),
dtype=np.float32) # heatmap
w_h_ = np.zeros((self.max_objs, 2),
dtype=np.float32) # width and height
regs = np.zeros((self.max_objs, 2), dtype=np.float32) # regression
# indexs
inds = np.zeros((self.max_objs, ), dtype=np.int64)
# 具体选择哪些index
ind_masks = np.zeros((self.max_objs, ), dtype=np.uint8)
for k, (bbox, label) in enumerate(zip(bboxes, labels)):
if flipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
# 对检测框也进行仿射变换
bbox[:2] = affine_transform(bbox[:2], trans_fmap)
bbox[2:] = affine_transform(bbox[2:], trans_fmap)
# 防止越界
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.fmap_size['w'] - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.fmap_size['h'] - 1)
# 得到高和宽
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
obj_c = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32) # 中心坐标-浮点型
obj_c_int = obj_c.astype(np.int32) # 整型的中心坐标
# 根据一元二次方程计算出最小的半径
radius = max(
0,
int(
gaussian_radius((math.ceil(h), math.ceil(w)),
self.gaussian_iou)))
# 得到高斯分布
draw_umich_gaussian(hmap[label], obj_c_int, radius)
w_h_[k] = 1. * w, 1. * h
# 记录偏移量
regs[k] = obj_c - obj_c_int # discretization error
# 当前是obj序列中的第k个 = fmap_w * cy + cx = fmap中的序列数
inds[k] = obj_c_int[1] * self.fmap_size['w'] + obj_c_int[0]
# 进行mask标记
ind_masks[k] = 1
return {
'image': img,
'hmap': hmap,
'w_h_': w_h_,
'regs': regs,
'inds': inds,
'ind_masks': ind_masks,
'c': center,
's': scale,
'img_id': img_id
}
def __getitem__(self, index):
img_id = self.images[index]
img_info = self.coco.loadImgs(ids=[img_id])[0]
img_path = os.path.join(self.img_dir, img_info['file_name'])
img = cv2.imread(img_path)
if 'calib' in img_info:
calib = np.array(img_info['calib'], dtype=np.float32)
else:
calib = self.calib
height, width = img.shape[0], img.shape[1]
c = np.array([img.shape[1] / 2., img.shape[0] / 2.])
if self.opt.keep_res:
s = np.array([self.opt.input_w, self.opt.input_h], dtype=np.int32)
else:
s = np.array([width, height], dtype=np.int32)
aug = False
if self.split == 'train' and np.random.random() < self.opt.aug_ddd:
aug = True
sf = self.opt.scale
cf = self.opt.shift
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
c[0] += img.shape[1] * np.clip(np.random.randn() * cf, -2 * cf,
2 * cf)
c[1] += img.shape[0] * np.clip(np.random.randn() * cf, -2 * cf,
2 * cf)
trans_input = get_affine_transform(
c, s, 0, [self.opt.input_w, self.opt.input_h])
inp = cv2.warpAffine(img,
trans_input, (self.opt.input_w, self.opt.input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
# if self.split == 'train' and not self.opt.no_color_aug:
# color_aug(self._data_rng, inp, self._eig_val, self._eig_vec)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
num_classes = self.opt.num_classes
trans_output = get_affine_transform(
c, s, 0, [self.opt.output_w, self.opt.output_h])
hm = np.zeros((num_classes, self.opt.output_h, self.opt.output_w),
dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
dep = np.zeros((self.max_objs, 1), dtype=np.float32)
rotbin = np.zeros((self.max_objs, 2), dtype=np.int64)
rotres = np.zeros((self.max_objs, 2), dtype=np.float32)
dim = np.zeros((self.max_objs, 3), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
rot_mask = np.zeros((self.max_objs), dtype=np.uint8)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
anns = self.coco.loadAnns(ids=ann_ids)
num_objs = min(len(anns), self.max_objs)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
for k in range(num_objs):
ann = anns[k]
bbox = self._coco_box_to_bbox(ann['bbox'])
cls_id = int(self.cat_ids[ann['category_id']])
if cls_id <= -99:
continue
# if flipped:
# bbox[[0, 2]] = width - bbox[[2, 0]] - 1
bbox[:2] = affine_transform(bbox[:2], trans_output)
bbox[2:] = affine_transform(bbox[2:], trans_output)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, self.opt.output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, self.opt.output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if h > 0 and w > 0:
radius = gaussian_radius((h, w))
radius = max(0, int(radius))
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
if cls_id < 0:
ignore_id = [_ for _ in range(num_classes)] \
if cls_id == - 1 else [- cls_id - 2]
if self.opt.rect_mask:
hm[ignore_id,
int(bbox[1]):int(bbox[3]) + 1,
int(bbox[0]):int(bbox[2]) + 1] = 0.9999
else:
for cc in ignore_id:
draw_gaussian(hm[cc], ct, radius)
hm[ignore_id, ct_int[1], ct_int[0]] = 0.9999
continue
draw_gaussian(hm[cls_id], ct, radius)
wh[k] = 1. * w, 1. * h
gt_det.append([ct[0], ct[1], 1] + \
self._alpha_to_8(self._convert_alpha(ann['alpha'])) + \
[ann['depth']] + (np.array(ann['dim']) / 1).tolist() + [cls_id])
if self.opt.reg_bbox:
gt_det[-1] = gt_det[-1][:-1] + [w, h] + [gt_det[-1][-1]]
# if (not self.opt.car_only) or cls_id == 1: # Only estimate ADD for cars !!!
if 1:
alpha = self._convert_alpha(ann['alpha'])
# print('img_id cls_id alpha rot_y', img_path, cls_id, alpha, ann['rotation_y'])
if alpha < np.pi / 6. or alpha > 5 * np.pi / 6.:
rotbin[k, 0] = 1
rotres[k, 0] = alpha - (-0.5 * np.pi)
if alpha > -np.pi / 6. or alpha < -5 * np.pi / 6.:
rotbin[k, 1] = 1
rotres[k, 1] = alpha - (0.5 * np.pi)
dep[k] = ann['depth']
dim[k] = ann['dim']
# print(' cat dim', cls_id, dim[k])
ind[k] = ct_int[1] * self.opt.output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1 if not aug else 0
rot_mask[k] = 1
# print('gt_det', gt_det)
# print('')
ret = {
'input': inp,
'hm': hm,
'dep': dep,
'dim': dim,
'ind': ind,
'rotbin': rotbin,
'rotres': rotres,
'reg_mask': reg_mask,
'rot_mask': rot_mask
}
if self.opt.reg_bbox:
ret.update({'wh': wh})
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.debug > 0 or not ('train' in self.split):
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 18), dtype=np.float32)
meta = {
'c': c,
's': s,
'gt_det': gt_det,
'calib': calib,
'image_path': img_path,
'img_id': img_id
}
ret['meta'] = meta
return ret
def __getitem__(self, index):
img_id = self.images[index]
img = cv2.imread(img_id)
height, width = img.shape[0], img.shape[1]
# YOLO标注转换
with warnings.catch_warnings():
warnings.simplefilter('ignore')
anns = np.loadtxt(self.anno[index]).reshape(-1, 5)
if anns.size:
x1 = width * (anns[:, 1] - anns[:, 3] / 2)
y1 = height * (anns[:, 2] - anns[:, 4] / 2)
x2 = width * (anns[:, 1] + anns[:, 3] / 2)
y2 = height * (anns[:, 2] + anns[:, 4] / 2)
anns[:, 1] = x1
anns[:, 2] = y1
anns[:, 3] = x2
anns[:, 4] = y2
num_objs = min(len(anns), self.max_objs)
# 数据变换
c = np.array([width / 2., height / 2.], dtype=np.float32)
s = max(height, width) * 1.0
rotation = 0
shear = 0
input_h, input_w = self.opt.input_h, self.opt.input_w
hflipped = False
vflipped = False
if self.split == 'train':
if self.shear:
shear = np.clip(np.random.randn() * self.shear, -self.shear,
self.shear)
if shear:
if shear < 0:
img = img[:, ::-1, :]
anns[:, [1, 3]] = width - anns[:, [3, 1]] - 1
M = np.array([[1, abs(shear), 0], [0, 1, 0]])
nW = width + abs(shear * height)
anns[:, [1, 3]] += ((anns[:, [2, 4]]) * abs(shear)).astype(int)
img = cv2.warpAffine(img, M, (int(nW), height))
if shear < 0:
img = img[:, ::-1, :]
anns[:, [1, 3]] = nW - anns[:, [3, 1]] - 1
c[0] = nW / 2.
s = max(nW, s)
width = nW
sf = self.scale
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
if self.hflip and np.random.random() < self.hflip:
hflipped = True
img = img[:, ::-1, :]
c[0] = width - c[0] - 1
if self.vflip and np.random.random() < self.vflip:
vflipped = True
img = img[::-1, :, :]
c[1] = height - c[1] - 1
# 旋转参数设置
if self.rotation:
rotation = np.clip(np.random.randn() * self.rotation,
-self.rotation, self.rotation)
trans_input = get_affine_transform(c, s, rotation, [input_w, input_h])
inp = cv2.warpAffine(img,
trans_input, (input_w, input_h),
flags=cv2.INTER_LINEAR)
inp = (inp.astype(np.float32) / 255.)
inp = (inp - self.mean) / self.std
inp = inp.transpose(2, 0, 1)
output_h = input_h // self.opt.down_ratio
output_w = input_w // self.opt.down_ratio
num_classes = self.num_classes
trans_output = get_affine_transform(c, s, rotation,
[output_w, output_h])
hm = np.zeros((num_classes, output_h, output_w), dtype=np.float32)
obj = np.zeros((output_h, output_w), dtype=np.float32)
wh = np.zeros((self.max_objs, 2), dtype=np.float32)
dense_wh = np.zeros((2, output_h, output_w), dtype=np.float32)
reg = np.zeros((self.max_objs, 2), dtype=np.float32)
ind = np.zeros((self.max_objs), dtype=np.int64)
reg_mask = np.zeros((self.max_objs), dtype=np.uint8)
cat_spec_wh = np.zeros((self.max_objs, num_classes * 2),
dtype=np.float32)
cat_spec_mask = np.zeros((self.max_objs, num_classes * 2),
dtype=np.uint8)
target = np.zeros((self.max_objs, 5), dtype=np.float32)
draw_gaussian = draw_msra_gaussian if self.opt.mse_loss else \
draw_umich_gaussian
gt_det = []
if self.opt.task in ['fcos']: #, 'ttf']: # using original target
trans_output = trans_input
output_w, output_h = input_w, input_h
for k in range(num_objs):
bbox = anns[k, 1:]
cls_id = int(anns[k, 0])
if hflipped:
bbox[[0, 2]] = width - bbox[[2, 0]] - 1
if vflipped:
bbox[[1, 3]] = height - bbox[[3, 1]] - 1
lt = affine_transform(bbox[:2], trans_output)
rb = affine_transform(bbox[2:], trans_output)
rt = affine_transform(bbox[[2, 1]], trans_output)
lb = affine_transform(bbox[[0, 3]], trans_output)
bbox[:2] = np.min([lt, rb, rt, lb], axis=0)
bbox[2:] = np.max([lt, rb, rt, lb], axis=0)
bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, output_w - 1)
bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, output_h - 1)
h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
if self.opt.task in ['fcos']: #, 'ttf']:
target[k] = cls_id, bbox[0], bbox[1], bbox[2], bbox[3]
if h > 0 and w > 0:
reg_mask[k] = 1
continue
if h > 0 and w > 0:
obj[int(bbox[1]):int(bbox[3]) + 1,
int(bbox[0]):int(bbox[2]) + 1] = 1
radius = gaussian_radius((math.ceil(h), math.ceil(w)))
radius = max(0, int(radius))
radius = 2 * radius / 3 if self.opt.mse_loss else radius
ct = np.array([(bbox[0] + bbox[2]) / 2,
(bbox[1] + bbox[3]) / 2],
dtype=np.float32)
ct_int = ct.astype(np.int32)
draw_gaussian(hm[cls_id], ct_int, radius)
wh[k] = 1. * w, 1. * h
ind[k] = ct_int[1] * output_w + ct_int[0]
reg[k] = ct - ct_int
reg_mask[k] = 1
# reg_mask[k] = 2 - w * h / output_w / output_h
cat_spec_wh[k, cls_id * 2:cls_id * 2 + 2] = wh[k]
cat_spec_mask[k, cls_id * 2:cls_id * 2 + 2] = 1
if self.opt.dense_wh:
draw_dense_reg(dense_wh, hm.max(axis=0), ct_int, wh[k],
radius)
gt_det.append([
ct[0] - w / 2, ct[1] - h / 2, ct[0] + w / 2, ct[1] + h / 2,
1, cls_id
])
if self.opt.task in ['fcos']: #, 'ttf']:
ret = {'input': inp, 'target': target, 'mask': reg_mask}
return ret
ret = {
'input': inp,
'hm': hm,
'reg_mask': reg_mask,
'ind': ind,
'wh': wh
}
if self.opt.dense_wh:
hm_a = hm.max(axis=0, keepdims=True)
dense_wh_mask = np.concatenate([hm_a, hm_a], axis=0)
ret.update({'dense_wh': dense_wh, 'dense_wh_mask': dense_wh_mask})
del ret['wh']
elif self.opt.cat_spec_wh:
ret.update({
'cat_spec_wh': cat_spec_wh,
'cat_spec_mask': cat_spec_mask
})
del ret['wh']
if self.opt.reg_offset:
ret.update({'reg': reg})
if self.opt.reg_obj:
ret.update({'obj': obj[np.newaxis]})
if self.opt.debug > 0 or not self.split == 'train':
gt_det = np.array(gt_det, dtype=np.float32) if len(gt_det) > 0 else \
np.zeros((1, 6), dtype=np.float32)
meta = {'c': c, 's': s, 'gt_det': gt_det, 'img_id': img_id}
ret['meta'] = meta
return ret
