def __getitem__(self, idx):
db_rec = copy.deepcopy(self.db[idx])
image_file = db_rec['image']
filename = db_rec['filename'] if 'filename' in db_rec else ''
imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''
if self.data_format == 'zip':
from utils import zipreader
data_numpy = zipreader.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
else:
data_numpy = cv2.imread(
image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if self.color_rgb:
data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
if data_numpy is None:
logger.error('=> fail to read {}'.format(image_file))
raise ValueError('Fail to read {}'.format(image_file))
joints = db_rec['joints_3d']
joints_vis = db_rec['joints_3d_vis']
c = db_rec['center']
s = db_rec['scale']
score = db_rec['score'] if 'score' in db_rec else 1
r = 0
if self.is_train:
if (np.sum(joints_vis[:, 0]) > self.num_joints_half_body
and np.random.rand() < self.prob_half_body):
c_half_body, s_half_body = self.half_body_transform(
joints, joints_vis)
if c_half_body is not None and s_half_body is not None:
c, s = c_half_body, s_half_body
sf = self.scale_factor
rf = self.rotation_factor
s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) \
if random.random() <= 0.6 else 0
if self.flip and random.random() <= 0.5:
data_numpy = data_numpy[:, ::-1, :]
joints, joints_vis = fliplr_joints(joints, joints_vis,
data_numpy.shape[1],
self.flip_pairs)
c[0] = data_numpy.shape[1] - c[0] - 1
trans = get_affine_transform(c, s, r, self.image_size)
input = cv2.warpAffine(
data_numpy,
trans, (int(self.image_size[0]), int(self.image_size[1])),
flags=cv2.INTER_LINEAR)
for i in range(self.num_joints):
if joints_vis[i, 0] > 0.0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
keypoint_input = copy.deepcopy(input)
keypoint_joints = copy.deepcopy(joints)
keypoint_joints_vis = copy.deepcopy(joints_vis)
parts_input = copy.deepcopy(input)
parts_joints = copy.deepcopy(joints)
parts_joints_vis = copy.deepcopy(joints_vis)
if self.transform:
if len(self.occlusion_transforms_keypoint) > 0:
sample = {
'image': keypoint_input,
'target': keypoint_joints,
'joints_vis': keypoint_joints_vis
}
choose_transform = random.choice(
self.occlusion_transforms_keypoint)
sample = choose_transform(sample)
sample = self.transform(sample)
keypoint_input = sample['image']
keypoint_joints = sample['target']
keypoint_joints_vis = sample['joints_vis']
if len(self.occlusion_transforms_parts) > 0:
sample = {
'image': parts_input,
'target': parts_joints,
'joints_vis': parts_joints_vis
}
choose_transform = random.choice(
self.occlusion_transforms_parts)
sample = choose_transform(sample)
sample = self.transform(sample)
parts_input = sample['image']
parts_joints = sample['target']
parts_joints_vis = sample['joints_vis']
# Casual image tranform
sample = {
'image': input,
'target': joints,
'joints_vis': joints_vis
}
sample = self.transform(sample)
input = sample['image']
joints = sample['target']
joints_vis = sample['joints_vis']
target, target_weight = self.generate_target(joints, joints_vis)
target = torch.from_numpy(target)
target_weight = torch.from_numpy(target_weight)
input_combined = {
'normal': input,
'keypoints_occluded': keypoint_input,
'parts_occluded': parts_input
}
meta = {
'image': image_file,
'filename': filename,
'imgnum': imgnum,
'joints': joints,
'joints_vis': joints_vis,
'parts_joints_vis': parts_joints_vis,
'center': c,
'scale': s,
'rotation': r,
'score': score
}
return input_combined, target, target_weight, meta
def __getitem__(self, idx):
d = copy.deepcopy(self.data[idx])
img_id = d['img_id']
img_path = d['img_path']
data_numpy = cv.imread(img_path, cv.IMREAD_COLOR)
if data_numpy is None:
raise ValueError('fail to read {}'.format(img_path))
if self.color_rgb:
data_numpy = cv.cvtColor(data_numpy, cv.COLOR_BGR2RGB)
joints = d['joints'][:, :2]
# 0: Not marked; 1: Not visible; 2: Normal point
joints_vis = d['joints'][:, -1].reshape((-1, 1))
center = d['center']
scale = d['scale']
score = d['score'] if 'score' in d else 1
rotation = 0
if self.stage == 'train':
# x:（1 + 0.05） * （1 ~1.6） = (1.05~1.68)
# y:（1 + 0.05） * （1 ~1.8） = (1.05~1.89)
scale[0] *= (1 + self.basic_ext)
scale[1] *= (1 + self.basic_ext)
rand = np.random.rand() if self.rand_ext else 1.0
scale[0] *= (1 + rand * self.x_ext)
rand = np.random.rand() if self.rand_ext else 1.0
scale[1] *= (1 + rand * self.y_ext)
else:
# x: 1.09, y: 1.135
scale[0] *= (1 + self.test_x_ext)
scale[1] *= (1 + self.test_y_ext)
if scale[0] > self.w_h_ratio * scale[1]:
scale[1] = scale[0] * 1.0 / self.w_h_ratio
else:
scale[0] = scale[1] * 1.0 * self.w_h_ratio
# augmentation
if self.stage == 'train':
if (np.sum(joints_vis[:, 0] > 0) > self.num_keypoints_half_body
and np.random.rand() < self.prob_half_body):
c_half_body, s_half_body = self.half_body_transform(
joints, joints_vis)
if c_half_body is not None and s_half_body is not None:
center, scale = c_half_body, s_half_body
# scale
rand = random.uniform(1 + self.scale_factor_low,
1 + self.scale_factor_high)
scale_ratio = self.scale_shrink_ratio * rand
scale *= scale_ratio
# rotation
if random.random() <= self.prob_rotation:
rotation = random.uniform(-self.rotation_factor,
self.rotation_factor)
# flip
if random.random() <= self.prob_flip:
data_numpy = data_numpy[:, ::-1, :]
joints, joints_vis = flip_joints(joints, joints_vis,
data_numpy.shape[1],
self.flip_pairs)
center[0] = data_numpy.shape[1] - center[0] - 1
trans = get_affine_transform(center, scale, rotation, self.input_shape)
img = cv.warpAffine(
data_numpy,
trans, (int(self.input_shape[1]), int(self.input_shape[0])),
flags=cv.INTER_LINEAR)
if self.transform:
img = self.transform(img)
if self.stage == 'train':
for i in range(self.keypoint_num):
if joints_vis[i, 0] > 0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
# Label 0 when out of image range.
if joints[i, 0] < 0 or joints[i, 0] > self.input_shape[1] - 1 or joints[i, 1] < 0 \
or joints[i, 1] > self.input_shape[0] - 1:
joints_vis[i, 0] = 0
valid = torch.from_numpy(joints_vis).float()
labels_num = len(self.gaussian_kernels)
labels = np.zeros(
(labels_num, self.keypoint_num, *self.output_shape))
for i in range(labels_num):
labels[i] = self.generate_heatmap(
joints, valid, kernel=self.gaussian_kernels[i])
labels = torch.from_numpy(labels).float()
return img, valid, labels
else:
return img, score, center, scale, img_id
def __getitem__(self, idx):
d = copy.deepcopy(self.data[idx])
img_id = d['img_id']
img_path = d['img_path']
data_numpy = cv2.imread(img_path, cv2.IMREAD_COLOR)
if data_numpy is None:
raise ValueError('fail to read {}'.format(img_path))
if self.color_rgb:
data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB)
joints = d['joints'][:, :2]
joints_vis = d['joints'][:, -1].reshape((-1, 1))
center = d['center']
scale = d['scale']
score = d['score'] if 'score' in d else 1
rotation = 0
if self.stage == 'train':
scale[0] *= (1 + self.basic_ext)
scale[1] *= (1 + self.basic_ext)
rand = np.random.rand() if self.rand_ext else 1.0
scale[0] *= (1 + rand * self.x_ext)
rand = np.random.rand() if self.rand_ext else 1.0
scale[1] *= (1 + rand * self.y_ext)
else:
scale[0] *= (1 + self.test_x_ext)
scale[1] *= (1 + self.test_y_ext)
# fit the ratio
if scale[0] > self.w_h_ratio * scale[1]:
scale[1] = scale[0] * 1.0 / self.w_h_ratio
else:
scale[0] = scale[1] * 1.0 * self.w_h_ratio
# augmentation
if self.stage == 'train':
# half body
if (np.sum(joints_vis[:, 0] > 0) > self.num_keypoints_half_body
and np.random.rand() < self.prob_half_body):
c_half_body, s_half_body = self.half_body_transform(
joints, joints_vis)
if c_half_body is not None and s_half_body is not None:
center, scale = c_half_body, s_half_body
# scale
rand = random.uniform(
1 + self.scale_factor_low, 1 + self.scale_factor_high)
scale_ratio = self.scale_shrink_ratio * rand
scale *= scale_ratio
# rotation
if random.random() <= self.prob_rotation:
rotation = random.uniform(
-self.rotation_factor, self.rotation_factor)
# flip
if random.random() <= self.prob_flip:
data_numpy = data_numpy[:, ::-1, :]
joints, joints_vis = flip_joints(
joints, joints_vis, data_numpy.shape[1], self.flip_pairs)
center[0] = data_numpy.shape[1] - center[0] - 1
trans = get_warpmatrix(rotation, center * 2.0,
np.array([self.input_shape[1], self.input_shape[0]], dtype=float) - 1.0, scale)
trans_inv = get_warpmatrix_inverse(rotation, center * 2.0,
np.array([self.input_shape[1], self.input_shape[0]], dtype=float) - 1.0, scale)
img = cv2.warpAffine(data_numpy, trans, (int(self.input_shape[1]), int(self.input_shape[0])),
flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
if self.transform:
img = self.transform(img)
if self.stage == 'train':
for i in range(self.keypoint_num):
if joints_vis[i, 0] > 0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans_inv)
if joints[i, 0] < 0 \
or joints[i, 0] > self.input_shape[1] - 1 \
or joints[i, 1] < 0 \
or joints[i, 1] > self.input_shape[0] - 1:
joints_vis[i, 0] = 0
valid = torch.from_numpy(joints_vis).float()
labels_num = len(self.gaussian_kernels)
labels = np.zeros(
(labels_num, self.keypoint_num, *self.output_shape))
for i in range(labels_num):
labels[i] = self.generate_heatmap(
joints, valid, kernel=self.gaussian_kernels[i])
labels = torch.from_numpy(labels).float()
return img, valid, labels
else:
return img, score, center, scale, img_id
def __getitem__(self, idx):
db_rec = copy.deepcopy(self.db[idx])
image_file = db_rec['image']
filename = db_rec['filename'] if 'filename' in db_rec else ''
imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''
if self.data_format == 'zip':
from utils import zipreader
data_numpy = zipreader.imread(image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
else:
data_numpy = cv2.imread(image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if data_numpy is None:
logger.error('=> fail to read {}'.format(image_file))
raise ValueError('Fail to read {}'.format(image_file))
joints = db_rec['joints_3d']
joints_vis = db_rec['joints_3d_vis']
c = db_rec['center']
s = db_rec['scale']
score = db_rec['score'] if 'score' in db_rec else 1
r = 0
if self.is_train:
sf = self.scale_factor
rf = self.rotation_factor
s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
r = np.clip(np.random.randn()*rf, -rf*2, rf*2) if random.random() <= 0.6 else 0
if self.flip and random.random() <= 0.5:
data_numpy = data_numpy[:, ::-1, :]
joints, joints_vis = fliplr_joints(joints, joints_vis, data_numpy.shape[1], self.flip_pairs)
c[0] = data_numpy.shape[1] - c[0] - 1
trans = get_affine_transform(c, s, r, self.image_size)
input = cv2.warpAffine(data_numpy, trans, (int(self.image_size[0]), int(self.image_size[1])), flags=cv2.INTER_LINEAR)
if self.transform:
input = self.transform(input)
for i in range(self.num_joints):
if joints_vis[i, 0] > 0.0:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
target, target_weight = self.generate_target(joints, joints_vis)
target = torch.from_numpy(target)
target_weight = torch.from_numpy(target_weight)
meta = {
'image': image_file,
'filename': filename,
'imgnum': imgnum,
'joints': joints,
'joints_vis': joints_vis,
'center': c,
'scale': s,
'rotation': r,
'score': score
}
return input, target, target_weight, meta