class BottomUp(BasePose):
"""Bottom-up pose detectors.
Args:
backbone (dict): Backbone modules to extract feature.
keypoint_head (dict): Keypoint head to process feature.
train_cfg (dict): Config for training. Default: None.
test_cfg (dict): Config for testing. Default: None.
pretrained (str): Path to the pretrained models.
loss_pose (None): Deprecated arguments. Please use
`loss_keypoint` for heads instead.
"""
def __init__(self,
backbone,
keypoint_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
loss_pose=None):
super().__init__()
self.fp16_enabled = False
self.backbone = builder.build_backbone(backbone)
if keypoint_head is not None:
if 'loss_keypoint' not in keypoint_head and loss_pose is not None:
warnings.warn(
'`loss_pose` for BottomUp is deprecated, '
'use `loss_keypoint` for heads instead. See '
'https://github.com/open-mmlab/mmpose/pull/382'
' for more information.', DeprecationWarning)
keypoint_head['loss_keypoint'] = loss_pose
self.keypoint_head = builder.build_head(keypoint_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.use_udp = test_cfg.get('use_udp', False)
self.parser = HeatmapParser(self.test_cfg)
self.init_weights(pretrained=pretrained)
@property
def with_keypoint(self):
"""Check if has keypoint_head."""
return hasattr(self, 'keypoint_head')
def init_weights(self, pretrained=None):
"""Weight initialization for model."""
self.backbone.init_weights(pretrained)
if self.with_keypoint:
self.keypoint_head.init_weights()
@auto_fp16(apply_to=('img', ))
def forward(self,
img=None,
targets=None,
masks=None,
joints=None,
img_metas=None,
return_loss=True,
return_heatmap=False,
**kwargs):
"""Calls either forward_train or forward_test depending on whether
return_loss is True.
Note:
batch_size: N
num_keypoints: K
num_img_channel: C
img_width: imgW
img_height: imgH
heatmaps weight: W
heatmaps height: H
max_num_people: M
Args:
img(torch.Tensor[NxCximgHximgW]): Input image.
targets(List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
masks(List(torch.Tensor[NxHxW])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
heatmaps for ae loss
img_metas(dict):Information about val&test
By default this includes:
- "image_file": image path
- "aug_data": input
- "test_scale_factor": test scale factor
- "base_size": base size of input
- "center": center of image
- "scale": scale of image
- "flip_index": flip index of keypoints
return loss(bool): Option to 'return_loss'. 'return_loss=True' for
training, 'return_loss=False' for validation & test
return_heatmap (bool) : Option to return heatmap.
Returns:
dict|tuple: if 'return_loss' is true, then return losses.
Otherwise, return predicted poses, scores, image
paths and heatmaps.
"""
if return_loss:
return self.forward_train(img, targets, masks, joints, img_metas,
**kwargs)
return self.forward_test(
img, img_metas, return_heatmap=return_heatmap, **kwargs)
def forward_train(self, img, targets, masks, joints, img_metas, **kwargs):
"""Forward the bottom-up model and calculate the loss.
Note:
batch_size: N
num_keypoints: K
num_img_channel: C
img_width: imgW
img_height: imgH
heatmaps weight: W
heatmaps height: H
max_num_people: M
Args:
img(torch.Tensor[NxCximgHximgW]): Input image.
targets(List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
masks(List(torch.Tensor[NxHxW])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
heatmaps for ae loss
img_metas(dict):Information about val&test
By default this includes:
- "image_file": image path
- "aug_data": input
- "test_scale_factor": test scale factor
- "base_size": base size of input
- "center": center of image
- "scale": scale of image
- "flip_index": flip index of keypoints
Returns:
dict: The total loss for bottom-up
"""
output = self.backbone(img)
if self.with_keypoint:
output = self.keypoint_head(output)
# if return loss
losses = dict()
if self.with_keypoint:
keypoint_losses = self.keypoint_head.get_loss(
output, targets, masks, joints)
losses.update(keypoint_losses)
return losses
def forward_dummy(self, img):
"""Used for computing network FLOPs.
See ``tools/get_flops.py``.
Args:
img (torch.Tensor): Input image.
Returns:
Tensor: Outputs.
"""
output = self.backbone(img)
if self.with_keypoint:
output = self.keypoint_head(output)
return output
def forward_test(self, img, img_metas, return_heatmap=False, **kwargs):
"""Inference the bottom-up model.
Note:
Batchsize = N (currently support batchsize = 1)
num_img_channel: C
img_width: imgW
img_height: imgH
Args:
flip_index (List(int)):
aug_data (List(Tensor[NxCximgHximgW])): Multi-scale image
test_scale_factor (List(float)): Multi-scale factor
base_size (Tuple(int)): Base size of image when scale is 1
center (np.ndarray): center of image
scale (np.ndarray): the scale of image
"""
assert img.size(0) == 1
assert len(img_metas) == 1
img_metas = img_metas[0]
aug_data = img_metas['aug_data']
test_scale_factor = img_metas['test_scale_factor']
base_size = img_metas['base_size']
center = img_metas['center']
scale = img_metas['scale']
result = {}
aggregated_heatmaps = None
tags_list = []
for idx, s in enumerate(sorted(test_scale_factor, reverse=True)):
image_resized = aug_data[idx].to(img.device)
features = self.backbone(image_resized)
if self.with_keypoint:
outputs = self.keypoint_head(features)
if self.test_cfg.get('flip_test', True):
# use flip test
features_flipped = self.backbone(
torch.flip(image_resized, [3]))
if self.with_keypoint:
outputs_flipped = self.keypoint_head(features_flipped)
else:
outputs_flipped = None
_, heatmaps, tags = get_multi_stage_outputs(
outputs,
outputs_flipped,
self.test_cfg['num_joints'],
self.test_cfg['with_heatmaps'],
self.test_cfg['with_ae'],
self.test_cfg['tag_per_joint'],
img_metas['flip_index'],
self.test_cfg['project2image'],
base_size,
align_corners=self.use_udp)
aggregated_heatmaps, tags_list = aggregate_results(
s,
aggregated_heatmaps,
tags_list,
heatmaps,
tags,
test_scale_factor,
self.test_cfg['project2image'],
self.test_cfg.get('flip_test', True),
align_corners=self.use_udp)
# average heatmaps of different scales
aggregated_heatmaps = aggregated_heatmaps / float(
len(test_scale_factor))
tags = torch.cat(tags_list, dim=4)
# perform grouping
grouped, scores = self.parser.parse(aggregated_heatmaps, tags,
self.test_cfg['adjust'],
self.test_cfg['refine'])
preds = get_group_preds(
grouped,
center,
scale, [aggregated_heatmaps.size(3),
aggregated_heatmaps.size(2)],
use_udp=self.use_udp)
image_paths = []
image_paths.append(img_metas['image_file'])
if return_heatmap:
output_heatmap = aggregated_heatmaps.detach().cpu().numpy()
else:
output_heatmap = None
result['preds'] = preds
result['scores'] = scores
result['image_paths'] = image_paths
result['output_heatmap'] = output_heatmap
return result
def show_result(self,
img,
result,
skeleton=None,
kpt_score_thr=0.3,
bbox_color=None,
pose_kpt_color=None,
pose_limb_color=None,
radius=4,
thickness=1,
font_scale=0.5,
win_name='',
show=False,
show_keypoint_weight=False,
wait_time=0,
out_file=None):
"""Draw `result` over `img`.
Args:
img (str or Tensor): The image to be displayed.
result (list[dict]): The results to draw over `img`
(bbox_result, pose_result).
skeleton (list[list]): The connection of keypoints.
kpt_score_thr (float, optional): Minimum score of keypoints
to be shown. Default: 0.3.
pose_kpt_color (np.array[Nx3]`): Color of N keypoints.
If None, do not draw keypoints.
pose_limb_color (np.array[Mx3]): Color of M limbs.
If None, do not draw limbs.
radius (int): Radius of circles.
thickness (int): Thickness of lines.
font_scale (float): Font scales of texts.
win_name (str): The window name.
show (bool): Whether to show the image. Default: False.
show_keypoint_weight (bool): Whether to change the transparency
using the predicted confidence scores of keypoints.
wait_time (int): Value of waitKey param.
Default: 0.
out_file (str or None): The filename to write the image.
Default: None.
Returns:
Tensor: Visualized image only if not `show` or `out_file`
"""
img = mmcv.imread(img)
img = img.copy()
img_h, img_w, _ = img.shape
pose_result = []
for res in result:
pose_result.append(res['keypoints'])
for _, kpts in enumerate(pose_result):
# draw each point on image
if pose_kpt_color is not None:
assert len(pose_kpt_color) == len(kpts)
for kid, kpt in enumerate(kpts):
x_coord, y_coord, kpt_score = int(kpt[0]), int(
kpt[1]), kpt[2]
if kpt_score > kpt_score_thr:
if show_keypoint_weight:
img_copy = img.copy()
r, g, b = pose_kpt_color[kid]
cv2.circle(img_copy, (int(x_coord), int(y_coord)),
radius, (int(r), int(g), int(b)), -1)
transparency = max(0, min(1, kpt_score))
cv2.addWeighted(
img_copy,
transparency,
img,
1 - transparency,
0,
dst=img)
else:
r, g, b = pose_kpt_color[kid]
cv2.circle(img, (int(x_coord), int(y_coord)),
radius, (int(r), int(g), int(b)), -1)
# draw limbs
if skeleton is not None and pose_limb_color is not None:
assert len(pose_limb_color) == len(skeleton)
for sk_id, sk in enumerate(skeleton):
pos1 = (int(kpts[sk[0] - 1, 0]), int(kpts[sk[0] - 1, 1]))
pos2 = (int(kpts[sk[1] - 1, 0]), int(kpts[sk[1] - 1, 1]))
if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0
and pos1[1] < img_h and pos2[0] > 0
and pos2[0] < img_w and pos2[1] > 0
and pos2[1] < img_h
and kpts[sk[0] - 1, 2] > kpt_score_thr
and kpts[sk[1] - 1, 2] > kpt_score_thr):
r, g, b = pose_limb_color[sk_id]
if show_keypoint_weight:
img_copy = img.copy()
X = (pos1[0], pos2[0])
Y = (pos1[1], pos2[1])
mX = np.mean(X)
mY = np.mean(Y)
length = ((Y[0] - Y[1])**2 + (X[0] - X[1])**2)**0.5
angle = math.degrees(
math.atan2(Y[0] - Y[1], X[0] - X[1]))
stickwidth = 2
polygon = cv2.ellipse2Poly(
(int(mX), int(mY)),
(int(length / 2), int(stickwidth)), int(angle),
0, 360, 1)
cv2.fillConvexPoly(img_copy, polygon,
(int(r), int(g), int(b)))
transparency = max(
0,
min(
1, 0.5 *
(kpts[sk[0] - 1, 2] + kpts[sk[1] - 1, 2])))
cv2.addWeighted(
img_copy,
transparency,
img,
1 - transparency,
0,
dst=img)
else:
cv2.line(
img,
pos1,
pos2, (int(r), int(g), int(b)),
thickness=thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file)
return img
class BottomUp(BasePose):
"""Bottom-up pose detectors.
Args:
backbone (dict): Backbone modules to extract feature.
keypoint_head (dict): Keypoint head to process feature.
train_cfg (dict): Config for training. Default: None.
test_cfg (dict): Config for testing. Default: None.
pretrained (str): Path to the pretrained models.
loss_pose (dict): Config for loss. Default: None.
"""
def __init__(self,
backbone,
keypoint_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
loss_pose=None):
super().__init__()
self.backbone = builder.build_backbone(backbone)
if keypoint_head is not None:
self.keypoint_head = builder.build_head(keypoint_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
self.parser = HeatmapParser(self.test_cfg)
self.loss = build_loss(loss_pose)
self.init_weights(pretrained=pretrained)
@property
def with_keypoint(self):
"""Check if has keypoint_head."""
return hasattr(self, 'keypoint_head')
def init_weights(self, pretrained=None):
"""Weight initialization for model."""
self.backbone.init_weights(pretrained)
if self.with_keypoint:
self.keypoint_head.init_weights()
def forward(self,
img=None,
targets=None,
masks=None,
joints=None,
img_metas=None,
return_loss=True,
**kwargs):
"""Calls either forward_train or forward_test depending on whether
return_loss is True.
Note:
batch_size: N
num_keypoints: K
num_img_channel: C
img_weight: imgW
img_height: imgH
heatmaps weight: W
heatmaps height: H
max_num_people: M
Args:
img(torch.Tensor[NxCximgHximgW]): Input image.
targets(List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
masks(List(torch.Tensor[NxHxW])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
heatmaps for ae loss
return loss(bool): Option to 'return_loss'. 'return_loss=True' for
training, 'return_loss=False' for validation & test
img_metas(dict):Information about val&test
By default this includes:
- "image_file": image path
- "aug_data": input
- "test_scale_factor": test scale factor
- "base_size": base size of input
- "center": center of image
- "scale": scale of image
- "flip_index": flip index of keypoints
Returns:
if 'return_loss' is true, then return losses. Otherwise, return
predicted poses, scores and image paths.
"""
if return_loss:
return self.forward_train(img, targets, masks, joints, img_metas,
**kwargs)
else:
return self.forward_test(img, img_metas, **kwargs)
def forward_train(self, img, targets, masks, joints, img_metas, **kwargs):
"""Forward the bottom-up model and calculate the loss.
Note:
batch_size: N
num_keypoints: K
num_img_channel: C
img_weight: imgW
img_height: imgH
heatmaps weight: W
heatmaps height: H
max_num_people: M
Args:
img(torch.Tensor[NxCximgHximgW]): Input image.
targets(List(torch.Tensor[NxKxHxW])): Multi-scale target heatmaps.
masks(List(torch.Tensor[NxHxW])): Masks of multi-scale target
heatmaps
joints(List(torch.Tensor[NxMxKx2])): Joints of multi-scale target
heatmaps for ae loss
img_metas(dict):Information about val&test
By default this includes:
- "image_file": image path
- "aug_data": input
- "test_scale_factor": test scale factor
- "base_size": base size of input
- "center": center of image
- "scale": scale of image
- "flip_index": flip index of keypoints
Returns:
losses (dict): the total loss for bottom-up
"""
output = self.backbone(img)
if self.with_keypoint:
output = self.keypoint_head(output)
heatmaps_losses, push_losses, pull_losses = self.loss(
output, targets, masks, joints)
losses = dict()
loss = 0
for idx in range(len(targets)):
if heatmaps_losses[idx] is not None:
heatmaps_loss = heatmaps_losses[idx].mean(dim=0)
loss = loss + heatmaps_loss
if push_losses[idx] is not None:
push_loss = push_losses[idx].mean(dim=0)
loss = loss + push_loss
if pull_losses[idx] is not None:
pull_loss = pull_losses[idx].mean(dim=0)
loss = loss + pull_loss
losses['all_loss'] = loss
return losses
def forward_test(self, img, img_metas, **kwargs):
"""Inference the bottom-up model.
Note:
Batchsize = N (currently support batchsize = 1)
num_img_channel: C
img_weight: imgW
img_height: imgH
Args:
flip_index (List(int)):
aug_data (List(Tensor[NxCximgHximgW])): Multi-scale image
test_scale_factor (List(float)): Multi-scale factor
base_size (Tuple(int)): Base size of image when scale is 1
center (np.ndarray): center of image
scale (np.ndarray): the scale of image
"""
assert img.size(0) == 1
assert len(img_metas) == 1
img_metas = img_metas[0]
aug_data = img_metas['aug_data']
test_scale_factor = img_metas['test_scale_factor']
base_size = img_metas['base_size']
center = img_metas['center']
scale = img_metas['scale']
aggregated_heatmaps = None
tags_list = []
for idx, s in enumerate(sorted(test_scale_factor, reverse=True)):
image_resized = aug_data[idx].to(img.device)
outputs = self.backbone(image_resized)
outputs = self.keypoint_head(outputs)
if self.test_cfg['flip_test']:
# use flip test
outputs_flip = self.backbone(torch.flip(image_resized, [3]))
outputs_flip = self.keypoint_head(outputs_flip)
else:
outputs_flip = None
_, heatmaps, tags = get_multi_stage_outputs(
outputs, outputs_flip, self.test_cfg['num_joints'],
self.test_cfg['with_heatmaps'], self.test_cfg['with_ae'],
self.test_cfg['tag_per_joint'], img_metas['flip_index'],
self.test_cfg['project2image'], base_size)
aggregated_heatmaps, tags_list = aggregate_results(
s, aggregated_heatmaps, tags_list, heatmaps, tags,
test_scale_factor, self.test_cfg['project2image'],
self.test_cfg['flip_test'])
# average heatmaps of different scales
aggregated_heatmaps = aggregated_heatmaps / float(
len(test_scale_factor))
tags = torch.cat(tags_list, dim=4)
# perform grouping
grouped, scores = self.parser.parse(aggregated_heatmaps, tags,
self.test_cfg['adjust'],
self.test_cfg['refine'])
results = get_group_preds(
grouped, center, scale,
[aggregated_heatmaps.size(3),
aggregated_heatmaps.size(2)])
image_path = []
image_path.extend(img_metas['image_file'])
return results, scores, image_path