import os
import cv2
import mmcv
import numpy as np
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmpose.core.post_processing import oks_nms
from mmpose.datasets.pipelines import Compose
from mmpose.models import build_posenet
from mmpose.utils.hooks import OutputHook
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
[文档]def init_pose_model(config, checkpoint=None, device='cuda:0'):
"""Initialize a pose model from config file.
Args:
config (str or :obj:`mmcv.Config`): Config file path or the config
object.
checkpoint (str, optional): Checkpoint path. If left as None, the model
will not load any weights.
Returns:
nn.Module: The constructed detector.
"""
if isinstance(config, str):
config = mmcv.Config.fromfile(config)
elif not isinstance(config, mmcv.Config):
raise TypeError('config must be a filename or Config object, '
f'but got {type(config)}')
config.model.pretrained = None
model = build_posenet(config.model)
if checkpoint is not None:
# load model checkpoint
load_checkpoint(model, checkpoint, map_location=device)
# save the config in the model for convenience
model.cfg = config
model.to(device)
model.eval()
return model
def _xyxy2xywh(bbox_xyxy):
"""Transform the bbox format from x1y1x2y2 to xywh.
Args:
bbox_xyxy (np.ndarray): Bounding boxes (with scores), shaped (n, 4) or
(n, 5). (left, top, right, bottom, [score])
Returns:
np.ndarray: Bounding boxes (with scores),
shaped (n, 4) or (n, 5). (left, top, width, height, [score])
"""
bbox_xywh = bbox_xyxy.copy()
bbox_xywh[:, 2] = bbox_xywh[:, 2] - bbox_xywh[:, 0] + 1
bbox_xywh[:, 3] = bbox_xywh[:, 3] - bbox_xywh[:, 1] + 1
return bbox_xywh
def _xywh2xyxy(bbox_xywh):
"""Transform the bbox format from xywh to x1y1x2y2.
Args:
bbox_xywh (ndarray): Bounding boxes (with scores),
shaped (n, 4) or (n, 5). (left, top, width, height, [score])
Returns:
np.ndarray: Bounding boxes (with scores), shaped (n, 4) or
(n, 5). (left, top, right, bottom, [score])
"""
bbox_xyxy = bbox_xywh.copy()
bbox_xyxy[:, 2] = bbox_xyxy[:, 2] + bbox_xyxy[:, 0] - 1
bbox_xyxy[:, 3] = bbox_xyxy[:, 3] + bbox_xyxy[:, 1] - 1
return bbox_xyxy
def _box2cs(cfg, box):
"""This encodes bbox(x,y,w,h) into (center, scale)
Args:
x, y, w, h
Returns:
tuple: A tuple containing center and scale.
- np.ndarray[float32](2,): Center of the bbox (x, y).
- np.ndarray[float32](2,): Scale of the bbox w & h.
"""
x, y, w, h = box[:4]
input_size = cfg.data_cfg['image_size']
aspect_ratio = input_size[0] / input_size[1]
center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)
if w > aspect_ratio * h:
h = w * 1.0 / aspect_ratio
elif w < aspect_ratio * h:
w = h * aspect_ratio
# pixel std is 200.0
scale = np.array([w / 200.0, h / 200.0], dtype=np.float32)
scale = scale * 1.25
return center, scale
class LoadImage:
"""A simple pipeline to load image."""
def __init__(self, color_type='color', channel_order='rgb'):
self.color_type = color_type
self.channel_order = channel_order
def __call__(self, results):
"""Call function to load images into results.
Args:
results (dict): A result dict contains the img_or_path.
Returns:
dict: ``results`` will be returned containing loaded image.
"""
if isinstance(results['img_or_path'], str):
results['image_file'] = results['img_or_path']
img = mmcv.imread(results['img_or_path'], self.color_type,
self.channel_order)
elif isinstance(results['img_or_path'], np.ndarray):
results['image_file'] = ''
if self.color_type == 'color' and self.channel_order == 'rgb':
img = cv2.cvtColor(results['img_or_path'], cv2.COLOR_BGR2RGB)
else:
raise TypeError('"img_or_path" must be a numpy array or a str or '
'a pathlib.Path object')
results['img'] = img
return results
def _inference_single_pose_model(model,
img_or_path,
bboxes,
dataset,
return_heatmap=False):
"""Inference human bounding boxes.
num_bboxes: N
num_keypoints: K
Args:
model (nn.Module): The loaded pose model.
img_or_path (str | np.ndarray): Image filename or loaded image.
bboxes (list | np.ndarray): All bounding boxes (with scores),
shaped (N, 4) or (N, 5). (left, top, width, height, [score])
where N is number of bounding boxes.
dataset (str): Dataset name.
outputs (list[str] | tuple[str]): Names of layers whose output is
to be returned, default: None
Returns:
ndarray[NxKx3]: Predicted pose x, y, score.
heatmap[N, K, H, W]: Model output heatmap.
"""
cfg = model.cfg
device = next(model.parameters()).device
# build the data pipeline
channel_order = cfg.test_pipeline[0].get('channel_order', 'rgb')
test_pipeline = [LoadImage(channel_order=channel_order)
] + cfg.test_pipeline[1:]
test_pipeline = Compose(test_pipeline)
assert len(bboxes[0]) in [4, 5]
flip_pairs = None
if dataset in ('TopDownCocoDataset', 'TopDownOCHumanDataset',
'AnimalMacaqueDataset'):
flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[13, 14], [15, 16]]
elif dataset == 'TopDownCocoWholeBodyDataset':
body = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14],
[15, 16]]
foot = [[17, 20], [18, 21], [19, 22]]
face = [[23, 39], [24, 38], [25, 37], [26, 36], [27, 35], [28, 34],
[29, 33], [30, 32], [40, 49], [41, 48], [42, 47], [43, 46],
[44, 45], [54, 58], [55, 57], [59, 68], [60, 67], [61, 66],
[62, 65], [63, 70], [64, 69], [71, 77], [72, 76], [73, 75],
[78, 82], [79, 81], [83, 87], [84, 86], [88, 90]]
hand = [[91, 112], [92, 113], [93, 114], [94, 115], [95, 116],
[96, 117], [97, 118], [98, 119], [99, 120], [100, 121],
[101, 122], [102, 123], [103, 124], [104, 125], [105, 126],
[106, 127], [107, 128], [108, 129], [109, 130], [110, 131],
[111, 132]]
flip_pairs = body + foot + face + hand
elif dataset == 'TopDownAicDataset':
flip_pairs = [[0, 3], [1, 4], [2, 5], [6, 9], [7, 10], [8, 11]]
elif dataset == 'TopDownMpiiDataset':
flip_pairs = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]]
elif dataset == 'TopDownMpiiTrbDataset':
flip_pairs = [[0, 1], [2, 3], [4, 5], [6, 7],
[8, 9], [10, 11], [14, 15], [16, 22], [28, 34], [17, 23],
[29, 35], [18, 24], [30, 36], [19, 25], [31,
37], [20, 26],
[32, 38], [21, 27], [33, 39]]
elif dataset == 'TopDownH36MDataset':
flip_pairs = [[1, 4], [2, 5], [3, 6], [11, 14], [12, 15], [13, 16]]
elif dataset in ('OneHand10KDataset', 'FreiHandDataset', 'PanopticDataset',
'InterHand2DDataset'):
flip_pairs = []
elif dataset in 'Face300WDataset':
flip_pairs = [[0, 16], [1, 15], [2, 14], [3, 13], [4, 12], [5, 11],
[6, 10], [7, 9], [17, 26], [18, 25], [19, 24], [20, 23],
[21, 22], [31, 35], [32, 34], [36, 45], [37,
44], [38, 43],
[39, 42], [40, 47], [41, 46], [48, 54], [49,
53], [50, 52],
[61, 63], [60, 64], [67, 65], [58, 56], [59, 55]]
elif dataset in 'FaceAFLWDataset':
flip_pairs = [[0, 5], [1, 4], [2, 3], [6, 11], [7, 10], [8, 9],
[12, 14], [15, 17]]
elif dataset in 'FaceCOFWDataset':
flip_pairs = [[0, 1], [4, 6], [2, 3], [5, 7], [8, 9], [10, 11],
[12, 14], [16, 17], [13, 15], [18, 19], [22, 23]]
elif dataset in 'FaceWFLWDataset':
flip_pairs = [[0, 32], [1, 31], [2, 30], [3, 29], [4, 28], [5, 27],
[6, 26], [7, 25], [8, 24], [9, 23], [10, 22], [11, 21],
[12, 20], [13, 19], [14, 18], [15, 17], [33,
46], [34, 45],
[35, 44], [36, 43], [37, 42], [38, 50], [39,
49], [40, 48],
[41, 47], [60, 72], [61, 71], [62, 70], [63,
69], [64, 68],
[65, 75], [66, 74], [67, 73], [55, 59], [56,
58], [76, 82],
[77, 81], [78, 80], [87, 83], [86, 84], [88, 92],
[89, 91], [95, 93], [96, 97]]
elif dataset in 'AnimalFlyDataset':
flip_pairs = [[1, 2], [6, 18], [7, 19], [8, 20], [9, 21], [10, 22],
[11, 23], [12, 24], [13, 25], [14, 26], [15, 27],
[16, 28], [17, 29], [30, 31]]
elif dataset in 'AnimalHorse10Dataset':
flip_pairs = []
elif dataset in 'AnimalLocustDataset':
flip_pairs = [[5, 20], [6, 21], [7, 22], [8, 23], [9, 24], [10, 25],
[11, 26], [12, 27], [13, 28], [14, 29], [15, 30],
[16, 31], [17, 32], [18, 33], [19, 34]]
elif dataset in 'AnimalZebraDataset':
flip_pairs = [[3, 4], [5, 6]]
elif dataset in 'AnimalPoseDataset':
flip_pairs = [[0, 1], [2, 3], [8, 9], [10, 11], [12, 13], [14, 15],
[16, 17], [18, 19]]
else:
raise NotImplementedError()
batch_data = []
for bbox in bboxes:
center, scale = _box2cs(cfg, bbox)
# prepare data
data = {
'img_or_path':
img_or_path,
'center':
center,
'scale':
scale,
'bbox_score':
bbox[4] if len(bbox) == 5 else 1,
'bbox_id':
0, # need to be assigned if batch_size > 1
'dataset':
dataset,
'joints_3d':
np.zeros((cfg.data_cfg.num_joints, 3), dtype=np.float32),
'joints_3d_visible':
np.zeros((cfg.data_cfg.num_joints, 3), dtype=np.float32),
'rotation':
0,
'ann_info': {
'image_size': np.array(cfg.data_cfg['image_size']),
'num_joints': cfg.data_cfg['num_joints'],
'flip_pairs': flip_pairs
}
}
data = test_pipeline(data)
batch_data.append(data)
batch_data = collate(batch_data, samples_per_gpu=1)
if next(model.parameters()).is_cuda:
# scatter not work so just move image to cuda device
batch_data['img'] = batch_data['img'].to(device)
# get all img_metas of each bounding box
batch_data['img_metas'] = [
img_metas[0] for img_metas in batch_data['img_metas'].data
]
# forward the model
with torch.no_grad():
result = model(
img=batch_data['img'],
img_metas=batch_data['img_metas'],
return_loss=False,
return_heatmap=return_heatmap)
return result['preds'], result['output_heatmap']
[文档]def inference_top_down_pose_model(model,
img_or_path,
person_results,
bbox_thr=None,
format='xywh',
dataset='TopDownCocoDataset',
return_heatmap=False,
outputs=None):
"""Inference a single image with a list of person bounding boxes.
num_people: P
num_keypoints: K
bbox height: H
bbox width: W
Args:
model (nn.Module): The loaded pose model.
img_or_path (str| np.ndarray): Image filename or loaded image.
person_results (List(dict)): the item in the dict may contain
'bbox' and/or 'track_id'.
'bbox' (4, ) or (5, ): The person bounding box, which contains
4 box coordinates (and score).
'track_id' (int): The unique id for each human instance.
bbox_thr: Threshold for bounding boxes. Only bboxes with higher scores
will be fed into the pose detector. If bbox_thr is None, ignore it.
format: bbox format ('xyxy' | 'xywh'). Default: 'xywh'.
'xyxy' means (left, top, right, bottom),
'xywh' means (left, top, width, height).
dataset (str): Dataset name, e.g. 'TopDownCocoDataset'.
return_heatmap (bool) : Flag to return heatmap, default: False
outputs (list(str) | tuple(str)) : Names of layers whose outputs
need to be returned, default: None
Returns:
list[dict]: The bbox & pose info,
Each item in the list is a dictionary,
containing the bbox: (left, top, right, bottom, [score])
and the pose (ndarray[Kx3]): x, y, score
list[dict[np.ndarray[N, K, H, W] | torch.tensor[N, K, H, W]]]:
Output feature maps from layers specified in `outputs`.
Includes 'heatmap' if `return_heatmap` is True.
"""
# only two kinds of bbox format is supported.
assert format in ['xyxy', 'xywh']
pose_results = []
returned_outputs = []
if len(person_results) == 0:
return pose_results, returned_outputs
# Change for-loop preprocess each bbox to preprocess all bboxes at once.
bboxes = np.array([box['bbox'] for box in person_results])
# Select bboxes by score threshold
if bbox_thr is not None:
assert bboxes.shape[1] == 5
valid_idx = np.where(bboxes[:, 4] > bbox_thr)[0]
bboxes = bboxes[valid_idx]
person_results = [person_results[i] for i in valid_idx]
if format == 'xyxy':
bboxes_xyxy = bboxes
bboxes_xywh = _xyxy2xywh(bboxes)
else:
# format is already 'xywh'
bboxes_xywh = bboxes
bboxes_xyxy = _xywh2xyxy(bboxes)
# if bbox_thr remove all bounding box
if len(bboxes_xywh) == 0:
return [], []
with OutputHook(model, outputs=outputs, as_tensor=False) as h:
# poses is results['pred'] # N x 17x 3
poses, heatmap = _inference_single_pose_model(
model,
img_or_path,
bboxes_xywh,
dataset,
return_heatmap=return_heatmap)
if return_heatmap:
h.layer_outputs['heatmap'] = heatmap
returned_outputs.append(h.layer_outputs)
assert len(poses) == len(person_results), print(
len(poses), len(person_results), len(bboxes_xyxy))
for pose, person_result, bbox_xyxy in zip(poses, person_results,
bboxes_xyxy):
pose_result = person_result.copy()
pose_result['keypoints'] = pose
pose_result['bbox'] = bbox_xyxy
pose_results.append(pose_result)
return pose_results, returned_outputs
[文档]def inference_bottom_up_pose_model(model,
img_or_path,
pose_nms_thr=0.9,
return_heatmap=False,
outputs=None):
"""Inference a single image.
num_people: P
num_keypoints: K
bbox height: H
bbox width: W
Args:
model (nn.Module): The loaded pose model.
img_or_path (str| np.ndarray): Image filename or loaded image.
pose_nms_thr (float): retain oks overlap < pose_nms_thr, default: 0.9.
return_heatmap (bool) : Flag to return heatmap, default: False.
outputs (list(str) | tuple(str)) : Names of layers whose outputs
need to be returned, default: None.
Returns:
list[ndarray]: The predicted pose info.
The length of the list is the number of people (P).
Each item in the list is a ndarray, containing each person's
pose (ndarray[Kx3]): x, y, score.
list[dict[np.ndarray[N, K, H, W] | torch.tensor[N, K, H, W]]]:
Output feature maps from layers specified in `outputs`.
Includes 'heatmap' if `return_heatmap` is True.
"""
pose_results = []
returned_outputs = []
cfg = model.cfg
device = next(model.parameters()).device
# build the data pipeline
channel_order = cfg.test_pipeline[0].get('channel_order', 'rgb')
test_pipeline = [LoadImage(channel_order=channel_order)
] + cfg.test_pipeline[1:]
test_pipeline = Compose(test_pipeline)
# prepare data
data = {
'img_or_path': img_or_path,
'dataset': 'coco',
'ann_info': {
'image_size':
cfg.data_cfg['image_size'],
'num_joints':
cfg.data_cfg['num_joints'],
'flip_index':
[0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15],
}
}
data = test_pipeline(data)
data = collate([data], samples_per_gpu=1)
if next(model.parameters()).is_cuda:
# scatter to specified GPU
data = scatter(data, [device])[0]
else:
# just get the actual data from DataContainer
data['img_metas'] = data['img_metas'].data[0]
with OutputHook(model, outputs=outputs, as_tensor=False) as h:
# forward the model
with torch.no_grad():
result = model(
img=data['img'],
img_metas=data['img_metas'],
return_loss=False,
return_heatmap=return_heatmap)
if return_heatmap:
h.layer_outputs['heatmap'] = result['output_heatmap']
returned_outputs.append(h.layer_outputs)
for idx, pred in enumerate(result['preds']):
area = (np.max(pred[:, 0]) - np.min(pred[:, 0])) * (
np.max(pred[:, 1]) - np.min(pred[:, 1]))
pose_results.append({
'keypoints': pred[:, :3],
'score': result['scores'][idx],
'area': area,
})
# pose nms
keep = oks_nms(pose_results, pose_nms_thr, sigmas=None)
pose_results = [pose_results[_keep] for _keep in keep]
return pose_results, returned_outputs
[文档]def vis_pose_result(model,
img,
result,
radius=4,
thickness=1,
kpt_score_thr=0.3,
bbox_color='green',
dataset='TopDownCocoDataset',
show=False,
out_file=None):
"""Visualize the detection results on the image.
Args:
model (nn.Module): The loaded detector.
img (str | np.ndarray): Image filename or loaded image.
result (list[dict]): The results to draw over `img`
(bbox_result, pose_result).
radius (int): Radius of circles.
thickness (int): Thickness of lines.
kpt_score_thr (float): The threshold to visualize the keypoints.
skeleton (list[tuple()]): Default None.
show (bool): Whether to show the image. Default True.
out_file (str|None): The filename of the output visualization image.
"""
if hasattr(model, 'module'):
model = model.module
palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],
[230, 230, 0], [255, 153, 255], [153, 204, 255],
[255, 102, 255], [255, 51, 255], [102, 178, 255],
[51, 153, 255], [255, 153, 153], [255, 102, 102],
[255, 51, 51], [153, 255, 153], [102, 255, 102],
[51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],
[255, 255, 255]])
if dataset in ('TopDownCocoDataset', 'BottomUpCocoDataset',
'TopDownOCHumanDataset', 'AnimalMacaqueDataset'):
# show the results
skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],
[7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],
[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
pose_limb_color = palette[[
0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16
]]
pose_kpt_color = palette[[
16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0
]]
elif dataset == 'TopDownCocoWholeBodyDataset':
# show the results
skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],
[7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],
[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7], [16, 18],
[16, 19], [16, 20], [17, 21], [17, 22], [17, 23], [92, 93],
[93, 94], [94, 95], [95, 96], [92, 97], [97, 98], [98, 99],
[99, 100], [92, 101], [101, 102], [102, 103], [103, 104],
[92, 105], [105, 106], [106, 107], [107, 108], [92, 109],
[109, 110], [110, 111], [111, 112], [113, 114], [114, 115],
[115, 116], [116, 117], [113, 118], [118, 119], [119, 120],
[120, 121], [113, 122], [122, 123], [123, 124], [124, 125],
[113, 126], [126, 127], [127, 128], [128, 129], [113, 130],
[130, 131], [131, 132], [132, 133]]
pose_limb_color = palette[
[0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16] +
[16, 16, 16, 16, 16, 16] + [
0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16,
16
] + [
0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16,
16
]]
pose_kpt_color = palette[
[16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0] +
[0, 0, 0, 0, 0, 0] + [19] * (68 + 42)]
elif dataset == 'TopDownAicDataset':
skeleton = [[3, 2], [2, 1], [1, 14], [14, 4], [4, 5], [5, 6], [9, 8],
[8, 7], [7, 10], [10, 11], [11, 12], [13, 14], [1, 7],
[4, 10]]
pose_limb_color = palette[[
9, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 0, 7, 7
]]
pose_kpt_color = palette[[
9, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 0, 0
]]
elif dataset == 'TopDownMpiiDataset':
skeleton = [[1, 2], [2, 3], [3, 7], [7, 4], [4, 5], [5, 6], [7, 8],
[8, 9], [9, 10], [9, 13], [13, 12], [12, 11], [9, 14],
[14, 15], [15, 16]]
pose_limb_color = palette[[
16, 16, 16, 16, 16, 16, 7, 7, 0, 9, 9, 9, 9, 9, 9
]]
pose_kpt_color = palette[[
16, 16, 16, 16, 16, 16, 7, 7, 0, 0, 9, 9, 9, 9, 9, 9
]]
elif dataset == 'TopDownMpiiTrbDataset':
skeleton = [[13, 14], [14, 1], [14, 2], [1, 3], [2, 4], [3, 5], [4, 6],
[1, 7], [2, 8], [7, 8], [7, 9], [8, 10], [9, 11], [10, 12],
[15, 16], [17, 18], [19, 20], [21, 22], [23, 24], [25, 26],
[27, 28], [29, 30], [31, 32], [33, 34], [35, 36], [37, 38],
[39, 40]]
pose_limb_color = palette[[16] * 14 + [19] * 13]
pose_kpt_color = palette[[16] * 14 + [0] * 26]
elif dataset == 'TopDownH36MDataset':
skeleton = [[1, 2], [2, 3], [3, 4], [1, 5], [5, 6], [6, 7], [1, 8],
[8, 9], [9, 10], [10, 11], [9, 12], [12, 13], [13, 14],
[9, 15], [15, 16], [16, 17]]
pose_kpt_color = palette[[
9, 0, 0, 0, 16, 16, 16, 9, 9, 9, 9, 16, 16, 16, 0, 0, 0
]]
pose_limb_color = palette[[
0, 0, 0, 16, 16, 16, 9, 9, 9, 9, 16, 16, 16, 0, 0, 0
]]
elif dataset in ('OneHand10KDataset', 'FreiHandDataset',
'PanopticDataset'):
skeleton = [[1, 2], [2, 3], [3, 4], [4, 5], [1, 6], [6, 7], [7, 8],
[8, 9], [1, 10], [10, 11], [11, 12], [12, 13], [1, 14],
[14, 15], [15, 16], [16, 17], [1, 18], [18, 19], [19, 20],
[20, 21]]
pose_limb_color = palette[[
0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16
]]
pose_kpt_color = palette[[
0, 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16,
16
]]
elif dataset == 'InterHand2DDataset':
skeleton = [[1, 2], [2, 3], [3, 4], [5, 6], [6, 7], [7, 8], [9, 10],
[10, 11], [11, 12], [13, 14], [14, 15], [15, 16], [17, 18],
[18, 19], [19, 20], [4, 21], [8, 21], [12, 21], [16, 21],
[20, 21]]
pose_limb_color = palette[[
0, 0, 0, 4, 4, 4, 8, 8, 8, 12, 12, 12, 16, 16, 16, 0, 4, 8, 12, 16
]]
pose_kpt_color = palette[[
0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16,
0
]]
elif dataset == 'Face300WDataset':
# show the results
skeleton = []
pose_limb_color = palette[[]]
pose_kpt_color = palette[[19] * 68]
kpt_score_thr = 0
elif dataset == 'FaceAFLWDataset':
# show the results
skeleton = []
pose_limb_color = palette[[]]
pose_kpt_color = palette[[19] * 19]
kpt_score_thr = 0
elif dataset == 'FaceCOFWDataset':
# show the results
skeleton = []
pose_limb_color = palette[[]]
pose_kpt_color = palette[[19] * 29]
kpt_score_thr = 0
elif dataset == 'FaceWFLWDataset':
# show the results
skeleton = []
pose_limb_color = palette[[]]
pose_kpt_color = palette[[19] * 98]
kpt_score_thr = 0
elif dataset == 'AnimalHorse10Dataset':
skeleton = [[1, 2], [2, 13], [13, 17], [17, 22], [22, 18], [18, 12],
[12, 11], [11, 9], [9, 10], [10, 13], [3, 4], [4, 5],
[6, 7], [7, 8], [14, 15], [15, 16], [19, 20], [20, 21]]
pose_limb_color = palette[[4] * 10 + [6] * 2 + [6] * 2 + [7] * 2 +
[7] * 2]
pose_kpt_color = palette[[
4, 4, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 7, 7, 7, 4, 4, 7, 7, 7, 4
]]
elif dataset == 'AnimalFlyDataset':
skeleton = [[2, 1], [3, 1], [4, 1], [5, 4], [6, 5], [8, 7], [9, 8],
[10, 9], [12, 11], [13, 12], [14, 13], [16, 15], [17, 16],
[18, 17], [20, 19], [21, 20], [22, 21], [24, 23], [25, 24],
[26, 25], [28, 27], [29, 28], [30, 29], [31, 4], [32, 4]]
pose_limb_color = palette[[0] * 25]
pose_kpt_color = palette[[0] * 32]
elif dataset == 'AnimalLocustDataset':
skeleton = [[2, 1], [3, 2], [4, 3], [5, 4], [7, 6], [8, 7], [10, 9],
[11, 10], [12, 11], [14, 13], [15, 14], [16, 15], [18, 17],
[19, 18], [20, 19], [22, 21], [23, 22], [25, 24], [26, 25],
[27, 26], [29, 28], [30, 29], [31, 30], [33, 32], [34, 33],
[35, 34]]
pose_limb_color = palette[[0] * 26]
pose_kpt_color = palette[[0] * 35]
elif dataset == 'AnimalZebraDataset':
skeleton = [[2, 1], [3, 2], [4, 3], [5, 3], [6, 8], [7, 8], [8, 3],
[9, 8]]
pose_limb_color = palette[[0] * 8]
pose_kpt_color = palette[[0] * 9]
elif dataset in 'AnimalPoseDataset':
skeleton = [[1, 2], [1, 3], [2, 4], [1, 5], [2, 5], [5, 6], [6, 8],
[7, 8], [6, 9], [9, 13], [13, 17], [6, 10], [10, 14],
[14, 18], [7, 11], [11, 15], [15, 19], [7, 12], [12, 16],
[16, 20]]
pose_kpt_color = palette[[15] * 5 + [0] * 7 + [9] * 8]
pose_limb_color = palette[[15] * 5 + [0] * 3 + [0, 9, 9] * 4]
else:
raise NotImplementedError()
img = model.show_result(
img,
result,
skeleton,
radius=radius,
thickness=thickness,
pose_kpt_color=pose_kpt_color,
pose_limb_color=pose_limb_color,
kpt_score_thr=kpt_score_thr,
bbox_color=bbox_color,
show=show,
out_file=out_file)
return img