6年前：
GitHub - YuliangXiu/PoseFlow: PoseFlow: Efficient Online Pose Tracking (BMVC'18)
报错：
Clarification on min_keypoints in tracking · Issue #1411 · open-mmlab/mmpose · GitHub
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
from mmpose.core import OneEuroFilter, oks_iou
def _compute_iou(bboxA, bboxB):
"""Compute the Intersection over Union (IoU) between two boxes .
Args:
bboxA (list): The first bbox info (left, top, right, bottom, score).
bboxB (list): The second bbox info (left, top, right, bottom, score).
Returns:
float: The IoU value.
"""
x1 = max(bboxA[0], bboxB[0])
y1 = max(bboxA[1], bboxB[1])
x2 = min(bboxA[2], bboxB[2])
y2 = min(bboxA[3], bboxB[3])
inter_area = max(0, x2 - x1) * max(0, y2 - y1)
bboxA_area = (bboxA[2] - bboxA[0]) * (bboxA[3] - bboxA[1])
bboxB_area = (bboxB[2] - bboxB[0]) * (bboxB[3] - bboxB[1])
union_area = float(bboxA_area + bboxB_area - inter_area)
if union_area == 0:
union_area = 1e-5
warnings.warn('union_area=0 is unexpected')
iou = inter_area / union_area
return iou
def _track_by_iou(res, results_last, thr):
"""Get track id using IoU tracking greedily.
Args:
res (dict): The bbox & pose results of the person instance.
results_last (list[dict]): The bbox & pose & track_id info of the
last frame (bbox_result, pose_result, track_id).
thr (float): The threshold for iou tracking.
Returns:
int: The track id for the new person instance.
list[dict]: The bbox & pose & track_id info of the persons
that have not been matched on the last frame.
dict: The matched person instance on the last frame.
"""
bbox = list(res['bbox'])
max_iou_score = -1
max_index = -1
match_result = {}
for index, res_last in enumerate(results_last):
bbox_last = list(res_last['bbox'])
iou_score = _compute_iou(bbox, bbox_last)
if iou_score > max_iou_score:
max_iou_score = iou_score
max_index = index
if max_iou_score > thr:
track_id = results_last[max_index]['track_id']
match_result = results_last[max_index]
del results_last[max_index]
else:
track_id = -1
return track_id, results_last, match_result
def _track_by_oks(res, results_last, thr):
"""Get track id using OKS tracking greedily.
Args:
res (dict): The pose results of the person instance.
results_last (list[dict]): The pose & track_id info of the
last frame (pose_result, track_id).
thr (float): The threshold for oks tracking.
Returns:
int: The track id for the new person instance.
list[dict]: The pose & track_id info of the persons
that have not been matched on the last frame.
dict: The matched person instance on the last frame.
"""
pose = res['keypoints'].reshape((-1))
area = res['area']
max_index = -1
match_result = {}
if len(results_last) == 0:
return -1, results_last, match_result
pose_last = np.array(
[res_last['keypoints'].reshape((-1)) for res_last in results_last])
area_last = np.array([res_last['area'] for res_last in results_last])
oks_score = oks_iou(pose, pose_last, area, area_last)
max_index = np.argmax(oks_score)
if oks_score[max_index] > thr:
track_id = results_last[max_index]['track_id']
match_result = results_last[max_index]
del results_last[max_index]
else:
track_id = -1
return track_id, results_last, match_result
def _get_area(results):
"""Get bbox for each person instance on the current frame.
Args:
results (list[dict]): The pose results of the current frame
(pose_result).
Returns:
list[dict]: The bbox & pose info of the current frame
(bbox_result, pose_result, area).
"""
for result in results:
if 'bbox' in result:
result['area'] = ((result['bbox'][2] - result['bbox'][0]) *
(result['bbox'][3] - result['bbox'][1]))
else:
xmin = np.min(
result['keypoints'][:, 0][result['keypoints'][:, 0] > 0],
initial=1e10)
xmax = np.max(result['keypoints'][:, 0])
ymin = np.min(
result['keypoints'][:, 1][result['keypoints'][:, 1] > 0],
initial=1e10)
ymax = np.max(result['keypoints'][:, 1])
result['area'] = (xmax - xmin) * (ymax - ymin)
result['bbox'] = np.array([xmin, ymin, xmax, ymax])
return results
def _temporal_refine(result, match_result, fps=None):
"""Refine koypoints using tracked person instance on last frame.
Args:
results (dict): The pose results of the current frame
(pose_result).
match_result (dict): The pose results of the last frame
(match_result)
Returns:
(array): The person keypoints after refine.
"""
if 'one_euro' in match_result:
result['keypoints'][:, :2] = match_result['one_euro'](
result['keypoints'][:, :2])
result['one_euro'] = match_result['one_euro']
else:
result['one_euro'] = OneEuroFilter(result['keypoints'][:, :2], fps=fps)
return result['keypoints']
def get_track_id(results,
results_last,
next_id,
min_keypoints=3,
use_oks=False,
tracking_thr=0.3,
use_one_euro=False,
fps=None):
"""Get track id for each person instance on the current frame.
Args:
results (list[dict]): The bbox & pose results of the current frame
(bbox_result, pose_result).
results_last (list[dict], optional): The bbox & pose & track_id info
of the last frame (bbox_result, pose_result, track_id). None is
equivalent to an empty result list. Default: None
next_id (int): The track id for the new person instance.
min_keypoints (int): Minimum number of keypoints recognized as person.
0 means no minimum threshold required. Default: 3.
use_oks (bool): Flag to using oks tracking. default: False.
tracking_thr (float): The threshold for tracking.
use_one_euro (bool): Option to use one-euro-filter. default: False.
fps (optional): Parameters that d_cutoff
when one-euro-filter is used as a video input
Returns:
tuple:
- results (list[dict]): The bbox & pose & track_id info of the \
current frame (bbox_result, pose_result, track_id).
- next_id (int): The track id for the new person instance.
"""
if use_one_euro:
warnings.warn(
'In the future, get_track_id() will no longer perform '
'temporal refinement and the arguments `use_one_euro` and '
'`fps` will be deprecated. This part of function has been '
'migrated to Smoother (mmpose.core.Smoother). See '
'demo/top_down_pose_trackign_demo_with_mmdet.py for an '
'example.', DeprecationWarning)
if results_last is None:
results_last = []
results = _get_area(results)
if use_oks:
_track = _track_by_oks
else:
_track = _track_by_iou
for result in results:
track_id, results_last, match_result = _track(result, results_last,
tracking_thr)
if track_id == -1:
if np.count_nonzero(result['keypoints'][:, 1]) >= min_keypoints:
result['track_id'] = next_id
next_id += 1
else:
# If the number of keypoints detected is small,
# delete that person instance.
result['keypoints'][:, 1] = -10
result['bbox'] *= 0
result['track_id'] = -1
else:
result['track_id'] = track_id
if use_one_euro:
result['keypoints'] = _temporal_refine(
result, match_result, fps=fps)
del match_result
return results, next_id
def vis_pose_tracking_result(model,
img,
result,
radius=4,
thickness=1,
kpt_score_thr=0.3,
dataset='TopDownCocoDataset',
dataset_info=None,
show=False,
out_file=None):
"""Visualize the pose tracking 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_info is None and dataset is not None:
warnings.warn(
'dataset is deprecated.'
'Please set `dataset_info` in the config.'
'Check https://github.com/open-mmlab/mmpose/pull/663 for details.',
DeprecationWarning)
# TODO: These will be removed in the later versions.
if dataset in ('TopDownCocoDataset', 'BottomUpCocoDataset',
'TopDownOCHumanDataset'):
kpt_num = 17
skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],
[5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],
[8, 10], [1, 2], [0, 1], [0, 2], [1, 3], [2, 4],
[3, 5], [4, 6]]
elif dataset == 'TopDownCocoWholeBodyDataset':
kpt_num = 133
skeleton = [[15, 13], [13, 11], [16, 14], [14, 12], [11, 12],
[5, 11], [6, 12], [5, 6], [5, 7], [6, 8], [7, 9],
[8, 10], [1, 2], [0, 1], [0, 2],
[1, 3], [2, 4], [3, 5], [4, 6], [15, 17], [15, 18],
[15, 19], [16, 20], [16, 21], [16, 22], [91, 92],
[92, 93], [93, 94], [94, 95], [91, 96], [96, 97],
[97, 98], [98, 99], [91, 100], [100, 101], [101, 102],
[102, 103], [91, 104], [104, 105], [105, 106],
[106, 107], [91, 108], [108, 109], [109, 110],
[110, 111], [112, 113], [113, 114], [114, 115],
[115, 116], [112, 117], [117, 118], [118, 119],
[119, 120], [112, 121], [121, 122], [122, 123],
[123, 124], [112, 125], [125, 126], [126, 127],
[127, 128], [112, 129], [129, 130], [130, 131],
[131, 132]]
radius = 1
elif dataset == 'TopDownAicDataset':
kpt_num = 14
skeleton = [[2, 1], [1, 0], [0, 13], [13, 3], [3, 4], [4, 5],
[8, 7], [7, 6], [6, 9], [9, 10], [10, 11], [12, 13],
[0, 6], [3, 9]]
elif dataset == 'TopDownMpiiDataset':
kpt_num = 16
skeleton = [[0, 1], [1, 2], [2, 6], [6, 3], [3, 4], [4, 5], [6, 7],
[7, 8], [8, 9], [8, 12], [12, 11], [11, 10], [8, 13],
[13, 14], [14, 15]]
elif dataset in ('OneHand10KDataset', 'FreiHandDataset',
'PanopticDataset'):
kpt_num = 21
skeleton = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7],
[7, 8], [0, 9], [9, 10], [10, 11], [11, 12], [0, 13],
[13, 14], [14, 15], [15, 16], [0, 17], [17, 18],
[18, 19], [19, 20]]
elif dataset == 'InterHand2DDataset':
kpt_num = 21
skeleton = [[0, 1], [1, 2], [2, 3], [4, 5], [5, 6], [6, 7], [8, 9],
[9, 10], [10, 11], [12, 13], [13, 14], [14, 15],
[16, 17], [17, 18], [18, 19], [3, 20], [7, 20],
[11, 20], [15, 20], [19, 20]]
else:
raise NotImplementedError()
elif dataset_info is not None:
kpt_num = dataset_info.keypoint_num
skeleton = dataset_info.skeleton
for res in result:
track_id = res['track_id']
bbox_color = palette[track_id % len(palette)]
pose_kpt_color = palette[[track_id % len(palette)] * kpt_num]
pose_link_color = palette[[track_id % len(palette)] * len(skeleton)]
img = model.show_result(
img, [res],
skeleton,
radius=radius,
thickness=thickness,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
bbox_color=tuple(bbox_color.tolist()),
kpt_score_thr=kpt_score_thr,
show=show,
out_file=out_file)
return img
