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# Copyright (c) OpenMMLab. All rights reserved.
import abc
import argparse
import os.path as osp
from collections import defaultdict
from tempfile import TemporaryDirectory
import mmengine
import numpy as np
from mmaction.apis import detection_inference, pose_inference
from mmaction.utils import frame_extract
args = abc.abstractproperty()
args.det_config = 'demo/demo_configs/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py' # noqa: E501
args.det_checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth' # noqa: E501
args.det_score_thr = 0.5
args.pose_config = 'demo/demo_configs/td-hm_hrnet-w32_8xb64-210e_coco-256x192_infer.py' # noqa: E501
args.pose_checkpoint = 'https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth' # noqa: E501
def intersection(b0, b1):
l, r = max(b0[0], b1[0]), min(b0[2], b1[2])
u, d = max(b0[1], b1[1]), min(b0[3], b1[3])
return max(0, r - l) * max(0, d - u)
def iou(b0, b1):
i = intersection(b0, b1)
u = area(b0) + area(b1) - i
return i / u
def area(b):
return (b[2] - b[0]) * (b[3] - b[1])
def removedup(bbox):
def inside(box0, box1, threshold=0.8):
return intersection(box0, box1) / area(box0) > threshold
num_bboxes = bbox.shape[0]
if num_bboxes == 1 or num_bboxes == 0:
return bbox
valid = []
for i in range(num_bboxes):
flag = True
for j in range(num_bboxes):
if i != j and inside(bbox[i],
bbox[j]) and bbox[i][4] <= bbox[j][4]:
flag = False
break
if flag:
valid.append(i)
return bbox[valid]
def is_easy_example(det_results, num_person):
threshold = 0.95
def thre_bbox(bboxes, threshold=threshold):
shape = [sum(bbox[:, -1] > threshold) for bbox in bboxes]
ret = np.all(np.array(shape) == shape[0])
return shape[0] if ret else -1
if thre_bbox(det_results) == num_person:
det_results = [x[x[..., -1] > 0.95] for x in det_results]
return True, np.stack(det_results)
return False, thre_bbox(det_results)
def bbox2tracklet(bbox):
iou_thre = 0.6
tracklet_id = -1
tracklet_st_frame = {}
tracklets = defaultdict(list)
for t, box in enumerate(bbox):
for idx in range(box.shape[0]):
matched = False
for tlet_id in range(tracklet_id, -1, -1):
cond1 = iou(tracklets[tlet_id][-1][-1], box[idx]) >= iou_thre
cond2 = (
t - tracklet_st_frame[tlet_id] - len(tracklets[tlet_id]) <
10)
cond3 = tracklets[tlet_id][-1][0] != t
if cond1 and cond2 and cond3:
matched = True
tracklets[tlet_id].append((t, box[idx]))
break
if not matched:
tracklet_id += 1
tracklet_st_frame[tracklet_id] = t
tracklets[tracklet_id].append((t, box[idx]))
return tracklets
def drop_tracklet(tracklet):
tracklet = {k: v for k, v in tracklet.items() if len(v) > 5}
def meanarea(track):
boxes = np.stack([x[1] for x in track]).astype(np.float32)
areas = (boxes[..., 2] - boxes[..., 0]) * (
boxes[..., 3] - boxes[..., 1])
return np.mean(areas)
tracklet = {k: v for k, v in tracklet.items() if meanarea(v) > 5000}
return tracklet
def distance_tracklet(tracklet):
dists = {}
for k, v in tracklet.items():
bboxes = np.stack([x[1] for x in v])
c_x = (bboxes[..., 2] + bboxes[..., 0]) / 2.
c_y = (bboxes[..., 3] + bboxes[..., 1]) / 2.
c_x -= 480
c_y -= 270
c = np.concatenate([c_x[..., None], c_y[..., None]], axis=1)
dist = np.linalg.norm(c, axis=1)
dists[k] = np.mean(dist)
return dists
def tracklet2bbox(track, num_frame):
# assign_prev
bbox = np.zeros((num_frame, 5))
trackd = {}
for k, v in track:
bbox[k] = v
trackd[k] = v
for i in range(num_frame):
if bbox[i][-1] <= 0.5:
mind = np.Inf
for k in trackd:
if np.abs(k - i) < mind:
mind = np.abs(k - i)
bbox[i] = bbox[k]
return bbox
def tracklets2bbox(tracklet, num_frame):
dists = distance_tracklet(tracklet)
sorted_inds = sorted(dists, key=lambda x: dists[x])
dist_thre = np.Inf
for i in sorted_inds:
if len(tracklet[i]) >= num_frame / 2:
dist_thre = 2 * dists[i]
break
dist_thre = max(50, dist_thre)
bbox = np.zeros((num_frame, 5))
bboxd = {}
for idx in sorted_inds:
if dists[idx] < dist_thre:
for k, v in tracklet[idx]:
if bbox[k][-1] < 0.01:
bbox[k] = v
bboxd[k] = v
bad = 0
for idx in range(num_frame):
if bbox[idx][-1] < 0.01:
bad += 1
mind = np.Inf
mink = None
for k in bboxd:
if np.abs(k - idx) < mind:
mind = np.abs(k - idx)
mink = k
bbox[idx] = bboxd[mink]
return bad, bbox[:, None, :]
def bboxes2bbox(bbox, num_frame):
ret = np.zeros((num_frame, 2, 5))
for t, item in enumerate(bbox):
if item.shape[0] <= 2:
ret[t, :item.shape[0]] = item
else:
inds = sorted(
list(range(item.shape[0])), key=lambda x: -item[x, -1])
ret[t] = item[inds[:2]]
for t in range(num_frame):
if ret[t, 0, -1] <= 0.01:
ret[t] = ret[t - 1]
elif ret[t, 1, -1] <= 0.01:
if t:
if ret[t - 1, 0, -1] > 0.01 and ret[t - 1, 1, -1] > 0.01:
if iou(ret[t, 0], ret[t - 1, 0]) > iou(
ret[t, 0], ret[t - 1, 1]):
ret[t, 1] = ret[t - 1, 1]
else:
ret[t, 1] = ret[t - 1, 0]
return ret
def ntu_det_postproc(vid, det_results):
det_results = [removedup(x) for x in det_results]
label = int(vid.split('/')[-1].split('A')[1][:3])
mpaction = list(range(50, 61)) + list(range(106, 121))
n_person = 2 if label in mpaction else 1
is_easy, bboxes = is_easy_example(det_results, n_person)
if is_easy:
print('\nEasy Example')
return bboxes
tracklets = bbox2tracklet(det_results)
tracklets = drop_tracklet(tracklets)
print(f'\nHard {n_person}-person Example, found {len(tracklets)} tracklet')
if n_person == 1:
if len(tracklets) == 1:
tracklet = list(tracklets.values())[0]
det_results = tracklet2bbox(tracklet, len(det_results))
return np.stack(det_results)
else:
bad, det_results = tracklets2bbox(tracklets, len(det_results))
return det_results
# n_person is 2
if len(tracklets) <= 2:
tracklets = list(tracklets.values())
bboxes = []
for tracklet in tracklets:
bboxes.append(tracklet2bbox(tracklet, len(det_results))[:, None])
bbox = np.concatenate(bboxes, axis=1)
return bbox
else:
return bboxes2bbox(det_results, len(det_results))
def pose_inference_with_align(args, frame_paths, det_results):
# filter frame without det bbox
det_results = [
frm_dets for frm_dets in det_results if frm_dets.shape[0] > 0
]
pose_results, _ = pose_inference(args.pose_config, args.pose_checkpoint,
frame_paths, det_results, args.device)
# align the num_person among frames
num_persons = max([pose['keypoints'].shape[0] for pose in pose_results])
num_points = pose_results[0]['keypoints'].shape[1]
num_frames = len(pose_results)
keypoints = np.zeros((num_persons, num_frames, num_points, 2),
dtype=np.float32)
scores = np.zeros((num_persons, num_frames, num_points), dtype=np.float32)
for f_idx, frm_pose in enumerate(pose_results):
frm_num_persons = frm_pose['keypoints'].shape[0]
for p_idx in range(frm_num_persons):
keypoints[p_idx, f_idx] = frm_pose['keypoints'][p_idx]
scores[p_idx, f_idx] = frm_pose['keypoint_scores'][p_idx]
return keypoints, scores
def ntu_pose_extraction(vid, skip_postproc=False):
tmp_dir = TemporaryDirectory()
frame_paths, _ = frame_extract(vid, out_dir=tmp_dir.name)
det_results, _ = detection_inference(
args.det_config,
args.det_checkpoint,
frame_paths,
args.det_score_thr,
device=args.device,
with_score=True)
if not skip_postproc:
det_results = ntu_det_postproc(vid, det_results)
anno = dict()
keypoints, scores = pose_inference_with_align(args, frame_paths,
det_results)
anno['keypoint'] = keypoints
anno['keypoint_score'] = scores
anno['frame_dir'] = osp.splitext(osp.basename(vid))[0]
anno['img_shape'] = (1080, 1920)
anno['original_shape'] = (1080, 1920)
anno['total_frames'] = keypoints.shape[1]
anno['label'] = int(osp.basename(vid).split('A')[1][:3]) - 1
tmp_dir.cleanup()
return anno
def parse_args():
parser = argparse.ArgumentParser(
description='Generate Pose Annotation for a single NTURGB-D video')
parser.add_argument('video', type=str, help='source video')
parser.add_argument('output', type=str, help='output pickle name')
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--skip-postproc', action='store_true')
args = parser.parse_args()
return args
if __name__ == '__main__':
global_args = parse_args()
args.device = global_args.device
args.video = global_args.video
args.output = global_args.output
args.skip_postproc = global_args.skip_postproc
anno = ntu_pose_extraction(args.video, args.skip_postproc)
mmengine.dump(anno, args.output)
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