137 lines
5.4 KiB
Python
137 lines
5.4 KiB
Python
'''
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@ Date: 2021-03-28 21:23:34
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@ Author: Qing Shuai
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@ LastEditors: Qing Shuai
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@ LastEditTime: 2022-05-24 14:27:46
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@ FilePath: /EasyMocapPublic/apps/annotation/annot_keypoints.py
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'''
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from easymocap.annotator.basic_visualize import capture_screen, plot_skeleton_factory, resize_to_screen
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import os
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from os.path import join
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import numpy as np
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from easymocap.annotator import ImageFolder
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from easymocap.annotator import AnnotBase
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from easymocap.annotator import callback_select_bbox_corner, callback_select_bbox_center
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from easymocap.annotator import plot_text, plot_bbox_body, plot_bbox_factory, vis_active_bbox, vis_bbox, plot_skeleton
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from easymocap.annotator.keypoints_callback import callback_select_joints
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from easymocap.annotator.keypoints_keyboard import set_unvisible, set_unvisible_according_previous, set_face_unvisible, check_track, mirror_keypoints2d, mirror_keypoints2d_leg
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class Estimator:
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def __init__(self) -> None:
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import torch
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device = torch.device('cuda')
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from easymocap.estimator.HRNet import SimpleHRNet
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config = {
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'nof_joints': 17,
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'c': 48,
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'checkpoint_path': 'data/models/pose_hrnet_w48_384x288.pth'
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}
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self.pose_estimator = SimpleHRNet(device=device, **config)
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def _detect_with_bbox(self, param, rot):
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select = param['select']['bbox']
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if select == -1:
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return 0
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img = param['img0'].copy()
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annots = param['annots']['annots'][select]
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bboxes = [annots['bbox']]
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res = self.pose_estimator(img, bboxes, rot=rot)[0]
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# annots['keypoints'][:19] = res[:19].tolist()
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annots['keypoints'] = res.tolist()
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return res
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def _detect_with_previous(self, annotator, param, sigma):
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select = param['select']['bbox']
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if select == -1:
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return 0
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annots = param['annots']['annots'][select]
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pid = annots['personID']
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previous = annotator.previous()
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found = [d for d in previous['annots'] if d['personID'] == pid]
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if len(found) == 0:
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print('[Info] Not found {} in previous frame'.format(pid))
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return 0
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keypoints = np.array(found[0]['keypoints'])[None]
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bboxes = [annots['bbox']]
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img = param['img0'].copy()
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res = self.pose_estimator.predict_with_previous(img, bboxes, keypoints, sigma)[0]
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# annots['keypoints'][:19] = res[:19].tolist()
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annots['keypoints'] = res.tolist()
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return res
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def detect_with_previous_slow(self, annotator, param):
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"detect_with_previous_slow"
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self._detect_with_previous(annotator, param, sigma=1)
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def detect_with_previous_mid(self, annotator, param):
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"detect_with_previous_mid"
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self._detect_with_previous(annotator, param, sigma=3)
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def detect_with_previous_fast(self, annotator, param):
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self._detect_with_previous(annotator, param, sigma=5)
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def detect_with_bbox(self, annotator, param):
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"detect"
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self._detect_with_bbox(param, 0)
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def detect_with_bbox90(self, annotator, param):
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"detect 90"
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self._detect_with_bbox(param, 90)
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def detect_with_bbox180(self, annotator, param):
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"detect 90"
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self._detect_with_bbox(param, 180)
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def detect_with_bbox270(self, annotator, param):
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"detect 90"
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self._detect_with_bbox(param, -90)
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def annot_example(path, sub, image, annot, step, args):
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# define datasets
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dataset = ImageFolder(path, sub=sub, image=image, annot=annot)
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key_funcs = {
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'v': set_unvisible,
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'V': set_unvisible_according_previous,
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# 'f': set_face_unvisible,
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'c': check_track,
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'm': mirror_keypoints2d,
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'M': mirror_keypoints2d_leg,
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}
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if args.hrnet:
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estimator = Estimator()
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key_funcs['e'] = estimator.detect_with_bbox
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key_funcs['r'] = estimator.detect_with_bbox90
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key_funcs['t'] = estimator.detect_with_bbox180
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key_funcs['y'] = estimator.detect_with_bbox270
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# key_funcs['g'] = estimator.detect_with_previous_slow
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key_funcs['j'] = estimator.detect_with_previous_mid
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# callback of bounding box
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callbacks = [callback_select_bbox_corner, callback_select_bbox_center, callback_select_joints]
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# callback of keypoints
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# define visualize
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vis_funcs = [plot_skeleton_factory('body25'), vis_bbox, vis_active_bbox]
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if args.hand:
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vis_funcs += [plot_bbox_factory('bbox_handl2d'), plot_bbox_factory('bbox_handr2d'), plot_bbox_factory('bbox_face2d')]
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vis_funcs += [plot_skeleton_factory('handl'), plot_skeleton_factory('handr'), plot_skeleton_factory('face')]
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vis_funcs += [resize_to_screen, plot_text, capture_screen]
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# construct annotations
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annotator = AnnotBase(
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dataset=dataset,
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key_funcs=key_funcs,
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vis_funcs=vis_funcs,
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callbacks=callbacks,
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name=sub,
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step=step)
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while annotator.isOpen:
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annotator.run()
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if __name__ == "__main__":
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from easymocap.annotator import load_parser, parse_parser
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parser = load_parser()
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parser.add_argument('--hand', action='store_true')
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parser.add_argument('--hrnet', action='store_true',
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help='loading HRNet model to detect human pose')
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args = parse_parser(parser)
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for sub in args.sub:
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annot_example(args.path, image=args.image, annot=args.annot, sub=sub, step=args.step, args=args) |