214 lines
9.2 KiB
Python
214 lines
9.2 KiB
Python
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'''
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@ Date: 2021-03-05 19:29:49
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@ Author: Qing Shuai
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@ LastEditors: Qing Shuai
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@ LastEditTime: 2021-03-31 22:46:05
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@ FilePath: /EasyMocap/scripts/postprocess/eval_k3d.py
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'''
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# Evaluate any 3d keypoints
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from glob import glob
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from tqdm import tqdm
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from os.path import join
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import os
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import numpy as np
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from easymocap.dataset import CONFIG
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from easymocap.mytools.reader import read_keypoints3d
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from easymocap.mytools import read_camera
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from eval_utils import keypoints_error
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from pprint import pprint
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class Conversion:
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def __init__(self, type_i, type_o, type_e=None):
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names_i = CONFIG[type_i]['joint_names']
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names_o = CONFIG[type_o]['joint_names']
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if type_e is None:
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self.commons = [i for i in names_o if i in names_i]
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else:
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names_e = CONFIG[type_e]['joint_names']
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self.commons = [i for i in names_e if i in names_i and i in names_o]
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self.idx_i = [names_i.index(i) for i in self.commons]
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self.idx_o = [names_o.index(i) for i in self.commons]
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def inp(self, inp):
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return inp[..., self.idx_i, :]
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def out(self, out):
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return out[..., self.idx_o, :]
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def __call__(self, inp, out):
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return inp[..., self.idx_i, :], out[..., self.idx_o, :]
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def run_eval_keypoints(inp, out, type_i, type_o, step_gt, mode='single', args=None):
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# 遍历输出文件夹
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conversion = Conversion(type_i, type_o)
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inplists = sorted(glob(join(inp, '*.json')))[::step_gt]
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outlists = sorted(glob(join(out, '*.json')))[args.start:args.end]
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assert len(inplists) == len(outlists), '{} != {}'.format(len(inplists), len(outlists))
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results = []
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for nf, inpname in enumerate(tqdm(inplists)):
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outname = outlists[nf]
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gts = read_keypoints3d(inpname)
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ests = read_keypoints3d(outname)
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# 将GT转换到当前坐标系
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for gt in gts:
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gt['keypoints3d'] = conversion.inp(gt['keypoints3d'])
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if gt['keypoints3d'].shape[1] == 3:
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gt['keypoints3d'] = np.hstack([gt['keypoints3d'], np.ones((gt['keypoints3d'].shape[0], 1))])
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for est in ests:
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est['keypoints3d'] = conversion.out(est['keypoints3d'])
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if est['keypoints3d'].shape[1] == 3:
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est['keypoints3d'] = np.hstack([est['keypoints3d'], np.ones((est['keypoints3d'].shape[0], 1))])
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# 这一步将交换est的顺序
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if mode == 'single':
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# 单人的：直接匹配上
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pass
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elif mode == 'matched': # ID已经匹配过了
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pass
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else: # 进行匹配
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# 把估计的id都清空
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for est in ests:
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est['id'] = -1
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# 计算距离先
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kpts_gt = np.stack([v['keypoints3d'] for v in gts])
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kpts_dt = np.stack([v['keypoints3d'] for v in ests])
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distances = np.linalg.norm(kpts_gt[:, None, :, :3] - kpts_dt[None, :, :, :3], axis=-1)
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conf = (kpts_gt[:, None, :, -1] > 0) * (kpts_dt[None, :, :, -1] > 0)
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dist = (distances * conf).sum(axis=-1)/conf.sum(axis=-1)
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# 贪婪的匹配
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ests_new = []
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for igt, gt in enumerate(gts):
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bestid = np.argmin(dist[igt])
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ests_new.append(ests[bestid])
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ests = ests_new
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# 计算误差
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for i, data in enumerate(gts):
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kpts_gt = data['keypoints3d']
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kpts_est = ests[i]['keypoints3d']
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# 计算各种误差，存成字典
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result = keypoints_error(kpts_gt, kpts_est, conversion.commons, joint_level=args.joint, use_align=args.align)
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result['nf'] = nf
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result['id'] = data['id']
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results.append(result)
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write_to_csv(join(out, '..', 'report.csv'), results)
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return 0
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keys = list(results[list(results.keys())[0]][0].keys())
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reports = {}
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for pid, result in results.items():
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vals = {key: sum([res[key] for res in result])/len(result) for key in keys}
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reports[pid] = vals
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from tabulate import tabulate
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headers = [''] + keys
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table = []
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for pid, report in reports.items():
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res = ['{}'.format(pid)] + ['{:.2f}'.format(report[key]) for key in keys]
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table.append(res)
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savename = 'tmp.txt'
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print(tabulate(table, headers, tablefmt='fancy_grid'))
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print(tabulate(table, headers, tablefmt='fancy_grid'), file=open(savename, 'w'))
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def write_to_csv(filename, results):
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from tabulate import tabulate
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keys = list(results[0].keys())
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headers, table = [], []
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for key in keys:
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if isinstance(results[0][key], float):
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headers.append(key)
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table.append('{:.3f}'.format(sum([res[key] for res in results])/len(results)))
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print('>> Totally {} samples:'.format(len(results)))
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print(tabulate([table], headers, tablefmt='fancy_grid'))
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with open(filename, 'w') as f:
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# 写入头
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header = list(results[0].keys())
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f.write(','.join(header) + '\n')
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for res in results:
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f.write(','.join(['{}'.format(res[key]) for key in header]) + '\n')
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def run_eval_keypoints_mono(inp, out, type_i, type_o, type_e, step_gt, cam_path, mode='single'):
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conversion = Conversion(type_i, type_o, type_e)
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inplists = sorted(glob(join(inp, '*.json')))[::step_gt]
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# TODO:only evaluate a subset of views
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if len(args.sub) == 0:
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views = sorted(os.listdir(out))
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else:
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views = args.sub
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# read camera
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cameras = read_camera(join(cam_path, 'intri.yml'), join(cam_path, 'extri.yml'), views)
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cameras = {key:cameras[key] for key in views}
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if args.cam_res is not None:
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cameras_res = read_camera(join(args.cam_res, 'intri.yml'), join(args.cam_res, 'extri.yml'), views)
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cameras_res = {key:cameras_res[key] for key in views}
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results = []
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for view in views:
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outlists = sorted(glob(join(out, view, '*.json')))
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RT = cameras[view]['RT']
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for outname in outlists:
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basename = os.path.basename(outname)
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gtname = join(inp, basename)
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gts = read_keypoints3d(gtname)
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ests = read_keypoints3d(outname)
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# 将GT转换到当前坐标系
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for gt in gts:
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keypoints3d = conversion.inp(gt['keypoints3d'])
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conf = keypoints3d[:, -1:].copy()
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keypoints3d[:, -1] = 1
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keypoints3d = (RT @ keypoints3d.T).T
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gt['keypoints3d'] = np.hstack([keypoints3d, conf])
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for est in ests:
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est['keypoints3d'] = conversion.out(est['keypoints3d'])
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if est['keypoints3d'].shape[1] == 3:
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# 增加置信度为1
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est['keypoints3d'] = np.hstack([est['keypoints3d'], np.ones((est['keypoints3d'].shape[0], 1))])
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# 计算误差
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for i, data in enumerate(gts):
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kpts_gt = data['keypoints3d']
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kpts_est = ests[i]['keypoints3d']
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# 计算各种误差，存成字典
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result = keypoints_error(kpts_gt, kpts_est, conversion.commons, joint_level=args.joint, use_align=True)
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result['pid'] = data['id']
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result['view'] = view
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results.append(result)
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write_to_csv(join(out, '..', 'report.csv'), results)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('path', type=str)
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parser.add_argument('--out', type=str, default=None)
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parser.add_argument('--type_i', type=str, default='body25',
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help='Type of ground-truth keypoints')
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parser.add_argument('--type_o', type=str, default='body25',
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help='Type of output keypoints')
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parser.add_argument('--type_e', type=str, default=None,
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help='Type of evaluation keypoints')
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parser.add_argument('--mode', type=str, default='single', choices=['single', 'matched', 'greedy'],
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help='the mode of match 3d person')
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# parser.add_argument('--dataset', type=str, default='h36m')
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parser.add_argument('--start', type=int, default=0,
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help='frame start')
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parser.add_argument('--end', type=int, default=100000,
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help='frame end')
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parser.add_argument('--step', type=int, default=1,
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help='frame step')
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parser.add_argument('--step_gt', type=int, default=1)
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parser.add_argument('--joint', action='store_true',
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help='report each joint')
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parser.add_argument('--align', action='store_true',
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help='report each joint')
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# Multiple views dataset
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parser.add_argument('--mono', action='store_true',
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help='use this option if the estimated joints use monocular images. \
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The results are stored in different folders.')
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parser.add_argument('--sub', type=str, nargs='+', default=[],
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help='the sub folder lists when in video mode')
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parser.add_argument('--cam', type=str, default=None)
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parser.add_argument('--cam_res', type=str, default=None)
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parser.add_argument('--debug', action='store_true')
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args = parser.parse_args()
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if args.mono:
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run_eval_keypoints_mono(args.path, args.out, args.type_i, args.type_o, args.type_e, cam_path=args.cam, step_gt=args.step_gt, mode=args.mode)
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else:
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run_eval_keypoints(args.path, args.out, args.type_i, args.type_o, args.step_gt, mode=args.mode, args=args)
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