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| # Copyright (c) 2018-present, Facebook, Inc. | |
| # All rights reserved. | |
| # | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # | |
| import h5py | |
| import numpy as np | |
| mpii_metadata = { | |
| 'layout_name': 'mpii', | |
| 'num_joints': 16, | |
| 'keypoints_symmetry': [ | |
| [3, 4, 5, 13, 14, 15], | |
| [0, 1, 2, 10, 11, 12], | |
| ] | |
| } | |
| coco_metadata = { | |
| 'layout_name': 'coco', | |
| 'num_joints': 17, | |
| 'keypoints_symmetry': [ | |
| [1, 3, 5, 7, 9, 11, 13, 15], | |
| [2, 4, 6, 8, 10, 12, 14, 16], | |
| ] | |
| } | |
| h36m_metadata = { | |
| 'layout_name': 'h36m', | |
| 'num_joints': 17, | |
| 'keypoints_symmetry': [ | |
| [4, 5, 6, 11, 12, 13], | |
| [1, 2, 3, 14, 15, 16], | |
| ] | |
| } | |
| humaneva15_metadata = { | |
| 'layout_name': 'humaneva15', | |
| 'num_joints': 15, | |
| 'keypoints_symmetry': [ | |
| [2, 3, 4, 8, 9, 10], | |
| [5, 6, 7, 11, 12, 13] | |
| ] | |
| } | |
| humaneva20_metadata = { | |
| 'layout_name': 'humaneva20', | |
| 'num_joints': 20, | |
| 'keypoints_symmetry': [ | |
| [3, 4, 5, 6, 11, 12, 13, 14], | |
| [7, 8, 9, 10, 15, 16, 17, 18] | |
| ] | |
| } | |
| def suggest_metadata(name): | |
| names = [] | |
| for metadata in [mpii_metadata, coco_metadata, h36m_metadata, humaneva15_metadata, humaneva20_metadata]: | |
| if metadata['layout_name'] in name: | |
| return metadata | |
| names.append(metadata['layout_name']) | |
| raise KeyError('Cannot infer keypoint layout from name "{}". Tried {}.'.format(name, names)) | |
| def import_detectron_poses(path): | |
| # Latin1 encoding because Detectron runs on Python 2.7 | |
| data = np.load(path, encoding='latin1') | |
| kp = data['keypoints'] | |
| bb = data['boxes'] | |
| results = [] | |
| for i in range(len(bb)): | |
| if len(bb[i][1]) == 0: | |
| assert i > 0 | |
| # Use last pose in case of detection failure | |
| results.append(results[-1]) | |
| continue | |
| best_match = np.argmax(bb[i][1][:, 4]) | |
| # import ipdb;ipdb.set_trace() | |
| keypoints = kp[i][1][best_match].T.copy() | |
| results.append(keypoints) | |
| results = np.array(results) | |
| # return results[:, :, 4:6] # Soft-argmax | |
| return results[:, :, [0, 1, 3]] # Argmax + score | |
| def my_pose(path): | |
| data = np.load(path, encoding='latin1') | |
| def import_cpn_poses(path): | |
| data = np.load(path) | |
| kp = data['keypoints'] | |
| return kp[:, :, :2] | |
| def import_sh_poses(path): | |
| with h5py.File(path) as hf: | |
| positions = hf['poses'].value | |
| return positions.astype('float32') | |
| def suggest_pose_importer(name): | |
| if 'detectron' in name: | |
| return import_detectron_poses | |
| if 'cpn' in name: | |
| return import_cpn_poses | |
| if 'sh' in name: | |
| return import_sh_poses | |
| raise KeyError('Cannot infer keypoint format from name "{}". Tried detectron, cpn, sh.'.format(name)) | |