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68 lines
2.3 KiB
68 lines
2.3 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import numpy as np
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from mmpose.datasets.pipelines import Compose
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def _check_flip(origin_imgs, result_imgs):
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"""Check if the origin_imgs are flipped correctly."""
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h, w, c = origin_imgs.shape
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for i in range(h):
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for j in range(w):
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for k in range(c):
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if result_imgs[i, j, k] != origin_imgs[i, w - 1 - j, k]:
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return False
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return True
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def get_sample_data():
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ann_info = {}
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ann_info['image_size'] = np.array([256, 256])
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ann_info['heatmap_size'] = np.array([64, 64, 64])
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ann_info['heatmap3d_depth_bound'] = 400.0
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ann_info['heatmap_size_root'] = 64
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ann_info['root_depth_bound'] = 400.0
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ann_info['num_joints'] = 42
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ann_info['joint_weights'] = np.ones((ann_info['num_joints'], 1),
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dtype=np.float32)
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ann_info['use_different_joint_weights'] = False
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ann_info['flip_pairs'] = [[i, 21 + i] for i in range(21)]
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ann_info['inference_channel'] = list(range(42))
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ann_info['num_output_channels'] = 42
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ann_info['dataset_channel'] = list(range(42))
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results = {
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'image_file': 'tests/data/interhand2.6m/image69148.jpg',
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'center': np.asarray([200, 200], dtype=np.float32),
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'scale': 1.0,
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'rotation': 0,
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'joints_3d': np.zeros([42, 3], dtype=np.float32),
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'joints_3d_visible': np.ones([42, 3], dtype=np.float32),
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'hand_type': np.asarray([1, 0], dtype=np.float32),
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'hand_type_valid': 1,
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'rel_root_depth': 50.0,
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'rel_root_valid': 1,
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'ann_info': ann_info
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}
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return results
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def test_hand_transforms():
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results = get_sample_data()
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# load image
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pipeline = Compose([dict(type='LoadImageFromFile')])
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results = pipeline(results)
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# test random flip
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pipeline = Compose([dict(type='HandRandomFlip', flip_prob=1)])
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results_flip = pipeline(copy.deepcopy(results))
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assert _check_flip(results['img'], results_flip['img'])
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# test root depth target generation
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pipeline = Compose([dict(type='HandGenerateRelDepthTarget')])
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results_depth = pipeline(copy.deepcopy(results))
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assert results_depth['target'].shape == (1, )
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assert results_depth['target_weight'].shape == (1, )
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