def test_top_down_pipeline():
# test loading
data_prefix = 'tests/data/coco/'
ann_file = osp.join(data_prefix, 'test_coco.json')
coco = COCO(ann_file)
results = dict(image_file=osp.join(data_prefix, '000000000785.jpg'))
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['image_file'] == osp.join(data_prefix, '000000000785.jpg')
assert results['img'].shape == (425, 640, 3)
image_size = (425, 640)
ann_ids = coco.getAnnIds(785)
ann = coco.anns[ann_ids[0]]
num_joints = 17
joints_3d = np.zeros((num_joints, 3), dtype=np.float32)
joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float32)
for ipt in range(num_joints):
joints_3d[ipt, 0] = ann['keypoints'][ipt * 3 + 0]
joints_3d[ipt, 1] = ann['keypoints'][ipt * 3 + 1]
joints_3d[ipt, 2] = 0
t_vis = ann['keypoints'][ipt * 3 + 2]
if t_vis > 1:
t_vis = 1
joints_3d_visible[ipt, 0] = t_vis
joints_3d_visible[ipt, 1] = t_vis
joints_3d_visible[ipt, 2] = 0
center, scale = _box2cs(ann['bbox'][:4], image_size)
results['joints_3d'] = joints_3d
results['joints_3d_visible'] = joints_3d_visible
results['center'] = center
results['scale'] = scale
results['bbox_score'] = 1
results['ann_info'] = {}
results['ann_info']['flip_pairs'] = [[1, 2], [3, 4], [5, 6], [7, 8],
[9, 10], [11, 12], [13, 14], [15, 16]]
results['ann_info']['num_joints'] = num_joints
results['ann_info']['upper_body_ids'] = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
results['ann_info']['lower_body_ids'] = (11, 12, 13, 14, 15, 16)
results['ann_info']['use_different_joint_weights'] = False
results['ann_info']['joint_weights'] = np.array([
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
],
dtype=np.float32).reshape(
(num_joints, 1))
results['ann_info']['image_size'] = np.array([192, 256])
results['ann_info']['heatmap_size'] = np.array([48, 64])
# test filp
random_flip = TopDownRandomFlip(flip_prob=1.)
results_flip = random_flip(copy.deepcopy(results))
assert _check_flip(results['img'], results_flip['img'])
# test halfbody transform
halfbody_transform = TopDownHalfBodyTransform(num_joints_half_body=8,
prob_half_body=1.)
results_halfbody = halfbody_transform(copy.deepcopy(results))
assert (results_halfbody['scale'] <= results['scale']).all()
affine_transform = TopDownAffine()
results['rotation'] = 90
results_affine = affine_transform(copy.deepcopy(results))
assert results_affine['img'].shape == (256, 192, 3)
results = results_affine
to_tensor = ToTensor()
results_tensor = to_tensor(copy.deepcopy(results))
assert isinstance(results_tensor['img'], torch.Tensor)
assert results_tensor['img'].shape == torch.Size([3, 256, 192])
norm_cfg = {}
norm_cfg['mean'] = [0.485, 0.456, 0.406]
norm_cfg['std'] = [0.229, 0.224, 0.225]
normalize = NormalizeTensor(mean=norm_cfg['mean'], std=norm_cfg['std'])
results_normalize = normalize(copy.deepcopy(results_tensor))
_check_normalize(results_tensor['img'].data.numpy(),
results_normalize['img'].data.numpy(), norm_cfg)
generate_target = TopDownGenerateTarget(sigma=2, unbiased_encoding=False)
results_target = generate_target(copy.deepcopy(results_tensor))
assert 'target' in results_target
assert results_target['target'].shape == (
num_joints, results['ann_info']['heatmap_size'][1],
results['ann_info']['heatmap_size'][0])
assert 'target_weight' in results_target
assert results_target['target_weight'].shape == (num_joints, 1)
collect = Collect(keys=['img', 'target', 'target_weight'],
meta_keys=[
'image_file', 'center', 'scale', 'rotation',
'bbox_score', 'flip_pairs'
])
results_final = collect(results_target)
assert 'img_size' not in results_final['img_metas'].data
assert 'image_file' in results_final['img_metas'].data
def test_mesh_pipeline():
# load data
results = _load_test_data()
# data_prefix = 'tests/data/coco/'
# ann_file = osp.join(data_prefix, 'test_coco.json')
# coco = COCO(ann_file)
#
# results = dict(image_file=osp.join(data_prefix, '000000000785.jpg'))
# test loading image
transform = LoadImageFromFile()
results = transform(copy.deepcopy(results))
assert results['img'].shape == (1002, 1000, 3)
# test loading densepose IUV image without GT iuv image
transform = LoadIUVFromFile()
results_no_iuv = copy.deepcopy(results)
results_no_iuv['has_iuv'] = 0
results_no_iuv = transform(results_no_iuv)
assert results_no_iuv['iuv'] is None
# test loading densepose IUV image
results = transform(results)
assert results['iuv'].shape == (1002, 1000, 3)
assert results['iuv'][:, :, 0].max() <= 1
# test filp
random_flip = MeshRandomFlip(flip_prob=1.)
results_flip = random_flip(copy.deepcopy(results))
assert _check_flip(results['img'], results_flip['img'])
flip_iuv = results_flip['iuv']
flip_iuv[:, :, 1] = 255 - flip_iuv[:, :, 1]
assert _check_flip(results['iuv'], flip_iuv)
results = results_flip
# test filp without IUV image
results_no_iuv = random_flip(copy.deepcopy(results_no_iuv))
assert results_no_iuv['iuv'] is None
# test random scale and rotation
random_scale_rotation = MeshGetRandomScaleRotation()
results = random_scale_rotation(results)
# test affine
affine_transform = MeshAffine()
results_affine = affine_transform(copy.deepcopy(results))
assert results_affine['img'].shape == (256, 256, 3)
assert results_affine['iuv'].shape == (64, 64, 3)
results = results_affine
# test affine without IUV image
results_no_iuv['rotation'] = 30
results_no_iuv = affine_transform(copy.deepcopy(results_no_iuv))
assert results_no_iuv['iuv'] is None
# test channel noise
random_noise = MeshRandomChannelNoise()
results_noise = random_noise(copy.deepcopy(results))
results = results_noise
# transfer image to tensor
to_tensor = ToTensor()
results_tensor = to_tensor(copy.deepcopy(results))
assert isinstance(results_tensor['img'], torch.Tensor)
assert results_tensor['img'].shape == torch.Size([3, 256, 256])
# transfer IUV image to tensor
iuv_to_tensor = IUVToTensor()
results_tensor = iuv_to_tensor(results_tensor)
assert isinstance(results_tensor['part_index'], torch.LongTensor)
assert results_tensor['part_index'].shape == torch.Size([1, 64, 64])
max_I = results_tensor['part_index'].max().item()
assert (max_I == 0 or max_I == 1)
assert isinstance(results_tensor['uv_coordinates'], torch.FloatTensor)
assert results_tensor['uv_coordinates'].shape == torch.Size([2, 64, 64])
# transfer IUV image to tensor withou GT IUV image
results_no_iuv = iuv_to_tensor(results_no_iuv)
assert isinstance(results_no_iuv['part_index'], torch.LongTensor)
assert results_no_iuv['part_index'].shape == torch.Size([1, 64, 64])
max_I = results_no_iuv['part_index'].max().item()
assert (max_I == 0)
assert isinstance(results_no_iuv['uv_coordinates'], torch.FloatTensor)
assert results_no_iuv['uv_coordinates'].shape == torch.Size([2, 64, 64])
# test norm
norm_cfg = {}
norm_cfg['mean'] = [0.485, 0.456, 0.406]
norm_cfg['std'] = [0.229, 0.224, 0.225]
normalize = NormalizeTensor(mean=norm_cfg['mean'], std=norm_cfg['std'])
results_normalize = normalize(copy.deepcopy(results_tensor))
_check_normalize(results_tensor['img'].data.numpy(),
results_normalize['img'].data.numpy(), norm_cfg)
# test collect
collect = Collect(
keys=[
'img', 'joints_2d', 'joints_2d_visible', 'joints_3d',
'joints_3d_visible', 'pose', 'beta', 'part_index', 'uv_coordinates'
],
meta_keys=['image_file', 'center', 'scale', 'rotation', 'iuv_file'])
results_final = collect(results_normalize)
assert 'img_size' not in results_final['img_metas'].data
assert 'image_file' in results_final['img_metas'].data