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Samuel Callahan
SCallahan/
lung-ultrasound-scan-targ
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[36ab12]: / ViTPose / tests / test_datasets / test_hand_dataset.py

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# Copyright (c) OpenMMLab. All rights reserved.

import copy



import pytest

from mmcv import Config

from numpy.testing import assert_almost_equal



from mmpose.datasets import DATASETS

from tests.utils.data_utils import convert_db_to_output





def test_OneHand10K_dataset():

    dataset = 'OneHand10KDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/onehand10k.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/onehand10k/test_onehand10k.json',

        img_prefix='tests/data/onehand10k/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/onehand10k/test_onehand10k.json',

        img_prefix='tests/data/onehand10k/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'onehand10k'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])

    assert_almost_equal(infos['PCK'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_hand_coco_wholebody_dataset():

    dataset = 'HandCocoWholeBodyDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/coco_wholebody_hand.py').dataset_info

    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/coco/test_coco_wholebody.json',

        img_prefix='tests/data/coco/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/coco/test_coco_wholebody.json',

        img_prefix='tests/data/coco/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])

    assert_almost_equal(infos['PCK'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_FreiHand2D_dataset():

    dataset = 'FreiHandDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/freihand2d.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[224, 224],

        heatmap_size=[56, 56],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/freihand/test_freihand.json',

        img_prefix='tests/data/freihand/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/freihand/test_freihand.json',

        img_prefix='tests/data/freihand/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'freihand'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 8

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])

    assert_almost_equal(infos['PCK'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_RHD2D_dataset():

    dataset = 'Rhd2DDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/rhd2d.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/rhd/test_rhd.json',

        img_prefix='tests/data/rhd/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/rhd/test_rhd.json',

        img_prefix='tests/data/rhd/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'rhd2d'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 3

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])

    assert_almost_equal(infos['PCK'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_Panoptic2D_dataset():

    dataset = 'PanopticDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/panoptic_hand2d.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/panoptic/test_panoptic.json',

        img_prefix='tests/data/panoptic/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/panoptic/test_panoptic.json',

        img_prefix='tests/data/panoptic/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'panoptic_hand2d'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCKh', 'EPE', 'AUC'])

    assert_almost_equal(infos['PCKh'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_InterHand2D_dataset():

    dataset = 'InterHand2DDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/interhand2d.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=21,

        dataset_joints=21,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64],

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',

        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',

        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',

        img_prefix='tests/data/interhand2.6m/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',

        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',

        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',

        img_prefix='tests/data/interhand2.6m/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'interhand2d'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    assert len(custom_dataset.db) == 6



    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])

    print(infos, flush=True)

    assert_almost_equal(infos['PCK'], 1.0)

    assert_almost_equal(infos['AUC'], 0.95)

    assert_almost_equal(infos['EPE'], 0.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')





def test_InterHand3D_dataset():

    dataset = 'InterHand3DDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/interhand3d.py').dataset_info



    dataset_class = DATASETS.get(dataset)



    channel_cfg = dict(

        num_output_channels=42,

        dataset_joints=42,

        dataset_channel=[

            [

                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,

                34, 35, 36, 37, 38, 39, 40, 41

            ],

        ],

        inference_channel=[

            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,

            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,

            36, 37, 38, 39, 40, 41

        ])



    data_cfg = dict(

        image_size=[256, 256],

        heatmap_size=[64, 64, 64],

        heatmap3d_depth_bound=400.0,

        heatmap_size_root=64,

        root_depth_bound=400.0,

        num_output_channels=channel_cfg['num_output_channels'],

        num_joints=channel_cfg['dataset_joints'],

        dataset_channel=channel_cfg['dataset_channel'],

        inference_channel=channel_cfg['inference_channel'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',

        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',

        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',

        img_prefix='tests/data/interhand2.6m/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',

        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',

        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',

        img_prefix='tests/data/interhand2.6m/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'interhand3d'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    assert len(custom_dataset.db) == 4



    _ = custom_dataset[0]



    results = convert_db_to_output(

        custom_dataset.db, keys=['rel_root_depth', 'hand_type'], is_3d=True)

    infos = custom_dataset.evaluate(

        results, metric=['MRRPE', 'MPJPE', 'Handedness_acc'])

    assert_almost_equal(infos['MRRPE'], 0.0, decimal=5)

    assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5)

    assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5)

    assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5)

    assert_almost_equal(infos['Handedness_acc'], 1.0)



    with pytest.raises(KeyError):

        infos = custom_dataset.evaluate(results, metric='mAP')