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Samuel Callahan
SCallahan/
lung-ultrasound-scan-targ
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[c1b1c5]: / ViTPose / tests / test_apis / test_inference.py

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# Copyright (c) OpenMMLab. All rights reserved.

import copy



import numpy as np



from mmpose.apis import (inference_bottom_up_pose_model,

                         inference_top_down_pose_model, init_pose_model,

                         process_mmdet_results, vis_pose_result)

from mmpose.datasets import DatasetInfo





def test_top_down_demo():

    # COCO demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'coco/res50_coco_256x192.py',

        None,

        device='cpu')

    image_name = 'tests/data/coco/000000000785.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))



    person_result = []

    person_result.append({'bbox': [50, 50, 50, 100]})

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # AIC demo

    pose_model = init_pose_model(

        'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'aic/res50_aic_256x192.py',

        None,

        device='cpu')

    image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # OneHand10K demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'onehand10k/res50_onehand10k_256x256.py',

        None,

        device='cpu')

    image_name = 'tests/data/onehand10k/9.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # InterHand2DDataset demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'interhand2d/res50_interhand2d_all_256x256.py',

        None,

        device='cpu')

    image_name = 'tests/data/interhand2.6m/image2017.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # Face300WDataset demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'

        '300w/res50_300w_256x256.py',

        None,

        device='cpu')

    image_name = 'tests/data/300w/indoor_020.png'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # FaceAFLWDataset demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'aflw/res50_aflw_256x256.py',

        None,

        device='cpu')

    image_name = 'tests/data/aflw/image04476.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # FaceCOFWDataset demo

    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'

        'cofw/res50_cofw_256x256.py',

        None,

        device='cpu')

    image_name = 'tests/data/cofw/001766.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))

    # test a single image, with a list of bboxes.

    pose_results, _ = inference_top_down_pose_model(

        pose_model,

        image_name,

        person_result,

        format='xywh',

        dataset_info=dataset_info)

    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)





def test_bottom_up_demo():



    # build the pose model from a config file and a checkpoint file

    pose_model = init_pose_model(

        'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'

        'coco/res50_coco_512x512.py',

        None,

        device='cpu')



    image_name = 'tests/data/coco/000000000785.jpg'

    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(

        'dataset_info', None))



    pose_results, _ = inference_bottom_up_pose_model(

        pose_model, image_name, dataset_info=dataset_info)



    # show the results

    vis_pose_result(

        pose_model, image_name, pose_results, dataset_info=dataset_info)



    # test dataset_info without sigmas

    pose_model_copy = copy.deepcopy(pose_model)



    pose_model_copy.cfg.data.test.dataset_info.pop('sigmas')

    pose_results, _ = inference_bottom_up_pose_model(

        pose_model_copy, image_name, dataset_info=dataset_info)





def test_process_mmdet_results():

    det_results = [np.array([0, 0, 100, 100])]

    det_mask_results = None



    _ = process_mmdet_results(

        mmdet_results=(det_results, det_mask_results), cat_id=1)



    _ = process_mmdet_results(mmdet_results=det_results, cat_id=1)