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198 lines
6.7 KiB
198 lines
6.7 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.apis import (inference_bottom_up_pose_model,
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inference_top_down_pose_model, init_pose_model,
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process_mmdet_results, vis_pose_result)
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from mmpose.datasets import DatasetInfo
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def test_top_down_demo():
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# COCO demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'coco/res50_coco_256x192.py',
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None,
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device='cpu')
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image_name = 'tests/data/coco/000000000785.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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person_result = []
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person_result.append({'bbox': [50, 50, 50, 100]})
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# AIC demo
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pose_model = init_pose_model(
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'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'aic/res50_aic_256x192.py',
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None,
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device='cpu')
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image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# OneHand10K demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'onehand10k/res50_onehand10k_256x256.py',
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None,
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device='cpu')
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image_name = 'tests/data/onehand10k/9.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# InterHand2DDataset demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'interhand2d/res50_interhand2d_all_256x256.py',
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None,
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device='cpu')
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image_name = 'tests/data/interhand2.6m/image2017.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# Face300WDataset demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'300w/res50_300w_256x256.py',
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None,
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device='cpu')
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image_name = 'tests/data/300w/indoor_020.png'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# FaceAFLWDataset demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'aflw/res50_aflw_256x256.py',
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None,
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device='cpu')
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image_name = 'tests/data/aflw/image04476.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# FaceCOFWDataset demo
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/'
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'cofw/res50_cofw_256x256.py',
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None,
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device='cpu')
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image_name = 'tests/data/cofw/001766.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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# test a single image, with a list of bboxes.
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pose_results, _ = inference_top_down_pose_model(
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pose_model,
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image_name,
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person_result,
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format='xywh',
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dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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def test_bottom_up_demo():
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# build the pose model from a config file and a checkpoint file
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pose_model = init_pose_model(
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'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
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'coco/res50_coco_512x512.py',
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None,
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device='cpu')
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image_name = 'tests/data/coco/000000000785.jpg'
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dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
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'dataset_info', None))
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pose_results, _ = inference_bottom_up_pose_model(
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pose_model, image_name, dataset_info=dataset_info)
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# show the results
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vis_pose_result(
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pose_model, image_name, pose_results, dataset_info=dataset_info)
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# test dataset_info without sigmas
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pose_model_copy = copy.deepcopy(pose_model)
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pose_model_copy.cfg.data.test.dataset_info.pop('sigmas')
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pose_results, _ = inference_bottom_up_pose_model(
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pose_model_copy, image_name, dataset_info=dataset_info)
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def test_process_mmdet_results():
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det_results = [np.array([0, 0, 100, 100])]
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det_mask_results = None
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_ = process_mmdet_results(
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mmdet_results=(det_results, det_mask_results), cat_id=1)
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_ = process_mmdet_results(mmdet_results=det_results, cat_id=1)
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