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from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
process_mmdet_results, vis_pose_result)
from mmpose.datasets import DatasetInfo
from mmdet.apis import inference_detector, init_detector
det_model = init_detector(
"./external/faster_rcnn_r50_fpn_coco.py",
"./faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth",
device="cpu")
pose_model = init_pose_model(
"./external/hrnet_w48_coco_256x192.py",
"./hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth",
device="cpu")
dataset = pose_model.cfg.data['test']['type']
dataset_info = pose_model.cfg.data['test'].get('dataset_info', None)
dataset_info = DatasetInfo(dataset_info)
def infer(image):
mmdet_results = inference_detector(det_model, image)
person_results = process_mmdet_results(mmdet_results, 1)
pose_results, returned_outputs = inference_top_down_pose_model(
pose_model,
image,
person_results,
bbox_thr=0.3,
format='xyxy',
dataset=dataset,
dataset_info=dataset_info,
return_heatmap=False,
outputs=None)
print(pose_results)
print(returned_outputs)
return pose_results, returned_outputs
def draw(image, results):
return vis_pose_result(
pose_model,
image,
results,
dataset=dataset,
dataset_info=dataset_info,
kpt_score_thr=0.3,
radius=4,
thickness=3,
show=False,
out_file=None)
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