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import torch, torchvision
import mmpose
from mmcv.ops import get_compiling_cuda_version, get_compiler_version
import gradio as gr
import cv2
from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
vis_pose_result, process_mmdet_results)
from mmdet.apis import inference_detector, init_detector
pose_config = '/configs/topdown_heatmap_hrnet_w48_coco_256x192.py'
pose_checkpoint = '/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
det_config = '/configs/faster_rcnn_r50_fpn_1x_coco.py'
det_checkpoint = '/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
# initialize pose model
pose_model = init_pose_model(pose_config, pose_checkpoint, device='cpu')
# initialize detector
det_model = init_detector(det_config, det_checkpoint, device='cpu')
def predict(img):
mmdet_results = inference_detector(det_model, img)
person_results = process_mmdet_results(mmdet_results, cat_id=1)
pose_results, returned_outputs = inference_top_down_pose_model(
pose_model,
img,
person_results,
bbox_thr=0.3,
format='xyxy',
dataset=pose_model.cfg.data.test.type)
vis_result = vis_pose_result(
pose_model,
img,
pose_results,
dataset=pose_model.cfg.data.test.type,
show=False)
#vis_result = cv2.resize(vis_result, dsize=None, fx=0.5, fy=0.5)
return vis_result
example_list = ['/examples/demo2.png']
title = "Pose estimation"
description = "HPE"
article = "test MMpose"
# Create the Gradio demo
demo = gr.Interface(fn=predict,
inputs=gr.Image(),
outputs=[gr.Image(label='Prediction')],
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch(debug=False,
share=True)
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