mirror of https://github.com/open-mmlab/mmpose
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os
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import os.path as osp
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import warnings
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from argparse import ArgumentParser
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import requests
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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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vis_pose_result)
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from mmpose.models import AssociativeEmbedding, TopDown
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def parse_args():
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parser = ArgumentParser()
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parser.add_argument('img', help='Image file')
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parser.add_argument('config', help='Config file')
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parser.add_argument('checkpoint', help='Checkpoint file')
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parser.add_argument('model_name', help='The model name in the server')
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parser.add_argument(
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'--inference-addr',
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default='127.0.0.1:8080',
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help='Address and port of the inference server')
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parser.add_argument(
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'--device', default='cuda:0', help='Device used for inference')
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parser.add_argument(
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'--out-dir', default='vis_results', help='Visualization output path')
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args = parser.parse_args()
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return args
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def main(args):
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os.makedirs(args.out_dir, exist_ok=True)
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# Inference single image by native apis.
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model = init_pose_model(args.config, args.checkpoint, device=args.device)
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if isinstance(model, TopDown):
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pytorch_result, _ = inference_top_down_pose_model(
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model, args.img, person_results=None)
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elif isinstance(model, (AssociativeEmbedding, )):
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pytorch_result, _ = inference_bottom_up_pose_model(model, args.img)
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else:
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raise NotImplementedError()
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vis_pose_result(
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model,
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args.img,
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pytorch_result,
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out_file=osp.join(args.out_dir, 'pytorch_result.png'))
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# Inference single image by torchserve engine.
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url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
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with open(args.img, 'rb') as image:
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response = requests.post(url, image)
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server_result = response.json()
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vis_pose_result(
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model,
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args.img,
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server_result,
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out_file=osp.join(args.out_dir, 'torchserve_result.png'))
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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# Following strings of text style are from colorama package
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bright_style, reset_style = '\x1b[1m', '\x1b[0m'
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red_text, blue_text = '\x1b[31m', '\x1b[34m'
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white_background = '\x1b[107m'
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msg = white_background + bright_style + red_text
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msg += 'DeprecationWarning: This tool will be deprecated in future. '
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msg += blue_text + 'Welcome to use the unified model deployment toolbox '
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msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
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msg += reset_style
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warnings.warn(msg)
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