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