# Copyright (c) OpenMMLab. All rights reserved. from argparse import ArgumentParser import numpy as np import requests from mmocr.apis import init_detector, model_inference 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( '--score-thr', type=float, default=0.5, help='bbox score threshold') args = parser.parse_args() return args def main(args): # build the model from a config file and a checkpoint file model = init_detector(args.config, args.checkpoint, device=args.device) # test a single image model_results = model_inference(model, args.img) model.show_result( args.img, model_results, win_name='model_results', show=True, score_thr=args.score_thr) url = 'http://' + args.inference_addr + '/predictions/' + args.model_name with open(args.img, 'rb') as image: response = requests.post(url, image) serve_results = response.json() model.show_result( args.img, serve_results, show=True, win_name='serve_results', score_thr=args.score_thr) assert serve_results.keys() == model_results.keys() for key in serve_results.keys(): for model_result, serve_result in zip(model_results[key], serve_results[key]): if isinstance(model_result[0], (int, float)): assert np.allclose(model_result, serve_result) elif isinstance(model_result[0], str): assert model_result == serve_result else: raise TypeError if __name__ == '__main__': args = parse_args() main(args)