75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
from argparse import ArgumentParser
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import numpy as np
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import requests
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from mmdet.apis import inference_detector, init_detector, show_result_pyplot
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from mmdet.core import bbox2result
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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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'--score-thr', type=float, default=0.5, help='bbox score threshold')
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args = parser.parse_args()
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return args
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def parse_result(input, model_class):
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bbox = []
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label = []
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score = []
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for anchor in input:
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bbox.append(anchor['bbox'])
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label.append(model_class.index(anchor['class_name']))
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score.append([anchor['score']])
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bboxes = np.append(bbox, score, axis=1)
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labels = np.array(label)
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result = bbox2result(bboxes, labels, len(model_class))
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return result
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def main(args):
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# build the model from a config file and a checkpoint file
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model = init_detector(args.config, args.checkpoint, device=args.device)
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# test a single image
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model_result = inference_detector(model, args.img)
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for i, anchor_set in enumerate(model_result):
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anchor_set = anchor_set[anchor_set[:, 4] >= 0.5]
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model_result[i] = anchor_set
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# show the results
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show_result_pyplot(
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model,
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args.img,
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model_result,
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score_thr=args.score_thr,
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title='pytorch_result')
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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 = parse_result(response.json(), model.CLASSES)
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show_result_pyplot(
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model,
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args.img,
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server_result,
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score_thr=args.score_thr,
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title='server_result')
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for i in range(len(model.CLASSES)):
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assert np.allclose(model_result[i], server_result[i])
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if __name__ == '__main__':
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args = parse_args()
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main(args)
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