60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
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import toml
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import threading
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from loguru import logger
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import zmq
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from infer import Lane_model_infer
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import numpy as np
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lock = threading.Lock()
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response = {'code': 0, 'data': 0}
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# 处理server响应数据
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def server_resp(lane_infer_port):
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logger.info("lane server thread init success")
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global response
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context = zmq.Context()
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# 启动server
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socket = context.socket(zmq.REP)
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socket.bind(f"tcp://*:{lane_infer_port}")
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logger.info("lane infer server init success")
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while True:
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message = socket.recv_string()
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with lock:
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socket.send_pyobj(response)
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if __name__ == "__main__":
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cfg = toml.load('../cfg_infer_server.toml')
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# 配置日志输出
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logger.add(cfg['debug']['logger_filename'], format=cfg['debug']['logger_format'], retention = 5, level="INFO")
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camera_cfg = cfg['camera']
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# 连接摄像头server 巡线只需要连接前摄像头
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context = zmq.Context()
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camera_socket = context.socket(zmq.SUB)
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camera_socket.connect(f"tcp://*:{camera_cfg['front_camera_port']}")
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camera_socket.setsockopt(zmq.SUBSCRIBE, '')
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logger.info("connect camera success")
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# 初始化paddle推理器
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predictor = Lane_model_infer()
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logger.info("lane model load success")
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# 启动lane_infer_server线程
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mythread = threading.Thread(target=server_resp,
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args=(camera_cfg['lane_infer_port'],),
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daemon=True)
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mythread.start()
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while True:
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image_data_bytes = camera_socket.recv(320*240)
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image = np.frombuffer(image_data_bytes, dtype=np.uint8).reshape((240, 320, 3))
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result = predictor.infer(image)
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with lock:
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response['data'] = result
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mythread.join()
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logger.info("lane infer server exit")
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