5.24

lane_server/lane_infer_server.py

@@ -4,7 +4,7 @@ from loguru import logger
 import zmq
 from infer import Lane_model_infer
 import numpy as np
-
+import cv2
 lock = threading.Lock()
 response = {'code': 0, 'data': 0}
 
@@ -31,27 +31,28 @@ if __name__ == "__main__":
 
     # 配置日志输出
     logger.add(cfg['debug']['logger_filename'], format=cfg['debug']['logger_format'], retention = 5, level="INFO")
-    camera_cfg = cfg['camera']
 
     # 连接摄像头server 巡线只需要连接前摄像头
     context = zmq.Context()
     camera_socket = context.socket(zmq.SUB)
-    camera_socket.connect(f"tcp://*:{camera_cfg['front_camera_port']}")
-    camera_socket.setsockopt(zmq.SUBSCRIBE, '')
+    camera_socket.connect(f"tcp://localhost:{cfg['camera']['front_camera_port']}")
+    camera_socket.setsockopt_string(zmq.SUBSCRIBE, "")
     logger.info("connect camera success")
     # 初始化paddle推理器
     predictor = Lane_model_infer()
     logger.info("lane model load success")
     # 启动lane_infer_server线程
     mythread = threading.Thread(target=server_resp,
-                                args=(camera_cfg['lane_infer_port'],),
+                                args=(cfg['server']['lane_infer_port'],),
                                 daemon=True)
     mythread.start()
 
     while True:
-        image_data_bytes = camera_socket.recv(320*240)
-        image = np.frombuffer(image_data_bytes, dtype=np.uint8).reshape((240, 320, 3))
-        result = predictor.infer(image)
+        message = camera_socket.recv()
+        np_array = np.frombuffer(message, dtype=np.uint8)
+        frame = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
+        frame = cv2.resize(frame,(320,240))
+        result = predictor.infer(frame)
         with lock:
             response['data'] = result
     mythread.join()