feat: 增加循跡回歸雙服務端
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@@ -2,7 +2,8 @@ import paddle.inference as paddle_infer
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import numpy as np
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import paddle.vision.transforms as T
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class Lane_model_infer:
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def __init__(self, model_dir="./lane_model"):
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# def __init__(self, model_dir="./lane_model/7_12_6"):
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def __init__(self, model_dir="./lane_model/6_9"):
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# 初始化 paddle 推理
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self.model_dir = model_dir
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self.config = paddle_infer.Config(model_dir + "/model.pdmodel", model_dir + "/model.pdiparams")
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@@ -21,6 +22,9 @@ class Lane_model_infer:
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self.normalize_transform = T.Normalize(mean=[127.5], std=[127.5])
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# print(self.config.summary())
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def infer(self,src) -> np.ndarray:
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# 7_12_3 [60:240,:]
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# crop_src = src[60:240,:]
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# image = self.normalize_transform(crop_src)
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image = self.normalize_transform(src)
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image = image.transpose(2, 0, 1)
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image = np.expand_dims(image, axis=0)
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@@ -16,8 +16,8 @@ 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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logger.info("lane server thread init success")
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context = zmq.Context()
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# 启动 server
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99
lane_server/lane_infer_server1.py
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99
lane_server/lane_infer_server1.py
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@@ -0,0 +1,99 @@
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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 logging
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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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import cv2
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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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global response
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global model_id
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logger.info("lane server1 thread init success")
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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 infer1 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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def lane_infer1_server_main(queue):
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# context2 = zmq.Context()
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# socket_server = context2.socket(zmq.PUB)
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# socket_server.setsockopt(zmq.SNDHWM,10)
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# socket_server.bind("tcp://*:7778")
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if queue != None:
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class Handler(logging.Handler):
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def emit(self, record):
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log_entry = self.format(record)
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queue.put({'level': record.levelname.lower(), 'content': log_entry})
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# logger.remove()
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handler = Handler()
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logger.add(handler, format="{time:MM-DD HH:mm:ss} {message}", level="DEBUG")
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cfg = toml.load('/home/evan/Workplace/project_infer/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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# 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.REQ)
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camera_socket.connect(f"tcp://localhost:{cfg['camera']['front_camera_port']}")
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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(model_dir="./lane_model/7_12_6")
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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=(cfg['server']['lane_infer_port1'],),
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daemon=True)
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mythread.start()
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import signal
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import sys
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def signal_handler(signum, frame):
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logger.info("Received signal, exiting...")
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camera_socket.close()
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context.term()
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sys.exit(0)
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# 注册信号处理函数
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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while True:
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camera_socket.send_string("")
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message = camera_socket.recv()
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np_array = np.frombuffer(message, dtype=np.uint8)
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frame = cv2.imdecode(np_array, cv2.IMREAD_COLOR)
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frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
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frame = frame[60:240,:]
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result = predictor.infer(frame)
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with lock:
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response['data'] = result
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if cv2.waitKey(1) == 27:
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break
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mythread.join()
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logger.info("lane infer1 server exit")
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if __name__ == "__main__":
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lane_infer1_server_main(None)
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4
lane_server/lane_model/6_9/说明.txt
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4
lane_server/lane_model/6_9/说明.txt
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@@ -0,0 +1,4 @@
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十字增强
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补充十字_备份原数据
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与初代相似 直角弯内切 十字正常 红色球后的弯压线
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1
lane_server/lane_model/7_12_3/readme.txt
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1
lane_server/lane_model/7_12_3/readme.txt
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@@ -0,0 +1 @@
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crop_frame = frame[60:240,:]能用的
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8
lane_server/lane_model/说明.txt
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8
lane_server/lane_model/说明.txt
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@@ -0,0 +1,8 @@
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根目录下的模型 最初版本 很久没调了
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2024-04-28-19-19-15
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6_9 在 2024-04-28-19-19-15 数据基础上补充了十字 mobilenet v3 small 全图
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7_10_2 新标注的数据集 mobilenet v3 small 全图
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7_12_3 使用和 6_9 相同的数据集 mobilenet v3 large 裁剪上面 60 行像素 [60:240,:]
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7_12_6 使用和 6_9 相同的数据集 mobilenet v3 small 裁剪上面 60 行像素 [60:240,:]
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