之前的脚本
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import os
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import cv2
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import numpy as np
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import time
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import threading
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import queue
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import socket
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import json
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import sys
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from ais_bench.infer.interface import InferSession
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# ================== 导入控制模块 =====================
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from drone_controller.base_control import DroneController
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from drone_controller.pid_counter import PID
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# ===================================================
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class VideoStreaming:
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""" 📡 视频流采集与后台图传模块（支持 RTSP 自动重连） """
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def __init__(self, video_cfg, net_cfg):
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self.video_source = video_cfg.get("video_source", 0)
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self.jpeg_quality = video_cfg.get("jpeg_quality", 75)
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self.gs_ip = net_cfg.get("ground_station_ip", "127.0.0.1")
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self.udp_port = net_cfg.get("udp_port", 9999)
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# RTSP 特殊参数
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self.is_rtsp = isinstance(self.video_source, str) and self.video_source.startswith("rtsp://")
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self.cap = None
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self.frame_buffer = queue.Queue(maxsize=1) # 只保留最新一帧
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self.is_running = True
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self.capture_thread = None
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# 启动后台采集线程
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self._start_capture_thread()
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# 图传发送队列与线程（原样保留）
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self.tx_queue = queue.Queue(maxsize=2)
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self.sender_thread = threading.Thread(target=self._udp_stream_sender_worker, daemon=True)
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self.sender_thread.start()
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def _start_capture_thread(self):
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""" 独立线程负责从摄像头/RTSP 读取帧，避免主循环阻塞 """
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self.capture_thread = threading.Thread(target=self._capture_worker, daemon=True)
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self.capture_thread.start()
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def _capture_worker(self):
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""" 后台不断读取帧，存入 frame_buffer """
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while self.is_running:
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# 如果摄像头未打开或已断开，尝试连接
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if self.cap is None or not self.cap.isOpened():
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self._open_camera()
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if self.cap is None or not self.cap.isOpened():
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time.sleep(1) # 等待重连
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continue
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ret, frame = self.cap.read()
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if not ret:
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# 读取失败，关闭并标记需重连
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print("⚠️ RTSP 读取失败，准备重连...")
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self.cap.release()
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self.cap = None
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continue
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# 清空旧帧，只保留最新一帧
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while not self.frame_buffer.empty():
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try:
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self.frame_buffer.get_nowait()
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except queue.Empty:
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break
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self.frame_buffer.put(frame)
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if self.cap is not None:
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self.cap.release()
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def _open_camera(self):
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""" 打开摄像头或 RTSP 流，并设置低延迟参数 """
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try:
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print(f"📷 正在连接视频源: {self.video_source}")
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self.cap = cv2.VideoCapture(self.video_source)
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if self.is_rtsp:
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# 设置 RTSP 缓冲区大小（只保留 1 帧，降低延迟）
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self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# 设置读取超时（OpenCV 没有直接超时参数，但可尝试设置后端属性）
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# 某些平台支持以下设置：
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self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('H', '2', '6', '4'))
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# 强制使用 tcp 协议（某些 rtsp 默认 udp 会丢包严重）
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# 如果 rtsp 地址支持，可以改成 "rtsp://...?tcp"
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if "?" not in self.video_source:
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self.video_source += "?tcp"
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# 注意：有些相机需要重新初始化，这里简单打印提示
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print("💡 建议使用 TCP 传输: 在 RTSP URL 后添加 ?tcp")
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else:
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# USB 摄像头也可设置缓冲区
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self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
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# 可选：强制指定分辨率（根据你的相机调整）
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# self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
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# self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
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if self.cap.isOpened():
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print("✅ 视频源连接成功")
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else:
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print("❌ 视频源打开失败")
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except Exception as e:
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print(f"❌ 打开视频源异常: {e}")
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self.cap = None
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def read_frame(self):
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""" 主循环调用：从缓冲区获取最新一帧（非阻塞） """
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try:
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frame = self.frame_buffer.get_nowait()
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return True, frame
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except queue.Empty:
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# 没有新帧时返回 False，主循环可根据需要 sleep
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return False, None
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def push_to_stream(self, frame):
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""" 原图传推流逻辑保持不变 """
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try:
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send_frame = cv2.resize(frame, (640, 480))
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self.tx_queue.put_nowait(send_frame)
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except queue.Full:
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pass
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def _udp_stream_sender_worker(self):
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""" 原图传发送线程（未改动） """
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sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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server_address = (self.gs_ip, self.udp_port)
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print(f"📡 图传后台线程已启动，目标地面站 -> {self.gs_ip}:{self.udp_port}")
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while self.is_running:
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try:
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frame_to_send = self.tx_queue.get(timeout=1.0)
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if frame_to_send is None:
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break
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result, img_encode = cv2.imencode('.jpg', frame_to_send,
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[cv2.IMWRITE_JPEG_QUALITY, self.jpeg_quality])
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if not result:
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continue
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data = img_encode.tobytes()
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if len(data) > 65000:
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continue
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sock.sendto(data, server_address)
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except queue.Empty:
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continue
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except Exception as e:
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print(f"❌ 图传线程异常: {e}")
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time.sleep(0.1)
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sock.close()
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def release(self):
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""" 释放资源 """
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self.is_running = False
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if self.capture_thread is not None:
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self.capture_thread.join(timeout=2.0)
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if self.cap is not None:
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self.cap.release()
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self.tx_queue.put(None)
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class YOLO26UAVInfer:
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""" 🚀 昇腾 NPU 推理与图像后处理模块 """
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def __init__(self, model_cfg):
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model_path = model_cfg.get("model_path", "./om/yolo26n-balloon.om")
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device_id = model_cfg.get("device_id", 0)
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self.conf_threshold = model_cfg.get("conf_threshold", 0.25)
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"❌ 找不到模型文件: {model_path}")
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self.session = InferSession(device_id, model_path)
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self.input_width = 640
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self.input_height = 640
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def letterbox(self, img, new_shape=(640, 640), color=(114, 114, 114)):
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shape = img.shape[:2] # [height, width]
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]
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dw /= 2
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dh /= 2
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if shape[::-1] != new_unpad:
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img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
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return img, r, (dw, dh)
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def preprocess(self, frame):
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""" 图像预处理，输出符合 NPU 输入的 Tensor """
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input_tensor, ratio, dwdh = self.letterbox(frame)
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input_tensor = input_tensor.astype(np.float32) / 255.0
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input_tensor = np.transpose(input_tensor, (2, 0, 1))
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input_tensor = np.expand_dims(input_tensor, axis=0)
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input_tensor = np.ascontiguousarray(input_tensor)
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return input_tensor, ratio, dwdh
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def postprocess(self, outputs, ratio, dwdh, orig_shape):
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out = outputs[0]
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detections = []
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for i in range(out.shape[1]):
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box = out[0, i, :4]
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conf = out[0, i, 4]
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cls_id = int(out[0, i, 5])
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if conf > self.conf_threshold:
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x1, y1, x2, y2 = box
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# 基于原图比例还原坐标
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x1 = (x1 - dwdh[0]) / ratio
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y1 = (y1 - dwdh[1]) / ratio
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x2 = (x2 - dwdh[0]) / ratio
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y2 = (y2 - dwdh[1]) / ratio
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x1 = max(0, min(x1, orig_shape[1]))
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y1 = max(0, min(y1, orig_shape[0]))
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x2 = max(0, min(x2, orig_shape[1]))
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y2 = max(0, min(y2, orig_shape[0]))
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detections.append([x1, y1, x2, y2, conf, cls_id])
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return detections
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def draw_boxes(self, img, detections):
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""" 在图像上渲染精准目标框 """
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for det in detections:
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x1, y1, x2, y2, conf, cls_id = det
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cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
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label = f"Target: {conf:.2f}"
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cv2.putText(img, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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return img
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class UAVControlLoop:
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""" 🛸 无人机飞控闭环控制核心 """
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def __init__(self, fc_cfg, pid_y_cfg, pid_z_cfg):
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connection_string = fc_cfg.get("connection_string", "udpout:127.0.0.1:14550")
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baud_rate = fc_cfg.get("baud_rate", 57600)
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# 初始化控制驱动端
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self.uav = DroneController(connection_string=connection_string, baud=baud_rate)
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# 从 JSON 参数动态初始化 PID 调节器
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self.pid_y = PID(
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kp=pid_y_cfg["kp"], ki=pid_y_cfg["ki"], kd=pid_y_cfg["kd"],
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max_out=pid_y_cfg["max_out"], min_out=pid_y_cfg["min_out"]
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)
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self.pid_z = PID(
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kp=pid_z_cfg["kp"], ki=pid_z_cfg["ki"], kd=pid_z_cfg["kd"],
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max_out=pid_z_cfg["max_out"], min_out=pid_z_cfg["min_out"]
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)
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def start_uav(self):
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""" 连接并解锁无人机 """
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self.uav.connect()
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if not self.uav.is_armed and self.uav.current_mode not in ['GUIDED', 'OFFBOARD']:
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self.uav.arm()
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self.uav.print_status()
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self.uav.set_mode('OFFBOARD')
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def process_control(self, detections, orig_shape, frame):
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""" 根据检测结果执行 MAVLink 闭环控制逻辑 """
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img_center_x = orig_shape[1] / 2
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img_center_y = orig_shape[0] / 2
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current_mode = getattr(self.uav, 'current_mode', 'UNKNOWN')
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if len(detections) > 0:
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# 寻找置信度最高的目标
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best_det = max(detections, key=lambda x: x[4])
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x1, y1, x2, y2, conf, cls_id = best_det
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target_x = (x1 + x2) / 2
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target_y = (y1 + y2) / 2
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# 计算归一化误差
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err_x = (target_x - img_center_x) / img_center_x
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err_y = (target_y - img_center_y) / img_center_y
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# PID 计算转化为物理速度需求
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vy_cmd = self.pid_y.update(err_x) # 图像X正向 -> 飞机Vy右移
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vz_cmd = self.pid_z.update(err_y) # 图像Y正向 -> 飞机Vz下移 (NED系)
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vx_cmd = 0.0 # 保持前后平移为0
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# 视效叠加
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cv2.line(frame, (int(img_center_x), int(img_center_y)), (int(target_x), int(target_y)), (255, 0, 0), 2)
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cv2.putText(frame, f"MAVLink Out -> Err_X: {err_x:+.3f} Err_Y: {err_y:+.3f}", (20, 70),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
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# 执行机体速度控制
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if current_mode in ['GUIDED', 'OFFBOARD']:
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self.uav.send_body_velocity(vx=vx_cmd, vy=vy_cmd, vz=vz_cmd, yaw_rate=0.0)
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else:
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cv2.putText(frame, f"MANUAL MODE OVERRIDE ({current_mode})", (20, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
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(0, 0, 255), 2)
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else:
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# 目标丢失保护逻辑
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cv2.putText(frame, "MAVLink Out -> Target LOST (HOVER)", (20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
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(0, 0, 255), 2)
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# 清除 PID 积分，防止突跳
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if hasattr(self.pid_y, 'reset'): self.pid_y.reset()
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if hasattr(self.pid_z, 'reset'): self.pid_z.reset()
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if current_mode in ['GUIDED', 'OFFBOARD']:
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self.uav.send_body_velocity(vx=0.0, vy=0.0, vz=0.0, yaw_rate=0.0)
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# ==================== 主程序入口 ======================
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def main():
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# 默认加载同目录下的 config.json
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config_file = "config.json"
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# 允许在命令行指定 json 路径，例如: python3 infer_camera_modular.py config_outdoor.json
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if len(sys.argv) > 1:
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config_file = sys.argv[1]
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print(f"📖 正在从 {config_file} 加载系统配置...")
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try:
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with open(config_file, "r", encoding="utf-8") as f:
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cfg = json.load(f)
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except Exception as e:
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print(f"❌ 读取配置文件失败: {e}")
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sys.exit(1)
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# 实例化各子系统并注入配置
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streamer = VideoStreaming(cfg["video"], cfg["network"])
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detector = YOLO26UAVInfer(cfg["model"])
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controller = UAVControlLoop(cfg["flight_control"], cfg["pid_y"], cfg["pid_z"])
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# 启动无人机连接
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controller.start_uav()
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print("🚀 NPU 精准推理 [JSON配置动态加载版] 主循环启动...")
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try:
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while True:
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ret, frame = streamer.read_frame()
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if not ret:
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time.sleep(0.005)
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continue
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orig_shape = frame.shape[:2]
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# 1. 图像预处理
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input_tensor, ratio, dwdh = detector.preprocess(frame)
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# 2. NPU 硬件推理
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t_start = time.time()
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outputs = detector.session.infer([input_tensor])
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t_end = time.time()
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fps_pure = 1.0 / (t_end - t_start)
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# 3. 坐标反求与控制计算
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detections = detector.postprocess(outputs, ratio, dwdh, orig_shape)
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controller.process_control(detections, orig_shape, frame)
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# 4. 界面渲染
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frame = detector.draw_boxes(frame, detections)
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cv2.putText(frame, f"NPU Pure FPS: {fps_pure:.1f}", (20, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255),
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2)
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# 5. 推送至后台异步图传队列
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streamer.push_to_stream(frame)
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except KeyboardInterrupt:
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print("\n👋 接收到终止信号，安全退出中...")
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finally:
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streamer.release()
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print("程序运行结束。")
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if __name__ == "__main__":
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main()
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