UDP视频测试
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@@ -2,157 +2,182 @@ 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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from ais_bench.infer.interface import InferSession
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# ==================== 硬编码配置区域 ====================
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MODEL_PATH = "./om/yolo26n-balloon.om" # 你的 YOLO26 模型路径
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CONF_THRESHOLD = 0.25 # 置信度过滤阈值 (YOLO26 推荐 0.25)
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CAMERA_INDEX = 0 # 摄像头索引 (默认0，如果打不开可以尝试1或2)
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# ==================== 配置区域 ====================
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MODEL_PATH = "./om/yolo26n-balloon.om" # YOLO26 模型路径
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INPUT_VIDEO = "./test_video.mp4" # 输入的测试视频（或 0 代表摄像头）
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CONF_THRESHOLD = 0.25 # 置信度阈值
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# -------------- 无线图传网络配置 --------------
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GROUND_STATION_IP = "192.168.31.239" # 地面站的局域网 IP
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UDP_PORT = 9999
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JPEG_QUALITY = 75 # JPEG 压缩质量 (0-100)
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# ==================================================
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# 限制队列大小，发不完就直接丢帧，保证飞控算法实时性
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tx_queue = queue.Queue(maxsize=2)
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# ========================================================
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def udp_stream_sender_thread():
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""" 📡 后台图传专用线程 """
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sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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server_address = (GROUND_STATION_IP, UDP_PORT)
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print(f"📡 图传后台线程已启动...")
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class YOLO26CameraInfer:
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while True:
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try:
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frame_to_send = tx_queue.get()
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if frame_to_send is None:
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break
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# 压缩为 JPEG 字节流
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result, img_encode = cv2.imencode('.jpg', frame_to_send, [cv2.IMWRITE_JPEG_QUALITY, 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: # 严格防丢包/报错限制
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continue
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sock.sendto(data, server_address)
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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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class YOLO26UAVInfer:
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def __init__(self, model_path, device_id=0):
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self.device_id = device_id
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self.model_path = model_path
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# 自动检查模型文件是否存在，防止硬编码路径写错导致程序崩溃
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"找不到硬编码的模型文件: {model_path}，请检查路径是否正确。")
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# 创建 ais_bench 推理会话
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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]
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shape = img.shape[:2] # 原始图的高宽 [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 = new_shape[1] - new_unpad[0]
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dh = new_shape[0] - new_unpad[1]
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left = dw // 2
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right = dw - left
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top = dh // 2
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bottom = dh - top
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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, (left, top)
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return img, r, (dw, dh)
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def preprocess_frame(self, img_bgr):
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self.original_height, self.original_width = img_bgr.shape[:2]
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img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
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img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(
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img_rgb, (self.input_width, self.input_height)
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)
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img_input = (img_resized.astype(np.float32) / 255.0).astype(np.float32)
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img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, :, :, :])
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return img_input
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def infer(self, img_input):
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return self.session.infer([img_input])
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def postprocess(self, infer_result, conf_threshold):
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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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if isinstance(infer_result, (list, tuple)):
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output = infer_result[0]
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else:
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output = infer_result
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if output.ndim == 3:
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output = output[0]
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for det in output:
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confidence = det[4]
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if confidence < conf_threshold:
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continue
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x1_scale, y1_scale, x2_scale, y2_scale = det[0], det[1], det[2], det[3]
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cls_id = int(det[5])
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x1 = (x1_scale - self.pad_left) / self.ratio
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y1 = (y1_scale - self.pad_top) / self.ratio
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x2 = (x2_scale - self.pad_left) / self.ratio
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y2 = (y2_scale - self.pad_top) / self.ratio
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x1 = max(0, min(x1, self.original_width))
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y1 = max(0, min(y1, self.original_height))
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x2 = max(0, min(x2, self.original_width))
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y2 = max(0, min(y2, self.original_height))
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detections.append([x1, y1, x2, y2, confidence, cls_id])
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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 > CONF_THRESHOLD:
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x1, y1, x2, y2 = box
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# 严格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(image, detections):
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def draw_boxes(img, detections):
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for det in detections:
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x1, y1, x2, y2, conf, cls_id = det
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x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
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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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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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label = f"Class: {cls_id} {conf:.2f}"
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(label_w, label_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
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cv2.rectangle(image, (x1, y1 - label_h - 5), (x1 + label_w, y1), (0, 255, 0), -1)
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cv2.putText(image, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
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def main():
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detector = YOLO26UAVInfer(MODEL_PATH)
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cap = cv2.VideoCapture(INPUT_VIDEO)
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return image
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if not cap.isOpened():
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print("❌ 无法打开视频源")
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return
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# 启动后台无线电图传线程
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sender_thread = threading.Thread(target=udp_stream_sender_thread, daemon=True)
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sender_thread.start()
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print("🚀 NPU 精准推理主循环启动...")
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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# 【核心修正】完全保留读取到的原图大小，坚决不在这一步进行 cv2.resize！
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orig_shape = frame.shape[:2]
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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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# 1. 严格使用原图进行标准无畸变 letterbox 图像预处理
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input_tensor, ratio, dwdh = detector.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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t_start = time.time()
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# 2. NPU 硬件推理
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outputs = detector.session.infer([input_tensor])
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# 3. 坐标反求（恢复到大图的分辨率，保证百分之百精准）
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detections = detector.postprocess(outputs, ratio, dwdh, orig_shape)
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t_end = time.time()
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fps_pure = 1.0 / (t_end - t_start)
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# 4. 寻找置信度最高的目标并计算 MAVLink 飞控误差
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if len(detections) > 0:
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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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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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# 画中心对准线
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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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else:
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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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# 5. 在原始分辨率大图上，渲染精准目标框
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frame = 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), 2)
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# 🚀【核心优化点】只有在进入发送队列的前一秒，才将画好完美框的图 resize 为轻量化分辨率
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send_frame = cv2.resize(frame, (640, 480))
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# 6. 非阻塞式尝试推入图传队列
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try:
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tx_queue.put_nowait(send_frame)
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except queue.Full:
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pass # 队列满则丢弃该帧，绝不积压延迟
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cap.release()
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tx_queue.put(None)
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print("程序运行结束。")
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if __name__ == "__main__":
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print("正在初始化 YOLOv26 离线推理引擎...")
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# 传入硬编码的模型路径
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yolo_infer = YOLO26CameraInfer(MODEL_PATH)
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# 打开硬编码指定的摄像头
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cap = cv2.VideoCapture(CAMERA_INDEX)
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if not cap.isOpened():
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print(f"错误：无法打开摄像头索引 {CAMERA_INDEX}，请检查物理连接。")
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exit()
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# 尝试设置合适的分辨率
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
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print(f"初始化成功！当前模型：{MODEL_PATH} | 过滤阈值：{CONF_THRESHOLD}")
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print("开始实时检测，在画面窗口按下 'q' 键可退出...")
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fps_start_time = 0
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while True:
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ret, frame = cap.read()
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if not ret:
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continue
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# 预处理与 NPU 推理
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input_tensor = yolo_infer.preprocess_frame(frame)
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raw_result = yolo_infer.infer(input_tensor)
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# 后处理（使用硬编码的置信度）
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detections = yolo_infer.postprocess(raw_result, conf_threshold=CONF_THRESHOLD)
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# 绘制结果
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frame = draw_boxes(frame, detections)
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# FPS 实时计算与显示
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fps_end_time = time.time()
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time_diff = fps_end_time - fps_start_time
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fps = 1 / time_diff if time_diff > 0 else 0
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fps_start_time = fps_end_time
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cv2.putText(frame, f"FPS: {fps:.1f}", (20, 40),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
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cv2.imshow("YOLOv26 Real-time Detection", frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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print("检测已结束。")
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main()
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