一些小更新
This commit is contained in:
@@ -9,7 +9,7 @@ from ais_bench.infer.interface import InferSession
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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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INPUT_VIDEO = 0 # 摄像头编号
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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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-155
@@ -1,155 +0,0 @@
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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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from ais_bench.infer.interface import InferSession
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# ==================== 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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INPUT_VIDEO = "./test_video.mp4" # 输入的 24 帧测试视频
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OUTPUT_VIDEO = "./output_result.mp4" # 检测完成后保存的新视频路径
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CONF_THRESHOLD = 0.25 # 置信度阈值
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# ==================================================
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class YOLO26UAVInfer:
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def __init__(self, model_path, device_id=0):
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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]
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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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left, top = dw // 2, dh // 2
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right, bottom = dw - left, dh - top
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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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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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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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detections = []
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output = infer_result[0] if isinstance(infer_result, (list, tuple)) else infer_result
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if output.ndim == 3: 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: 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 = max(0, min((x1_scale - self.pad_left) / self.ratio, self.original_width))
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y1 = max(0, min((y1_scale - self.pad_top) / self.ratio, self.original_height))
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x2 = max(0, min((x2_scale - self.pad_left) / self.ratio, self.original_width))
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y2 = max(0, min((y2_scale - self.pad_top) / self.ratio, self.original_height))
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detections.append([x1, y1, x2, y2, confidence, cls_id])
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return detections
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def draw_boxes(image, 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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# 画绿色的目标检测框
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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(image, f"Target:{cls_id} {conf:.2f}", (x1, y1 - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
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return image
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if __name__ == "__main__":
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yolo_infer = YOLO26UAVInfer(MODEL_PATH)
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# 1. 打开输入视频
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cap = cv2.VideoCapture(INPUT_VIDEO)
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if not cap.isOpened():
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print(f"错误：无法打开输入视频 {INPUT_VIDEO}")
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exit()
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# 读取输入视频的各种属性，用于配置保存视频的写入器
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frame_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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video_fps = cap.get(cv2.CAP_PROP_FPS)
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img_center_x, img_center_y = frame_w / 2, frame_h / 2
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# 2. 【核心点】初始化视频保存器 (VideoWriter)
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# mp4v 是最通用、在 Linux 下不需要额外配置库的编码格式
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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video_writer = cv2.VideoWriter(OUTPUT_VIDEO, fourcc, video_fps, (frame_w, frame_h))
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print(f"开始处理视频并渲染可视化结果...")
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print(f"原视频属性: {frame_w}x{frame_h} @ {video_fps:.2f} FPS")
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print(f"处理后的视频将实时保存到: {OUTPUT_VIDEO}")
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fps_start_time = time.time()
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frame_count = 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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break
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frame_count += 1
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# NPU 执行推理
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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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detections = yolo_infer.postprocess(raw_result, conf_threshold=CONF_THRESHOLD)
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# 如果检测到目标，计算偏差并画可视化指示线
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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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# 画一条从画面中心指向目标中心的蓝色对准线，直观展示控制走向
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cv2.line(frame, (int(img_center_x), int(img_center_y)),
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(int(target_x), int(target_y)), (255, 0, 0), 2)
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# 把计算好的、原本要发给飞控的误差值实时写在视频上
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cv2.putText(frame, f"MAVLink Out -> Err_X: {err_x:+.3f} Err_Y: {err_y:+.3f}",
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(20, 70), 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)",
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(20, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
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# 渲染框和实时运行 FPS
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frame = draw_boxes(frame, detections)
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end_time = time.time()
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curr_fps = 1 / (end_time - fps_start_time) if (end_time - fps_start_time) > 0 else video_fps
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fps_start_time = end_time
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cv2.putText(frame, f"NPU Process-FPS: {curr_fps:.1f}", (20, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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# 3. 【核心点】把这一帧画好的画面写入新视频文件
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video_writer.write(frame)
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if frame_count % 24 == 0:
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print(f"已处理并写入 {frame_count} 帧...")
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# 4. 释放资源，这一步至关重要！不 release 视频文件会损坏打不开
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cap.release()
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video_writer.release()
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print(f"🎉 处理完毕！完整的检测结果视频已成功保存至: {OUTPUT_VIDEO}")
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@@ -0,0 +1,191 @@
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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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from ais_bench.infer.interface import InferSession
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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
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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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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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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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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] # 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, 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 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 > 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(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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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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def main():
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detector = YOLO26UAVInfer(MODEL_PATH)
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cap = cv2.VideoCapture(INPUT_VIDEO)
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from control import UAVController
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uav = UAVController()
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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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# 完全保留读取到的原图大小
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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),cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
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# ================ MAVLink 动作调用 =================
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uav.update_control(has_target=True, err_x=err_x, err_y=err_y)
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# =================================================
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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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# ================ MAVLink 丢失保护 =================
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uav.update_control(has_target=False)
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# =================================================
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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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# 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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main()
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-175
@@ -1,175 +0,0 @@
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import cv2
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import math
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import time
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import serial
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import numpy as np
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from ais_bench.infer.interface import InferSession
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from pymavlink import mavutil
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class DroneDetector:
|
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def __init__(self, model_path, serial_port='/dev/ttyAMA0', baudrate=57600):
|
||||
self.session = InferSession(0, model_path)
|
||||
self.input_size = 640
|
||||
# 串口/MAVLink
|
||||
self.serial_port = serial_port
|
||||
self.baudrate = baudrate
|
||||
self.mav = None
|
||||
self.init_serial()
|
||||
|
||||
def init_serial(self):
|
||||
"""初始化串口（如果用于自定义协议）或 MAVLink 连接"""
|
||||
try:
|
||||
self.mav = mavutil.mavlink_connection(self.serial_port, baud=self.baudrate)
|
||||
print(f"MAVLink 已连接到 {self.serial_port} 波特率 {self.baudrate}")
|
||||
except Exception as e:
|
||||
print(f"串口打开失败: {e}")
|
||||
self.mav = None
|
||||
|
||||
def letterbox(self, img, color=(114, 114, 114)):
|
||||
shape = img.shape[:2]
|
||||
r = min(self.input_size / shape[0], self.input_size / shape[1])
|
||||
new_unpad = (int(round(shape[1] * r)), int(round(shape[0] * r)))
|
||||
dw = self.input_size - new_unpad[0]
|
||||
dh = self.input_size - new_unpad[1]
|
||||
left = dw // 2
|
||||
right = dw - left
|
||||
top = dh // 2
|
||||
bottom = dh - top
|
||||
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||||
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
|
||||
return img, r, (left, top)
|
||||
|
||||
def preprocess(self, img):
|
||||
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
||||
img_resized, self.ratio, (self.pad_left, self.pad_top) = self.letterbox(img_rgb)
|
||||
img_input = img_resized.astype(np.float32) / 255.0
|
||||
img_input = np.ascontiguousarray(np.transpose(img_input, (2, 0, 1))[np.newaxis, ...])
|
||||
return img_input
|
||||
|
||||
def postprocess(self, output, conf_thresh=0.5):
|
||||
detections = []
|
||||
if output.ndim == 3:
|
||||
output = output[0]
|
||||
for det in output:
|
||||
raw_conf = det[4]
|
||||
conf = 1.0 / (1.0 + math.exp(-raw_conf))
|
||||
if conf < conf_thresh:
|
||||
continue
|
||||
x1, y1, x2, y2 = det[0], det[1], det[2], det[3]
|
||||
# 映射回原图坐标
|
||||
x1 = (x1 - self.pad_left) / self.ratio
|
||||
y1 = (y1 - self.pad_top) / self.ratio
|
||||
x2 = (x2 - self.pad_left) / self.ratio
|
||||
y2 = (y2 - self.pad_top) / self.ratio
|
||||
cls_id = int(det[5])
|
||||
detections.append({
|
||||
'bbox': (x1, y1, x2, y2),
|
||||
'confidence': conf,
|
||||
'class': cls_id,
|
||||
'center': ((x1 + x2) / 2, (y1 + y2) / 2)
|
||||
})
|
||||
return detections
|
||||
|
||||
def send_mavlink_obstacle(self, angle_deg, distance_m):
|
||||
"""发送 DISTANCE_SENSOR 消息，表示目标方向与距离"""
|
||||
if not self.mav:
|
||||
return
|
||||
self.mav.mav.distance_sensor_send(
|
||||
0, # time_boot_ms
|
||||
0, # min_distance cm
|
||||
10000, # max_distance cm
|
||||
int(distance_m * 100), # current_distance cm
|
||||
0, # type (0=laser)
|
||||
0, # id
|
||||
int(angle_deg / 360.0 * 65535), # orientation (0~360° -> 0~65535)
|
||||
0 # covariance
|
||||
)
|
||||
|
||||
def send_custom_serial(self, detections):
|
||||
"""通过串口发送自定义格式的数据（例如 JSON）"""
|
||||
if not self.serial or not self.serial.is_open:
|
||||
return
|
||||
import json
|
||||
data = {
|
||||
'timestamp': time.time(),
|
||||
'detections': detections
|
||||
}
|
||||
self.serial.write((json.dumps(data) + '\n').encode())
|
||||
|
||||
def detect_and_send(self, frame, send_method='mavlink', conf_thresh=0.5):
|
||||
"""对单帧图像进行检测，并发送结果"""
|
||||
input_tensor = self.preprocess(frame)
|
||||
outputs = self.session.infer([input_tensor])
|
||||
detections = self.postprocess(outputs[0], conf_thresh)
|
||||
if not detections:
|
||||
return detections
|
||||
# 假设只发送置信度最高的那个目标
|
||||
best = detections[0]
|
||||
if send_method == 'mavlink':
|
||||
# 计算角度和距离（需要根据相机参数和实际尺寸估算）
|
||||
# 这里简单示例：根据目标在图像中的偏移量估算角度
|
||||
cx = best['center'][0]
|
||||
img_w = frame.shape[1]
|
||||
angle = (cx - img_w / 2) / (img_w / 2) * 45 # 水平视场角90°，偏移量映射为±45°
|
||||
# 距离估算：根据目标在图像中的高度（假设目标实际高度1m）
|
||||
# 需要相机焦距等，简化：使用固定值10米
|
||||
distance = 10.0
|
||||
self.send_mavlink_obstacle(angle, distance)
|
||||
elif send_method == 'custom':
|
||||
self.send_custom_serial(detections)
|
||||
return detections
|
||||
|
||||
def run_on_camera(self, camera_id=0, conf_thresh=0.5, send_method='mavlink'):
|
||||
cap = cv2.VideoCapture(camera_id)
|
||||
if not cap.isOpened():
|
||||
print("摄像头打开失败")
|
||||
return
|
||||
print("开始检测，按 q 退出")
|
||||
while True:
|
||||
ret, frame = cap.read()
|
||||
if not ret:
|
||||
break
|
||||
detections = self.detect_and_send(frame, send_method, conf_thresh)
|
||||
# 绘制检测框（调试）
|
||||
for det in detections:
|
||||
x1, y1, x2, y2 = map(int, det['bbox'])
|
||||
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
cv2.putText(frame, f"{det['class']}:{det['confidence']:.2f}", (x1, y1 - 5),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
|
||||
cv2.imshow("Detection", frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord('q'):
|
||||
break
|
||||
cap.release()
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
def run_on_image(self, image_path, conf_thresh=0.5, send_method='mavlink'):
|
||||
frame = cv2.imread(image_path)
|
||||
if frame is None:
|
||||
print("图片读取失败")
|
||||
return
|
||||
detections = self.detect_and_send(frame, send_method, conf_thresh)
|
||||
print(f"检测到 {len(detections)} 个目标")
|
||||
for d in detections:
|
||||
print(d)
|
||||
# 显示结果
|
||||
for det in detections:
|
||||
x1, y1, x2, y2 = map(int, det['bbox'])
|
||||
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
|
||||
cv2.putText(frame, f"{det['class']}:{det['confidence']:.2f}", (x1, y1 - 5),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
|
||||
cv2.imshow("Detection", frame)
|
||||
cv2.waitKey(0)
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
MODEL_PATH = "./om/yolo26n-drone.om"
|
||||
SERIAL_PORT = "/dev/ttyAMA0" # 根据实际修改
|
||||
BAUD = 57600
|
||||
|
||||
detector = DroneDetector(MODEL_PATH, SERIAL_PORT, BAUD)
|
||||
|
||||
# 选择模式：摄像头或单张图片
|
||||
# detector.run_on_camera(camera_id=0, conf_thresh=0.5, send_method='mavlink')
|
||||
detector.run_on_image("test.jpg", conf_thresh=0.5, send_method='mavlink')
|
||||
Reference in New Issue
Block a user