删除视频处理功能，简化图像处理逻辑，更新横向赛道边缘检测相关函数，移除可视化功能，增强错误处理信息。
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@ -9,7 +9,7 @@ current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.dirname(os.path.dirname(current_dir))
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sys.path.append(project_root)
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from utils.detect_track import detect_yellow_track, visualize_track_detection, detect_horizontal_track_edge, visualize_horizontal_track_edge
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from utils.detect_track import detect_horizontal_track_edge
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def process_image(image_path, save_dir=None, show_steps=False):
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"""处理单张图像"""
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@ -37,110 +37,11 @@ def process_image(image_path, save_dir=None, show_steps=False):
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if save_dir:
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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base_name = os.path.basename(image_path)
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save_path = os.path.join(save_dir, f"result_{base_name}")
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# 可视化并保存
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visualize_horizontal_track_edge(image_path, save_path=save_path, observe=show_steps)
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print(f"结果已保存到: {save_path}")
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else:
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print("未能检测到黄色赛道")
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return edge_point, edge_info
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def process_video(video_path, save_path=None, show_output=True):
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"""处理视频"""
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print(f"处理视频: {video_path}")
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# 打开视频文件
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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print("无法打开视频文件")
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return
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# 获取视频基本信息
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fps = cap.get(cv2.CAP_PROP_FPS)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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print(f"视频信息: {width}x{height}, {fps} FPS, 总帧数: {total_frames}")
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# 如果需要保存，创建VideoWriter
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if save_path:
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fourcc = cv2.VideoWriter_fourcc(*'XVID')
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out = cv2.VideoWriter(save_path, fourcc, fps, (width, height))
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# 处理计数器
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frame_count = 0
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processed_count = 0
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start_time = time.time()
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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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# 每5帧处理一次以提高性能
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if frame_count % 5 == 0:
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processed_count += 1
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# 检测赛道
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edge_point, edge_info = detect_horizontal_track_edge(frame, observe=False)
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# 创建结果图像（使用visualize_horizontal_track_edge函数）
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if edge_point is not None and edge_info is not None:
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result_frame = visualize_horizontal_track_edge(frame, observe=False)
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else:
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# 如果未检测到赛道，显示原始帧并添加警告
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result_frame = frame.copy()
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cv2.putText(result_frame, "未检测到横向赛道", (width//4, height//2),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
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# 添加帧计数
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cv2.putText(result_frame, f"帧: {frame_count}/{total_frames}",
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(width - 200, height - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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# 保存或显示结果
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if save_path:
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out.write(result_frame)
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if show_output:
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# 将交点信息显示在窗口标题上
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if edge_point is not None and edge_info is not None:
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intersection_point = edge_info['intersection_point']
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distance_to_bottom = edge_info['distance_to_bottom']
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title = f'赛道检测 - 交点:({intersection_point[0]},{intersection_point[1]}) 底距:{distance_to_bottom:.1f}px'
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else:
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title = '赛道检测 - 未检测到交点'
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cv2.imshow(title, result_frame)
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key = cv2.waitKey(1) & 0xFF
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if key == 27: # ESC键退出
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break
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# 每秒更新一次处理进度
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if frame_count % int(fps) == 0:
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elapsed = time.time() - start_time
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percent = frame_count / total_frames * 100
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print(f"进度: {percent:.1f}% ({frame_count}/{total_frames}), 已用时间: {elapsed:.1f}秒")
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# 清理
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cap.release()
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if save_path:
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out.release()
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cv2.destroyAllWindows()
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# 输出统计信息
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total_time = time.time() - start_time
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print(f"视频处理完成，总时间: {total_time:.2f}秒")
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print(f"实际处理帧数: {processed_count}/{frame_count}")
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if processed_count > 0:
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print(f"平均每帧处理时间: {total_time/processed_count:.3f}秒")
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def main():
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parser = argparse.ArgumentParser(description='黄色赛道检测演示程序')
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parser.add_argument('--input', type=str, default='res/path/image_20250513_162556.png', help='输入图像或视频的路径')
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@ -171,8 +72,8 @@ def main():
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# 获取输出目录
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output_dir = os.path.dirname(args.output)
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process_image(args.input, output_dir, args.show)
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else: # video
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process_video(args.input, args.output, args.show)
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else:
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print("错误：不支持的视频类型")
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if __name__ == "__main__":
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main()
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@ -999,159 +999,6 @@ def detect_horizontal_track_edge(image, observe=False, delay=1000):
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return bottom_edge_point, edge_info
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def visualize_horizontal_track_edge(image, save_path=None, observe=False, delay=500):
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"""
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可视化横向赛道边缘检测过程，显示中间结果和最终分析
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参数:
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image: 输入图像，可以是文件路径或者已加载的图像数组
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save_path: 保存结果图像的路径（可选）
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observe: 是否输出中间状态信息和可视化结果，默认为False
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delay: 展示每个步骤的等待时间(毫秒)，默认为500ms
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"""
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# 如果输入是字符串（文件路径），则加载图像
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if isinstance(image, str):
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img = cv2.imread(image)
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else:
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img = image.copy()
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if img is None:
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print("无法加载图像")
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return
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# 获取图像尺寸
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height, width = img.shape[:2]
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# 创建输出图像
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output = img.copy()
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# 执行横向赛道检测
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edge_point, edge_info = detect_horizontal_track_edge(img, observe=False)
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# 如果成功检测到边缘
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if edge_point is not None and edge_info is not None:
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# 获取信息
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center_x = width // 2
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# 绘制中线
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cv2.line(output, (center_x, 0), (center_x, height), (0, 0, 255), 2)
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# 获取关键信息
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selected_slope = edge_info['slope']
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distance_to_center = edge_info['distance_to_center']
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intersection_point = edge_info['intersection_point']
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distance_to_bottom = edge_info['distance_to_bottom']
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is_horizontal = edge_info['is_horizontal']
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# 绘制检测到的点，兼容旧版本的edge_info字典
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if 'points' in edge_info and edge_info['points']:
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for point in edge_info['points']:
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cv2.circle(output, point, 3, (0, 255, 0), -1)
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# 标记边缘点
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cv2.circle(output, edge_point, 10, (0, 0, 255), -1)
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# 使用斜率画一条线来表示边缘方向
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line_length = 200
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end_x = edge_point[0] + line_length
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end_y = int(edge_point[1] + selected_slope * line_length)
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start_x = edge_point[0] - line_length
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start_y = int(edge_point[1] - selected_slope * line_length)
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cv2.line(output, (start_x, start_y), (end_x, end_y), (0, 255, 0), 2)
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# 标记中线与横向线的交点 (高亮显示)
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cv2.circle(output, intersection_point, 12, (255, 0, 255), -1)
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cv2.circle(output, intersection_point, 5, (255, 255, 255), -1)
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# 画出交点到底部的距离线
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cv2.line(output, intersection_point, (intersection_point[0], height), (255, 255, 0), 2)
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# 添加信息文本
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cv2.putText(output, f"边缘点: ({edge_point[0]}, {edge_point[1]})", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"到中线距离: {distance_to_center}像素", (10, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"斜率: {selected_slope:.4f}", (10, 90),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"是否水平: {is_horizontal}", (10, 120),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"点数量: {edge_info['points_count']}", (10, 150),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"到底部距离: {distance_to_bottom:.1f}像素", (10, 180),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(output, f"中线交点: ({intersection_point[0]}, {intersection_point[1]})", (10, 210),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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# 绘制交点计算的可视化内容
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# 用线条显示边缘点到中线的连接（交点计算的辅助线）
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cv2.line(output, edge_point, (center_x, edge_point[1]), (255, 153, 51), 2)
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# 在边缘点处标注其y值
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cv2.putText(output, f"y={edge_point[1]}", (edge_point[0] + 10, edge_point[1] - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 153, 51), 2)
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# 在交点处标注其坐标
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cv2.putText(output, f"({intersection_point[0]}, {intersection_point[1]})",
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(intersection_point[0] + 10, intersection_point[1] - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
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# 用箭头显示从边缘点到交点的路径（表示计算过程）
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cv2.arrowedLine(output, edge_point, intersection_point, (102, 204, 255), 2)
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# 添加交点计算的方程说明
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equation_text = f"y = {selected_slope:.2f} * (x - {edge_point[0]}) + {edge_point[1]}"
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cv2.putText(output, equation_text, (10, 240),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (102, 204, 255), 2)
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else:
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# 如果没有检测到，添加提示
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cv2.putText(output, "未检测到横向赛道", (width//4, height//2),
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cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
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# 如果提供了保存路径，保存结果图像
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if save_path:
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cv2.imwrite(save_path, output)
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if observe:
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print(f"结果已保存到: {save_path}")
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# 显示结果
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if observe:
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# 转换到HSV颜色空间
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 黄色的HSV范围
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lower_yellow = np.array([20, 100, 100])
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upper_yellow = np.array([30, 255, 255])
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# 创建黄色的掩码
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 应用形态学操作
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kernel = np.ones((5, 5), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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# 应用掩码，只保留黄色部分
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yellow_only = cv2.bitwise_and(img, img, mask=mask)
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# 创建包含所有处理步骤的结果图像
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result = np.hstack((img, yellow_only, output))
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# 调整大小以便查看
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scale_percent = 50 # 缩放到原来的50%
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width = int(result.shape[1] * scale_percent / 100)
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height = int(result.shape[0] * scale_percent / 100)
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dim = (width, height)
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resized = cv2.resize(result, dim, interpolation=cv2.INTER_AREA)
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# 显示结果
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cv2.imshow('横向赛道边缘检测', resized)
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cv2.waitKey(delay)
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if save_path is None: # 如果没有保存，则等待按键
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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return output
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# 用法示例
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if __name__ == "__main__":
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# 替换为实际图像路径
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