#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 视觉检测模块 提供靶心检测、距离估算、图像保存等功能 """ import cv2 import numpy as np import os import math from maix import image import config from logger_manager import logger_manager def save_calibration_image(frame, laser_pos, photo_dir=None): """ 保存激光校准图像（带标注） 在找到的激光点位置绘制圆圈，便于检查算法是否正确 Args: frame: 原始图像帧 laser_pos: 找到的激光点坐标 (x, y) photo_dir: 照片存储目录，如果为None则使用 config.PHOTO_DIR Returns: str: 保存的文件路径，如果保存失败则返回 None """ # 检查是否启用图像保存 if not config.SAVE_IMAGE_ENABLED: return None if photo_dir is None: photo_dir = config.PHOTO_DIR try: # 确保照片目录存在 try: if photo_dir not in os.listdir("/root"): os.mkdir(photo_dir) except: pass # 生成文件名 try: all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))] img_count = len(all_images) except: img_count = 0 x, y = laser_pos filename = f"{photo_dir}/calibration_{int(x)}_{int(y)}_{img_count:04d}.bmp" logger = logger_manager.logger if logger: logger.info(f"保存校准图像: {filename}, 激光点: ({x}, {y})") # 转换图像为 OpenCV 格式以便绘制 img_cv = image.image2cv(frame, False, False) # 绘制激光点圆圈（用绿色圆圈标出找到的激光点） cv2.circle(img_cv, (int(x), int(y)), 10, (0, 255, 0), 2) # 外圈：绿色，半径10 cv2.circle(img_cv, (int(x), int(y)), 5, (0, 255, 0), 2) # 中圈：绿色，半径5 cv2.circle(img_cv, (int(x), int(y)), 2, (0, 255, 0), -1) # 中心点：绿色实心 # 可选：绘制十字线帮助定位 cv2.line(img_cv, (int(x - 20), int(y)), (int(x + 20), int(y)), (0, 255, 0), 1) # 水平线 cv2.line(img_cv, (int(x), int(y - 20)), (int(x), int(y + 20)), (0, 255, 0), 1) # 垂直线 # 转换回 MaixPy 图像格式并保存 result_img = image.cv2image(img_cv, False, False) result_img.save(filename) if logger: logger.debug(f"校准图像已保存: {filename}") return filename except Exception as e: logger = logger_manager.logger if logger: logger.error(f"保存校准图像失败: {e}") import traceback logger.error(traceback.format_exc()) return None def detect_circle_v3(frame, laser_point=None): """检测图像中的靶心（优先清晰轮廓，其次黄色区域）- 返回椭圆参数版本 增加红色圆圈检测，验证黄色圆圈是否为真正的靶心 如果提供 laser_point，会选择最接近激光点的目标 Args: frame: 图像帧 laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择 Returns: (result_img, best_center, best_radius, method, best_radius1, ellipse_params) """ img_cv = image.image2cv(frame, False, False) best_center = best_radius = best_radius1 = method = None ellipse_params = None # HSV 黄色掩码检测（模糊靶心） hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV) h, s, v = cv2.split(hsv) # 调整饱和度策略：稍微增强，不要过度 s = np.clip(s * 1.1, 0, 255).astype(np.uint8) hsv = cv2.merge((h, s, v)) # 放宽 HSV 阈值范围（针对模糊图像的关键调整） lower_yellow = np.array([7, 80, 0]) # 饱和度下限降低，捕捉淡黄色 upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满 mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow) # 调整形态学操作 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel) contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 存储所有有效的黄色-红色组合 valid_targets = [] if contours_yellow: for cnt_yellow in contours_yellow: area = cv2.contourArea(cnt_yellow) perimeter = cv2.arcLength(cnt_yellow, True) # 计算圆度 if perimeter > 0: circularity = (4 * np.pi * area) / (perimeter * perimeter) else: circularity = 0 logger = logger_manager.logger if area > 50 and circularity > 0.7: if logger: logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}") # 尝试拟合椭圆 yellow_center = None yellow_radius = None yellow_ellipse = None if len(cnt_yellow) >= 5: (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow) yellow_ellipse = ((x, y), (width, height), angle) axes_minor = min(width, height) radius = axes_minor / 2 yellow_center = (int(x), int(y)) yellow_radius = int(radius) else: (x, y), radius = cv2.minEnclosingCircle(cnt_yellow) yellow_center = (int(x), int(y)) yellow_radius = int(radius) yellow_ellipse = None # 如果检测到黄色圆圈，再检测红色圆圈进行验证 if yellow_center and yellow_radius: # HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围） # 红色范围1: 0-10度（接近0度的红色） lower_red1 = np.array([0, 80, 0]) upper_red1 = np.array([10, 255, 255]) mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1) # 红色范围2: 170-180度（接近180度的红色） lower_red2 = np.array([170, 80, 0]) upper_red2 = np.array([180, 255, 255]) mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2) # 合并两个红色掩码 mask_red = cv2.bitwise_or(mask_red1, mask_red2) # 形态学操作 kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red) contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) found_valid_red = False if contours_red: # 找到所有符合条件的红色圆圈 for cnt_red in contours_red: area_red = cv2.contourArea(cnt_red) perimeter_red = cv2.arcLength(cnt_red, True) if perimeter_red > 0: circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red) else: circularity_red = 0 # 红色圆圈也应该有一定的圆度 if area_red > 50 and circularity_red > 0.6: # 计算红色圆圈的中心和半径 if len(cnt_red) >= 5: (x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red) radius_red = min(w_red, h_red) / 2 red_center = (int(x_red), int(y_red)) red_radius = int(radius_red) else: (x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red) red_center = (int(x_red), int(y_red)) red_radius = int(radius_red) # 计算黄色和红色圆心的距离 if red_center: dx = yellow_center[0] - red_center[0] dy = yellow_center[1] - red_center[1] distance = np.sqrt(dx*dx + dy*dy) # 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍） max_distance = yellow_radius * 1.5 # 红色圆圈应该比黄色圆圈大（外圈） if distance < max_distance and red_radius > yellow_radius * 0.8: found_valid_red = True logger = logger_manager.logger if logger: logger.info(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), 红心({red_center}), 距离:{distance:.1f}, 黄半径:{yellow_radius}, 红半径:{red_radius}") # 记录这个有效目标 valid_targets.append({ 'center': yellow_center, 'radius': yellow_radius, 'ellipse': yellow_ellipse, 'area': area }) break if not found_valid_red: logger = logger_manager.logger if logger: logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别") # 从所有有效目标中选择最佳目标 if valid_targets: if laser_point: # 如果有激光点，选择最接近激光点的目标 best_target = None min_distance = float('inf') for target in valid_targets: dx = target['center'][0] - laser_point[0] dy = target['center'][1] - laser_point[1] distance = np.sqrt(dx*dx + dy*dy) if distance < min_distance: min_distance = distance best_target = target if best_target: best_center = best_target['center'] best_radius = best_target['radius'] ellipse_params = best_target['ellipse'] method = "v3_ellipse_red_validated_laser_selected" best_radius1 = best_radius * 5 else: # 如果没有激光点，选择面积最大的目标 best_target = max(valid_targets, key=lambda t: t['area']) best_center = best_target['center'] best_radius = best_target['radius'] ellipse_params = best_target['ellipse'] method = "v3_ellipse_red_validated" best_radius1 = best_radius * 5 result_img = image.cv2image(img_cv, False, False) return result_img, best_center, best_radius, method, best_radius1, ellipse_params def estimate_distance(pixel_radius): """根据像素半径估算实际距离（单位：米）""" if not pixel_radius: return 0.0 return (config.REAL_RADIUS_CM * config.FOCAL_LENGTH_PIX) / pixel_radius / 100.0 def compute_laser_position(circle_center, laser_point, radius, method): """计算激光相对于靶心的偏移量（单位：厘米）""" if not all([circle_center, radius, method]): return None, None cx, cy = circle_center lx, ly = 320, 230 # 根据检测方法动态调整靶心物理半径（简化模型） circle_r = (radius / 4.0) * 20.0 if method == "模糊" else (68 / 16.0) * 20.0 dx = lx - cx dy = ly - cy return dx / (circle_r / 100.0), -dy / (circle_r / 100.0) def save_shot_image(result_img, center, radius, method, ellipse_params, laser_point, distance_m, photo_dir=None): """ 保存射击图像（带标注） 即使没有检测到靶心也会保存图像，文件名会标注 "no_target" 确保保存的图像总是包含激光十字线 Args: result_img: 处理后的图像对象（可能已经包含激光十字线或检测标注） center: 靶心中心坐标 (x, y)，可能为 None（未检测到靶心） radius: 靶心半径，可能为 None（未检测到靶心） method: 检测方法，可能为 None（未检测到靶心） ellipse_params: 椭圆参数 ((center, (width, height), angle)) 或 None laser_point: 激光点坐标 (x, y) distance_m: 距离（米），可能为 None（未检测到靶心） photo_dir: 照片存储目录，如果为None则使用 config.PHOTO_DIR Returns: str: 保存的文件路径，如果保存失败或未启用则返回 None """ # 检查是否启用图像保存 if not config.SAVE_IMAGE_ENABLED: return None if photo_dir is None: photo_dir = config.PHOTO_DIR try: # 确保照片目录存在 try: if photo_dir not in os.listdir("/root"): os.mkdir(photo_dir) except: pass # 生成文件名 # 统计所有图片文件（包括 .bmp 和 .jpg） try: all_images = [f for f in os.listdir(photo_dir) if f.endswith(('.bmp', '.jpg', '.jpeg'))] img_count = len(all_images) except: img_count = 0 x, y = laser_point # 如果未检测到靶心，在文件名中标注 if center is None or radius is None: method_str = "no_target" distance_str = "000" else: method_str = method or "unknown" distance_str = str(round((distance_m or 0.0) * 100)) filename = f"{photo_dir}/{method_str}_{int(x)}_{int(y)}_{distance_str}_{img_count:04d}.bmp" logger = logger_manager.logger if logger: if center and radius: logger.info(f"结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}") if ellipse_params: (ell_center, (width, height), angle) = ellipse_params logger.info(f"椭圆 -> 中心: ({ell_center[0]:.1f}, {ell_center[1]:.1f}), 长轴: {max(width, height):.1f}, 短轴: {min(width, height):.1f}, 角度: {angle:.1f}°") else: logger.info(f"结果 -> 未检测到靶心，保存原始图像（激光点: ({x}, {y})）") # 转换图像为 OpenCV 格式以便绘制 img_cv = image.image2cv(result_img, False, False) # 确保激光十字线被绘制（使用OpenCV在图像上绘制，确保可见性） laser_color = (config.LASER_COLOR[0], config.LASER_COLOR[1], config.LASER_COLOR[2]) thickness = max(config.LASER_THICKNESS, 2) # 至少2像素宽，确保可见 length = max(config.LASER_LENGTH, 10) # 至少10像素长 # 绘制激光十字线（水平线） cv2.line(img_cv, (int(x - length), int(y)), (int(x + length), int(y)), laser_color, thickness) # 绘制激光十字线（垂直线） cv2.line(img_cv, (int(x), int(y - length)), (int(x), int(y + length)), laser_color, thickness) # 绘制激光点 cv2.circle(img_cv, (int(x), int(y)), max(thickness, 3), laser_color, -1) # 如果检测到靶心，绘制靶心标注 if center and radius: cx, cy = center if ellipse_params: (ell_center, (width, height), angle) = ellipse_params cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1]) # 绘制椭圆 cv2.ellipse(img_cv, (cx_ell, cy_ell), (int(width/2), int(height/2)), angle, 0, 360, (0, 255, 0), 2) cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1) # 绘制短轴 minor_length = min(width, height) / 2 minor_angle = angle + 90 if width >= height else angle minor_angle_rad = math.radians(minor_angle) dx_minor = minor_length * math.cos(minor_angle_rad) dy_minor = minor_length * math.sin(minor_angle_rad) pt1_minor = (int(cx_ell - dx_minor), int(cy_ell - dy_minor)) pt2_minor = (int(cx_ell + dx_minor), int(cy_ell + dy_minor)) cv2.line(img_cv, pt1_minor, pt2_minor, (0, 0, 255), 2) else: # 绘制圆形靶心 cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2) cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1) # 如果检测到靶心，绘制从激光点到靶心的连线（可选，用于可视化偏移） cv2.line(img_cv, (int(x), int(y)), (cx, cy), (255, 255, 0), 1) # 转换回 MaixPy 图像格式并保存 result_img = image.cv2image(img_cv, False, False) result_img.save(filename) if logger: if center and radius: logger.debug(f"图像已保存（含靶心标注）: {filename}") else: logger.debug(f"图像已保存（无靶心，含激光十字线）: {filename}") return filename except Exception as e: logger = logger_manager.logger if logger: logger.error(f"保存图像失败: {e}") import traceback logger.error(traceback.format_exc()) return None