#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 离线测试脚本：直接复用 detect_circle 逻辑进行测试 运行环境：MaixPy (Sipeed MAIX) """ import sys import os # import time from maix import image, time import cv2 import numpy as np import math # ==================== 全局配置 (与 test_main.py 保持一致) ==================== REAL_RADIUS_CM = 20 # 靶心实际半径（厘米） def detect_circle_v3(frame, laser_point=None, img_cv=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 优化： 1. 缩图到 MAX_DET_DIM 后再做 HSV/形态学，最长边 640->320 可获得 ~4x 加速 2. 红色掩码在黄色轮廓循环外只计算一次，避免 N 次重复计算 3. img_cv 可由外部传入（与其他线程共享转换结果），为 None 时自动转换 Args: frame: 图像帧（img_cv 为 None 时使用） laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择 img_cv: 已转换的 numpy BGR/RGB 图像；不为 None 时跳过 image2cv 转换 Returns: (result_img, best_center, best_radius, method, best_radius1, ellipse_params) """ if img_cv is None: img_cv = image.image2cv(frame, False, False) from datetime import datetime print(f"[detect_circle_v3] begin {datetime.now()}") # -- 1. 缩图加速（与三角形路径保持一致） h_orig, w_orig = img_cv.shape[:2] MAX_DET_DIM = 320 long_side = max(h_orig, w_orig) if long_side > MAX_DET_DIM: det_scale = MAX_DET_DIM / long_side img_det = cv2.resize(img_cv, (int(w_orig * det_scale), int(h_orig * det_scale)), interpolation=cv2.INTER_LINEAR) inv_scale = 1.0 / det_scale # 检测坐标 -> 原始坐标的倍率 else: img_det = img_cv inv_scale = 1.0 # 激光点映射到检测分辨率 lp_det = None if laser_point is not None: lp_det = (laser_point[0] / inv_scale, laser_point[1] / inv_scale) best_center = best_radius = best_radius1 = method = None ellipse_params = None print(f"[detect_circle_v3] step 1 fin {datetime.now()}") # -- 2. HSV + 黄色掩码 hsv = cv2.cvtColor(img_det, cv2.COLOR_RGB2HSV) h, s, v = cv2.split(hsv) s = np.clip(s * 1.1, 0, 255).astype(np.uint8) hsv = cv2.merge((h, s, v)) lower_yellow = np.array([7, 80, 0]) upper_yellow = np.array([32, 255, 255]) mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel) print(f"[detect_circle_v3] step 2 fin {datetime.now()}") # -- 3. 红色掩码：在循环外只算一次 mask_red = cv2.bitwise_or( cv2.inRange(hsv, np.array([0, 80, 0]), np.array([10, 255, 255])), cv2.inRange(hsv, np.array([170, 80, 0]), np.array([180, 255, 255])), ) 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) # 预先把红色轮廓筛选成 (center, radius) 列表，后续直接查表 red_candidates = [] for cnt_r in contours_red: ar = cv2.contourArea(cnt_r) if ar <= 50: continue pr = cv2.arcLength(cnt_r, True) if pr <= 0 or (4 * np.pi * ar) / (pr * pr) <= 0.6: continue if len(cnt_r) >= 5: (xr, yr), (wr, hr), _ = cv2.fitEllipse(cnt_r) red_candidates.append({"center": (int(xr), int(yr)), "radius": int(min(wr, hr) / 2)}) else: (xr, yr), rr = cv2.minEnclosingCircle(cnt_r) red_candidates.append({"center": (int(xr), int(yr)), "radius": int(rr)}) print(f"[detect_circle_v3] step 3 fin {datetime.now()}") # -- 4. 黄色轮廓循环（复用上面的红色候选列表） contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) valid_targets = [] for cnt_yellow in contours_yellow: area = cv2.contourArea(cnt_yellow) if area <= 50: continue perimeter = cv2.arcLength(cnt_yellow, True) if perimeter <= 0: continue circularity = (4 * np.pi * area) / (perimeter * perimeter) if circularity <= 0.7: continue print(f"[target] -> 面积:{area:.1f}, 圆度:{circularity:.2f}") if len(cnt_yellow) >= 5: (x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow) yellow_ellipse = ((x, y), (width, height), angle) yellow_center = (int(x), int(y)) yellow_radius = int(min(width, height) / 2) else: (x, y), radius = cv2.minEnclosingCircle(cnt_yellow) yellow_center = (int(x), int(y)) yellow_radius = int(radius) yellow_ellipse = None # 在预筛好的红色候选中匹配 matched = False for rc in red_candidates: ddx = yellow_center[0] - rc["center"][0] ddy = yellow_center[1] - rc["center"][1] dist_centers = math.hypot(ddx, ddy) if dist_centers < yellow_radius * 1.5 and rc["radius"] > yellow_radius * 0.8: print(f"[target] -> 找到匹配的红圈: 黄心({yellow_center}), " f"红心({rc['center']}), 距离:{dist_centers:.1f}, " f"黄半径:{yellow_radius}, 红半径:{rc['radius']}") valid_targets.append({ "center": yellow_center, "radius": yellow_radius, "ellipse": yellow_ellipse, "area": area, }) matched = True break if not matched : print("Debug -> 未找到匹配的红色圆圈，可能是误识别") print(f"[detect_circle_v3] step 4 fin {datetime.now()}") # -- 5. 选最佳目标，坐标还原到原始分辨率 if valid_targets: if lp_det: best_target = min(valid_targets, key=lambda t: (t["center"][0] - lp_det[0]) ** 2 + (t["center"][1] - lp_det[1]) ** 2) method = "v3_ellipse_red_validated_laser_selected" else: best_target = max(valid_targets, key=lambda t: t["area"]) method = "v3_ellipse_red_validated" bc = best_target["center"] br = best_target["radius"] be = best_target["ellipse"] if inv_scale != 1.0: best_center = (int(bc[0] * inv_scale), int(bc[1] * inv_scale)) best_radius = int(br * inv_scale) if be is not None: (ex, ey), (ew, eh), ea = be be = ((ex * inv_scale, ey * inv_scale), (ew * inv_scale, eh * inv_scale), ea) else: best_center = bc best_radius = br ellipse_params = be best_radius1 = best_radius * 5 result_img = image.cv2image(img_cv, False, False) print(f"[detect_circle_v3] step 5 fin {datetime.now()}") return result_img, best_center, best_radius, method, best_radius1, ellipse_params def run_offline_test(image_path): """读取图片，检测圆，绘制结果，保存图片""" # 1. 检查文件是否存在 if not os.path.exists(image_path): print(f"[ERROR] 找不到图片文件: {image_path}") return # 2. 使用 maix.image 读取图片 (适配 MaixPy v4) try: # 使用 image.load 读取文件，返回 Image 对象 img = image.load(image_path) print(f"[INFO] 成功读取图片: {image_path} (尺寸: {img.width()}x{img.height()})") except Exception as e: print(f"[ERROR] 读取图片失败: {e}") print("提示：请确认 MaixPy 版本是否为 v4，且图片路径正确。") return # 3. 调用 detect_circle_v3 函数 print("[INFO] 正在调用 detect_circle_v3 进行检测...") start_time = time.ticks_ms() result_img, center, radius, method, radius1, ellipse_params = detect_circle_v3(img) cost_time = time.ticks_ms() - start_time print(f"[INFO] 检测完成，耗时: {cost_time}ms") print(f" 结果 -> 圆心: {center}, 半径: {radius}, 方法: {method}") if ellipse_params: (ell_center, (width, height), angle) = ellipse_params print( 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}°") # 4. 绘制辅助线（可选，用于调试） if center and radius: # 为了绘制椭圆，需要转换回 cv2 图像 img_cv = image.image2cv(result_img, False, False) cx, cy = center # 如果有椭圆参数，绘制椭圆 if ellipse_params: (ell_center, (width, height), angle) = ellipse_params cx_ell, cy_ell = int(ell_center[0]), int(ell_center[1]) # 确定长轴和短轴 if width >= height: # width 是长轴，height 是短轴 axes_major = width axes_minor = height major_angle = angle # 长轴角度就是 angle minor_angle = angle + 90 # 短轴角度 = 长轴角度 + 90度 else: # height 是长轴，width 是短轴 axes_major = height axes_minor = width major_angle = angle + 90 # 长轴角度 = width角度 + 90度 minor_angle = angle # 短轴角度就是 angle # 使用 OpenCV 绘制椭圆（绿色，线宽2） cv2.ellipse(img_cv, (cx_ell, cy_ell), # 中心点 (int(width / 2), int(height / 2)), # 半宽、半高 angle, # 旋转角度（OpenCV需要原始angle） 0, 360, # 起始和结束角度 (0, 255, 0), # 绿色 (RGB格式) 2) # 线宽 # 绘制椭圆中心点（红色） cv2.circle(img_cv, (cx_ell, cy_ell), 3, (255, 0, 0), -1) import math # 绘制短轴（蓝色线条） minor_length = axes_minor / 2 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) # 蓝色 (RGB格式) else: # 如果没有椭圆参数，绘制圆形（红色） cv2.circle(img_cv, (cx, cy), radius, (0, 0, 255), 2) cv2.circle(img_cv, (cx, cy), 2, (0, 0, 255), -1) # 转换回 maix image result_img = image.cv2image(img_cv, False, False) # 定义颜色对象用于文字 try: color_black = image.Color.from_rgb(0, 0, 0) except AttributeError: color_black = image.Color(0, 0, 0) # D. 添加文字信息 FOCAL_LENGTH_PIX = 1900 d = (REAL_RADIUS_CM * FOCAL_LENGTH_PIX) / radius1 / 100.0 info_str = f"R:{radius} M:{method} D:{d:.2f}" print(info_str) # 计算文字位置，防止超出图片边界 r_outer = int(radius * 11.0) if radius else 100 text_y = cy - r_outer - 20 if cy > r_outer + 20 else cy + r_outer + 20 # 调用 draw_string result_img.draw_string(0, 0, info_str, color=color_black, scale=1.0) # 5. 保存结果图片 output_path = image_path.replace(".bmp", "_result.bmp") output_path = image_path.replace(".jpg", "_result.jpg") try: result_img.save(output_path, quality=100) print(f"[SUCCESS] 结果已保存至: {output_path}") except Exception as e: print(f"[ERROR] 保存图片失败: {e}") if __name__ == "__main__": # ================= 配置区域 ================= # 1. 设置要测试的图片路径 # 建议将图片放在与脚本同级目录，或者使用绝对路径 TARGET_IMAGE = "/root/phot/None_314_258_0_0041.bmp" TARGET_DIR = "/root/phot" # 修改为你想要读取的目录路径 # 支持的图片格式 IMAGE_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.bmp'] # ================= 执行区域 ================= if 'TARGET_DIR' in locals(): # 读取目录下所有图片文件，过滤掉 _result.jpg 后缀的文件 image_files = [] if os.path.exists(TARGET_DIR) and os.path.isdir(TARGET_DIR): for filename in os.listdir(TARGET_DIR): # 检查文件扩展名 if any(filename.lower().endswith(ext) for ext in IMAGE_EXTENSIONS): # 过滤掉 _result.jpg 后缀的文件 if not filename.endswith('_result.jpg'): filepath = os.path.join(TARGET_DIR, filename) if os.path.isfile(filepath): image_files.append(filepath) # 按文件名排序（可选） image_files.sort() print(f"[INFO] 在目录 {TARGET_DIR} 中找到 {len(image_files)} 张图片") # 处理每张图片 for img_path in image_files: print(f"\n{'=' * 10} 开始处理: {img_path} {'=' * 10}") run_offline_test(img_path) else: print(f"[ERROR] 目录不存在或不是有效目录: {TARGET_DIR}") else: run_offline_test(TARGET_IMAGE)