diff --git a/test/test_decect.py b/test/test_decect.py new file mode 100644 index 0000000..f1a566d --- /dev/null +++ b/test/test_decect.py @@ -0,0 +1,330 @@ +#!/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) \ No newline at end of file