diff --git a/test/test_decect_circle_v4.py b/test/test_decect_circle_v4.py new file mode 100644 index 0000000..abd5249 --- /dev/null +++ b/test/test_decect_circle_v4.py @@ -0,0 +1,617 @@ +#!/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 + +# ==================== 全局配置 (与 test_main.py 保持一致) ==================== +REAL_RADIUS_CM = 20 # 靶心实际半径（厘米） + + +# ==================== 复制的核心算法 ==================== +# 注意：这里直接复制了 detect_circle 的逻辑，避免 import main 导致的冲突 + + +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 + + if area > 50 and circularity > 0.7: + print(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, 50, 40]) + 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, 50, 40]) + 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 > 30 and circularity_red > 0.4: + # 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 * 0.7: + found_valid_red = True + print( + 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: + print("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 detect_circle(frame): + """检测图像中的靶心（优先清晰轮廓，其次黄色区域）""" + img_cv = image.image2cv(frame, False, False) + # gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY) + # blurred = cv2.GaussianBlur(gray, (5, 5), 0) + # edged = cv2.Canny(blurred, 50, 150) + # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # ceroded = cv2.erode(cv2.dilate(edged, kernel), kernel) + + # contours, _ = cv2.findContours(ceroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + # best_center = best_radius = best_radius1 = method = None + + # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV) + # h, s, v = cv2.split(hsv) + # s = np.clip(s * 2, 0, 255).astype(np.uint8) + # hsv = cv2.merge((h, s, v)) + # lower_yellow = np.array([7, 80, 0]) + # upper_yellow = np.array([32, 255, 182]) + # mask = cv2.inRange(hsv, lower_yellow, upper_yellow) + # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) + # mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel) + # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + # if contours: + # largest = max(contours, key=cv2.contourArea) + # if cv2.contourArea(largest) > 50: + # (x, y), radius = cv2.minEnclosingCircle(largest) + # best_center = (int(x), int(y)) + # best_radius = int(radius) + # best_radius1 = radius * 5 + # method = "v2" + + # auto + # R:31 M:v2 D:2.410110127692767 + # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV) + # h, s, v = cv2.split(hsv) + + # # 1. 增强饱和度（模糊照片需要更强的增强） + # s = np.clip(s * 2.5, 0, 255).astype(np.uint8) # 从2.0改为2.5 + + # # 2. 增强亮度（模糊照片可能偏暗） + # v = np.clip(v * 1.2, 0, 255).astype(np.uint8) # 新增：提升亮度 + + # hsv = cv2.merge((h, s, v)) + + # # 3. 放宽HSV颜色范围（特别是模糊照片） + # # 降低饱和度下限，提高亮度上限 + # lower_yellow = np.array([5, 50, 30]) # H:5-35, S:50-255, V:30-255 + # upper_yellow = np.array([35, 255, 255]) + + # mask = cv2.inRange(hsv, lower_yellow, upper_yellow) + + # # 4. 增强形态学操作（连接被分割的区域） + # kernel_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + # kernel_large = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9)) # 更大的核 + + # # 先开运算去除噪声 + # mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel_small) + # # 多次膨胀连接区域（模糊照片需要更多膨胀） + # mask = cv2.dilate(mask, kernel_large, iterations=2) # 增加迭代次数 + # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_large) # 闭运算填充空洞 + + # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + # if contours: + # largest = max(contours, key=cv2.contourArea) + # area = cv2.contourArea(largest) + # if area > 50: + # # 5. 使用面积计算等效半径（更准确） + # equivalent_radius = np.sqrt(area / np.pi) + + # # 6. 同时使用minEnclosingCircle作为备选（取较大值） + # (x, y), enclosing_radius = cv2.minEnclosingCircle(largest) + + # # 取两者中的较大值，确保不遗漏 + # radius = max(equivalent_radius, enclosing_radius) + + # best_center = (int(x), int(y)) + # best_radius = int(radius) + # best_radius1 = radius * 5 + # method = "v2" + + # codegee + # R:24 M:v2 D:3.061493895819174 + # R:22 M:v2 D:3.3644971681267077 np.clip(s * 1.1, 0, 255) + hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV) + h, s, v = cv2.split(hsv) + + # 2. 调整饱和度策略： + # 不要暴力翻倍，可以尝试稍微增强，或者使用 CLAHE 增强亮度/对比度 + # 这里我们稍微增加一点饱和度，并确保不溢出 + s = np.clip(s * 1.1, 0, 255).astype(np.uint8) + # 对亮度通道 v 也可以做一点 CLAHE 处理来增强对比度（可选） + # clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) + # v = clahe.apply(v) + + hsv = cv2.merge((h, s, v)) + + # 3. 放宽 HSV 阈值范围（针对模糊图像的关键调整） + # 降低 S 的下限 (80 -> 35)，提高 V 的上限 (182 -> 255) + 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 = cv2.inRange(hsv, lower_yellow, upper_yellow) + + # 4. 调整形态学操作 + # 去掉 MORPH_OPEN，因为它会减小面积。 + # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域 + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) + # 再进行一次膨胀，确保边缘被包含进来 + # mask = cv2.dilate(mask, kernel, iterations=1) + + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if contours: + largest = max(contours, key=cv2.contourArea) + + # 这里可以适当降低面积阈值，或者保持不变 + if cv2.contourArea(largest) > 50: + # (x, y), radius = cv2.minEnclosingCircle(largest) + # best_center = (int(x), int(y)) + # best_radius = int(radius) + + # --- 核心修改开始 --- + # 1. 尝试拟合椭圆 (需要轮廓点至少为5个) + if len(largest) >= 5: + # 返回值: ((中心x, 中心y), (长轴, 短轴), 旋转角度) + (x, y), (axes_major, axes_minor), angle = cv2.fitEllipse(largest) + + # 2. 计算半径 + # 选项A：取长短轴的平均值 (比较稳健) + # radius = (axes_major + axes_minor) / 4 + + # 选项B：直接取短轴的一半 (抗模糊最强，推荐) + radius = axes_minor / 2 + + best_center = (int(x), int(y)) + best_radius = int(radius) + method = "v2_ellipse" + else: + # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle + (x, y), radius = cv2.minEnclosingCircle(largest) + best_center = (int(x), int(y)) + best_radius = int(radius) + method = "v2" + # --- 核心修改结束 --- + + # 你的后续逻辑 + best_radius1 = radius * 5 + + # operas 4.5 + # R:25 M:v2 D:2.9554872521538527 + # hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV) + # h, s, v = cv2.split(hsv) + + # # 1. 适度增强饱和度（不要过度，否则噪声也会增强） + # s = np.clip(s * 1.5, 0, 255).astype(np.uint8) + # hsv = cv2.merge((h, s, v)) + + # # 2. 放宽 HSV 阈值范围（关键改动） + # # - 饱和度下限从 80 降到 40（捕捉淡黄色） + # # - 亮度上限从 182 提高到 255（允许更亮的黄色） + # lower_yellow = np.array([7, 40, 30]) + # upper_yellow = np.array([35, 255, 255]) + + # mask = cv2.inRange(hsv, lower_yellow, upper_yellow) + + # # 3. 调整形态学操作：用 CLOSE 替代 OPEN + # # CLOSE（先膨胀后腐蚀）：填充内部空洞，连接相邻区域 + # # OPEN（先腐蚀后膨胀）：会缩小区域，不适合模糊图像 + # kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7)) # 稍大的核 + # mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) + # mask = cv2.dilate(mask, kernel, iterations=1) # 额外膨胀，确保边缘被包含 + + # contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + # if contours: + # largest = max(contours, key=cv2.contourArea) + # if cv2.contourArea(largest) > 50: + # (x, y), radius = cv2.minEnclosingCircle(largest) + # best_center = (int(x), int(y)) + # best_radius = int(radius) + # best_radius1 = radius * 5 + # method = "v2" + + # # --- 新增：将 Mask 叠加到原图上用于调试 --- + # # 创建一个彩色掩码（红色通道为255，其他为0） + # mask_overlay = np.zeros_like(img_cv) + # mask_overlay[:, :, 2] = mask # 将掩码放在红色通道 (BGR中的R) + # + # cv2.addWeighted(img_cv, 0.6, mask_overlay, 0.4, 0, img_cv) + + result_img = image.cv2image(img_cv, False, False) + return result_img, best_center, best_radius, method, best_radius1 + + +def detect_circle_v2(frame): + """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""" + global REAL_RADIUS_CM + img_cv = image.image2cv(frame, False, False) + + best_center = best_radius = best_radius1 = method = None + ellipse_params = None # 存储椭圆参数 ((x, y), (axes_major, axes_minor), angle) + + # 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 = cv2.inRange(hsv, lower_yellow, upper_yellow) + + # 调整形态学操作 + # 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域 + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) + + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if contours: + largest = max(contours, key=cv2.contourArea) + + if cv2.contourArea(largest) > 50: + # 尝试拟合椭圆 (需要轮廓点至少为5个) + if len(largest) >= 5: + # 返回值: ((中心x, 中心y), (width, height), 旋转角度) + # 注意：width 和 height 是外接矩形的尺寸，不是长轴和短轴 + (x, y), (width, height), angle = cv2.fitEllipse(largest) + + # 保存椭圆参数（保持原始顺序，用于绘制） + ellipse_params = ((x, y), (width, height), angle) + + # 计算半径：使用较小的尺寸作为短轴 + axes_minor = min(width, height) + radius = axes_minor / 2 + + best_center = (int(x), int(y)) + best_radius = int(radius) + method = "v2_ellipse" + else: + # 如果点太少无法拟合椭圆，降级回 minEnclosingCircle + (x, y), radius = cv2.minEnclosingCircle(largest) + best_center = (int(x), int(y)) + best_radius = int(radius) + method = "v2" + ellipse_params = None # 圆形，没有椭圆参数 + + best_radius1 = radius * 5 + + result_img = image.cv2image(img_cv, False, False) + return result_img, best_center, best_radius, method, best_radius1, ellipse_params + + +# ==================== 测试逻辑 ==================== + +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_v2 函数 + print("[INFO] 正在调用 detect_circle_v2 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)