621 lines
27 KiB
Python
621 lines
27 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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离线测试脚本：直接复用 detect_circle 逻辑进行测试
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运行环境：MaixPy (Sipeed MAIX)
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"""
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import sys
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import os
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# import time
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from maix import image,time
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import cv2
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import numpy as np
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# ==================== 全局配置 (与 test_main.py 保持一致) ====================
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REAL_RADIUS_CM = 20 # 靶心实际半径（厘米）
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# ==================== 复制的核心算法 ====================
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# 注意：这里直接复制了 detect_circle 的逻辑，避免 import main 导致的冲突
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def detect_circle_v3(frame, laser_point=None):
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"""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
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增加红色圆圈检测，验证黄色圆圈是否为真正的靶心
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如果提供 laser_point，会选择最接近激光点的目标
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Args:
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frame: 图像帧
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laser_point: 激光点坐标 (x, y)，用于多目标场景下的目标选择
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Returns:
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(result_img, best_center, best_radius, method, best_radius1, ellipse_params)
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"""
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img_cv = image.image2cv(frame, False, False)
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best_center = best_radius = best_radius1 = method = None
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ellipse_params = None
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# HSV 黄色掩码检测（模糊靶心）
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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# 调整饱和度策略：稍微增强，不要过度
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s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
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hsv = cv2.merge((h, s, v))
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# 放宽 HSV 阈值范围（针对模糊图像的关键调整）
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lower_yellow = np.array([7, 80, 0]) # 饱和度下限降低，捕捉淡黄色
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upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 调整形态学操作
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_yellow = cv2.morphologyEx(mask_yellow, cv2.MORPH_CLOSE, kernel)
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contours_yellow, _ = cv2.findContours(mask_yellow, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 存储所有有效的黄色-红色组合
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valid_targets = []
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if contours_yellow:
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for cnt_yellow in contours_yellow:
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area = cv2.contourArea(cnt_yellow)
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perimeter = cv2.arcLength(cnt_yellow, True)
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# 计算圆度
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if perimeter > 0:
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circularity = (4 * np.pi * area) / (perimeter * perimeter)
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else:
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circularity = 0
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logger = get_logger()
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if area > 50 and circularity > 0.7:
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if logger:
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logger.info(f"[target] -> 面积:{area}, 圆度:{circularity:.2f}")
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# 尝试拟合椭圆
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yellow_center = None
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yellow_radius = None
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yellow_ellipse = None
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if len(cnt_yellow) >= 5:
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(x, y), (width, height), angle = cv2.fitEllipse(cnt_yellow)
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yellow_ellipse = ((x, y), (width, height), angle)
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axes_minor = min(width, height)
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radius = axes_minor / 2
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yellow_center = (int(x), int(y))
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yellow_radius = int(radius)
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else:
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(x, y), radius = cv2.minEnclosingCircle(cnt_yellow)
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yellow_center = (int(x), int(y))
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yellow_radius = int(radius)
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yellow_ellipse = None
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# 如果检测到黄色圆圈，再检测红色圆圈进行验证
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if yellow_center and yellow_radius:
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# HSV 红色掩码检测（红色在HSV中跨越0度，需要两个范围）
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# 红色范围1: 0-10度（接近0度的红色）
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lower_red1 = np.array([0, 80, 0])
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upper_red1 = np.array([10, 255, 255])
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mask_red1 = cv2.inRange(hsv, lower_red1, upper_red1)
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# 红色范围2: 170-180度（接近180度的红色）
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lower_red2 = np.array([170, 80, 0])
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upper_red2 = np.array([180, 255, 255])
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mask_red2 = cv2.inRange(hsv, lower_red2, upper_red2)
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# 合并两个红色掩码
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mask_red = cv2.bitwise_or(mask_red1, mask_red2)
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# 形态学操作
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kernel_red = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask_red = cv2.morphologyEx(mask_red, cv2.MORPH_CLOSE, kernel_red)
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contours_red, _ = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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found_valid_red = False
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if contours_red:
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# 找到所有符合条件的红色圆圈
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for cnt_red in contours_red:
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area_red = cv2.contourArea(cnt_red)
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perimeter_red = cv2.arcLength(cnt_red, True)
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if perimeter_red > 0:
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circularity_red = (4 * np.pi * area_red) / (perimeter_red * perimeter_red)
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else:
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circularity_red = 0
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# 红色圆圈也应该有一定的圆度
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if area_red > 50 and circularity_red > 0.6:
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# 计算红色圆圈的中心和半径
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if len(cnt_red) >= 5:
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(x_red, y_red), (w_red, h_red), angle_red = cv2.fitEllipse(cnt_red)
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radius_red = min(w_red, h_red) / 2
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red_center = (int(x_red), int(y_red))
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red_radius = int(radius_red)
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else:
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(x_red, y_red), radius_red = cv2.minEnclosingCircle(cnt_red)
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red_center = (int(x_red), int(y_red))
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red_radius = int(radius_red)
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# 计算黄色和红色圆心的距离
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if red_center:
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dx = yellow_center[0] - red_center[0]
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dy = yellow_center[1] - red_center[1]
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distance = np.sqrt(dx*dx + dy*dy)
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# 圆心距离阈值：应该小于黄色半径的某个倍数（比如1.5倍）
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max_distance = yellow_radius * 1.5
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# 红色圆圈应该比黄色圆圈大（外圈）
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if distance < max_distance and red_radius > yellow_radius * 0.8:
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found_valid_red = True
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logger = get_logger()
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if logger:
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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}")
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# 记录这个有效目标
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valid_targets.append({
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'center': yellow_center,
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'radius': yellow_radius,
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'ellipse': yellow_ellipse,
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'area': area
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})
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break
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if not found_valid_red:
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logger = get_logger()
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if logger:
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logger.debug("Debug -> 未找到匹配的红色圆圈，可能是误识别")
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# 从所有有效目标中选择最佳目标
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if valid_targets:
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if laser_point:
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# 如果有激光点，选择最接近激光点的目标
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best_target = None
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min_distance = float('inf')
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for target in valid_targets:
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dx = target['center'][0] - laser_point[0]
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dy = target['center'][1] - laser_point[1]
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distance = np.sqrt(dx*dx + dy*dy)
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if distance < min_distance:
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min_distance = distance
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best_target = target
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if best_target:
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best_center = best_target['center']
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best_radius = best_target['radius']
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ellipse_params = best_target['ellipse']
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method = "v3_ellipse_red_validated_laser_selected"
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best_radius1 = best_radius * 5
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else:
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# 如果没有激光点，选择面积最大的目标
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best_target = max(valid_targets, key=lambda t: t['area'])
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best_center = best_target['center']
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best_radius = best_target['radius']
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ellipse_params = best_target['ellipse']
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method = "v3_ellipse_red_validated"
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best_radius1 = best_radius * 5
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result_img = image.cv2image(img_cv, False, False)
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return result_img, best_center, best_radius, method, best_radius1, ellipse_params
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def detect_circle(frame):
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"""检测图像中的靶心（优先清晰轮廓，其次黄色区域）"""
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img_cv = image.image2cv(frame, False, False)
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# gray = cv2.cvtColor(img_cv, cv2.COLOR_RGB2GRAY)
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# blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# edged = cv2.Canny(blurred, 50, 150)
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# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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# ceroded = cv2.erode(cv2.dilate(edged, kernel), kernel)
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# contours, _ = cv2.findContours(ceroded, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# best_center = best_radius = best_radius1 = method = None
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# hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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# h, s, v = cv2.split(hsv)
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# s = np.clip(s * 2, 0, 255).astype(np.uint8)
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# hsv = cv2.merge((h, s, v))
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# lower_yellow = np.array([7, 80, 0])
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# upper_yellow = np.array([32, 255, 182])
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# mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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# mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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# mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
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# contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# if contours:
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# largest = max(contours, key=cv2.contourArea)
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# if cv2.contourArea(largest) > 50:
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# (x, y), radius = cv2.minEnclosingCircle(largest)
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# best_center = (int(x), int(y))
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# best_radius = int(radius)
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# best_radius1 = radius * 5
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# method = "v2"
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# auto
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# R:31 M:v2 D:2.410110127692767
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# hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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# h, s, v = cv2.split(hsv)
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# # 1. 增强饱和度（模糊照片需要更强的增强）
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# s = np.clip(s * 2.5, 0, 255).astype(np.uint8) # 从2.0改为2.5
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# # 2. 增强亮度（模糊照片可能偏暗）
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# v = np.clip(v * 1.2, 0, 255).astype(np.uint8) # 新增：提升亮度
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# hsv = cv2.merge((h, s, v))
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# # 3. 放宽HSV颜色范围（特别是模糊照片）
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# # 降低饱和度下限，提高亮度上限
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# lower_yellow = np.array([5, 50, 30]) # H:5-35, S:50-255, V:30-255
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# upper_yellow = np.array([35, 255, 255])
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# mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# # 4. 增强形态学操作（连接被分割的区域）
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# kernel_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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# kernel_large = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 9)) # 更大的核
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# # 先开运算去除噪声
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# mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel_small)
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# # 多次膨胀连接区域（模糊照片需要更多膨胀）
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# mask = cv2.dilate(mask, kernel_large, iterations=2) # 增加迭代次数
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# mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel_large) # 闭运算填充空洞
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# contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# if contours:
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# largest = max(contours, key=cv2.contourArea)
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# area = cv2.contourArea(largest)
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# if area > 50:
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# # 5. 使用面积计算等效半径（更准确）
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# equivalent_radius = np.sqrt(area / np.pi)
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# # 6. 同时使用minEnclosingCircle作为备选（取较大值）
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# (x, y), enclosing_radius = cv2.minEnclosingCircle(largest)
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# # 取两者中的较大值，确保不遗漏
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# radius = max(equivalent_radius, enclosing_radius)
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# best_center = (int(x), int(y))
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# best_radius = int(radius)
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# best_radius1 = radius * 5
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# method = "v2"
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# codegee
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# R:24 M:v2 D:3.061493895819174
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# R:22 M:v2 D:3.3644971681267077 np.clip(s * 1.1, 0, 255)
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hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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h, s, v = cv2.split(hsv)
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# 2. 调整饱和度策略：
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# 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
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# 这里我们稍微增加一点饱和度，并确保不溢出
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s = np.clip(s * 1.1, 0, 255).astype(np.uint8)
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# 对亮度通道 v 也可以做一点 CLAHE 处理来增强对比度（可选）
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# clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
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# v = clahe.apply(v)
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hsv = cv2.merge((h, s, v))
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# 3. 放宽 HSV 阈值范围（针对模糊图像的关键调整）
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# 降低 S 的下限 (80 -> 35)，提高 V 的上限 (182 -> 255)
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lower_yellow = np.array([7, 80, 0]) # 饱和度下限降低，捕捉淡黄色
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upper_yellow = np.array([32, 255, 255]) # 亮度上限拉满
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mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# 4. 调整形态学操作
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# 去掉 MORPH_OPEN，因为它会减小面积。
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# 使用 MORPH_CLOSE (先膨胀后腐蚀) 来填充内部小黑洞，连接近邻区域
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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# 再进行一次膨胀，确保边缘被包含进来
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# mask = cv2.dilate(mask, kernel, iterations=1)
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if contours:
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largest = max(contours, key=cv2.contourArea)
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# 这里可以适当降低面积阈值，或者保持不变
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if cv2.contourArea(largest) > 50:
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# (x, y), radius = cv2.minEnclosingCircle(largest)
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# best_center = (int(x), int(y))
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# best_radius = int(radius)
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# --- 核心修改开始 ---
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# 1. 尝试拟合椭圆 (需要轮廓点至少为5个)
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if len(largest) >= 5:
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# 返回值: ((中心x, 中心y), (长轴, 短轴), 旋转角度)
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(x, y), (axes_major, axes_minor), angle = cv2.fitEllipse(largest)
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# 2. 计算半径
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# 选项A：取长短轴的平均值 (比较稳健)
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# radius = (axes_major + axes_minor) / 4
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# 选项B：直接取短轴的一半 (抗模糊最强，推荐)
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radius = axes_minor / 2
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best_center = (int(x), int(y))
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best_radius = int(radius)
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method = "v2_ellipse"
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else:
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# 如果点太少无法拟合椭圆，降级回 minEnclosingCircle
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(x, y), radius = cv2.minEnclosingCircle(largest)
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best_center = (int(x), int(y))
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best_radius = int(radius)
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method = "v2"
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# --- 核心修改结束 ---
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# 你的后续逻辑
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best_radius1 = radius * 5
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# operas 4.5
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# R:25 M:v2 D:2.9554872521538527
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# hsv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2HSV)
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# h, s, v = cv2.split(hsv)
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# # 1. 适度增强饱和度（不要过度，否则噪声也会增强）
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# s = np.clip(s * 1.5, 0, 255).astype(np.uint8)
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# hsv = cv2.merge((h, s, v))
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# # 2. 放宽 HSV 阈值范围（关键改动）
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# # - 饱和度下限从 80 降到 40（捕捉淡黄色）
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# # - 亮度上限从 182 提高到 255（允许更亮的黄色）
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# lower_yellow = np.array([7, 40, 30])
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# upper_yellow = np.array([35, 255, 255])
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# mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
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# # 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_test2" # 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)
|