51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
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# -*- coding:utf-8 -*-
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# @Author len
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# @Create 2023/11/21 21:16
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import cv2
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import numpy as np
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# 读取图像
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image_path = 'xdl/1.png' # 请替换为您图像的实际路径
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image_bgr = cv2.imread(image_path)
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# 检查图像是否成功加载
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if image_bgr is not None:
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# 将图像从 BGR 颜色空间转换到 LAB 颜色空间
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image_lab = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2LAB)
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# 定义红框区域的坐标 (x, y, width, height)
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# 请用实际的红框坐标替换
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x, y, w, h = 280, 340, 60, 60 # 替换为红框的实际坐标
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# 裁剪 LAB 图像以获取红框内的区域
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cropped_lab = image_lab[y:y+h, x:x+w]
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# 计算裁剪区域的平均 LAB 值
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avg_lab = np.mean(cropped_lab.reshape(-1, 3), axis=0)
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# 定义阈值范围
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threshold_range = 10
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lower_red_threshold = np.clip(avg_lab - threshold_range, 0, 255)
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upper_red_threshold = np.clip(avg_lab + threshold_range, 0, 255)
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# 输出红色 LAB 颜色阈值
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lower = lower_red_threshold.astype(int)
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upper = upper_red_threshold.astype(int)
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print('红色的 LAB 颜色阈值下界:', lower)
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print('红色的 LAB 颜色阈值上界:', upper)
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print("{}, {}, {}, {}, {}, {}".format(lower[0], upper[0], lower[1], upper[1], lower[2], upper[2]))
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# 在原图像上绘制红框
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cv2.rectangle(image_bgr, (x, y), (x + w, y + h), (0, 0, 255), 2)
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# 显示图像
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cv2.imshow('Image with Red Rectangle', image_bgr)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# 保存处理后的图像
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cv2.imwrite("image_with_red_rectangle.jpg", image_bgr)
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else:
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print("图像无法加载。")
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