feat:补充20240529RGBtest3内胜哥的lab色彩空间test文件

20240529RGBtest3/xs/20240711lab.py

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+import cv2
+import numpy as np
+import os
+
+# 读取文件夹中的所有图片文件
+def read_images_from_folder(folder):
+    images = []
+    for filename in os.listdir(folder):
+        img = cv2.imread(os.path.join(folder, filename))
+        if img is not None:
+            images.append((filename, img))
+    return images
+
+# Lab颜色空间的a阈值分割，同时处理灰度值大于190的像素
+def threshold_lab_a_and_high_gray(image, lower_threshold=0, upper_threshold=20):
+    lab_image = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
+    _, a, _ = cv2.split(lab_image)
+
+    # 创建一个与a通道相同大小的黑色图像
+    binary_image = np.zeros_like(a)
+
+    # 将a通道中值在指定范围内的像素设置为白色（255）
+    binary_image[(a >= lower_threshold) & (a <= upper_threshold)] = 255
+
+    # 为灰度值大于190的像素创建二值图
+    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    high_gray_image = np.zeros_like(gray_image)
+    high_gray_image[gray_image > 170] = 255
+
+    # 从a通道阈值图中移除灰度值大于190的像素
+    final_image = cv2.bitwise_and(binary_image, binary_image, mask=np.bitwise_not(high_gray_image))
+
+    return binary_image, high_gray_image, final_image
+
+# 拼接并显示所有图片
+def concatenate_images(original, images, filename, scale=0.5):
+    # 将所有单通道图像转换为三通道图像
+    resized_imgs = []
+    for img in images:
+        if len(img.shape) == 2:  # 单通道图像
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        # 缩放图像
+        img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
+        resized_imgs.append(img)
+
+    # 将原图也转换为相同大小和缩放
+    resized_original = cv2.resize(original, (int(original.shape[1] * scale), int(original.shape[0] * scale)))
+
+    # 水平拼接第一行和第二行
+    top_row = cv2.hconcat([resized_original, resized_imgs[0]])
+    bottom_row = cv2.hconcat([resized_imgs[1], resized_imgs[2]])
+
+    # 垂直拼接所有行
+    final_image = cv2.vconcat([top_row, bottom_row])
+
+    # 显示图片
+    cv2.imshow(f"Combined Images - {filename}", final_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+def main():
+    folder = r'F:\images'  # 替换为你的文件夹路径
+    images = read_images_from_folder(folder)
+
+    for filename, image in images:
+        lab_thresh, high_gray, final_image = threshold_lab_a_and_high_gray(image, lower_threshold=115, upper_threshold=135)
+        concatenate_images(image, [lab_thresh, high_gray, final_image], filename, scale=0.5)  # 添加缩放因子
+
+if __name__ == "__main__":
+    main()
+