mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 14:54:07 +00:00
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import cv2
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import numpy as np
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import os
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# 读取文件夹中的所有图片文件
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def read_images_from_folder(folder):
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images = []
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for filename in os.listdir(folder):
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img = cv2.imread(os.path.join(folder, filename))
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if img is not None:
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images.append((filename, img))
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return images
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# Lab颜色空间的a阈值分割，同时处理灰度值大于190的像素
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def threshold_lab_a_and_high_gray(image, lower_threshold=0, upper_threshold=20):
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lab_image = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
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_, a, _ = cv2.split(lab_image)
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# 创建一个与a通道相同大小的黑色图像
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binary_image = np.zeros_like(a)
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# 将a通道中值在指定范围内的像素设置为白色（255）
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binary_image[(a >= lower_threshold) & (a <= upper_threshold)] = 255
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# 为灰度值大于190的像素创建二值图
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gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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high_gray_image = np.zeros_like(gray_image)
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high_gray_image[gray_image > 170] = 255
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# 从a通道阈值图中移除灰度值大于190的像素
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final_image = cv2.bitwise_and(binary_image, binary_image, mask=np.bitwise_not(high_gray_image))
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return binary_image, high_gray_image, final_image
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# 拼接并显示所有图片
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def concatenate_images(original, images, filename, scale=0.5):
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# 将所有单通道图像转换为三通道图像
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resized_imgs = []
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for img in images:
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if len(img.shape) == 2: # 单通道图像
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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# 缩放图像
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img = cv2.resize(img, (int(img.shape[1] * scale), int(img.shape[0] * scale)))
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resized_imgs.append(img)
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# 将原图也转换为相同大小和缩放
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resized_original = cv2.resize(original, (int(original.shape[1] * scale), int(original.shape[0] * scale)))
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# 水平拼接第一行和第二行
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top_row = cv2.hconcat([resized_original, resized_imgs[0]])
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bottom_row = cv2.hconcat([resized_imgs[1], resized_imgs[2]])
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# 垂直拼接所有行
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final_image = cv2.vconcat([top_row, bottom_row])
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# 显示图片
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cv2.imshow(f"Combined Images - {filename}", final_image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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def main():
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folder = r'F:\images' # 替换为你的文件夹路径
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images = read_images_from_folder(folder)
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for filename, image in images:
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lab_thresh, high_gray, final_image = threshold_lab_a_and_high_gray(image, lower_threshold=115, upper_threshold=135)
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concatenate_images(image, [lab_thresh, high_gray, final_image], filename, scale=0.5) # 添加缩放因子
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if __name__ == "__main__":
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main()
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