工具代码剪切背景

# import cv2
# import numpy as np
#
# def imshow_image(image):
#     cv2.namedWindow('image',cv2.WINDOW_AUTOSIZE)
#     cv2.imshow("image",image)
#     cv2.waitKey()
#     cv2.destroyAllWindows()
#
# def Crop_image(image):
#     image=cv2.imread(image)
#     if image is None:
#         print('Unable to load image!')
#     else:
#         # print(image.shape)
#         image_gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
#         # print(image_gray.shape)
#         # 最大类间方差法(大津算法)，thresh会被忽略，自动计算一个阈值
#         image_adaptive_threshold = cv2.adaptiveThreshold(image_gray,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,15,2)
#         # image_adaptive_threshold = cv2.medianBlur(image_adaptive_threshold, 7)
#         # 创建膨胀和腐蚀的结构元素
#         kernel = np.ones((5, 5), np.uint8)
#         # 对图像进行膨胀
#         dilated_image = cv2.dilate(image_adaptive_threshold, kernel, iterations=1)
#         # 创建一个与输入图像大小相同的遮罩
#         image_fill = dilated_image.copy()
#         # 定义填充起始点
#         h,w=dilated_image.shape[:2]
#         start_seed = (w//2, h//2)
#         seed_pixel_value = dilated_image[start_seed[1], start_seed[0]]
#         # 输出种子点的像素值
#         print("Seed Pixel Value:", seed_pixel_value)
#         # 如果起始点的像素值小于 255，将小于 255 的像素值都设置为 255
#
#         if seed_pixel_value == 255:
#             pass
#         else:
#             dilated_image[start_seed[1], start_seed[0]] = 255
#             print("New pixel value at seed point:", dilated_image[start_seed[1], start_seed[0]])
#
#         # imshow_image(image_fill)
#         # 创建一个与输入图像大小相同的遮罩
#         mask = np.zeros((dilated_image.shape[0] + 2, dilated_image.shape[1] + 2),
#                         dtype=np.uint8)
#         # 定义填充的新像素值
#         new_value = 255
#         # 定义填充的连通区域的低、高像素差值范围
#         lo_diff = 50
#         hi_diff = 200
#         # 执行泛洪填充
#         cv2.floodFill(image_fill, mask, start_seed, new_value, (lo_diff, lo_diff, lo_diff),
#                       (hi_diff, hi_diff, hi_diff),
#                       flags=cv2.FLOODFILL_FIXED_RANGE)
#
#         # imshow_image(image_fill)
#
#         # 找到泛洪填充后的掩码中的最小矩形区域
#         contours, _ = cv2.findContours(image_fill, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#         if contours:
#             # 获取最大的轮廓区域
#             largest_contour = max(contours, key=cv2.contourArea)
#             # 找到最大轮廓的边界框
#             x, y, w, h = cv2.boundingRect(largest_contour)
#
#             # 根据边界框剪切原始图像
#             cropped_image = image[y:y + h, x:x + w]
#
#             cv2.imwrite('crop_images',cropped_image)
#
#
# imgfile="D:\glioma_datasets\\20230719Slice\\36\\36_flair.nii2\\36_flair.nii2_020.jpg"
# Crop_image(imgfile)

import os
import cv2
import numpy as np

def crop_and_save_images(input_folder, output_folder):
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)

    image_files = os.listdir(input_folder)
    for image_file in image_files:
        if image_file.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp')):
            image_path = os.path.join(input_folder, image_file)
            image = cv2.imread(image_path)
            if image is not None:
                image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                image_adaptive_threshold = cv2.adaptiveThreshold(image_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 15, 2)
                kernel = np.ones((5, 5), np.uint8)
                dilated_image = cv2.dilate(image_adaptive_threshold, kernel, iterations=1)

                h, w = dilated_image.shape[:2]
                start_seed = (w // 2, h // 2)
                seed_pixel_value = dilated_image[start_seed[1], start_seed[0]]

                if seed_pixel_value != 255:
                    dilated_image[start_seed[1], start_seed[0]] = 255

                mask = np.zeros((dilated_image.shape[0] + 2, dilated_image.shape[1] + 2), dtype=np.uint8)
                new_value = 255
                lo_diff = 50
                hi_diff = 200
                cv2.floodFill(dilated_image, mask, start_seed, new_value, (lo_diff, lo_diff, lo_diff),
                              (hi_diff, hi_diff, hi_diff), flags=cv2.FLOODFILL_FIXED_RANGE)

                contours, _ = cv2.findContours(dilated_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
                if contours:
                    largest_contour = max(contours, key=cv2.contourArea)
                    x, y, w, h = cv2.boundingRect(largest_contour)
                    cropped_image = image[y:y + h, x:x + w]

                    output_path = os.path.join(output_folder, image_file)
                    cv2.imwrite(output_path, cropped_image)
            else:
                print(f"Unable to load image: {image_path}")

if __name__ == "__main__":
    input_folder = "A"  # Replace with your input folder
    output_folder = "B"  # Replace with your output folder
    crop_and_save_images(input_folder, output_folder)