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一、原始数据

二、分别读取数据

1、gdal读取的array 

2、pil读取的array

 

三、 gdal转pil

image= np.rollaxis(image , 0, 3)

 转换结果

总的代码 

用gdal库读取tif影像并填充边缘，并根据窗口大小滑动裁剪裁剪

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def clip_picture(file_path,a):slide_window = 1024  # 大的滑动窗口step_length = 1024sat_list = os.listdir(file_path) for file in sat_list:Image_Path = os.path.join(file_path,file)image=gdal.Open(Image_Path)width = image.RasterXSizeheight = image.RasterYSize# image = Image.open(Image_Path)# width = image.size[0]  # 获取图像的宽# height = image.size[1]  # 获取图像的高right_fill = step_length - (width % step_length)bottom_fill = step_length - (height % step_length)width_path_number = int((width + right_fill) / step_length)  # 横向切成的小图的数量height_path_number = int((height + bottom_fill) / step_length)  # 纵向切成的小图的数量#print(width_path_number, height_path_number)# image = np.array(image)image=image.ReadAsArray()if a=='tif':image= np.rollaxis(image , 0, 3)image = cv2.copyMakeBorder(image, top=0, bottom=bottom_fill, left=0, right=right_fill,borderType=cv2.BORDER_CONSTANT, value=0)image = cv2.copyMakeBorder(image, top=step_length // 2, bottom=step_length // 2, left=step_length // 2,right=step_length // 2,borderType=cv2.BORDER_CONSTANT, value=0)  # 填充1/2步长的外边框# 2.将膨胀后的大图按照滑窗裁剪tar = './dataset/train/'target=tarimage_crop_addr = target  # 图像裁剪后存储的文件夹# image = Image.fromarray(image)  # 将图片格式从numpy转回PILimage=Image.fromarray(np.uint8(image))l = 0if a=='tif':for j in range(height_path_number):for i in range(width_path_number):box = (i * step_length, j * step_length, i * step_length + slide_window, j * step_length + slide_window)small_image = image.crop(box)small_image.save(image_crop_addr + file[:-4] + '({},{})@{:04d}_sat.tif'.format(j, i, l), quality=95)l = l + 1if a=='png':for j in range(height_path_number):for i in range(width_path_number):box = (i * step_length, j * step_length, i * step_length + slide_window, j * step_length + slide_window)small_image = image.crop(box)small_image.save(image_crop_addr + file[:-4] + '({},{})@{:04d}_mask.png'.format(j, i, l), quality=95)l = l + 1​