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from math import ceil

from torchvision.transforms.functional import to_tensor, to_pil_image

from PIL import Image

import torch

# 填充到最接近base整数倍的长和宽图像大小

def get_padding_pic_mask(origin_png, result_png, result_mask_png, rgb=[[130, 206, 255]], base=4):

    # C, H, W

    src = Image.open(origin_png)

    src = to_tensor(src)

    # print(src.shape)  # torch.Size([3, 800, 600])

    # channel: (R, G, B) / 255

    origin_h, origin_w = src.shape[1], src.shape[2]

    print('原图像大小, height: {}, width: {}'.format(origin_h, origin_w))

    h = ceil(origin_h / base) * base

    w = ceil(origin_w / base) * base

    img = torch.ones(3, h, w)

    # 如果想要填充是黑色则注释掉上一句，换下面这一句

    # img = torch.zeros(3, h, w)

    img[:, :origin_h, :origin_w] = src

    # 保存填充后的图片

    to_pil_image(img).save(result_png)

    # 处理一下mask

    mask = torch.tensor(rgb) / 255

    mask = mask.view(3, 1, 1).repeat(1, h, w)

    # 保存填充后的mask

    to_pil_image(mask).save(result_mask_png)

# 图像输出后我们需要clip一下

def clip_unpadding(input_png, output_png, origin_h, origin_w):

    # C, H, W

    img = Image.open(input_png)

    img = to_tensor(img)

    img = img[:, :origin_h, :origin_w]

    # 保存裁剪后的图片

    to_pil_image(img).save(output_png)

if __name__ == '__main__':

    # origin_png = 'pic/pic.jpg'

    # result_png = 'pic/pic_padding.jpg'

    # result_mask_png = 'pic/mask_padding.jpg'

    # get_padding_pic_mask(origin_png, result_png, result_mask_png)

    input_png = 'pic/pic_padding.jpg'

    output_png = 'pic/pic_clip.jpg'

    # 原图像大小, height: 567, width: 390

    clip_unpadding(input_png, output_png, 567, 390)

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from PIL import Image

def resize_img(origin_png, resize_png, height, width):

    img = Image.open(origin_png)

    img = img.resize((width, height))

    img.save(resize_png)

if __name__ == '__main__':

    origin_png = 'pic/white.jpg'

    resize_png = 'pic/white_resize.png'

    resize_img(origin_png, resize_png, 800, 600)