import torch
from PIL import Image
import matplotlib.pyplot as plt
from extract.getim import load_image
from torchvision import transforms
import cv2
import matplotlib.pyplot as plt



transform = transforms.Compose([
    transforms.ToTensor(),  # 将numpy数组或PIL.Image读的图片转换成(C,H, W)的Tensor格式且/255归一化到[0,1.0]之间
])  # 来自ImageNet的mean和variance


# fcontent = load_image("./ori/0.jpg",transform=None,shape=[512, 256])



def show_cut(path, left, upper, right, lower):
    """
        原图与所截区域相比较
    :param path: 图片路径
    :param left: 区块左上角位置的像素点离图片左边界的距离
    :param upper：区块左上角位置的像素点离图片上边界的距离
    :param right：区块右下角位置的像素点离图片左边界的距离
    :param lower：区块右下角位置的像素点离图片上边界的距离
     故需满足：lower > upper、right > left
    """

    img = path

    # print("This image's size: {}".format(img.size))  # (W, H)
    # img.save("kkk.jpg")
    # plt.figure("Image Contrast")
    #
    # plt.subplot(1, 2, 1)
    # plt.title('origin')
    #
    # plt.imshow(img)
    # plt.axis('off')
    #
    # box = (left, upper, right, lower)
    # roi = img.crop(box)
    #
    # plt.subplot(1, 2, 2)
    # plt.title('roi')
    # plt.imshow(roi)
    # plt.axis('off')
    # plt.show()


def image_cut_save(path, left, upper, right, lower):
    """
        所截区域图片保存
    :param path: 图片路径
    :param left: 区块左上角位置的像素点离图片左边界的距离
    :param upper：区块左上角位置的像素点离图片上边界的距离
    :param right：区块右下角位置的像素点离图片左边界的距离
    :param lower：区块右下角位置的像素点离图片上边界的距离
     故需满足：lower > upper、right > left
    :param save_path: 所截图片保存位置
    """
    img = path  # 打开图像
    box = (left, upper, right, lower)
    roi = img.crop(box)
    # roi.save(save_path)
    return transform(roi)


    # 保存截取的图片



# def getcontent(fcontent,gap):
#     Intgap=gap/9
#     a=torch.Tensor()
#     for i in range(10):
#         pic_path = fcontent
#         # pic_save_dir_path = './out2/0-'+str(i)+".jpg"
#         left, upper, right, lower = Intgap*i, 0, Intgap*i+gap, gap
#         a=torch.cat([a,image_cut_save(pic_path, left, upper, right, lower).unsqueeze(1)],dim=1)
#     return a
# def cobtwoten(image_path):
#     fcontent = load_image(image_path, transform=None, shape=[512, 256])
#     Intgap = 256
#     a = torch.Tensor()
#     for i in range(2):
#         pic_path = fcontent
#         #pic_save_dir_path = './out2/0-' + str(i) + ".jpg"
#         left, upper, right, lower = Intgap * i, 0, Intgap * i + Intgap, Intgap
#         a = torch.cat([a, image_cut_save(pic_path, left, upper, right, lower).unsqueeze(1)], dim=1)
#     return a.unsqueeze(0)

def cobtwoten(image_path):
    fcontent = load_image(image_path, transform=None, shape=[256, 128])
    Intgap = 128/9
    a = torch.Tensor()
    for i in range(10):
        pic_path = fcontent
        #pic_save_dir_path = './out2/0-' + str(i) + ".jpg"
        left, upper, right, lower = Intgap * i, 0, Intgap * i + 128, 128
        a = torch.cat([a, image_cut_save(pic_path, left, upper, right, lower).unsqueeze(1)], dim=1)
    return a.unsqueeze(0)

def cobtwoten256(image_path):
    fcontent = load_image(image_path, transform=None, shape=[512,256])
    Intgap = 256/9
    a = torch.Tensor()
    for i in range(10):
        pic_path = fcontent
        #pic_save_dir_path = './out2/0-' + str(i) + ".jpg"
        left, upper, right, lower = Intgap * i, 0, Intgap * i + 256, 256
        a = torch.cat([a, image_cut_save(pic_path, left, upper, right, lower).unsqueeze(1)], dim=1)
    return a.unsqueeze(0)

#
# fcontent = load_image("./extract/image/0.jpg",transform=None,shape=[256,128])
# Intgap = 128
# a = torch.Tensor()
# for i in range(2):
#     pic_path = fcontent
#     pic_save_dir_path = './out2/0-'+str(i)+".jpg"
#     left, upper, right, lower = Intgap * i, 0, Intgap * i + 128, 128
#     a = torch.cat([a, image_cut_save(pic_path, left, upper, right, lower,pic_save_dir_path).unsqueeze(1)], dim=1)
# print(a.shape)
import numpy as np
def imgsave(image, path):
    image = image.squeeze(0)
    image = image.permute(1, 2, 0)
    image_np = image.cpu().numpy()*255
    image_np = image_np.astype(np.uint8)
    Image.fromarray(image_np).save(path)  # 直接保存PIL图像对象

# lik=["0"]
# for name in lik:
#     videos=cobtwoten("./extract/image/0.jpg").permute(0, 2, 1, 3, 4)
#     print(videos.shape)
#     for i in range(10):
#         frame = videos[:, i, :, :]
#         imgsave(frame, "./out2/"+str(i)+".jpg")