pytorch学习（三）—图像的加载/读取方式

拼搏现实的明天。 2023-06-06 12:01 21阅读 0赞

import matplotlib.pyplot as plt
    import skimage.io as io
    import cv2
    from PIL import Image
    import numpy as np
    import torch
    
    #使用Skimage读取图像
    # skimage.io imread()-----np.ndarray,  (H x W x C), [0, 255],RGB
    img_skimage = io.imread('./data/dog.jpg')
    print(img_skimage.shape)
    
    #使用opencv读取图像
    # cv2.imread()------np.array, (H x W xC), [0, 255], BGR
    img_cv = cv2.imread('./data/dog.jpg')
    print(img_cv.shape)
    
    #使用PIL读取图像
    # (H x W x C), [0, 255], RGB
    img_PIL = Image.open('./data/dog.jpg')
    img_PIL_l = np.array(img_PIL)
    print(img_PIL_l.shape)
    
    plt.figure()
    for i, im in enumerate([img_skimage,img_cv,img_PIL_l]):
        ax = plt.subplot(1,3,i + 1)
        ax.imshow(im)
        #plt.pause(0.01)
        
     
    # ------------np.ndarray转为torch.Tensor------------------------------------
    # numpy image: H x W x C  (0,1,2)
    # torch image: C x H x W  (2,0,1)
    # np.transpose( xxx,  (2, 0, 1))   # 将 H x W x C 转化为 C x H x W
    tensor_skimage = torch.from_numpy(np.transpose(img_skimage,(2,0,1)))
    print(tensor_skimage.shape)
    tensor_cv = torch.from_numpy(np.transpose(img_cv,(2,0,1)))
    print(tensor_cv.shape)
    tensor_pil = torch.from_numpy(np.transpose(img_PIL_l,(2,0,1)))
    print(tensor_pil.shape)
    
    #plt只支持数组对象
    #ax.imshow(im),im可以是数列类格式、或者PIL图片。
    '''
    plt.figure()
    for i, img in enumerate([tensor_skimage,tensor_cv,tensor_pil]):
        ax = plt.subplot(1,3,i + 1)
        ax.imshow(img)
    ''' 
    
    # np.transpose( xxx,  (2, 0, 1))   # 将 C x H x W 转化为 H x W x C
    img_skimage_2 = np.transpose(tensor_skimage.numpy(), (1, 2, 0))
    print(img_skimage_2.shape)
    img_cv_2 = np.transpose(tensor_cv.numpy(), (1, 2, 0))
    print(img_cv_2.shape)
    img_pil_2 = np.transpose(tensor_pil.numpy(), (1, 2, 0))
    print(img_pil_2.shape)
    plt.figure()
    for i, im in enumerate([img_skimage_2, img_cv_2, img_pil_2]):
        ax = plt.subplot(1, 3, i + 1)
        ax.imshow(im)
        
        
    #opencv读取的图像为BGR，首先需要转为RGB
    img_cv = cv2.cvtColor(img_cv,cv2.COLOR_BGR2RGB)
    
    #转torch.Tensor
    tensor_cv = torch.from_numpy(img_cv)
    
    #tensor转numpy
    numpy_cv = tensor_cv.numpy()
    
    plt.figure()
    plt.title("cv")
    plt.imshow(numpy_cv)
    plt.show()