pytorch常用函数介绍

transforms.ToTensor()

transforms.ToTensor() 可以将 PIL.Image/numpy.ndarray 数据进转化为torch.FloadTensor，并归一化到[0, 1.0]：

• 取值范围为[0, 255]的PIL.Image，转换成形状为[C, H, W]，取值范围是[0, 1.0]的torch.FloadTensor；
• 形状为[H, W, C]的numpy.ndarray，转换成形状为[C, H, W]，取值范围是[0, 1.0]的torch.FloadTensor。
  transforms.ToPILImage则是将Tensor转化为PIL.Image

img_path = "./data/img_37.jpg"

# transforms.ToTensor()
transform1 = transforms.Compose([
    transforms.ToTensor(), # range [0, 255] -> [0.0,1.0]
    ]
)

##numpy.ndarray
img = cv2.imread(img_path)# 读取图像
img1 = transform1(img) # 归一化到 [0.0,1.0]
print("img1 = ",img1)
# 转化为numpy.ndarray并显示
img_1 = img1.numpy()*255
img_1 = img_1.astype('uint8')
img_1 = np.transpose(img_1, (1,2,0))
cv2.imshow('img_1', img_1)
cv2.waitKey()

##PIL
img = Image.open(img_path).convert('RGB') # 读取图像
img2 = transform1(img) # 归一化到 [0.0,1.0]
print("img2 = ",img2)
#转化为PILImage并显示
img_2 = transforms.ToPILImage()(img2).convert('RGB')
print("img_2 = ",img_2)
img_2.show()