LAB空间科普：

同RGB颜色空间相比（见博客《光与色的故事--颜色空间浅析》），Lab是一种不常用的色彩空间。它是在1931年国际照明委员会（CIE）制定的颜色度量国际标准的基础上建立起来的。1976年，经修改后被正式命名为CIELab。它是一种设备无关的颜色系统，也是一种基于生理特征的颜色系统。这也就意味着，它是用数字化的方法来描述人的视觉感应。Lab颜色空间中的L分量用于表示像素的亮度，取值范围是[0,100],表示从纯黑到纯白；a表示从红色到绿色的范围，取值范围是[127,-128]；b表示从黄色到蓝色的范围，取值范围是[127,-128]。

完整转换源代码

说明：

1. 2代表to
2. 这里全部写成方法的形式，也可以专门再封一个class
3. 例如tensor2im表示tensor转换成image、rgb2lab表示rgb空间转换成lab空间
4. 缺少的头文件包记得import

def rgb2lab(in_img,mean_cent=False):
    from skimage import color
    img_lab = color.rgb2lab(in_img)
    if(mean_cent):
        img_lab[:,:,0] = img_lab[:,:,0]-50
    return img_lab

def tensor2np(tensor_obj):
    # change dimension of a tensor object into a numpy array
    return tensor_obj[0].cpu().float().numpy().transpose((1,2,0))

def np2tensor(np_obj):
     # change dimenion of np array into tensor array
    return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1)))

def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False):
    # image tensor to lab tensor
    from skimage import color

    img = tensor2im(image_tensor)
    img_lab = color.rgb2lab(img)
    if(mc_only):
        img_lab[:,:,0] = img_lab[:,:,0]-50
    if(to_norm and not mc_only):
        img_lab[:,:,0] = img_lab[:,:,0]-50
        img_lab = img_lab/100.

    return np2tensor(img_lab)

def tensorlab2tensor(lab_tensor,return_inbnd=False):
    from skimage import color
    import warnings
    warnings.filterwarnings("ignore")

    lab = tensor2np(lab_tensor)*100.
    lab[:,:,0] = lab[:,:,0]+50

    rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1)
    if(return_inbnd):
        # convert back to lab, see if we match
        lab_back = color.rgb2lab(rgb_back.astype('uint8'))
        mask = 1.*np.isclose(lab_back,lab,atol=2.)
        mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis])
        return (im2tensor(rgb_back),mask)
    else:
        return im2tensor(rgb_back)

def load_image(path):
    if(path[-3:] == 'dng'):
        import rawpy
        with rawpy.imread(path) as raw:
            img = raw.postprocess()
    elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png' or path[-4:]=='jpeg'):
        import cv2
        return cv2.imread(path)[:,:,::-1]
    else:
        img = (255*plt.imread(path)[:,:,:3]).astype('uint8')

    return img

def rgb2lab(input):
    from skimage import color
    return color.rgb2lab(input / 255.)

def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
    image_numpy = image_tensor[0].cpu().float().numpy()
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
    return image_numpy.astype(imtype)

def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
    return torch.Tensor((image / factor - cent)
                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))

def tensor2vec(vector_tensor):
    return vector_tensor.data.cpu().numpy()[:, :, 0, 0]


def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.):
# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.):
    image_numpy = image_tensor[0].cpu().float().numpy()
    image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor
    return image_numpy.astype(imtype)

def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.):
# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.):
    return torch.Tensor((image / factor - cent)
                        [:, :, :, np.newaxis].transpose((3, 2, 0, 1)))

参考

• https://baike.baidu.com/item/RGB%E9%A2%9C%E8%89%B2%E7%A9%BA%E9%97%B4/20868274?fr=aladdin