How to visualize the output?

[jundanl]

Thanks for your great project. However, I have trouble visualizing the predictions. The image looks very strange, could you please tell me how to correctly visualize them?

[irismanchen]

same question here

[jundanl]

I'm really looking forward to visualizing the prediction R and S……

[zhangmohan]

I have the same question, have you solved it?

[windj007]

:heavy_plus_sign: @lixx2938 In your previous work Learning Intrinsic Image Decomposition from Watching the World the model predicts two images - 3-channel log-reflectance and 1-channel log-lighting - so I could do torch.exp(log_r + log_s).

But in this repository the model predicts two 1-channel images. How to use them to reconstruct the original image?

[jundanl]

I think I solve this question now. Please read the code on computing Reconstruction loss. Here is my explanation.

Gray-scale shading assumption

1. A colorful pixel can be decomposed into:
   1. RGB = intensity * chromaticity
   2. That means,
      • input_rgb_image = img_intensity * img_chromaticity
        • Note that, input image here is represented in linear RGB space. The photo loaded from the disk is usually represented in sRGB space. You can find the conversion code in this project. Be careful the input image for the network is in sRGB space, but when computing reconstruction loss, the image is in linear RGB space.
      • R = R_intensity * R_chromaticity
      • S = S_intensity * S_chromaticity
2. Under gray-scale shading (white lighting) assumption, above can be rewritten as,
   • S = S_intensity
   • img_chromaticity = R_chromaticity
   • R = R_intensity * img_chromaticity
3. For the more specific explanation for intensity and chromaticity, I recommend you to read:
   • Intrinsic images in the wild
   • Revisiting Deep Intrinsic Image Decompositions
4. You can find all these code in the project (especially in the script of data_loader): (if not, please look at code in IIW and SAW repositories)
   • rgb to srgb
   • srgb to rgb
   • compute intensity (it's actually the avarage value of RGB channels)
   • compute chromaticity

Network prediction

1. the outputs of the network actually are:
   • S_pred = log(S_intensity)
   • R_ pred = log(R_intensity)
2. If you want to get S and R:
   • S = exp(S_pred)
   • R = exp(R_pred) * img_chromaticity

Reconstruct original input image

1. RGB_reconst_image = S * R
2. If you want to visualize it, convert RGB_reconst_image into sRGB space
   • sRGB space is suitable for human eyes

[shapin94]

Thanks to @tlsshh, I've also solved the visualization problem. Here is my implementation code with different image set (Not IIW nor SAW) (Based on test_iiw.py)

opt = TrainOptions().parse()  # set CUDA_VISIBLE_DEVICES before import torch

#root = "/home/zl548/phoenix24/"
#full_root = root +'/phoenix/S6/zl548/'

model = create_model(opt)
minc_loader = get_minc_loader(batch_size=16)
   for i, (x,y) in enumerate(minc_loader):
       x = x.float().cuda()
       x = x.view(16, 3, 256, 256)
       output_R, output_S = model.test_minc(x)

       for j in range(0,output_R.size(0)):

           prediction_R = output_R.data[j,:,:,:]
           prediction_R = torch.exp(prediction_R)#.repeat(1,3,1,1)

           prediction_S = output_S.data[j,:,:,:]
           prediction_S = torch.exp(prediction_S)#.repeat(1,3,1,1)

           # calc chromaticity
           srgb_img = x.data[j,:,:,:]
           rgb_img = srgb_to_rgb(np.transpose(srgb_img.cpu().numpy(), (1,2,0)))
           rgb_img[rgb_img <1e-4] = 1e-4
           chromaticity = rgb_to_chromaticity(rgb_img) # opt 1
           # opt 2 chromaticity = rgb_to_chromaticity(srgb_img.cpu().numpy())
           chromaticity = torch.from_numpy(np.transpose(chromaticity, (2,0,1))).contiguous().float()

           p_R = torch.mul(chromaticity, prediction_R.cpu())
           p_R_np = p_R.cpu().numpy()
           p_R_np = np.transpose(p_R_np, (1,2,0))
           p_R_np = cv2.cvtColor(p_R_np, cv2.COLOR_BGR2RGB)

           p_S_np = np.transpose(prediction_S.cpu().numpy(), (1,2,0))
           p_S_np = np.squeeze(p_S_np, axis=2)

           save('D:/minc_decomposition/yuv/' + str(i) + '_' + str(j) + '_albedo.png', p_R_np)
           save('D:/minc_decomposition/yuv/' + str(i) + '_' + str(j) + '_shading.png', p_S_np)
           cv2.imwrite('D:/minc_decomposition/yuv/' + str(i) + '_' + str(j) + '_original.png',np.transpose(srgb_img.cpu().numpy(),(1,2,0)))
       print('save data done')

For better understanding, here's my additional code for test_data function and data_loader

Here's test_data function which I added on intrinsic_model.py

    def test_minc(self, input_):
        prediction_R, prediction_S = self.netG.forward(input_)
        return prediction_R, prediction_S

Here's data_loader function which I added on data_loader

class TestDataSet(Dataset):
    """
    Custom Test DataSet class 
    """
    def __init__(self, transform=None):
        x_test = []
        y_test = []
        test_file_name = 'D:/labels/test2.txt' # image list
        test_file = open(test_file_name, 'r')
        test_list = test_file.readlines()
        test_file.close()

        print('start loading test data')
        for i in range(len(test_list)):
            img = cv2.imread('D:/MINC/minc-2500/minc-2500/' + test_list[i].rstrip('\n')) # load image
            img = cv2.resize(img,(256,256)) #cv2 load image with bgr format
            img = np.transpose(img3, (2,0,1))

            x_test.append(img)
            y_test.append(1)#convert_class(label[1])) # ex) 'brick'  #since given project doest not require image label, I randomly gave 1 as a label

        print('loading test data done')
        x = np.asarray(x_test)
        y = np.asarray(y_test)
        #y_test = np_utils.to_categorical(y_test[:len(test_list)], num_classes)

        self.len = len(y)
        #self.transform = transform
        self.x_data = torch.from_numpy(x)
        self.y_data = torch.from_numpy(y)

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len

def get_minc_loader(batch_size,
                    shuffle=True,
                    show_sample=False,
                    num_workers = 2,
                    pin_memory = True):
    # load dataset
    test_dataset = TestDataSet()

    test_loader = torch.utils.data.DataLoader(
        test_dataset ,batch_size= batch_size, shuffle=False,
        pin_memory=pin_memory)

    return test_loader