Not getting repaired images

[Bruceeeee]

Thanks for your work.I run eval.py with your pretrained model and undistort images with resampling.py.However the unpaired images are almost same as the origin images.Besides, I find the values in .mat file generated by eval.py are much smaller than that generated by dataset_generated.py.

[xiaoyu258]

What's the resolution of the input? For the pre-trained model, it is best to use the image at 256*256. If you use different resolution, you should resize the image to 256*256 first, and then estimate the flow, finally resize the estimated flow and multiply the flow by a factor according to the resolution. For example, if your original resolution is 512*512, you should multiply the flow by 2 after you upsample the flow.

[nicolasmetallo]

Thank you very much for your work.

I have found the same issue with undistorted images being very similar to original images. Multiplying the flow has not produced good results. This is what I have at the moment (reformatted your evaluation code a bit):

def load_weights(model, path):
    model = nn.DataParallel(model) # if pre-trained
    model = model.cuda() if torch.cuda.is_available() else model
    model.load_state_dict(torch.load(path))
    return model.eval()

def transform_image(image_path):
    transform = transforms.Compose([transforms.Resize((256,256)),
                                        transforms.ToTensor(),
                                        transforms.Normalize(
                                            (0.5, 0.5, 0.5), 
                                            (0.5, 0.5, 0.5))])
    im = Image.open(image_path).convert('RGB')
    im_npy = np.asarray(im.resize((256,256)))
    im_tensor = transform(im).unsqueeze(0)
    if torch.cuda.is_available(): im_tensor = im_tensor.cuda()
    return im_tensor,im_npy

def rectify_image(image_path, multi=1):
    from resample.resampling import rectification
    im_tensor,im_npy = transform_image(image_path)
    middle = model_en(im_tensor)
    flow_output = model_de(middle)
    return rectification(im_npy, flow_output.data.cpu().numpy()[0]*multi)

model_en = EncoderNet([1,1,1,1,2])
model_de = DecoderNet([1,1,1,1,2])
model_class = ClassNet()

model_en = load_weights(model_en, './model_en.pkl')
model_de = load_weights(model_de, './model_de.pkl')
model_class = load_weights(model_class, './model_class.pkl')

testImgPath = 'test_images'
testImgs = [x for x in [Path(testImgPath).rglob(e) for e in ('*.jpg','*.png')] for x in x]
imgPath = testImgs[2]

resImg,resMsk = rectify_image(imgPath)

This is the output

[Bruceeeee]

Thank you for your reply. The cause of my problem is I have just a single GPU and I modified the code of loading pretranined weights. #model_en.load_state_dict(torch.load('model_en.pkl') model_en.load_state_dict(torch.load('model_en.pkl', strict=False) I fixed this by just empolying nn.DataParrellel to load weights. @nicolasmetallo,Is it possible that the image is too difficult for model to undistort?

[nicolasmetallo]

Hi Bruce,

The image that I used on my comment comes from the paper itself so there shouldn't be a problem. If I multiply the flow it eventually gets it right, but overall the quality is very low. I'm probably running something wrong, hopefully @xiaoyu258 can help us.

[xiaoyu258]

@nicolasmetallo , I have a long trip this time, sorry for the late reply.

Here is the result before fitting.

The result after fitting.

This is the input, can you try this image directly?

[valeriopaolicelli]

Thanks for your work, is really interesting! But, I have probably the same issue discussed here. I'm evaluating this image:

it has size 500x500. I have resized to 256x256, then performed the evaluation on one GPU, but the result is the following:

The code is essentially the same one published by you:

transform = transforms.Compose([transforms.Resize((256, 256)),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

print("-> Build model")
model_en = EncoderNet([1, 1, 1, 1, 2])
model_de = DecoderNet([1, 1, 1, 1, 2])
model_class = ClassNet()

print("-> Load weights")
""" ... load weight code """

model_en.eval()
model_de.eval()
model_class.eval()

testImgPath = './distortedImages'
saveFlowPath = './undistortedImage'

correct = 0
print("-> Start eval")
for index, file in enumerate(os.listdir(testImgPath)):
    imgPath = os.path.abspath(os.path.join(testImgPath, file))
    disimgs = Image.open(imgPath).convert('RGB')
    im_npy = np.asarray(disimgs.resize((256, 256)))

    disimgs = transform(disimgs)

    use_GPU = torch.cuda.is_available()
    if use_GPU:
        disimgs = disimgs.cuda()

    disimgs = disimgs.view(1, 3, 256, 256)
    disimgs = Variable(disimgs)

    middle = model_en(disimgs)
    flow_output = model_de(middle)
    clas = model_class(middle)

    _, predicted = torch.max(clas.data, 1)
    if predicted.cpu().numpy()[0] == index:
        correct += 1

    u = flow_output.data.cpu().numpy()[0][0]
    v = flow_output.data.cpu().numpy()[0][1]
    print("--> Image:", file)
    multi = 2
    resImg, resMsk = rectification(im_npy, flow_output.data.cpu().numpy()[0]*multi)
    img_out = Image.fromarray(resImg)

    img_out.save('undistortedImage/' + file)

Thanks a lot for your support!

[JKANG94]

@valeriopaolicelli ，did you solve this problem?

[JKANG94]

@xiaoyu258 I have got the results before fitting. How to get the results after fitting?

[JKANG94]

How to do model fitting?

[AnastasiiaMishchenko]

Dear @xiaoyu258 , thank you very much for your work! I would like to ask if it is possible to adjust the code to work with horizontal wave distortion? I introduced changes in distortion_model.py file as well as in dataset_generation.py but after the resampling the image stayed the same. Thank you in advance!

[huynhbaobk]

@nicolasmetallo , I have a long trip this time, sorry for the late reply.

Here is the result before fitting.

The result after fitting.

This is the input, can you try this image directly?

Could you help to show how to get the result 'after fitting' ? Thank you so much @xiaoyu258 xi

[nikhilparmar]

rectification

Hi you send me the code for evaluating on my image. I tried to run the eval.py but all I am getting is just the output.mat.

[psj-yyds]

So why do I have the same image before and after correction，who can help me?thank you