Hello, I would like to ask. I want to match the pictures that have not been taught. Can this project be realized? Do you have any recommended open source projects to realize the generation?

I have downloaded the he models for shadow detection, and put i into ./checkpoints. When I run: python test.py --dataset_mode demo --model detect --netG dsd --dataset_root ../Samples/ I get the following error: FileNotFoundError: [Errno 2] No such file or directory: '/SynShadow-main/src/models/n etworks/base/resnext/resnext_101_32x4d.pth'

How to infer an image.

Hello, your work is very exciting, thank you for your contribution to this field. There is no training code for DHAN. I would like to know further details about DHAN under the setting of SynShadow->ISTD+. Can you provide the corresponding code? I saw the evaluation results in the paper. Thank you for your work and look forward to your reply.

Hello, Thank for sharing your code. I want to know if there is a script to generate Synthesized image(with shadow-free image, matte, attenuation parameter)!

A fix was needed to proceed with the current application's Docker image build, as an error was shown when building the required wheels. After the corresponding update of the required packages, the Docker image build process is accomplished without any inconvenience.

This repository is such a great effort; thanks for sharing it.

Hello,

I would like to test your model of shadow removal on a random image but I am a bit confused where to save the image as well as which command to type in.

For now, I saved the image under datasets/test/, but when I run

python test.py --dataset_root ../datasets/

I get the following error:

AssertionError: ../datasets/test/input is not a valid directory

If I create an input directory, then the program is asking me for a target directory and a maskdirectory. Both should contain the same number of elements as the input directory.

But if I test on a random image, I do not have any target or mask, so I do not know how to proceed. Could you please help me?

Thanks! Guillaume

Thank you for sharing. I want to train spmnet on ISTD, but I got the error after some epochs:

C:\anaconda\envs\taka1\lib\site-packages\numpy\lib\polynomial.py:628: RuntimeWarning: invalid value encountered in true_divide lhs /= scale

Intel MKL ERROR: Parameter 4 was incorrect on entry to DGELSD. Traceback (most recent call last): File "train.py", line 31, in for i, data_i in enumerate(dataloader, start=iter_counter.epoch_iter): File "C:\anaconda\envs\taka1\lib\site-packages\torch\utils\data\dataloader.py", line 521, in next data = self._next_data() File "C:\anaconda\envs\taka1\lib\site-packages\torch\utils\data\dataloader.py", line 561, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "C:\anaconda\envs\taka1\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\anaconda\envs\taka1\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\SynShadow\src\data\paired_dataset.py", line 82, in getitem return self.postprocess(input_dict) File "C:\SynShadow\src\data\base_dataset.py", line 49, in postprocess d['input'], d['target'], d['mask'].squeeze()) File "C:\SynShadow\src\util\illum_affine_model.py", line 98, in fit_brightening_params 1 File "<array_function internals>", line 6, in polyfit File "C:\anaconda\envs\taka1\lib\site-packages\numpy\lib\polynomial.py", line 629, in polyfit c, resids, rank, s = lstsq(lhs, rhs, rcond) File "<array_function internals>", line 6, in lstsq File "C:\anaconda\envs\taka1\lib\site-packages\numpy\linalg\linalg.py", line 2306, in lstsq x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj) File "C:\anaconda\envs\taka1\lib\site-packages\numpy\linalg\linalg.py", line 100, in _raise_linalgerror_lstsq raise LinAlgError("SVD did not converge in Linear Least Squares") numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares

Thanks.
I revise code in illum_affine_model.py line 100 except ValueError: -> except : Then, I didn't get error.

On the other hand, when I trained spmnet on ISTD,w_l2 and b_l2 loss decreased fastly, but L1 loss didn't decrease. The results from this checkpoints are bad. I trained spmnet on SynShadow(USR/shadowfree). W_l2 and b_l2 loss decreased slowly, and L1 loss also decreased. The results are good.

In addition,do you have some validation set to evaluate benchmark during training?

Thanks for your great work. Could you please provide an complete instruction for synthetic dataset generation? A little confusing about the darken function. I don't find the definition of the parameter "opt.intercepts_mode".

Thank you~

Hello, Thanks for sharing your work. Could you provide base body models and ShapeNetCore.v2 in shadow_matte_generator subdirectory. Some link is failure.