khanhha/crack_segmentation

This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

crack_segmentation's Issues

Have you encountered this problem?Thanks to answer

Traceback (most recent call last):
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/visdom/init.py", line 711, in _send
data=json.dumps(msg),
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/visdom/init.py", line 677, in _handle_post
r = self.session.post(url, data=data)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/requests/sessions.py", line 578, in post
return self.request('POST', url, data=data, json=json, **kwargs)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/requests/sessions.py", line 530, in request
resp = self.send(prep, **send_kwargs)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/requests/sessions.py", line 643, in send
r = adapter.send(request, **kwargs)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/requests/adapters.py", line 510, in send
raise ProxyError(e, request=request)
requests.exceptions.ProxyError: HTTPConnectionPool(host='127.0.0.1', port=7890): Max retries exceeded with url: http://localhost:8097/events (Caused by ProxyError('Cannot connect to proxy.', RemoteDisconnected('Remote end closed connection without response')))
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6192.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6192.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6219.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6219.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6221.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6221.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6225.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6225.bmp']
0%| | 0/39 [00:00<?, ?it/s]bar 0%| | 0/39 [00:00<?, ?it/s]
Epoch 1 --- Training --- :: 0%| | 0/39 [00:00<?, ?it/s]item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6194.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6194.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6220.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6220.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6229.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6229.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6228.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6228.bmp']
item ['/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260/6206.jpg', '/home/featurize/data/DeepCrack-datasets/CrackTree260/CrackTree260_gt/6206.bmp']
/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/torch/nn/functional.py:693: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)
return torch._C._nn.max_pool2d_with_indices(input, kernel_size, stride, padding, dilation, ceil_mode)
/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/torch/nn/functional.py:3613: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode)
Epoch 1 --- Training --- :: 0%| | 0/39 [00:00<?, ?it/s]
Traceback (most recent call last):
File "train.py", line 198, in
main()
File "train.py", line 67, in main
pred = trainer.train_op(data, target)
File "/cloud/DeepCrack-master/codes/trainer.py", line 39, in train_op
pred_output, pred_fuse5, pred_fuse4, pred_fuse3, pred_fuse2, pred_fuse1 = self.model(input)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 166, in forward
return self.module(*inputs[0], **kwargs[0])
File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/cloud/DeepCrack-master/codes/model/deepcrack.py", line 154, in forward
output = self.final(torch.cat([fuse5,fuse4,fuse3,fuse2,fuse1],1))
RuntimeError: Sizes of tensors must match except in dimension 2. Got 600 and 592 (The offending index is 0)

Mask creation tool

Hi khanhha,

thanks for providing this dataset. This is really fantastic.
How did you produce the masks for the images?
Which tool did you use ... I only found annotation-tools that can to polyline or rectangles. But it seems that you have really hand-drawn the cracks?

Thanks for any help
Stefan

Hello, where can I see the paper of this model?

Code license

Hi khanhha,

I'm a developer working on my open-sourced projects and I found that your github repo crack_segmentation very useful! Thanks a lot for your contribution to the open-sourced community!

By the way, would you mind adding a license such as MIT license to your github repository? This kind of license will confirm the open sourced usage of this repo and perhaps more people will use it without any license problem.

I’m looking forward to your reply and thanks by advance.

Best,
xcqiutim

Patchwise inference in unet_inference.py

Hi @khanhha , thanks for the repo and the pretrained model. I note in inference_unet.py that you perform once inference on the entire image and then on patches of images and then merge the patches to form the final picture.

N typically the patch-based inference performs better. Is there any reason you took the patch-based approach?

What is this project license?

Can it be used for commercial purposes?

the link of pretraind weight of Resnet is invaild, pls resent agina.

in the utils.py

in line number 30
use this code to fix it

def cuda(x):
return x.cuda(non_blocking=True) if torch.cuda.is_available() else x

where is unet_resnet_101

Hi, I would like to try [unet_resnet_101], but the link provided in README is not available.
@khanhha Coul you please upload it again? Thank you so much.

Hi

Would it be possible to share the training dataset you used? I saw you reckoned that you were using the largest crack segmentation dataset available. It would be super helpful to have a link of those dataset or links if they are not combined already.
Thanks and looking forward to your response.

我跑通了您的代码，但是我没有搜到您论文的链接，可以分享您论文的题目和链接吗？

the pretrained weight of resnet is invalid

I want to cite your work on github in my paper related to UAV bridge inspection, but the weight of the pretrained weight of resnet is invalid, please check it on your github so that I can cite your work, thanks!

Bro in the code their is problem

use this for inference :-
python inference_unet.py -img_dir ./test_images -model_path ./model.pth -model_type resnet101 -out_viz_dir ./viz_result -out_pred_dir ./pred_result -threshold 0.5

Model not training with resnet101 and resnet34

When I am trying to train the model with resnet101, I am getting the following error. Please help me figure this out.

0%| | 0/14848 [00:00<?, ?it/s]
Epoch 0: 0%| | 0/14848 [00:00<?, ?it/s]total images = 17469
create resnet101 model
Started training model from epoch 0
Traceback (most recent call last):
File "train_unet.py", line 251, in
train(train_loader, model, criterion, optimizer, validate, args)
File "train_unet.py", line 118, in train
masks_pred = model(input_var)
File "/opt/apps/Anaconda3/2019.03/envs/powerai16_ibm/lib/python3.6/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/home/nys09/unet/unet_transfer.py", line 233, in forward
dec5 = self.dec5(torch.cat([center, conv5], 1))
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 7 and 6 in dimension 2 at /opt/anaconda/conda-bld/pytorch_1551416914958/work/aten/src/THC/generic/THCTensorMath.cu:83

where is the joint_transforms

When I was reading train_tiramisu.py, I found that the code need to import JointRandomSizedCrop from joint_transforms.
However, I did not find the joint_transforms. Whether it is a package, or you deleted it?

Which paper can I study the model structure?

Crack mask is much more wider than the real crack

    Thank you very much for your sharing! Datasets about crack are really hard to find. But i've found out that some crack mask in dataset are much more wider than the real crack, let's take Volker_DSC01704_219_142_1156_1117.jpg in this dataset as example, crack width in the mask is about 10 pixels, while the real crack width is about 2 or 3 pixel width. 
    Is crack label width 3 times larger than the crack 's real width? I think mismatch crack width would effect the segmentation model' s performance, as the BCE loss would take crack pixel gray value into account, and i don't know how to generate accurate crack pixel width based on these wider masks
   Any advice or suggestions would be appreciated! Thank you again!

Where does the dataset Rissbilder_for_Florian come from？

Rissbilder_for_Florian dataset is in your data，but I can't find its source.
Can you show it? Thank you very much.