Deep-Image-Matting

This is tensorflow implementation for paper "Deep Image Matting".

Thanks to Davi Frossard, "vgg16_weights.npz" can be found in his blog: "https://www.cs.toronto.edu/~frossard/post/vgg16/"

2017-8-25: Now this code can be used to train, but the data is owned by company.I'll try my best to provide code and model that can do inference.Fix bugs about memory leak when training and change one of randomly crop size from 640 to 620 for boundary security issue.This can be avoid by preparing training data more carefully. Besides, it can save model and restore pre-trained model now, and can test on alphamatting set at rum time.

2017-9-1: Validation code and tensorboard view on 'alphamatting' dataset are added. Some bugs on compositional_loss and validation code are fixed. Missed 'fc6' layer is added now. And the decoder structure is exactly same with paper despide of replacing unpooling with deconvolution layer which means the network is more complex than before. The weight Wi of two loss is still vague, I'm trying to find best weight structure. Currently, general boundary is easy to predit. But some details or complex foregrounds like bike is still bad.

2017-9-14: Latest version of code has following changes:

1. Rearrange the order of preprocessing so that there is no ground truth shift in preprocessing.(Composite bg,fg,alpha first then resize, or resize bg,fg,alpha then composite. My suggestion is that composition should always happen after resize.) The result RGB images of those two preprocessing order are slightly different from each other, although it's hard to tell the difference by eye.)
2. Replace deconvolution with unpooling. Because in the experiment, it is shown that deconvolution is always hard to learn detailed information (like hair). And because of using unpooling, batch_size is also changed from 5 to 1 ( The code is not decent now, just can work).
3. Another thing need to mentioned here is that when we training on single complex sample like bike, even with deconvolution (not unpooling), the network can overfitting. But deconvolution can't converge on whole dataset. (Maybe I didn't training enough time : lr = 1e-5 with 5 days training, can't converge). Discussion about 'whether deconvolution can replace unpooling' is welcomed!
4. Add hard mode to allow training on tough samples

2018-2-19: I was working on other projects recently, so long time no maintaining this repo. In issues, I noticed some great comments may give the hint that why previous work can't reach author's performance! Here is some idea you can apply to improve this work:

1. Preparing training set using author's code (I used to work with scipy.misc which has too many weird auto-settings, it hurts the performance! If you want to use scipy.misc, make sure you understand this lib very well. Or: try PIL or opencv, there won't be too much troublesome things).
2. Generate trimap using random dilation and random erosion both! Previous code used random dilation only which is a fatal mistake!
3. Testing time ,use original size (or resize it to the closest number which can be divided by 32). I don't have free GPU to keep working on this, so above suggestions are not verified to be useful. If it helps, let me know : )

2018-4-24: Because I changed implementation of 'unpool' so the test code can't work now. I have no plan to modify this repo but probably restart a new repo for Image Matting with brand new algorithm in near future.

My Chinese blog about the implementation of this paper http://blog.leanote.com/post/calebge/Deep-Image-Matting%E5%A4%8D%E7%8E%B0%E8%BF%87%E7%A8%8B%E6%80%BB%E7%BB%93

Usage

simply run:
python test.py --alpha --rgb
sample:
python test.py --alpha=./test_data/alpha/1.png --rgb=./test_data/RGB/1.png

Pretrained Model

Can be found here:
https://drive.google.com/open?id=0B6l9O8aWij8fSnZBa2o4OUpsb1k
notice that you have to create a folder './model' under root and then put those pretrained model in there.

Important notification:

1. The pretrained model is trained on private dataset, which has large difference from authors data, so it performs struggling on author's data. You can test the model by feeding test_data.
2. 'fc6' is transformed into convolution operation by tricks proposed in FCN paper. This paper also follows this way. But in this code, convolutionarized 'fc6' is replaced by plain convolution whose weights and biases are initialilzed randomly.
3. Even test on our own data, this model still can't reach the performance mentioned in paper.

Salience Object Detection

Here is my implementation about paper "Deeply Supervised Salient Object Detection with Short Connections" in CVPR2017. Source code won't be pubulished because I did some modification in network structure, but trained model and inference code are available. Now it's only version 1, try it if u are interested!
https://github.com/Joker316701882/Salience-Object-Detection