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Project: Deep-Image-Matting (GitHub Link)

• Deep-Image-Matting-master
  • plot_model.py
  • custom_layers
    • unpooling_layer.py
    • __init__.py
    • scale_layer.py
  • unit_tests.py
  • train_refinement.py
  • segnet.py
  • clean.sh
  • config.py
  • images
  • valid_names.txt
  • LICENSE
  • demo.py
  • model.svg
  • refinement.svg
  • parallel_model.svg
  • train_encoder_decoder.py
  • migrate.py
  • data_generator.py
  • predit_single.py
  • model.py
  • Combined_Dataset
    • Test_set
      • Composition_code_revised.py
    • Training_set
      • Composition_code_revised.py
  • test_alphamatting.py
  • train.py
  • README.md
  • utils.py
  • history
  • test.py
  • .gitignore
  • vgg16.py
  • train_final.py
  • pre_process.py

Just in case you are interested, Deep Image Matting v2 is an upgraded version of this.

Deep Image Matting

This repository is to reproduce Deep Image Matting.

Dependencies

• NumPy
• Tensorflow 1.9.0
• Keras 2.1.6
• OpenCV

Dataset

Adobe Deep Image Matting Dataset

Follow the instruction to contact author for the dataset.

MSCOCO

Go to MSCOCO to download:

• 2014 Train images

PASCAL VOC

Go to PASCAL VOC to download:

• VOC challenge 2008 training/validation data
• The test data for the VOC2008 challenge

ImageNet Pretrained Models

Download VGG16 into "models" folder.

Usage

Data Pre-processing

Extract training images:

$ python pre_process.py

Train

$ python train.py

If you want to visualize during training, run in your terminal:

$ tensorboard --logdir path_to_current_dir/logs

Demo

Download pre-trained Deep Image Matting Model to "models" folder then run:

$ python demo.py

Image/Trimap	Output/GT	New BG/Compose