Keras Image Segmentation

Semantic Segmentation easy code for keras users.

We use cityscape dataset for training various models.

Use pretrained VGG16 weight for FCN and U-net! You can download weights offered by keras.

Tested Env

File	Description
train.py	Train various models.
test.py	Predict one picture what you want.
dataest_parser/make_h5.py	Parse cityscape dataset and make h5py file.
dataest_parser/generator.py	Data_generator with augmentation using data.h5
model/	Folder that contains various models for semantic segmentation
segmentation_dh/	Experiment folder for Anthony Kim(useless contents for users)
segmentation_tk/	Experiment folder for TaeKang Woo(useless contents for users)
temp/	Folder that contains various scripts we used(useless contents for users)

We used only three classes in the cityscape dataset for a simple implementation.

Person, Car, and Road.

First, you have to make .h5 file with data!

python3 dataset_parser/make_h5.py --path "/downloaded/leftImg8bit/path/" --gtpath "/downloaded/gtFine/path/"

After you run above command, 'data.h5' file will appear in dataset_parser folder.

Second, Train your model!

python3 train.py --model fcn

Option	Description
--model	Model to train. ['fcn', 'unet', 'pspnet']
--train_batch	Batch size for train.
--val_batch	Batch size for validation.
--lr_init	Initial learning rate.
--lr_decay	How much to decay the learning rate.
--vgg	Pretrained vgg16 weight path.

Finally, test your model!

python3 test.py --model fcn

Option	Description
--model	Model to test. ['fcn', 'unet', 'pspnet']
--img_path	The image path you want to test