Image Segmentation Keras : Implementation of Segnet, FCN, UNet and other models in Keras.
Implememnation of various Deep Image Segmentation models in keras.
Our Other Repositories
Models
FCN8
FCN32
Simple Segnet
VGG Segnet
U-Net
VGG U-Net
Getting Started
Prerequisites
Keras 2.0
opencv for python
Theano
sudo apt-get install python-opencv
sudo pip install --upgrade theano
sudo pip install --upgrade keras
Preparing the data for training
You need to make two folders
The filenames of the annotation images should be same as the filenames of the RGB images.
The size of the annotation image for the corresponding RGB image should be same.
For each pixel in the RGB image, the class label of that pixel in the annotation image would be the value of the blue pixel.
Example code to generate annotation images :
import cv2import numpy as npann_img = np.zeros((30,30,3)).astype('uint8')ann_img[ 3 , 4 ] = 1 # this would set the label of pixel 3,4 as 1cv2.imwrite( "ann_1.png" ,ann_img )
Only use bmp or png format for the annotation images.
Download the sample prepared dataset
Download and extract the following:
https://drive.google.com/file/d/0B0d9ZiqAgFkiOHR1NTJhWVJMNEU/view?usp=sharing
Place the dataset1/ folder in data/
Visualizing the prepared data
You can also visualize your prepared annotations for verification of the prepared data.
python visualizeDataset.py
--images="data/dataset1/images_prepped_train/"
--annotations="data/dataset1/annotations_prepped_train/"
--n_classes=10
Downloading the Pretrained VGG Weights
You need to download the pretrained VGG-16 weights trained on imagenet if you want to use VGG based models
mkdir datacd data
wget "https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_th_dim_ordering_th_kernels.h5"
Training the Model
To train the model run the following command:
THEANO_FLAGS=device=gpu,floatX=float32 python train.py
--save_weights_path=weights/ex1
--train_images="data/dataset1/images_prepped_train/"
--train_annotations="data/dataset1/annotations_prepped_train/"
--val_images="data/dataset1/images_prepped_test/"
--val_annotations="data/dataset1/annotations_prepped_test/"
--n_classes=10
--input_height=320
--input_width=640
--model_name="vgg_segnet"
Choose model_name from vgg_segnet vgg_unet, vgg_unet2, fcn8, fcn32
Getting the predictions
To get the predictions of a trained model
THEANO_FLAGS=device=gpu,floatX=float32 python predict.py
--save_weights_path=weights/ex1
--epoch_number=0
--test_images="data/dataset1/images_prepped_test/"
--output_path="data/predictions/"
--n_classes=10
--input_height=320
--input_width=640
--model_name="vgg_segnet"