Image Segmentation Keras

Implememnation of various Deep Image Segmentation models in keras

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 python3-opencv
pip3 install --upgrade theano
pip3 install --upgrade keras

Set Keras to Theano

Find and open keras.json

Should be in home/.keras/
ctrl+h to show hidden dir/files

Change Backend to Theano:

{
    "floatx": "float32",
    "epsilon": 1e-07,
    "backend": "theano",
    "image_data_format": "channels_last"
}

Preparing the data for training

You need to make two folders

Images Folder - For all the training images
Annotations Folder - For the corresponding ground truth segmentation images

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 cv2
import numpy as np

ann_img = np.zeros((30,30,3)).astype('uint8')
ann_img[ 3 , 4 ] = 1 # this would set the label of pixel 3,4 as 1

cv2.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

python3 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

cd 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=cuda,floatX=float32  python3  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=cuda,floatX=float32  python3  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"

c351 / image-segmentation-keras Goto Github PK