Image Segmentation

Models

• FCN32
• VGG Segnet
• VGG U-Net

Graphs Models

Getting Started

Prerequisites

• OpenCV or "sudo apt-get install python-opencv"
• Theano
• Keras

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.

Downloading the Pretrained VGG Weights

You need to download the pretrained VGG-16 weights trained on imagenet

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:

python  train.py \
 --save_weights_path=model \
 --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 \
 --epochs=5 \
 --input_height=320 \
 --input_width=640 \
 --model_name="vgg_segnet"

Choose model_name from vgg_segnet vgg_unet, fcn32

Getting the predictions

To get the predictions of a trained model

python  predict.py \
 --save_weights_path=model \
 --test_images="data/dataset1/images_prepped_test/" \
 --output_path="data/predictions/" \
 --n_classes=10 \
 --input_height=320 \
 --input_width=640 \
 --model_name="vgg_segnet"