image-segmentation-keras-py3

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image-segmentation-keras-py3

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Implementation of Segnet, FCN, UNet and other models in Keras. (Python 3)

image-segmentation-keras-py3 최신버젼 다운로드

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# This repository is python 3 version of https://github.com/divamgupta/image-segmentation-keras

## Before you start:

Modify your keras.json file as following:

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

# Image Segmentation Keras : Implementation of Segnet, FCN, UNet and other models in 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

```shell
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

*  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 :

```python
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.

```shell
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

```shell
mkdir data
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:

```shell
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

```shell
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" 
```

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