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Pixelwise Segmentation

Pixelwise segmentation for document images

Introduction

This repository contains the source code for training an encoder model for document image segmentation.

Installation

Either clone the repository via git clone https://github.com/qurator-spk/sbb_pixelwise_segmentation.git or download and unpack the ZIP.

Pretrained encoder

Download our pretrained weights and add them to a pretrained_model folder:
https://qurator-data.de/sbb_pixelwise_segmentation/pretrained_encoder/

Helpful tools

Tool to extract 2-D or 3-D RGB images from PAGE-XML data. In the former case, the output will be 1 2-D image array which each class has filled with a pixel value. In the case of a 3-D RGB image, each class will be defined with a RGB value and beside images, a text file of classes will also be produced.

Convert COCO GT or results for a single image to a segmentation map and write it to disk.

Extract region classes and their colours in mask (pseg) images. Allows the color map as free dict parameter, and comes with a default that mimics PageViewer's coloring for quick debugging; it also warns when regions do overlap.

Usage

Train

To train a model, run: python train.py with config_params.json

Ground truth format

Lables for each pixel are identified by a number. So if you have a binary case, n_classes should be set to 2 and labels should be 0 and 1 for each class and pixel.

In the case of multiclass, just set n_classes to the number of classes you have and the try to produce the labels by pixels set from 0 , 1 ,2 .., n_classes-1. The labels format should be png. Our lables are 3 channel png images but only information of first channel is used. If you have an image label with height and width of 10, for a binary case the first channel should look like this:

Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         ...,
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]

This means that you have an image by 10*10*3 and pixel[0,0] belongs to class 1 and pixel[0,1] belongs to class 0.

A small sample of training data for binarization experiment can be found here, Training data sample, which contains images and lables folders.

Training , evaluation and output

The train and evaluation folders should contain subfolders of images and labels.
The output folder should be an empty folder where the output model will be written to.

Parameter configuration

  • patches: If you want to break input images into smaller patches (input size of the model) you need to set this parameter to true. In the case that the model should see the image once, like page extraction, patches should be set to false.
  • n_batch: Number of batches at each iteration.
  • n_classes: Number of classes. In the case of binary classification this should be 2.
  • n_epochs: Number of epochs.
  • input_height: This indicates the height of model's input.
  • input_width: This indicates the width of model's input.
  • weight_decay: Weight decay of l2 regularization of model layers.
  • augmentation: If you want to apply any kind of augmentation this parameter should first set to true.
  • flip_aug: If true, different types of filp will be applied on image. Type of flips is given with "flip_index" in train.py file.
  • blur_aug: If true, different types of blurring will be applied on image. Type of blurrings is given with "blur_k" in train.py file.
  • scaling: If true, scaling will be applied on image. Scale of scaling is given with "scales" in train.py file.
  • rotation_not_90: If true, rotation (not 90 degree) will be applied on image. Rotation angles are given with "thetha" in train.py file.
  • rotation: If true, 90 degree rotation will be applied on image.
  • binarization: If true,Otsu thresholding will be applied to augment the input data with binarized images.
  • scaling_bluring: If true, combination of scaling and blurring will be applied on image.
  • scaling_binarization: If true, combination of scaling and binarization will be applied on image.
  • scaling_flip: If true, combination of scaling and flip will be applied on image.
  • continue_training: If true, it means that you have already trained a model and you would like to continue the training. So it is needed to provide the dir of trained model with "dir_of_start_model" and index for naming the models. For example if you have already trained for 3 epochs then your last index is 2 and if you want to continue from model_1.h5, you can set "index_start" to 3 to start naming model with index 3.
  • weighted_loss: If true, this means that you want to apply weighted categorical_crossentropy as loss fucntion. Be carefull if you set to truethe parameter "is_loss_soft_dice" should be false
  • data_is_provided: If you have already provided the input data you can set this to true. Be sure that the train and eval data are in "dir_output". Since when once we provide training data we resize and augment them and then we write them in sub-directories train and eval in "dir_output".
  • dir_train: This is the directory of "images" and "labels" (dir_train should include two subdirectories with names of images and labels ) for raw images and labels. Namely they are not prepared (not resized and not augmented) yet for training the model. When we run this tool these raw data will be transformed to suitable size needed for the model and they will be written in "dir_output" in train and eval directories. Each of train and eval include "images" and "labels" sub-directories.