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Image Inpainting

Update 06/21/2020: I haven't changed the way the interface is coded or the way the calculations are done, although both are important. The first could be done with PyQt, the second with numba. This update is just to make the project easier to read and to use. But there is definitely room for improvement.

Description

Image inpainting makes it possible to erase elements present in an image and replace them with a plausible background, in particular by reproducing textures when the area to be filled is relatively large and by propagating linear structures such as contours.

This project has been coded in 2018 by Jean Vassoyan and Antoine Moulin - students at Télécom Paris - under the supervision of Alasdair Newson. It is based on the method described in [1].

Reference [1] Criminisi, Antonio and Pérez, Patrick and Toyama, Kentaro. Region Filling and Object Removal by Exemplar-Based Image Inpainting. In IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2004.

Getting started

Step 1: Install GIMP here https://www.gimp.org/downloads/. It is useful for the calls made in useful_functions.py.

Step 2: Clone the repository.

  • git clone https://github.com/moulinantoine/image-inpainting.git
  • cd image-inpainting

Step 3: Install the environment.

  • pip install virtualenv
  • virtualenv inpainting-env
  • source inpainting-env/bin/activate (Linux) or source inpainting-env/Scripts/activate (Windows)
  • pip install -r requirements.txt

Step 4: Launch the General User Interface (GUI). python -m GUI.py.

Use the GUI

There are two GUI files: GUI.py and GUI_for_tests.py. The first one makes it possible to make an inpainting on a single image by drawing a mask on it or loading it from your own directory. However, no data are recorded about this inpainting and you cannot compare statistical performance. The second one makes it possible to record data about an inpainting made on a whole dataset of images. However you cannot draw your own masks with, to work properly, it requires a file named mask.jpg in the folder of each image of the dataset.

In order to use GUI.py:

  1. Click on Browse to search your image
  2. Draw a mask on the image (or load one if it already exists)
  3. Choose the parameters (if the number of clusters equals 1, the algorithm will use a restricted search area)
  4. Choose the frequency of the display (in GIMP)
  5. Click on Start inpainting

Here is an example of how the GUI is (note that instead of loading the mask, one can directly draw on the image):

The GUI for the tests is similar (although it has not been updated, see the first remark). In order to use GUI_for_tests.py:

  1. Click on Browse to search the folder containing your data set
  2. Select the parameter you want to change during the tests, and its range
  3. Select the others parameters
  4. Write a short description of the test
  5. Click on Start the test

The class Files

There are 3 class files: image_inpainting.py, pixel_inpainting.py, test.py.

image_inpainting.py contains the class ImageInpainting and all the methods necessary to make an inpainting.

pixel_inpainting.py contains the class PixelInpainting used to represent a pixel in the image and is used by image_inpainting.py.

test.py contains a class Test that representents a test made on one image. It is used by the GUI_for_tests.py file.

The dataset directory

The dataset directory is used by the GUI_for_tests.py file. It contains a set of folders which are called data1, data2, data3, etc. In each data folder, you can find a file called image.jpg, another one called mask.jpg and a directory called tests which contains python objects from the Test class. It is important to follow this rule about the names of image and mask in data directories. Otherwise the program cannot find them.

The other files

useful_functions.py: it contains two viewimage functions which call GIMP with specific commands. Please check that these commands are adapted to your laptop before running the program.

Inspect_Data.py: it contains a few functions to explore the data generated with the tests made with GUI_for_test.py.

Examples

Here are two examples of inpainting obtained using our GUI:

  • Removing someone from a selfie

  • Removing a stoplight

NB: if you want to do an inpainting without using any optimization method you can just chose method number 1 (clustering on pixels) with a number of clusters equal to 1.