Image completion is a challenging problem because it requires a high-level recognition of scenes. This project tries to achieve object removal from images and get the base image reconstructed based on surrounding colours and objects using conditional GANs.
This project is an implementation of cGANs discussed in the paper for [General Image Completion]
The models are tweaked a little and implemented to remove objects from images and reconstruct the image without the object.
[Esc]: To quit the windowed application.
[f]: To filter out the masked object.
[n]: To go to the next image.
[r]: To refresh and undo all the masking in the current image.
The folder that contains the images to be used in the project. Currently the project requires images of dimensions 400 x 400 which can be changed in the main.py file.
This folder contains the pretrained model that is trained on mscoco dataset and the model definition file which is written in tensorflow.
The main file to run the program. The code runs as an OpenCV windowed application.
The requirements file for the project
To install the dependencies type
sudo pip3 install -r requirements.txt
To run the application type
python3 main.py
This will run the demo as an OpenCV application
The project requires the following packages:
OpenCV and OpenCV_python 3.3.0.10
Tensorflow 1.10.1
Numpy 1.13.3
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent