Image-to-Image Translation Using Conditional Adversarial Networks

night2day	edges2shoes	facades

cityscapes	maps

• pix2pix-cGAN-on-maps-dataset.ipynb(train)
• inference.py

You can download pretrained model from the table below

• In Google Colabo with 12GB RAM

• 5K images of training set and in 40k steps

training:

• For training, you can run pix2pix-cGAN-on-maps-dataset.ipynb file.

1- For inference, first clone this repository using the following command:

https://github.com/NahidEbrahimian/paint-pix2pix-tensorflow

2- In ./paint-pix2pix-tensorflow directory, run the following command to install requirements:

pip install -r requirements.txt

3- Then, according to the images above and model input and output according to the following, select your favorite dataset name that you want to run inference(for example: maps, cityscapes or ...)

model input and output for each dataset in images above:

• for cityscapes,facades, maps --> right image = model input and left image = output

• for edges2shoes, night2day --> right image = model input and left image = output

4- Put your input images in ./input/dataset_name directory(dataset_name refers to favorite dataset name that you selected in previous step)

5- run the following command

dataset_name: favorite dataset name that selected in step2

input: your input image

python3 inference_img.py --input input/maps/01.jpg --dataset_name maps

1- For run Inference using paint and QT on edges2shoes dataset, Ffrst clone this repository using the following command:

git clone https://github.com/NahidEbrahimian/paint-pix2pix-tensorflow

2- In ./paint-pix2pix-tensorflow directory, run the following command to install requirements:

pip install -r requirements.txt

3- Then, run the following command:

python3 qt.py