ConditionalGAN - sketch2face

This is tensorflow implementation based on pix2pix. I specifically tried to train the model to convert a human face sketch image to a photo-like image.

Need improvements: It works for edges extracted from real photos, but not very well for hand-drawn sketch.

Differences from the paper:

• To avoid the fast convergence of discriminator, updated generator twice for each training step.
  (Borrowed the idea from https://github.com/carpedm20/DCGAN-tensorflow)

Output Example

After 2 epochs training, from randomly chosen test set:

input	output	ground truth

Data

CelebA Used first 99% of them (200,573 images) as a training set and the others (2,026 images) as a test set.

1. Crop and resize images into 256 x 256 (target image)
2. Extract edges from target images to get sketch images using opencv canny (input image)

Usage

To download dataset and start training:

$ python main.py --download_data=True

To train a model with downloaded data:

$ python main.py

To test the model with a canvas UI:

$ python main.py --mode=test

Environment

• python (3.5.3)

• tensorflow-gpu (1.1.0)

• opencv-python (3.2.0)

• google-api-python-client (1.6.2)

• requests (2.13.0)

• tqdm (4.11.2)