A TensorFlow implementation of "Deep Painterly Harmonization".
Requirements:
- Tensorflow (tested with version 1.13)
- OpenCV (if you want to run the post processing you will also need to install the contrib modules)
- scikit-learn
- SciPy
- NumPy
You also need to download the VGG19 model weights from here.
Run deep_painterly_harmonization.py -c content.png -s painting.png -o output.png.
Optional Arguments:
-m: Mask image (a black image with the inserted objects marked in white). If not provided, the program generates one automatically.-a: Hyperparameter regarding how stylized the target painting is. Corresponds to the output of the painting estimator in the paper.--vgg: Path of the the VGG19 weights.--max_size: Maximum height or width of the images. Default is set to 700.--num_cores: Number of processes to use for the post processing.--only: Can be used to run only parts of the image generation pipeline (pass1,pass2,post). For example to skip the second pass, you could run the program with--only pass1 post. When skipping the first pass or only running the post processing, you might want to provide the output of the previous skipped stage with--inter_res.
To see a description of the other arguments, run deep_painterly_harmonization.py -h.
- There were a few cases where the original code of the authors contradicted the statements in their paper. In these cases I tried to follow the code.
- The histogram loss for the second pass is implemented but does not seem to function properly and is deactivated for now.
- The painting estimater to classify art styles is not implemented yet. You can set the style value with the parameter
-a.
- See the original code for the paper.
- This blog post by Sylvain Gugger was also really helpful.
These are the results for a few of the original examples by the authors. You can find all examples in the output/ directory.
To generate these examples, use the script run_examples.py.
