The existing image generation models cannot synthesize photo-realistic meal images which contextualize the amount of each ingredient used in a recipe.
We tackled the challenge of including contextual information when generating realistic meal images by automatically adjusting the amount of ingredients in the image generation process through latent space interpolation.

• requirements.txt: environment of Python 3.8 & required packages
• environment.yaml: conda environment named ys2

• Download Recipe1M dataset from http://pic2recipe.csail.mit.edu/ and place it inside CS470_HnC/data/Recipe1M/.
  

• CS470_HnC/data/Recipe1M/
    images/
        train/
        val/
        test/
    recipe1M/
        det_ingrs.json
        layer1.json
        layer2.json
  

• run python clean_recipes_with_canonical_ingrs.py to generate ./data/Recipe1M/recipes_withImage.json which contains simplified recipes with images (N=402760).

• CS470_HnC/retrieval_model/train_word2vec.py: Train Word2Vec to Generate models/word2vec_recipes.bin.

• Download UPMC-Food-101 dataset from HERE and place it inside CS470_HnC/retrieval_model/.pretrain_upmc/.
  
• CS470_HnC/retrieval_model/pretrain_upmc/train_upmc.py: Train Image Encoder on UPMC-Food-101 dataset.
• The training process can be viewed HERE.

• CS470_HnC/retrieval_model/run_retrieval.sh: Train Attention-based Retrieval Model.
• The training process can be viewed HERE.

• CS470_HnC/cookgan/run.sh: Train CookGAN on salad.
• The training process can be viewed HERE.

• CS470_HnC/made_a_little_cookgan/run_interpolation.ipynb: Generate Meal Image with Ingredient List & Conduct Appropriate Interpolation.
• CS470_HnC/made_a_little_cookgan/interpolation_example/: Example Interpolation Results. See this.
• The output can be previewed from the run_interpolation.ipynb jupyter notebook. The step-by-step instruction is given in the file itself.