This README will explain how to set-up and run the code for LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on Colour.
- Download LLD-Icons PNG files from: https://data.vision.ee.ethz.ch/sagea/lld/data/LLD-icon_PNG.zip
- Set up a Python 3.5 environment with the packages mentioned below
- Run get_colors.py (change the PATH variable to main directory)
- In your current directory a file 'colors.csv' will be created with 3 columns (name of file, top 3 colors in file, amount of each of the top 3 colors) - Run change_colors_to_words.py (change path variable to main directory)
- A one hot encoding of the colors extracetd previously will be created in the data folder - Change the path of the data and the one-hot-encoding csv on read_images.py - Run main.py
| File | Function | Parameters |
|---|---|---|
| get_colors.py | Uses the logos to find RGB centroids for the KMeans clusters for each image (output:colors.csv) | PATH - of working directory, TRAINING - true in training mode, VERBOSE - wether to print details |
| change_colors_to_words.py | Uses colors.csv to create a one-hot-encoding for the labels (output:one_hot_encoding_color_icon.csv) | PATH - of working directory, TRAINING - true in training mode, VERBOSE - wether to print details |
| acgan.py | The class of the ACGAN | |
| main.py | The main class. Use this to start training the model and tweak the parameters. | gan_type - only ACWGANGP, dataset - only available dataset lld, epoch - number of epochs to train, batch_size - size of batch to train, z_dim - dimension of noise vector, checkpoint_dir-to save checkpoint, result_dir-directory to save results, log_dir-tensorboard log directory |
| ops.py | Defines the layers. | |
| utils.py | Defines useful functions. | |
| read_images.py | Defines two methods, to read images, and labels in one hot encoding format. | Data_PATH, label_csv_PATH, IMAGE_SIZE - size of the images, CATEGORIES - classes, BATCH_SIZE - size of batch, VERBOSE - wether to print details |
| make_confusion_matrix.py | Used to get the confusion matrix to get the results as in paper. |
Python 3.5 tensorflow (1.4+) numpy (1.13.1 and 1.14.1) PIL (pillow) (5.1.0) pandas (0.20.3) webcolors (1.8.1) scikit-learn (0.19.0) matplotlib (2.12.2) cv2 (opencv-python) (3.4.0.12)
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