Skateboard Decks
3

Skateboard Decks

This dataset contains around 2,000 images of skateboard decks.

The default image size is 128x128, but you can also specify different size during training, e.g with a relativitic GAN:

mantra train relativistic_gan --dataset decks --image-dim 256 256

In the data processing logic, vertical and horizontal flipping have been applied as data augmentation.

Importing

To import this dataset to your project, run:

mantra import RJT1990/data/decks

Usage

Example usage in code:

from data.decks.data import SkateboardDecks d = SkateboardDecks() print(d.X.__class__) ## <class 'numpy.ndarray'> print(d.X.shape) ## (5680, 128, 128, 3)

Data processing
import glob import numpy as np import os from mantraml.data.Dataset import Dataset, cachedata from mantraml.data.ImageDataset import ImageDataset class SkateboardDecks(ImageDataset): # These class variables contain metadata on the Dataset data_name = 'Skateboard Decks' data_tags = ['decks'] files = ['decks.tar.gz'] has_labels = False image_dim = (128, 128) # default - can override with command line @cachedata def X(self): """ This method extracts inputs from the data. The output should be an np.ndarray that can be processed by the model. Returns -------- np.ndarray - of data inputs (X vector) """ images = glob.glob(os.path.join(self.extracted_data_path, '*%s' % self.file_format)) training_data = [] self.unprocessed_images = [] self.image_file_names = [] for image_name in images: image_data = self.get_image(image_name, resize_height=self.image_shape[0], resize_width=self.image_shape[1], crop=True, normalize=self.normalize) if image_data.shape == self.image_shape: training_data.append(image_data) self.image_file_names.append(image_name.split(self.extracted_data_path +'/')[-1]) else: self.unprocessed_images.append((image_name, 'Image shape of extracted image differed from self.image_shape : %s' % image_name)) training_data = np.array(training_data) training_data = np.append(training_data, np.flip(training_data, axis=2), axis=0) training_data = np.append(training_data, np.flipud(training_data), axis=0) return training_data
All files
decks / README.md
31 lines | 733 bytes

Skateboard Decks

This dataset contains around 2,000 images of skateboard decks.

The default image size is 128x128, but you can also specify different size during training, e.g with a relativitic GAN:

mantra train relativistic_gan --dataset decks --image-dim 256 256

In the data processing logic, vertical and horizontal flipping have been applied as data augmentation.

Importing

To import this dataset to your project, run:

mantra import RJT1990/data/decks

Usage

Example usage in code:

from data.decks.data import SkateboardDecks d = SkateboardDecks() print(d.X.__class__) ## <class 'numpy.ndarray'> print(d.X.shape) ## (5680, 128, 128, 3)