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########################################################################
#
# Functions for downloading the Knifey-Spoony data-set from the internet
# and loading it into memory. Note that this only loads the file-names
# for the images in the data-set and does not load the actual images.
#
# Implemented in Python 3.5
#
########################################################################
#
# This file is part of the TensorFlow Tutorials available at:
#
# https://github.com/Hvass-Labs/TensorFlow-Tutorials
#
# Published under the MIT License. See the file LICENSE for details.
#
# Copyright 2016 by Magnus Erik Hvass Pedersen
#
########################################################################
from dataset import load_cached
import download
import os
########################################################################
# Directory where you want to download and save the data-set.
# Set this before you start calling any of the functions below.
data_dir = "data/knifey-spoony/"
# Directory for the training-set after copying the files using copy_files().
train_dir = os.path.join(data_dir, "train/")
# Directory for the test-set after copying the files using copy_files().
test_dir = os.path.join(data_dir, "test/")
# URL for the data-set on the internet.
data_url = "https://github.com/Hvass-Labs/knifey-spoony/raw/master/knifey-spoony.tar.gz"
########################################################################
# Various constants for the size of the images.
# Use these constants in your own program.
# Width and height of each image.
img_size = 200
# Number of channels in each image, 3 channels: Red, Green, Blue.
num_channels = 3
# Shape of the numpy-array for an image.
img_shape = [img_size, img_size, num_channels]
# Length of an image when flattened to a 1-dim array.
img_size_flat = img_size * img_size * num_channels
# Number of classes.
num_classes = 3
########################################################################
# Public functions that you may call to download the data-set from
# the internet and load the data into memory.
def maybe_download_and_extract():
"""
Download and extract the Knifey-Spoony data-set if it doesn't already exist
in data_dir (set this variable first to the desired directory).
"""
download.maybe_download_and_extract(url=data_url, download_dir=data_dir)
def load():
"""
Load the Knifey-Spoony data-set into memory.
This uses a cache-file which is reloaded if it already exists,
otherwise the Knifey-Spoony data-set is created and saved to
the cache-file. The reason for using a cache-file is that it
ensure the files are ordered consistently each time the data-set
is loaded. This is important when the data-set is used in
combination with Transfer Learning as is done in Tutorial #09.
:return:
A DataSet-object for the Knifey-Spoony data-set.
"""
# Path for the cache-file.
cache_path = os.path.join(data_dir, "knifey-spoony.pkl")
# If the DataSet-object already exists in a cache-file
# then load it, otherwise create a new object and save
# it to the cache-file so it can be loaded the next time.
dataset = load_cached(cache_path=cache_path,
in_dir=data_dir)
return dataset
def copy_files():
"""
Copy all the files in the training-set to train_dir
and copy all the files in the test-set to test_dir.
This creates the directories if they don't already exist,
and it overwrites the images if they already exist.
The images are originally stored in a directory-structure
that is incompatible with e.g. the Keras API. This function
copies the files to a dir-structure that works with e.g. Keras.
"""
# Load the Knifey-Spoony dataset.
# This is very fast as it only gathers lists of the files
# and does not actually load the images into memory.
dataset = load()
# Copy the files to separate training- and test-dirs.
dataset.copy_files(train_dir=train_dir, test_dir=test_dir)
########################################################################
if __name__ == '__main__':
# Download and extract the data-set if it doesn't already exist.
maybe_download_and_extract()
# Load the data-set.
dataset = load()
# Get the file-paths for the images and their associated class-numbers
# and class-labels. This is for the training-set.
image_paths_train, cls_train, labels_train = dataset.get_training_set()
# Get the file-paths for the images and their associated class-numbers
# and class-labels. This is for the test-set.
image_paths_test, cls_test, labels_test = dataset.get_test_set()
# Check if the training-set looks OK.
# Print some of the file-paths for the training-set.
for path in image_paths_train[0:5]:
print(path)
# Print the associated class-numbers.
print(cls_train[0:5])
# Print the class-numbers as one-hot encoded arrays.
print(labels_train[0:5])
# Check if the test-set looks OK.
# Print some of the file-paths for the test-set.
for path in image_paths_test[0:5]:
print(path)
# Print the associated class-numbers.
print(cls_test[0:5])
# Print the class-numbers as one-hot encoded arrays.
print(labels_test[0:5])
########################################################################