forked from rchurchley/IMA-Deep-Learning
/
make_datasets.py
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/
make_datasets.py
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import deepsix
from deepsix import *
from deepsix.download import *
from deepsix.tidy import make_dataset
from deepsix.utils import ensure_directory, alter_images
import IPython
from PIL import Image
from numpy.random import randint
def download(keywords, count):
urls = deepsix.download.flickr.urls_tagged(
keywords,
api_key='Flickr_API_key.txt')
deepsix.download.get_images_from_urls(
urls,
max_count=count,
output_directory='images/flickr-raw')
def create(number):
ensure_directory('images/black')
for i in range(1, number + 1):
Im = Image.new('L', [64, 64], color=0)
Im.save('images/black/{}.bmp'.format(i), 'BMP')
def create_colours(number):
ensure_directory('images/solid')
for i in range(1, number + 1):
random = (randint(255), randint(255), randint(255))
Im = Image.new('RGB', [64, 64], color=random)
Im.save('images/solid/{}.bmp'.format(i), 'BMP')
def resize(input_directory, output_directory):
alter_images(
deepsix.anomalies.resize,
args=64,
input_directory=input_directory,
output_directory=output_directory)
def alter(input_directory, output_directory):
alter_images(
procedure=deepsix.anomalies.add_rectangle,
args=16,
input_directory=input_directory,
output_directory=output_directory,
output_format='BMP'
)
if __name__ == '__main__':
create(5000)
alter('images/black', 'images/black+rect')
make_dataset('images/black', 'images/black+rect', 'data/black+rect')
create_colours(5000)
alter('images/solid', 'images/solid+rect')
make_dataset('images/solid', 'images/solid+rect', 'data/solid+rect')
download('nikon', 1000)
resize('images/flickr-raw', 'images/flickr')
alter('images/flickr', 'images/flickr+rect')
make_dataset('images/flickr', 'images/flickr+rect', 'data/flickr+rect')