def test_load_image(tmpdir):
file_path = tmpdir.join("Image.jpg")
img = Image.new("RGB", (30, 30))
img.save(file_path.strpath)
img = utils.load_image("Image.jpg", file_path.dirname)
assert (isinstance(img, np.ndarray))
assert (img.shape == (32, 24, 3))
def test_load_image(tmpdir):
d = TemporaryDirectory()
img = Image.new("RGB", (30, 30))
img.save(d.name + "/Image.jpg")
img = utils.load_image("Image.jpg", d.name)
assert (isinstance(img, np.ndarray))
assert (img.shape == (32, 24, 3))
def load_pair(fname):
f = utils.load_image(fname + '_firefox.png')
print(f.shape)
c = utils.load_image(fname + '_chrome.png')
print(c.shape)
return [f, c]
import argparse
import random
from autowebcompat import network, utils
parser = argparse.ArgumentParser()
parser.add_argument('network', type=str, choices=network.SUPPORTED_NETWORKS, help='Select the network to use for training')
parser.add_argument('optimizer', type=str, choices=network.SUPPORTED_OPTIMIZERS, help='Select the optimizer to use for training')
args = parser.parse_args()
labels = utils.read_labels()
utils.prepare_images()
all_image_names = [i for i in utils.get_images() if i in labels]
all_images = sum([[i + '_firefox.png', i + '_chrome.png'] for i in all_image_names], [])
image = utils.load_image(all_images[0])
input_shape = image.shape
BATCH_SIZE = 32
EPOCHS = 50
def load_pair(fname):
f = utils.load_image(fname + '_firefox.png')
print(f.shape)
c = utils.load_image(fname + '_chrome.png')
print(c.shape)
return [f, c]
images_train = random.sample(all_image_names, int(len(all_image_names) * 0.9))
images_test = [i for i in all_image_names if i not in set(images_train)]
import random
from autowebcompat import network
from autowebcompat import utils
labels = utils.read_labels()
utils.prepare_images()
all_images = utils.get_images()
image = utils.load_image(all_images[0] + '_firefox.png')
input_shape = image.shape
BATCH_SIZE = 32
EPOCHS = 50
def load_pair(fname):
f = utils.load_image(fname + '_firefox.png')
print(f.shape)
c = utils.load_image(fname + '_chrome.png')
print(c.shape)
return [f, c]
images_train = random.sample(all_images, int(len(all_images) * 0.9))
images_test = [i for i in all_images if i not in set(images_train)]
def couples_generator(images):
for i in images:
yield load_pair(i), labels[i]
