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]