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import os
import wandb
from glob import glob
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import utils
def read_image(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_png(image, channels=3)
image.set_shape([None, None, 3])
image = tf.cast(image, dtype=tf.float32) / 255.0
return image
def peak_signal_noise_ratio(y_true, y_pred):
return tf.image.psnr(y_pred, y_true, max_val=255.0)
def plot_results(images, titles, figure_size=(12, 12)):
fig = plt.figure(figsize=figure_size)
for i in range(len(images)):
fig.add_subplot(1, len(images), i + 1).set_title(titles[i])
_ = plt.imshow(images[i])
plt.axis("off")
plt.show()
def closest_number(n, m):
q = int(n / m)
n1 = m * q
if (n * m) > 0:
n2 = m * (q + 1)
else:
n2 = m * (q - 1)
if abs(n - n1) < abs(n - n2):
return n1
return n2
def init_wandb(project_name, experiment_name, wandb_api_key):
if project_name is not None and experiment_name is not None:
os.environ["WANDB_API_KEY"] = wandb_api_key
wandb.init(project=project_name, name=experiment_name, sync_tensorboard=True)
def download_lol_dataset():
utils.get_file(
"lol_dataset.zip",
"https://github.com/soumik12345/enhance-me/releases/download/v0.1/lol_dataset.zip",
cache_dir="./",
cache_subdir="./datasets",
extract=True,
)
low_images = sorted(glob("./datasets/lol_dataset/our485/low/*"))
enhanced_images = sorted(glob("./datasets/lol_dataset/our485/high/*"))
assert len(low_images) == len(enhanced_images)
test_low_images = sorted(glob("./datasets/lol_dataset/eval15/low/*"))
test_enhanced_images = sorted(glob("./datasets/lol_dataset/eval15/high/*"))
assert len(test_low_images) == len(test_enhanced_images)
return (low_images, enhanced_images), (test_low_images, test_enhanced_images)
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