utils.py

import numpy as np
import cv2
import tensorflow as tf
DIV2K_RGB_MEAN = np.array([0.4488, 0.4371, 0.4040]) * 255


def resolve_single(model, lr):
    return resolve(model, tf.expand_dims(lr, axis=0))[0]


def resolve(model, lr_batch):
    lr_batch = tf.cast(lr_batch, tf.float32)
    sr_batch = model(lr_batch)
    sr_batch = tf.clip_by_value(sr_batch, 0, 255)
    sr_batch = tf.round(sr_batch)
    sr_batch = tf.cast(sr_batch, tf.uint8)
    return sr_batch


def evaluate(model, dataset):
    psnr_values = []
    for lr, hr in dataset:
        sr = resolve(model, lr)
        #cv2.imwrite("img.jpg", sr[0])
        psnr_value = psnr(hr, sr)[0]
        psnr_values.append(psnr_value)
    return tf.reduce_mean(psnr_values)


def normalize(x, rgb_mean=DIV2K_RGB_MEAN):
    return (x - rgb_mean) / 127.5


def denormalize(x, rgb_mean=DIV2K_RGB_MEAN):
    return x * 127.5 + rgb_mean


def normalize_01(x):
    """Normalizes RGB images to [0, 1]."""
    return x / 255.0


def normalize_m11(x):
    """Normalizes RGB images to [-1, 1]."""
    return x / 127.5 - 1


def denormalize_m11(x):
    """Inverse of normalize_m11."""
    return (x + 1) * 127.5


def psnr(x1, x2):
    return tf.image.psnr(x1, x2, max_val=255)


def subpixel_conv2d(scale):
    return lambda x: tf.nn.depth_to_space(x, scale)