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app.py

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+import os
+import cv2
+import random
+import numpy as np
+from glob import glob
+from PIL import Image, ImageOps
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+
+def charbonnier_loss(y_true, y_pred):
+    return tf.reduce_mean(tf.sqrt(tf.square(y_true - y_pred) + tf.square(1e-3)))
+
+
+def peak_signal_noise_ratio(y_true, y_pred):
+    return tf.image.psnr(y_pred, y_true, max_val=255.0)
+    
+ 
+model = tf.keras.models.load_model('./MIRNet.h5', custom_objects={'charbonnier_loss':                   
+charbonnier_loss, "peak_signal_noise_ratio":peak_signal_noise_ratio})
+
+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 infer(original_image):
+    image = keras.preprocessing.image.img_to_array(original_image)
+    image = image.astype("float32") / 255.0
+    image = np.expand_dims(image, axis=0)
+    output = model.predict(image)
+    output_image = output[0] * 255.0
+    output_image = output_image.clip(0, 255)
+    output_image = output_image.reshape(
+        (np.shape(output_image)[0], np.shape(output_image)[1], 3)
+    )
+    output_image = Image.fromarray(np.uint8(output_image))
+    original_image = Image.fromarray(np.uint8(original_image))
+    return output_image