Create model.h5

model.h5

@@ -1,3 +1,331 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7e0bd457aa184c3a8fde411375b292e4f7776aaf7cfc9c29661f577309be451c
-size 441222364
+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
+
+from google.colab import drive
+drive.mount('/content/gdrive')
+
+
+random.seed(10)
+
+IMAGE_SIZE = 128
+BATCH_SIZE = 4
+MAX_TRAIN_IMAGES = 300
+
+
+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 random_crop(low_image, enhanced_image):
+    low_image_shape = tf.shape(low_image)[:2]
+    low_w = tf.random.uniform(
+        shape=(), maxval=low_image_shape[1] - IMAGE_SIZE + 1, dtype=tf.int32
+    )
+    low_h = tf.random.uniform(
+        shape=(), maxval=low_image_shape[0] - IMAGE_SIZE + 1, dtype=tf.int32
+    )
+    enhanced_w = low_w
+    enhanced_h = low_h
+    low_image_cropped = low_image[
+        low_h : low_h + IMAGE_SIZE, low_w : low_w + IMAGE_SIZE
+    ]
+    enhanced_image_cropped = enhanced_image[
+        enhanced_h : enhanced_h + IMAGE_SIZE, enhanced_w : enhanced_w + IMAGE_SIZE
+    ]
+    return low_image_cropped, enhanced_image_cropped
+
+
+def load_data(low_light_image_path, enhanced_image_path):
+    low_light_image = read_image(low_light_image_path)
+    enhanced_image = read_image(enhanced_image_path)
+    low_light_image, enhanced_image = random_crop(low_light_image, enhanced_image)
+    return low_light_image, enhanced_image
+
+
+def get_dataset(low_light_images, enhanced_images):
+    dataset = tf.data.Dataset.from_tensor_slices((low_light_images, enhanced_images))
+
+    dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
+
+    dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
+    return dataset
+
+
+train_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/low/*"))[:MAX_TRAIN_IMAGES]
+train_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/high/*"))[:MAX_TRAIN_IMAGES]
+
+val_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/low/*"))[MAX_TRAIN_IMAGES:]
+val_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/our485/high/*"))[MAX_TRAIN_IMAGES:]
+
+test_low_light_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/eval15/low/*"))
+test_enhanced_images = sorted(glob("/content/gdrive/MyDrive/dataset/lol_dataset/eval15/high/*"))
+
+
+train_dataset = get_dataset(train_low_light_images, train_enhanced_images)
+val_dataset = get_dataset(val_low_light_images, val_enhanced_images)
+
+
+print("Train Dataset:", train_dataset)
+print("Val Dataset:", val_dataset)
+
+
+def selective_kernel_feature_fusion(
+    multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3
+):
+    channels = list(multi_scale_feature_1.shape)[-1]
+    combined_feature = layers.Add()(
+        [multi_scale_feature_1, multi_scale_feature_2, multi_scale_feature_3]
+    )
+    gap = layers.GlobalAveragePooling2D()(combined_feature)
+    channel_wise_statistics = tf.reshape(gap, shape=(-1, 1, 1, channels))
+    compact_feature_representation = layers.Conv2D(
+        filters=channels // 8, kernel_size=(1, 1), activation="relu"
+    )(channel_wise_statistics)
+    feature_descriptor_1 = layers.Conv2D(
+        channels, kernel_size=(1, 1), activation="softmax"
+    )(compact_feature_representation)
+    feature_descriptor_2 = layers.Conv2D(
+        channels, kernel_size=(1, 1), activation="softmax"
+    )(compact_feature_representation)
+    feature_descriptor_3 = layers.Conv2D(
+        channels, kernel_size=(1, 1), activation="softmax"
+    )(compact_feature_representation)
+    feature_1 = multi_scale_feature_1 * feature_descriptor_1
+    feature_2 = multi_scale_feature_2 * feature_descriptor_2
+    feature_3 = multi_scale_feature_3 * feature_descriptor_3
+    aggregated_feature = layers.Add()([feature_1, feature_2, feature_3])
+    return aggregated_feature
+
+
+
+
+def spatial_attention_block(input_tensor):
+    average_pooling = tf.reduce_max(input_tensor, axis=-1)
+    average_pooling = tf.expand_dims(average_pooling, axis=-1)
+    max_pooling = tf.reduce_mean(input_tensor, axis=-1)
+    max_pooling = tf.expand_dims(max_pooling, axis=-1)
+    concatenated = layers.Concatenate(axis=-1)([average_pooling, max_pooling])
+    feature_map = layers.Conv2D(1, kernel_size=(1, 1))(concatenated)
+    feature_map = tf.nn.sigmoid(feature_map)
+    return input_tensor * feature_map
+
+
+def channel_attention_block(input_tensor):
+    channels = list(input_tensor.shape)[-1]
+    average_pooling = layers.GlobalAveragePooling2D()(input_tensor)
+    feature_descriptor = tf.reshape(average_pooling, shape=(-1, 1, 1, channels))
+    feature_activations = layers.Conv2D(
+        filters=channels // 8, kernel_size=(1, 1), activation="relu"
+    )(feature_descriptor)
+    feature_activations = layers.Conv2D(
+        filters=channels, kernel_size=(1, 1), activation="sigmoid"
+    )(feature_activations)
+    return input_tensor * feature_activations
+
+
+def dual_attention_unit_block(input_tensor):
+    channels = list(input_tensor.shape)[-1]
+    feature_map = layers.Conv2D(
+        channels, kernel_size=(3, 3), padding="same", activation="relu"
+    )(input_tensor)
+    feature_map = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(
+        feature_map
+    )
+    channel_attention = channel_attention_block(feature_map)
+    spatial_attention = spatial_attention_block(feature_map)
+    concatenation = layers.Concatenate(axis=-1)([channel_attention, spatial_attention])
+    concatenation = layers.Conv2D(channels, kernel_size=(1, 1))(concatenation)
+    return layers.Add()([input_tensor, concatenation])
+
+
+# Recursive Residual Modules
+
+
+def down_sampling_module(input_tensor):
+    channels = list(input_tensor.shape)[-1]
+    main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation="relu")(
+        input_tensor
+    )
+    main_branch = layers.Conv2D(
+        channels, kernel_size=(3, 3), padding="same", activation="relu"
+    )(main_branch)
+    main_branch = layers.MaxPooling2D()(main_branch)
+    main_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(main_branch)
+    skip_branch = layers.MaxPooling2D()(input_tensor)
+    skip_branch = layers.Conv2D(channels * 2, kernel_size=(1, 1))(skip_branch)
+    return layers.Add()([skip_branch, main_branch])
+
+
+def up_sampling_module(input_tensor):
+    channels = list(input_tensor.shape)[-1]
+    main_branch = layers.Conv2D(channels, kernel_size=(1, 1), activation="relu")(
+        input_tensor
+    )
+    main_branch = layers.Conv2D(
+        channels, kernel_size=(3, 3), padding="same", activation="relu"
+    )(main_branch)
+    main_branch = layers.UpSampling2D()(main_branch)
+    main_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(main_branch)
+    skip_branch = layers.UpSampling2D()(input_tensor)
+    skip_branch = layers.Conv2D(channels // 2, kernel_size=(1, 1))(skip_branch)
+    return layers.Add()([skip_branch, main_branch])
+
+
+# MRB Block
+def multi_scale_residual_block(input_tensor, channels):
+    # features
+    level1 = input_tensor
+    level2 = down_sampling_module(input_tensor)
+    level3 = down_sampling_module(level2)
+    # DAU
+    level1_dau = dual_attention_unit_block(level1)
+    level2_dau = dual_attention_unit_block(level2)
+    level3_dau = dual_attention_unit_block(level3)
+    # SKFF
+    level1_skff = selective_kernel_feature_fusion(
+        level1_dau,
+        up_sampling_module(level2_dau),
+        up_sampling_module(up_sampling_module(level3_dau)),
+    )
+    level2_skff = selective_kernel_feature_fusion(
+        down_sampling_module(level1_dau), level2_dau, up_sampling_module(level3_dau)
+    )
+    level3_skff = selective_kernel_feature_fusion(
+        down_sampling_module(down_sampling_module(level1_dau)),
+        down_sampling_module(level2_dau),
+        level3_dau,
+    )
+    # DAU 2
+    level1_dau_2 = dual_attention_unit_block(level1_skff)
+    level2_dau_2 = up_sampling_module((dual_attention_unit_block(level2_skff)))
+    level3_dau_2 = up_sampling_module(
+        up_sampling_module(dual_attention_unit_block(level3_skff))
+    )
+    # SKFF 2
+    skff_ = selective_kernel_feature_fusion(level1_dau_2, level2_dau_2, level3_dau_2)
+    conv = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(skff_)
+    return layers.Add()([input_tensor, conv])
+
+
+
+
+def recursive_residual_group(input_tensor, num_mrb, channels):
+    conv1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
+    for _ in range(num_mrb):
+        conv1 = multi_scale_residual_block(conv1, channels)
+    conv2 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(conv1)
+    return layers.Add()([conv2, input_tensor])
+
+
+def mirnet_model(num_rrg, num_mrb, channels):
+    input_tensor = keras.Input(shape=[None, None, 3])
+    x1 = layers.Conv2D(channels, kernel_size=(3, 3), padding="same")(input_tensor)
+    for _ in range(num_rrg):
+        x1 = recursive_residual_group(x1, num_mrb, channels)
+    conv = layers.Conv2D(3, kernel_size=(3, 3), padding="same")(x1)
+    output_tensor = layers.Add()([input_tensor, conv])
+    return keras.Model(input_tensor, output_tensor)
+
+
+model = mirnet_model(num_rrg=3, num_mrb=2, channels=64)
+
+
+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)
+
+
+optimizer = keras.optimizers.Adam(learning_rate=1e-4)
+model.compile(
+    optimizer=optimizer, loss=charbonnier_loss, metrics=[peak_signal_noise_ratio]
+)
+
+history = model.fit(
+    train_dataset,
+    validation_data=val_dataset,
+    #epochs traning cycles set krna k lia
+    epochs=1,
+    callbacks=[
+        keras.callbacks.ReduceLROnPlateau(
+            monitor="val_peak_signal_noise_ratio",
+            factor=0.5,
+            patience=5,
+            verbose=1,
+            min_delta=1e-7,
+            mode="max",
+        )
+    ],
+)
+
+plt.plot(history.history["loss"], label="train_loss")
+plt.plot(history.history["val_loss"], label="val_loss")
+plt.xlabel("Epochs")
+plt.ylabel("Loss")
+plt.title("Train and Validation Losses Over Epochs", fontsize=14)
+plt.legend()
+plt.grid()
+plt.show()
+
+
+plt.plot(history.history["peak_signal_noise_ratio"], label="train_psnr")
+plt.plot(history.history["val_peak_signal_noise_ratio"], label="val_psnr")
+plt.xlabel("Epochs")
+plt.ylabel("PSNR")
+plt.title("Train and Validation PSNR Over Epochs", fontsize=14)
+plt.legend()
+plt.grid()
+plt.show()
+
+
+
+
+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("float16") / 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
+
+
+
+for low_light_image in random.sample(test_low_light_images, 2):
+    original_image = Image.open(low_light_image)
+    enhanced_image = infer(original_image)
+    plot_results(
+        [original_image, ImageOps.autocontrast(original_image), enhanced_image],
+        ["Original", "PIL Autocontrast", "MIRNet Enhanced"],
+        (20, 12),
+    )