model/unet.py

import tensorflow as tf
from tensorflow.keras import layers, models

def conv_block(input_tensor, num_filters):
    x = layers.Conv2D(num_filters, (3, 3), padding="same")(input_tensor)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)

    x = layers.Conv2D(num_filters, (3, 3), padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    return x

def encoder_block(input_tensor, num_filters):
    x = conv_block(input_tensor, num_filters)
    p = layers.MaxPooling2D((2, 2))(x)
    return x, p

def decoder_block(input_tensor, concat_tensor, num_filters):
    x = layers.Conv2DTranspose(num_filters, (2, 2), strides=(2, 2), padding="same")(input_tensor)
    x = layers.concatenate([x, concat_tensor])
    x = conv_block(x, num_filters)
    return x

def build_unet(input_shape=(256, 256, 3)):
    """Builds a U-Net architecture for image segmentation."""
    inputs = layers.Input(shape=input_shape)

    # Encoder
    e1, p1 = encoder_block(inputs, 64)
    e2, p2 = encoder_block(p1, 128)
    e3, p3 = encoder_block(p2, 256)
    e4, p4 = encoder_block(p3, 512)

    # Bridge
    b = conv_block(p4, 1024)

    # Decoder
    d1 = decoder_block(b, e4, 512)
    d2 = decoder_block(d1, e3, 256)
    d3 = decoder_block(d2, e2, 128)
    d4 = decoder_block(d3, e1, 64)

    # Output (1 class for Oil Spill vs Background)
    outputs = layers.Conv2D(1, (1, 1), padding="same", activation="sigmoid")(d4)

    model = models.Model(inputs, outputs, name="U-Net")
    return model

if __name__ == "__main__":
    model = build_unet()
    model.summary()