#https://youtu.be/csFGTLT6_WQ # u-net model import os import segmentation_models as sm from keras.layers import ( BatchNormalization, Conv2D, Conv2DTranspose, Dropout, Input, MaxPooling2D, concatenate, ) from keras.models import Model os.environ["SM_FRAMEWORK"] = "tf.keras" os.environ["_TF_KERAS_FRAMEWORK_NAME"] = "tf.keras" sm.set_framework('tf.keras') ################################################################ def SpecSeg(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS): #Build the model inputs = Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)) s = inputs #Contraction path c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(s) c1 = Dropout(0.1)(c1) c1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c1) c1 = BatchNormalization(axis=-1)(c1) p1 = MaxPooling2D((2, 2))(c1) c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p1) c2 = Dropout(0.1)(c2) c2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c2) c2 = BatchNormalization(axis=-1)(c2) p2 = MaxPooling2D((2, 2))(c2) c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p2) c3 = Dropout(0.2)(c3) c3 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c3) c3 = BatchNormalization(axis=-1)(c3) p3 = MaxPooling2D((2, 2))(c3) c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p3) c4 = Dropout(0.2)(c4) c4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c4) c4 = BatchNormalization(axis=-1)(c4) p4 = MaxPooling2D((2, 2))(c4) c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(p4) c5 = Dropout(0.3)(c5) c5 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c5) c5 = BatchNormalization(axis=-1)(c5) #Expansive path u6 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5) u6 = concatenate([u6, c4]) c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u6) c6 = Dropout(0.2)(c6) c6 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c6) u7 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6) u7 = concatenate([u7, c3]) c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u7) c7 = Dropout(0.2)(c7) c7 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c7) u8 = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7) u8 = concatenate([u8, c2]) c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u8) c8 = Dropout(0.1)(c8) c8 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c8) u9 = Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8) u9 = concatenate([u9, c1], axis=3) c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(u9) c9 = Dropout(0.1)(c9) c9 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='RandomNormal', padding='same')(c9) outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9) model = Model(inputs=[inputs], outputs=[outputs], name = 'SpecSeg') # model.summary() return model