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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+2.bmp filter=lfs diff=lfs merge=lfs -text
+2cleaned.bmp filter=lfs diff=lfs merge=lfs -text
+binarize.bmp filter=lfs diff=lfs merge=lfs -text

models.py

@@ -0,0 +1,113 @@
+
+from tensorflow import keras
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from tensorflow.keras import regularizers
+from tensorflow.keras import metrics
+import scipy.misc
+import os
+import numpy as np
+from tensorflow.keras.models import *
+from tensorflow.keras.layers import *
+from tensorflow.keras.optimizers import *
+
+
+def get_optimizer():
+    return Adam(lr=1e-4)
+
+def generator_model(pretrained_weights = None,input_size = (256,256,1),biggest_layer = 512):
+    
+    inputs = Input(input_size)
+    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
+    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
+    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
+    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
+    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
+    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
+    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
+    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
+    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
+    conv4 = Conv2D(biggest_layer//2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
+    conv4 = Conv2D(biggest_layer//2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
+    drop4 = Dropout(0.5)(conv4)
+    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
+
+    conv5 = Conv2D(biggest_layer, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
+    conv5 = Conv2D(biggest_layer, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
+    drop5 = Dropout(0.5)(conv5)
+
+    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
+
+    merge6 = concatenate ([drop4,up6])
+    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
+    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
+
+    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
+    merge7 = concatenate ([conv3,up7])
+
+    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
+    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
+
+    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
+    merge8 = concatenate ([conv2,up8])
+
+    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
+    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
+
+    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
+
+    merge9 = concatenate ([conv1,up9])  
+    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
+    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
+    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
+    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
+
+    model = Model(inputs = inputs, outputs = conv10)
+    
+    return model
+
+
+
+def discriminator_model(input_size = (256,256,1)):
+
+    def d_layer(layer_input, filters, f_size=4, bn=True):
+
+        d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
+        d = LeakyReLU(alpha=0.2)(d)
+        if bn:
+            d = BatchNormalization(momentum=0.8)(d)
+        return d
+
+    img_A = Input(input_size)
+    img_B = Input(input_size)
+
+    df=64
+
+    combined_imgs = Concatenate(axis=-1)([img_A, img_B])
+
+    d1 = d_layer(combined_imgs, df, bn=False)
+    d2 = d_layer(d1, df*2)
+    d3 = d_layer(d2, df*4)
+    d4 = d_layer(d3, df*4)
+    
+    validity = Conv2D(1, kernel_size=4, strides=1, padding='same', activation='sigmoid')(d4)
+
+    discriminator = Model([img_A, img_B], validity)
+    discriminator.compile(loss='mse', optimizer=get_optimizer(), metrics = ['accuracy'])
+    
+    return discriminator
+
+
+
+def get_gan_network(discriminator, generator, input_size = (256,256,1)):
+    discriminator.trainable = False
+    
+    gan_input2 = Input(input_size)
+    
+    x = generator(gan_input2)
+    valid = discriminator([x,gan_input2])
+    gan = Model(inputs=[gan_input2], outputs=[valid,x])
+    gan.compile(loss=['mse','binary_crossentropy'],loss_weights=[1, 100], optimizer=get_optimizer(),metrics = ['accuracy'])
+    return gan
+