main.py

import arch
import tensorflow as tf
import data
import numpy as np
import random

from skimage.io import imread, imshow
from skimage.transform import resize
import matplotlib.pyplot as plt
from tqdm import tqdm 

np.random.seed = 42

IMG_WIDTH = 128
IMG_HEIGHT = 128
IMG_CHANNELS = 3

#################################

X_train,Y_train = data.train(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)
X_test = data.test(IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS)


#################################

model = arch.architecture(IMG_HEIGHT,IMG_WIDTH,IMG_CHANNELS)
print(model.summary())

################################

#Modelcheckpoint
checkpointer = tf.keras.callbacks.ModelCheckpoint('model_for_nuclei.h5', verbose=1, save_best_only=True)

callbacks = [
        tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),
        tf.keras.callbacks.TensorBoard(log_dir='logs')]

results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=16, epochs=25, callbacks=callbacks)

################################

idx = random.randint(0, len(X_train))


preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)
preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)
preds_test = model.predict(X_test, verbose=1)

 
preds_train_t = (preds_train > 0.5).astype(np.uint8)
preds_val_t = (preds_val > 0.5).astype(np.uint8)
preds_test_t = (preds_test > 0.5).astype(np.uint8)


################################

# Perform a sanity check on some random training samples
ix = random.randint(0, len(preds_train_t))
imshow(X_train[ix])
plt.show()
imshow(np.squeeze(Y_train[ix]))
plt.show()
imshow(np.squeeze(preds_train_t[ix]))
plt.show()

################################

# Perform a sanity check on some random validation samples
ix = random.randint(0, len(preds_val_t))
imshow(X_train[int(X_train.shape[0]*0.9):][ix])
plt.show()
imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))
plt.show()
imshow(np.squeeze(preds_val_t[ix]))
plt.show()