





import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
					# Initialising the CNN
					# (Sequential- Building the model layer by layer)
					UpperCAmelCase_      							=     models.Sequential()

					# Step 1 - Convolution
					# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
					# (3,3) is the kernel size (filter matrix)
					classifier.add(
					    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
					)

					# Step 2 - Pooling
					classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

					# Adding a second convolutional layer
					classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
					classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

					# Step 3 - Flattening
					classifier.add(layers.Flatten())

					# Step 4 - Full connection
					classifier.add(layers.Dense(units=128, activation="""relu"""))
					classifier.add(layers.Dense(units=1, activation="""sigmoid"""))

					# Compiling the CNN
					classifier.compile(
					    optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
					)

					# Part 2 - Fitting the CNN to the images

					# Load Trained model weights

					# from keras.models import load_model
					# regressor=load_model('cnn.h5')

					UpperCAmelCase_      							=     tf.keras.preprocessing.image.ImageDataGenerator(
					    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
					)

					UpperCAmelCase_      							=     tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

					UpperCAmelCase_      							=     train_datagen.flow_from_directory(
					    """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
					)

					UpperCAmelCase_      							=     test_datagen.flow_from_directory(
					    """dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
					)

					classifier.fit_generator(
					    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
					)

					classifier.save("""cnn.h5""")

					# Part 3 - Making new predictions

					UpperCAmelCase_      							=     tf.keras.preprocessing.image.load_img(
					    """dataset/single_prediction/image.png""", target_size=(64, 64)
					)
					UpperCAmelCase_      							=     tf.keras.preprocessing.image.img_to_array(test_image)
					UpperCAmelCase_      							=     np.expand_dims(test_image, axis=0)
					UpperCAmelCase_      							=     classifier.predict(test_image)
					# training_set.class_indices
					if result[0][0] == 0:
										UpperCAmelCase_      							=     """Normal"""
					if result[0][0] == 1:
										UpperCAmelCase_      							=     """Abnormality detected"""










from cva import destroyAllWindows, imread, imshow, waitKey





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[int]						)			-> Optional[int]:



					'''simple docstring'''




					_snake_case							,     _snake_case									=				img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__						):
										for j in range(UpperCamelCase__						):
															_snake_case									=				[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_      							=     imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_      							=     convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



