
'''simple docstring'''




import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt





class  A				:





     def __init__(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   str					=						''''''
         A       :   str					=						''''''
         A       :   Tuple					=						[]
         A       :   int					=						0
         A       :   Any					=						256
         A       :   Union[str, Any]					=						0
         A       :   Optional[Any]					=						0
         A       :   Optional[int]					=						0
         A       :   Optional[int]					=						0





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[str]					=						cva.imread(SCREAMING_SNAKE_CASE						,			0	)
         A       :   Optional[int]					=						copy.deepcopy(self.img	)
         A,  A,  A       :   List[str]					=						plt.hist(self.img.ravel()						,			256						,			[0, 256]						,			label='''x'''	)
         A       :   Union[str, Any]					=						np.sum(SCREAMING_SNAKE_CASE	)
         for i in range(len(SCREAMING_SNAKE_CASE	)	):
             A       :   str					=						x[i] / self.k
             self.sk += prk
             A       :   List[str]					=						(self.L - 1) * self.sk
             if self.rem != 0:
                 A       :   Optional[Any]					=						int(last % last	)
             A       :   Any					=						int(last + 1 if self.rem >= 0.5 else last	)
             self.last_list.append(SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						int(np.ma.count(self.img	) / self.img[1].size	)
             A       :   Union[str, Any]					=						self.img[1].size
         for i in range(self.number_of_cols	):
             for j in range(self.number_of_rows	):
                 A       :   int					=						self.img[j][i]
                 if num != self.last_list[num]:
                     A       :   List[Any]					=						self.last_list[num]
         cva.imwrite('''output_data/output.jpg'''						,			self.img	)





     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         plt.hist(self.img.ravel()						,			256						,			[0, 256]	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         cva.imshow('''Output-Image'''						,			self.img	)
         cva.imshow('''Input-Image'''						,			self.original_image	)
         cva.waitKey(5000	)
         cva.destroyAllWindows()


if __name__ == "__main__":
      lowercase			:				Union[str, Any]    		=			os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
      lowercase			:				int    		=			ConstantStretch()
      stretcher.stretch(file_path)
      stretcher.plot_histogram()
      stretcher.show_image()








'''simple docstring'''

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)
 lowerCamelCase							:   Any  						=	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')

 lowerCamelCase							:   Optional[Any]  						=	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 lowerCamelCase							:   List[str]  						=	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

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




