






'''simple docstring'''



import math
import sys

import cva
import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						float     )	->      np.ndarray:

   """simple docstring"""
   # For applying gaussian function for each element in matrix.
   __a					  =     math.sqrt(SCREAMING_SNAKE_CASE__     )
   __a					  =     1 / (sigma * math.sqrt(2 * math.pi     ))
   return cons * np.exp(-((img / sigma) ** 2) * 0.5     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						int     )	->      np.ndarray:

   """simple docstring"""
   __a					  =     kernel_size // 2
   return img[x - half : x + half + 1, y - half : y + half + 1]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						float     )	->      np.ndarray:

   """simple docstring"""
   # Creates a gaussian kernel of given dimension.
   __a					  =     np.zeros((kernel_size, kernel_size)     )
   for i in range(0,       SCREAMING_SNAKE_CASE__     ):
      for j in range(0,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     math.sqrt(
             abs(i - kernel_size // 2     ) ** 2 + abs(j - kernel_size // 2     ) ** 2     )
   return vec_gaussian(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						np.ndarray,       SCREAMING_SNAKE_CASE__:						float,       SCREAMING_SNAKE_CASE__:						float,       SCREAMING_SNAKE_CASE__:						int,       )	->      np.ndarray:

   """simple docstring"""
   __a					  =     np.zeros(img.shape     )
   __a					  =     get_gauss_kernel(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a  ,      __a					  =     img.shape
   for i in range(kernel_size // 2,       size_x - kernel_size // 2     ):
      for j in range(kernel_size // 2,       size_y - kernel_size // 2     ):
         __a					  =     get_slice(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
         __a					  =     img_s - img_s[kernel_size // 2, kernel_size // 2]
         __a					  =     vec_gaussian(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
         __a					  =     np.multiply(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
         __a					  =     np.multiply(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
         __a					  =     np.sum(SCREAMING_SNAKE_CASE__     ) / np.sum(SCREAMING_SNAKE_CASE__     )
         __a					  =     val
   return imga



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						list     )	->      tuple:

   """simple docstring"""
   __a					  =     args[1] if args[1:] else '../image_data/lena.jpg'
   __a					  =     float(args[2]     ) if args[2:] else 1.0
   __a					  =     float(args[3]     ) if args[3:] else 1.0
   if args[4:]:
      __a					  =     int(args[4]     )
      __a					  =     kernel_size + abs(kernel_size % 2 - 1     )
   else:
      __a					  =     5
   return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
 __UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase   :							List[str]         =   parse_args(sys.argv)
 __UpperCamelCase   :							str         =   cva.imread(filename, 0)
 cva.imshow("""input image""", img)

 __UpperCamelCase   :							Dict         =   img / 255
 __UpperCamelCase   :							str         =   out.astype("""float32""")
 __UpperCamelCase   :							Union[str, Any]         =   bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 __UpperCamelCase   :							Tuple         =   out * 255
 __UpperCamelCase   :							Union[str, Any]         =   np.uinta(out)
 cva.imshow("""output image""", out)
 cva.waitKey(0)
 cva.destroyAllWindows()







'''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)
 __UpperCamelCase   :							Union[str, 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')

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

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

 __UpperCamelCase   :							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

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