






'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase      					=logging.get_logger(__name__)




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    str			  =    ['''pixel_values''']
 def __init__(				self      ,  __lowerCAmelCase = True      ,  __lowerCAmelCase = 32      ,  __lowerCAmelCase=PILImageResampling.BILINEAR      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: Union[str, Any]            =				do_resize
       UpperCamelCase_					: int            =				do_rescale
       UpperCamelCase_					: Optional[Any]            =				size_divisor
       UpperCamelCase_					: int            =				resample
       super().__init__(**__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase			):
       UpperCamelCase_     ,      UpperCamelCase_					: Any            =				get_image_size(__lowerCAmelCase			)
       # Rounds the height and width down to the closest multiple of size_divisor
       UpperCamelCase_					: Union[str, Any]            =				height // size_divisor * size_divisor
       UpperCamelCase_					: int            =				width // size_divisor * size_divisor
       UpperCamelCase_					: Optional[int]            =				resize(__lowerCAmelCase      ,  (new_h, new_w)      ,  resample=__lowerCAmelCase      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase			)
       return image
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  **__lowerCAmelCase			):
       return rescale(image=__lowerCAmelCase      ,  scale=__lowerCAmelCase      ,  data_format=__lowerCAmelCase      ,  **__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = ChannelDimension.FIRST      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: Dict            =				do_resize if do_resize is not None else self.do_resize
       UpperCamelCase_					: str            =				do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase_					: Dict            =				size_divisor if size_divisor is not None else self.size_divisor
       UpperCamelCase_					: str            =				resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
             raise ValueError("""size_divisor is required for resizing"""			)

       UpperCamelCase_					: Any            =				make_list_of_images(__lowerCAmelCase			)

       if not valid_images(__lowerCAmelCase			):
             raise ValueError("""Invalid image(s)"""			)

       # All transformations expect numpy arrays.
       UpperCamelCase_					: Union[str, Any]            =				[to_numpy_array(__lowerCAmelCase			) for img in images]

       if do_resize:
             UpperCamelCase_					: Optional[Any]            =				[self.resize(__lowerCAmelCase      ,  size_divisor=__lowerCAmelCase      ,  resample=__lowerCAmelCase			) for image in images]

       if do_rescale:
             UpperCamelCase_					: int            =				[self.rescale(__lowerCAmelCase      ,  scale=1 / 2_55			) for image in images]

       UpperCamelCase_					: List[Any]            =				[to_channel_dimension_format(__lowerCAmelCase      ,  __lowerCAmelCase			) for image in images]

       UpperCamelCase_					: Union[str, Any]            =				{"""pixel_values""": images}
       return BatchFeature(data=__lowerCAmelCase      ,  tensor_type=__lowerCAmelCase			)














'''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      					=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"






