
'''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)
					lowercase    :							Optional[int]      =					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')

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

					lowercase    :							Optional[int]      =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

					lowercase    :							Optional[int]      =					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

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









import cva
import numpy as np
class     a    :



      """simple docstring"""


      def __init__(      self					:			Optional[Any]		,      __lowercase					:			float		,      __lowercase					:			int					)							->      List[Any]:

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      tuple[cva.Mat, list[list[int]]]:
        __UpperCAmelCase							:	Optional[Any]           =  cva.imread(__lowercase		,      0					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  img.shape
        __UpperCAmelCase							:	list[list[int]]           =  []
        __UpperCAmelCase							:	Any           =  img.copy()
        __UpperCAmelCase							:	Tuple           =  cva.cvtColor(__lowercase		,      cva.COLOR_GRAY2RGB					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  np.gradient(__lowercase					)
        __UpperCAmelCase							:	int           =  dx**2
        __UpperCAmelCase							:	Any           =  dy**2
        __UpperCAmelCase							:	Any           =  dx * dy
        __UpperCAmelCase							:	Optional[int]           =  0.04
        __UpperCAmelCase							:	List[str]           =  self.window_size // 2
        for y in range(__lowercase		,      h - offset					):
          for x in range(__lowercase		,      w - offset					):
            __UpperCAmelCase							:	Tuple           =  ixx[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	List[Any]           =  iyy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	Optional[int]           =  ixy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	Optional[Any]           =  det - k * (trace**2)
            # Can change the value
            if r > 0.5:
              corner_list.append([x, y, r]					)
              color_img.itemset((y, x, 0)		,      0					)
              color_img.itemset((y, x, 1)		,      0					)
              color_img.itemset((y, x, 2)		,      255					)
        return color_img, corner_list


if __name__ == "__main__":
       a		:       int			     = HarrisCorner(0.04, 3)
       a     ,a		:       Any			     = edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)


