:py:mod:`HAlphaAnomalyzer._cell_average_calculator`
===================================================

.. py:module:: HAlphaAnomalyzer._cell_average_calculator


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   HAlphaAnomalyzer._cell_average_calculator._calculate_cell_average_per_image
   HAlphaAnomalyzer._cell_average_calculator._calculate_cell_average_per_batch
   HAlphaAnomalyzer._cell_average_calculator._calculate_cell_average



.. py:function:: _calculate_cell_average_per_image(image_path, label, grid_size=8)

   Calculate the average pixel value for each cell in a grid for a given 
   image.

   This function reads the image from the specified path in grayscale, 
   divides it into a grid of the given size, and computes the average pixel 
   value for each cell.

   Parameters
   ----------
   image_path : str
       The path to the image file.
   label : int
       The label indicating if the image is anomalous (1) or non-anomalous 
       (0).
   grid_size : int, optional
       The number of rows and columns to divide the image into, by default 8.

   Returns
   -------
   image_data : List[List[str, int, int, float, int]]
       A list containing the calculated average pixel values for each grid 
       cell in the image.


.. py:function:: _calculate_cell_average_per_batch(image_paths, label, grid_size=8, desc='Processing Training Images')

   Process a batch of training images and compute the average pixel value for each cell in each image.

   This function iterates through a list of image paths, processing each 
   image to calculate the average pixel values for its grid cells.

   Parameters
   ----------
   image_paths : List[str]
       A list of paths to the image files.
   label : int
       The label indicating if the images are anomalous (1) or non-anomalous 
       (0).
   grid_size : int, optional
       The number of rows and columns to divide each image into, by default 8.
   desc : str, optional
       Description for the tqdm progress bar, by default "Processing Training 
       Images".

   Returns
   -------
   result_df : pd.DataFrame
       A DataFrame containing the calculated average pixel values for each 
       grid cell in each training image.


.. py:function:: _calculate_cell_average(non_anomalous_paths=None, anomalous_paths=None, grid_size=8)

   Calculate the average pixel value for each cell in a grid for batches of 
   anomalous and/or non-anomalous training images.

   This function processes batches of anomalous and/or non-anomalous image 
   paths, computing the average pixel values for their grid cells.

   Parameters
   ----------
   non_anomalous_paths : List[str], optional
       A list of paths to non-anomalous image files, by default None.
   anomalous_paths : List[str], optional
       A list of paths to anomalous image files, by default None.
   grid_size : int, optional
       The number of rows and columns to divide each image into, by default 8.

   Returns
   -------
   result_df : pd.DataFrame
       A DataFrame containing the calculated average pixel values for each 
       grid cell in the anomalous and/or non-anomalous training image batches.