sharpedge.pooling_image
=======================

.. py:module:: sharpedge.pooling_image


Functions
---------

.. autoapisummary::

   sharpedge.pooling_image.pooling_image


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

.. py:function:: pooling_image(img, window_size, pooling_method=np.mean)

   Perform pooling on an image using a specified window size and pooling function.

   :param img: The input image as a 2D numpy array (grayscale) or 3D numpy array (RGB).
   :type img: numpy.ndarray
   :param window_size: The size of the pooling window (e.g., 10 for 10x10 windows).
   :type window_size: int
   :param pooling_method: The pooling function to apply to each window. Common options include
                          `numpy.mean`, `numpy.median`, `numpy.max`, and `numpy.min`. Default is `numpy.mean`.
   :type pooling_method: callable, optional

   :returns: The resized image, reduced by the pooling operation based on the specified
             window size and pooling function. For grayscale images, the result is a 2D array.
             For RGB images, the result is a 3D array normalized to the range [0.0, 1.0].
   :rtype: numpy.ndarray

   :raises TypeError: If `window_size` is not an integer or `pooling_method` is not callable.
   :raises ValueError: If the image dimensions are not divisible by the window size.

   .. rubric:: Examples

   >>> img = np.random.rand(100, 100)
   >>> pooled_img = pooling_image(img, window_size=10, pooling_method=np.mean)

   For an RGB image:
   >>> img_rgb = np.random.rand(100, 100, 3)
   >>> pooled_img = pooling_image(img_rgb, window_size=20, pooling_method=np.max)