:py:mod:`hypso.classification.WaterDetection.waterdetect.Image`
===============================================================

.. py:module:: hypso.classification.WaterDetection.waterdetect.Image


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

Classes
~~~~~~~

.. autoapisummary::

   hypso.classification.WaterDetection.waterdetect.Image.DWImageClustering




.. py:class:: DWImageClustering(bands, bands_keys, invalid_mask, config, glint_processor=None)

   .. py:property:: product_name


   .. py:method:: get_necessary_bands()

      Check the config file and all the parametrization to return a set of bands needed to run the algorithm


   .. py:method:: check_necessary_bands(bands, bands_keys, invalid_mask)

      Check if the bands_keys combination for the clustering algorithm are available in bands
      and if they all have the same shape
      :param invalid_mask: array mask with the invalid pixels
      :param bands: image bands available
      :param bands_keys: bands combination
      :return: bands and bands_keys


   .. py:method:: bands_to_columns()

      Convert self.bands to a column type matrix where each band is a column
      It follows the order of the keys ordered
      :return: column type matrix


   .. py:method:: apply_cluster(data)

      Apply the cluster algorithm to the data. Number of cluster is in self.best_k
      :param data: data to be clustered
      :return: Vector with the labels


   .. py:method:: find_best_k(data)

      Find the best number of clusters according to an metrics.
      :param data: data to be tested
      :return: number of clusters


   .. py:method:: calc_clusters_params(data, clusters_labels)

      Calculate parameters for each encountered cluster.
      Mean, Variance, Std-dev
      :param data: Clustered data
      :param clusters_labels: Labels for the data
      :return: List with cluster statistics


   .. py:method:: identify_water_cluster()

      Finds the water cluster within all the clusters.
      It can be done using MNDWI, MBWI or Mir2 bands
      :return: water cluster object


   .. py:method:: detect_cluster(param, logic, band1, band2=None)

      Detects a cluster according to a specific metrics
      :param param: Which parameter to search (mean, std-dev, variance,  ...)
      :param logic: Max or Min
      :param band1: The band related to the parameter
      :param band2:
      :return: Cluster object that satisfies the logic


   .. py:method:: supervised_classification(data, train_data, clusters_labels)

      Applies a machine learning supervised classification
      :param data: new data to be classified
      :param train_data: reference data
      :param clusters_labels: labels for the reference data
      :return: labels for the new data


   .. py:method:: apply_svm(data, clusters_labels, clusters_data)
      :staticmethod:

      Apply Support Vector Machine to classify data
      :param data: new data to be classified
      :param clusters_labels: labels for the reference data
      :param clusters_data: reference data
      :return: labels for the new data


   .. py:method:: apply_naive_bayes(data, clusters_labels, clusters_data)
      :staticmethod:

      Apply Naive Bayes classifier to classify data
      :param data: new data to be classified
      :param clusters_labels: labels for the reference data
      :param clusters_data: reference data
      :return: labels for the new data


   .. py:method:: apply_mlp(data, clusters_labels, clusters_data)
      :staticmethod:

      Apply Multilayer Perceptron classifier to classify data
      :param data: new data to be classified
      :param clusters_labels: labels for the reference data
      :param clusters_data: reference data
      :return: labels for the new data


   .. py:method:: get_cluster_param(clusters_params, k, param, band)


   .. py:method:: verify_cluster(clusters_params, k)


   .. py:method:: create_matrice_cluster(indices_array)

      Recreates the matrix with the original shape with the cluster labels for each pixel
      :param indices_array: position of the clustered pixels in the matrix
      :return: clustered image (0-no data, 1-water, 2, 3, ... - other)


   .. py:method:: split_data_by_bands(data, selected_keys)

      Gets data in column format (each band is a column) and returns only the desired bands
      :param data: data in column format
      :param selected_keys: bands keys to be extracted
      :return: data in column format only with the selected bands


   .. py:method:: create_product_name()


   .. py:method:: apply_canny_treshold()


   .. py:method:: apply_otsu_treshold()


   .. py:method:: run_detect_water(config=None)

      Runs the detect_water function
      :param config: Options dictionary for the processing
      :return: clustered matrix where 1= water


   .. py:method:: index_of_key(key)


   .. py:method:: separate_high_low_mndwi()


   .. py:method:: apply_clustering()