sisppeo.masks.waterdetect package

Submodules

sisppeo.masks.waterdetect.Common module

class sisppeo.masks.waterdetect.Common.DWConfig(config_file=None)

Bases: object

property average_results

property classifier

property clip_band

property clip_inf_value

property clip_sup_value

property clustering_bands

property clustering_method

property detect_water_cluster

get_option(section, key, evaluate)

property linkage

load_config_file(config_file)

property max_clusters

property max_train_size

property min_clusters

property min_positive_pixels

property min_train_size

return_defaults(section, key)

property score_index

property train_size

class sisppeo.masks.waterdetect.Common.DWutils

Bases: object

static calc_normalized_difference(img1, img2, mask=None)

Calc the normalized difference of given arrays (img1 - img2)/(img1 + img2). Updates the mask if any invalid numbers (ex. np.inf or np.nan) are encountered :param img1: first array :param img2: second array :param mask: initial mask, that will be updated :return: nd array filled with -9999 in the mask and the mask itself

static check_path(path_str, is_dir=False)

Check if the path/file exists and returns a Path variable with it :param path_str: path string to test :param is_dir: whether if it is a directory or a file :return: Path type variable

static create_bands_dict(bands_array, bands_order)

static get_train_test_data(data, train_size, min_train_size, max_train_size)

Split the provided data in train-test bunches :param min_train_size: minimum data quantity for train set :param max_train_size: maximum data quantity for train set :param train_size: percentage of the data to be used as train dataset :param data: data to be split :return: train and test datasets

static listify(lst, uniques=[])

sisppeo.masks.waterdetect.Image module

class sisppeo.masks.waterdetect.Image.DWImageClustering(bands, bands_keys, invalid_mask, config)

Bases: object

apply_canny_treshold()

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

apply_clustering()

static apply_naive_bayes(data, clusters_labels, clusters_data)

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

apply_otsu_treshold()

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

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

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

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)

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

find_best_k(data)

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

get_cluster_param(clusters_params, k, param, band)

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

index_of_key(key)

run_detect_water(config=None)

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

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

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

Module contents