KMeansRSImage: Added get_random_spectra_from_each_cluster() and _im2spectra().

Former-commit-id: 25aea6ba

gms_preprocessing/algorithms/L2B_P.py

@@ -9,6 +9,7 @@ import scipy as sp
 import matplotlib.pyplot as plt
 from logging import Logger
 from sklearn.cluster import KMeans, k_means_
+from pandas import DataFrame
 
 from geoarray import GeoArray  # noqa F401  # flake8 issue
 
@@ -169,6 +170,7 @@ class SpectralResampler(object):
 class KMeansRSImage(object):
     _clusters = None
     _im_clust = None
+    _spectra = None
 
     def __init__(self, im, n_clusters):
         # type: (GeoArray, int) -> None
@@ -193,21 +195,19 @@ class KMeansRSImage(object):
         return self._im_clust
 
     def compute_clusters(self):
-        # implement like this: https://www.pyimagesearch.com/2014/05/26/opencv-python-k-means-color-clustering/
-        pixels2d = self.im.reshape((self.im.rows * self.im.cols, self.im.bands))
-
         kmeans = KMeans(n_clusters=self.n_clusters, random_state=0)
-        self.clusters = kmeans.fit(pixels2d)
+        self.clusters = kmeans.fit(self._im2spectra(self.im))
 
         return self.clusters
 
     def apply_clusters(self, image):
-        image = GeoArray(image)
-        pixels2d = self.im.reshape((image.rows * image.cols, image.bands))
-
-        labels = self.clusters.predict(pixels2d)
+        labels = self.clusters.predict(self._im2spectra(GeoArray(image)))
         return labels
 
+    @staticmethod
+    def _im2spectra(geoArr):
+        return geoArr.reshape((geoArr.rows * geoArr.cols, geoArr.bands))
+
     def plot_cluster_centers(self, figsize=(15, 5)):
         # type: (tuple) -> None
         """Show a plot of the cluster center signatures.
@@ -263,9 +263,30 @@ class KMeansRSImage(object):
         plt.show()
 
     def get_random_spectra_from_each_cluster(self, samplesize=50):
+        # type: (int) -> dict
         """Returns a given number of spectra randomly selected within each cluster.
 
-        E.g., 50 spectra of belonging to cluster 1, 50 spectra of belonging to cluster 2 and so on."""
-        # TODO
+        E.g., 50 spectra of belonging to cluster 1, 50 spectra of belonging to cluster 2 and so on.
+
+        :param samplesize:  number of spectra to be randomly selected from each cluster
+        :return:
+        """
+
+        # get DataFrame with columns [cluster_label, B1, B2, B3, ...]
+        df = DataFrame(self._im2spectra(self.im), columns=['B%s' % band for band in range(1, self.im.bands + 1)], )
+        df.insert(0, 'cluster_label', self.clusters.labels_)
+
+        # get random sample from each cluster and generate a dict like {cluster_label: random_sample}
+        random_samples = dict()
+        for label in range(self.n_clusters):
+            cluster_subset = df[df.cluster_label == label].loc[:, 'B1':]
+            # get random sample while filling it with duplicates of the same sample when cluster has not enough spectra
+            random_samples[label] = np.array(cluster_subset.sample(samplesize, replace=True))
+
+        return random_samples
+
+
+
+