KMeansRSImage: Added functions and properties to apply clustering, plot...

KMeansRSImage: Added functions and properties to apply clustering, plot cluster centers, plot cluster histogram, plot clustered image + Tests.

gms_preprocessing/algorithms/L2B_P.py

@@ -8,7 +8,7 @@ from scipy.interpolate import interp1d
 import scipy as sp
 import matplotlib.pyplot as plt
 from logging import Logger
-from sklearn.cluster import KMeans
+from sklearn.cluster import KMeans, k_means_
 
 from geoarray import GeoArray  # noqa F401  # flake8 issue
 
@@ -167,21 +167,105 @@ class SpectralResampler(object):
 
 
 class KMeansRSImage(object):
+    _clusters = None
+    _im_clust = None
+
     def __init__(self, im, n_clusters):
         # type: (GeoArray, int) -> None
         self.im = im
         self.n_clusters = n_clusters
 
+    @property
+    def clusters(self):
+        # type: () -> k_means_.KMeans
+        if not self._clusters:
+            self._clusters = self.compute_clusters()
+        return self._clusters
+
+    @clusters.setter
+    def clusters(self, clusters):
+        self._clusters = clusters
+
+    @property
+    def im_clust(self):
+        if self._im_clust is None:
+            self._im_clust = self.clusters.labels_.reshape((self.im.rows, self.im.cols))
+        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)
-        out = kmeans.fit(pixels2d)
+        self.clusters = kmeans.fit(pixels2d)
+
+        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)
+        return labels
+
+    def plot_cluster_centers(self, figsize=(15, 5)):
+        # type: (tuple) -> None
+        """Show a plot of the cluster center signatures.
+
+        :param figsize:     figure size (inches)
+        """
+        plt.figure(figsize=figsize)
+        for i, center_signature in enumerate(self.clusters.cluster_centers_):
+            plt.plot(range(1, self.im.bands + 1), center_signature, label='Cluster #%s' % (i + 1))
+
+        plt.title('KMeans cluster centers for %s clusters' % self.n_clusters)
+        plt.xlabel('Spectral band')
+        plt.ylabel('Pixel values')
+        plt.legend(loc='upper right')
 
-        print(out)
+        plt.show()
 
+    def plot_cluster_histogram(self, figsize=(15, 5)):
+        # type: (tuple) -> None
+        """Show a histogram indicating the proportion of each cluster label in percent.
+
+        :param figsize:     figure size (inches)
+        """
+        # grab the number of different clusters and create a histogram
+        # based on the number of pixels assigned to each cluster
+        numLabels = np.arange(0, len(np.unique(self.clusters.labels_)) + 1)
+        hist, bins = np.histogram(self.clusters.labels_, bins=numLabels)
+
+        # normalize the histogram, such that it sums to 100
+        hist = hist.astype("float")
+        hist /= hist.sum() / 100
+
+        # plot the histogram as bar plot
+        plt.figure(figsize=figsize)
+
+        plt.bar(bins[:-1], hist, width=1)
+
+        plt.title('Proportion of cluster labels (%s clusters)' % self.n_clusters)
+        plt.xlabel('# Cluster')
+        plt.ylabel('Proportion [%]')
+
+        plt.show()
+
+    def plot_clustered_image(self, figsize=(15, 15)):
+        # type: (tuple) -> None
+        """Show a the clustered image.
+
+        :param figsize:     figure size (inches)
+        """
+        plt.figure(figsize=figsize)
+        rows, cols = self.im_clust.shape[:2]
+        plt.imshow(self.im_clust, plt.cm.prism, interpolation='none', extent=(0, cols, rows, 0))
+        plt.show()
 
+    def get_random_spectra_from_each_cluster(self, samplesize=50):
+        """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
 
 

tests/test_kmeans.py

@@ -9,14 +9,19 @@ Tests for gms_preprocessing.algorithms.L2B_P.KMeansRSImage
 """
 
 import unittest
-import numpy as np
 import os
+import matplotlib
+import numpy as np
+
+from sklearn.cluster import k_means_
 
-from geoarray import GeoArray
+matplotlib.use('Template')  # disables matplotlib figure popups
 
-from gms_preprocessing import __file__
-from gms_preprocessing.config import set_config
-from gms_preprocessing.algorithms.L2B_P import KMeansRSImage
+from geoarray import GeoArray  # noqa E402 module level import not at top of file
+
+from gms_preprocessing import __file__  # noqa E402 module level import not at top of file
+from gms_preprocessing.config import set_config  # noqa E402 module level import not at top of file
+from gms_preprocessing.algorithms.L2B_P import KMeansRSImage  # noqa E402 module level import not at top of file
 
 
 testdata = os.path.join(os.path.dirname(__file__),
@@ -36,3 +41,22 @@ class Test_KMeansRSImage(unittest.TestCase):
 
     def test_compute_clusters(self):
         self.kmeans.compute_clusters()
+        self.assertIsInstance(self.kmeans.clusters, k_means_.KMeans)
+
+    def test_apply_clusters(self):
+        labels = self.kmeans.apply_clusters(self.geoArr)
+        self.assertIsInstance(labels, np.ndarray)
+        self.assertTrue(labels.size == self.geoArr.rows * self.geoArr.cols)
+
+    def test_get_random_spectra_from_each_cluster(self):
+        self.kmeans.get_random_spectra_from_each_cluster()
+        # TODO
+
+    def test_plot_cluster_centers(self):
+        self.kmeans.plot_cluster_centers()
+
+    def test_plot_cluster_histogram(self):
+        self.kmeans.plot_cluster_histogram()
+
+    def test_plot_clustered_image(self):
+        self.kmeans.plot_clustered_image()