Merge branch 'master' of github.com:GatorSense/hsi_toolkit_py

Author: Yutai

util/BatchPlotSpectra/an_hsi_image_sub_for_demo.mat

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+#function [SpecDist] = PlotSpectraDistribution(Spectra, WaveLengths,SampStuff, FigNum);
+###
+######################################################################
+### A FUNCTION THAT CREATES & DISPLAYS SPECTRA AS A 2D HISTOGRAM   ###
+###    SPECTRA ARE ASSUMED REFLECTANCES OR EMISSIVITIES IN [0,1]   ###
+###    SPECTRA ARE MAPPED TO INTEGERS BETWEEN 0 AND 100 (OR < 100) ###
+######################################################################
+###
+### INPUTS:
+###   I1. Spectra IS A NUMBER SPECTRA x NUMBER BANDS...        ###
+###       ...ARRAY OF REFLECTANCES OR EMISSIVITIES             ###
+###   I2. WaveLengths IS A VECTOR OF THE SPECTRAL WAVELENGTHS  ###
+###   I3. SampStuff IS A VECTOR CONTAINING                     ###
+###          SampInt:       FRACTIONAL SIZE OF HISTOGRAM BINS  ###
+###          IntSampInt:    INT VERSION OF SampInt             ###
+###          IntTopReflect: INT VALUE OF MAX REF/EMIS BIN      ###
+###   I4. FigNum IS THE INDEX OF THE FIGURE TO USE FOR DISPLAY ###
+###          IF FigNum < 1, DO NOT DISPLAY ANYTHING            ###
+###
+### OUTPUTS:
+###   O1. SpecDist IS THE 2D HISTOGRAM                         ###
+##################################################################
+###                                                            ###
+### MATLAB AUTHOR:      Darth Gader                            ###
+### PYTHON AUTHOR:      Ron Fick                               ###
+### LAST UPDATE: 021519                                        ###
+###                                                            ###
+##################################################################
+def PlotSpectraDistribution(Spectra, WaveLengths, SampStuff, FigNum):
+
+    import numpy as np
+    from scipy import signal
+    import cv2
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+    from matplotlib import cm
+    
+    ##
+    ### INITIALIZE PARAMETERS ###
+    
+    SampInt       = SampStuff[0]
+    IntSampInt    = SampStuff[1]
+    IntTopReflect = SampStuff[2]
+    SMOOTHSIZE    = [3,3]
+    NumWave       = np.size(Spectra, 1)
+    SpecDist      = np.zeros((IntTopReflect, NumWave))
+    assert NumWave == np.size(WaveLengths), 'Wavelength sizes don''t match'
+    
+    ### MAP SPECTRA TO [0, 100] ###
+    MappedSpectra = np.minimum(100, (Spectra*99)+1)
+    MappedSpectra = np.maximum(1, np.round(MappedSpectra/SampInt)*SampInt)
+    
+    ##
+    ### MAKE A HISTOGRAM FOR EACH WAVELENGTH ###
+    for k in range(NumWave):
+        SpecDist[:, k] = np.histogram(MappedSpectra[:, k], np.arange(0, IntTopReflect+IntSampInt, IntSampInt))[0]
+    
+    ### SMOOTH BY TAKING A LOCAL MAX FOLLOWED BY A LOCAL AVERAGE ###
+    SpecDist   = cv2.dilate(SpecDist, np.ones((3,3)), iterations=1)
+    SpecDist   = signal.convolve2d(SpecDist, (1/np.prod(SMOOTHSIZE))*np.ones(SMOOTHSIZE), 'same')
+    
+    ##
+    ### DISPLAY AS MESH ###
+    if(FigNum > 0):
+        XAxis      = WaveLengths;
+        YAxis      = np.arange(0, IntTopReflect, IntSampInt).T
+        X, Y = np.meshgrid(XAxis, YAxis)
+        fig        = plt.figure(FigNum)
+        ax         = fig.gca(projection='3d')
+        surf       = ax.plot_surface(X, Y, SpecDist, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+        fig.colorbar(surf, shrink=0.5, aspect=5)
+        plt.title('Spectra Histogram')
+        plt.xlabel('Wavelength (nm)')
+        plt.ylabel('Reflectance')
+        plt.show()
+
+### END OF FUNCTION ###
+#######################

util/BatchPlotSpectra/batchplotspectra.py

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+import scipy.io as sio
+import numpy as np
+from batchplotspectra import PlotSpectraDistribution
+
+an_hsi_image_sub_for_demo = sio.loadmat('an_hsi_image_sub_for_demo.mat')
+hsi_img_sub = an_hsi_image_sub_for_demo['hsi_img_sub']
+wavelengths = an_hsi_image_sub_for_demo['wavelengths']
+mask_sub = an_hsi_image_sub_for_demo['mask_sub']
+
+x_dims, y_dims, band_dims = hsi_img_sub.shape
+
+mat_data = np.reshape(hsi_img_sub, (x_dims*y_dims, band_dims))
+
+mask_reshaped = np.reshape(mask_sub, (x_dims*y_dims))
+
+masked_data = mat_data[mask_reshaped == 1]
+
+PlotSpectraDistribution(masked_data, wavelengths, [1,1,100], 1)