renamed spectral.py to stim_spectral.py; modified envi_batch_predict to return R…

…GB array in case user wants to save the fig

renamed spectral.py to stim_spectral.py; modified envi_batch_predict to return R…
…GB array in case user wants to save the fig
sberisha
1 parent eccf10ff
Showing 2 changed files with 5 additions and 56 deletions Show diff stats
python/classify.py
python/spectral.py
@@ -12,7 +12,7 @@ import sklearn.metrics
 import scipy
 import scipy.misc
 import envi
-import spectral
+import stim_spectral
 import random
 import progressbar
 import matplotlib.pyplot as plt
@@ -176,11 +176,13 @@ def envi_batch_predict(E, C, batch=10000):
         else:
             Tv = numpy.concatenate((Tv, C.predict(Fv.transpose()).transpose()), 0)
         tempmask = E.batchmask()
-        Lv = spectral.unsift2(Tv, tempmask)
+        Lv = stim_spectral.unsift2(Tv, tempmask)
         Cv = label2class(Lv.squeeze(), background=0)
         RGB = class2color(Cv)
         plt.imshow(RGB)
         plt.pause(0.05)
         Fv = E.loadbatch(batch)   
         i = i + 1
-        bar.update(len(Tv))
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+        bar.update(len(Tv))
+
+    return RGB
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-# -*- coding: utf-8 -*-
-"""
-Created on Sun Jul 23 13:52:22 2017
-
-@author: david
-"""
-import numpy
-
-#sift a 2D hyperspectral image into a PxB matrix where P is the number of pixels and B is the number of bands
-def sift2(I, mask = []):
-    
-    #get the shape of the input array
-    S = I.shape
-    
-    #convert that array into a 1D matrix
-    M = numpy.reshape(I, (S[0], S[1] * S[2]))
-    
-    #gif no mask is provided, just return all pixels
-    if mask == []:
-        return M
-
-    #if a mask is provided, only return pixels corresponding to that mask
-    flatmask = numpy.reshape(mask, (S[1] * S[2]))
-    i = numpy.flatnonzero(flatmask)                     #get the nonzero indices
-    return M[:, i]                                      #return pixels corresponding to the masked values
-
-def unsift2(M, mask):
-    
-    #get the size of the input matrix
-    S = M.shape
-    
-    #count the number of nonzero values in the mask
-    nnz = numpy.count_nonzero(mask)
-    
-    #the number of masked values should be the same as the number of pixels in the input matrix
-    if len(S) == 1:
-    	if not S[0] == nnz:
-    		print("ERROR: expected " + str(nnz) + " pixels based on the mask but there are " + str(S[0]) + " in the matrix.")
-    elif not S[1] == nnz:
-        print("ERROR: expected " + str(nnz) + " pixels based on the mask but there are " + str(S[1]) + " in the matrix.")
-    
-    
-    i = numpy.nonzero(mask)
-    
-    if len(S) == 1:
-    	I = numpy.zeros((1, mask.shape[0], mask.shape[1]), dtype=M.dtype)
-    else:
-    	I = numpy.zeros((M.shape[0], mask.shape[0], mask.shape[1]), dtype=M.dtype)
-    I[:, i[0], i[1]] = M
-    return I
-
-#create a function that sifts a color image
-#input: image name, mask  
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