From f3de42f83d400e423932a2e28a72d9ee65ab704d Mon Sep 17 00:00:00 2001
From: sberisha <sberisha@Forrestmoon.EE.e.UH.EDU>
Date: Tue, 1 Aug 2017 16:09:53 -0500
Subject: [PATCH] added stim_spectral.py

---
 python/stim_spectral.py | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+), 0 deletions(-)
 create mode 100644 python/stim_spectral.py

diff --git a/python/stim_spectral.py b/python/stim_spectral.py
new file mode 100644
index 0000000..6f56001
--- /dev/null
+++ b/python/stim_spectral.py
@@ -0,0 +1,53 @@
+# -*- 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  
\ No newline at end of file
--
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