added digital staining functions in Python

David Mayerich
1 parent 71ee1c23
Showing 3 changed files with 35 additions and 11 deletions Show diff stats
python/classify.py
python/envi.py
python/spectral.py
@@ -12,6 +12,7 @@ import sklearn.metrics
 import scipy
 import scipy.misc
 import envi
+import random
  
 #generate a 2D color class map using a stack of binary class images
 #input: C is a C x Y x X binary image
@@ -120,16 +121,27 @@ def prob2roc(P, t_vals, mask=[]):
     return sklearn.metrics.roc_curve(labels, scores)
  
 #convert a label image to a C x Y x X class image
-def label2class(L):
+def label2class(L, background=[]):
     unique = numpy.unique(L)
+
+    if not background == []:                                                #if a background value is specified
+        unique = numpy.delete(unique, numpy.nonzero(unique == background))  #remove it from the label array
     s = L.shape
-    s = numpy.append(numpy.array((len(unique)-1)), s)
-    print(s)
+    s = numpy.append(numpy.array((len(unique))), s)
     C = numpy.zeros(s, dtype=numpy.bool)
-    for i in range(1, len(unique)):
-        C[i-1, :, :] = L == unique[i]
+    for i in range(0, len(unique)):
+        C[i, :, :] = L == unique[i]
     return C
  
+#randomizes a given mask to include a subset of n pixels in the original
+def random_mask(M, n):
+    idx = numpy.flatnonzero(M)
+    new_idx = numpy.random.permutation(idx)
+    new_mask = numpy.zeros(M.shape, dtype=numpy.bool)
+    new_mask[numpy.unravel_index(new_idx[0:n], new_mask.shape)] = True
+    return new_mask
+
+
 #Function to convert a set of class labels to a matrix of neuron responses for an ANN
  
 #Function CNN extraction function
 \ No newline at end of file
@@ -185,7 +185,7 @@ class envi:
     def __init__(self, filename, headername = "", mask = []):
         self.open(filename, headername)
         if mask == []:
-            self.mask = numpy.ones((self.header.samples, self.header.lines), dtype=numpy.bool)
+            self.mask = numpy.ones((self.header.lines, self.header.samples), dtype=numpy.bool)
         else:
             self.mask = mask
         self.idx = 0                                                               #initialize the batch IDX to 0 for batch reading
@@ -304,7 +304,9 @@ class envi:
     #read a batch of data based on the mask
     def loadbatch(self, npixels):
         i = numpy.flatnonzero(self.mask)                                      #get the indices of valid pixels
-        npixels = min(npixels, len(i) - self.idx - 1)                        #if there aren't enough pixels, change the batch size
+        if len(i) == self.idx:													#if all of the pixels have been read, return an empyt array
+        	return []
+        npixels = min(npixels, len(i) - self.idx)                        #if there aren't enough pixels, change the batch size
         B = self.header.bands
  
         batch = numpy.zeros((B, npixels), dtype=self.header.data_type)          #allocate space for the batch
@@ -316,8 +318,6 @@ class envi:
                 self.file.readinto(pixel)                                       #read a single pixel
                 batch[:, n] = pixel                                             #save the pixel into the batch matrix
                 self.idx = self.idx + 1
-                #print("idx: " + str(self.idx))
-                #print("tell(): " + str(self.file.tell()))
             return batch
         elif self.header.interleave == "bsq":
             print("ERROR: BSQ batch loading isn't implemented yet!")
@@ -327,6 +327,18 @@ class envi:
     #returns the current batch index         
     def getidx(self):
         return self.idx
+
+    #returns an image of the pixels that have been read using batch loading
+    def batchmask(self):
+    	#allocate a new mask
+    	outmask = numpy.zeros(self.mask.shape, dtype=numpy.bool)
+
+    	#zero out any unclassified pixels 
+    	idx = self.getidx()
+    	i = numpy.nonzero(self.mask)
+    	outmask[i[0][0:idx], i[1][0:idx]] = self.mask[i[0][0:idx], i[1][0:idx]]
+    	return outmask
+
  
     def __del__(self):
         self.file.close()
 \ No newline at end of file
@@ -13,14 +13,14 @@ def sift2(I, mask = []):
     S = I.shape
  
     #convert that array into a 1D matrix
-    M = numpy.reshape(I, (S[2], S[0] * S[1]))
+    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[0] * S[1]))
+    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