diff --git a/python/classify.py b/python/classify.py
index 61f8fd8..ef6e610 100644
--- a/python/classify.py
+++ b/python/classify.py
@@ -16,7 +16,7 @@ import envi
 #generate a 2D color class map using a stack of binary class images
 #input: C is a C x Y x X binary image
 #output: an RGB color image with a unique color for each class
-def classcolor2(C):
+def class2color(C):
     
     #determine the number of classes
     nc = C.shape[0]
@@ -119,6 +119,17 @@ 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):
+    unique = numpy.unique(L)
+    s = L.shape
+    s = numpy.append(numpy.array((len(unique)-1)), s)
+    print(s)
+    C = numpy.zeros(s, dtype=numpy.bool)
+    for i in range(1, len(unique)):
+        C[i-1, :, :] = L == unique[i]
+    return C
+
 #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
diff --git a/python/envi.py b/python/envi.py
index 4b3f9c5..b4619fc 100644
--- a/python/envi.py
+++ b/python/envi.py
@@ -182,12 +182,13 @@ class envi_header:
         f.close()
         
 class envi:
-    def __init__(self, filename, headername = "", maskname = ""):
+    def __init__(self, filename, headername = "", mask = []):
         self.open(filename, headername)
-        if maskname == "":
+        if mask == []:
             self.mask = numpy.ones((self.header.samples, self.header.lines), dtype=numpy.bool)
         else:
-            self.mask = scipy.misc.imread(maskname, flatten=True).astype(numpy.bool)
+            self.mask = mask
+        self.idx = 0                                                               #initialize the batch IDX to 0 for batch reading
         
     def open(self, filename, headername = ""):
         if headername == "":
@@ -232,6 +233,7 @@ class envi:
         M = numpy.zeros((B, P), dtype=self.header.data_type)
         type_bytes = numpy.dtype(self.header.data_type).itemsize
         
+        prev_pos = self.file.tell()
         self.file.seek(0)
         if self.header.interleave == "bip":
             spectrum = numpy.zeros(B, dtype=self.header.data_type)
@@ -262,6 +264,7 @@ class envi:
                 M[:, p:p+i.shape[0]] = plane[:, i]
                 p = p + i.shape[0]
                 bar.update(l+1)
+        self.file.seek(prev_pos)
         return M
 
     def loadband(self, n):
@@ -271,11 +274,12 @@ class envi:
 
         band = numpy.zeros((Y, X), dtype=self.header.data_type)
         type_bytes = numpy.dtype(self.header.data_type).itemsize
-
+        
+        prev_pos = self.file.tell()
         if self.header.interleave == "bsq":
             self.file.seek(n * X * Y * type_bytes)
             self.file.readinto(band)
-
+        self.file.seek(prev_pos)
         return band
 
     #create a set of feature/target pairs for classification
@@ -296,7 +300,33 @@ class envi:
             T.append(t)
 
         return numpy.concatenate(F, 1).transpose(), numpy.concatenate(T)
-
+    
+    #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
+        B = self.header.bands
+        
+        batch = numpy.zeros((B, npixels), dtype=self.header.data_type)          #allocate space for the batch
+        pixel = numpy.zeros((B), dtype=self.header.data_type)                   #allocate space for a single pixel
+        type_bytes = numpy.dtype(self.header.data_type).itemsize                #calculate the size of a single value
+        if self.header.interleave == "bip":
+            for n in range(0, npixels):                                          #for each pixel in the batch
+                self.file.seek(i[self.idx] * B * type_bytes)                 #seek to the current pixel in the file
+                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!")
+        elif self.header.interleave == "bil":
+            print("ERROR: BIL batch loading isn't implemented yet!")        
+       
+    #returns the current batch index         
+    def getidx(self):
+        return self.idx
             
     def __del__(self):
         self.file.close()
\ No newline at end of file
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libgit2 0.21.4