diff --git a/python/classify.py b/python/classify.py
index 2cf4388..32e39fc 100644
--- a/python/classify.py
+++ b/python/classify.py
@@ -12,7 +12,10 @@ import sklearn.metrics
 import scipy
 import scipy.misc
 import envi
+import spectral
 import random
+import progressbar
+import matplotlib.pyplot as plt
 
 #generate a 2D color class map using a stack of binary class images
 #input: C is a C x Y x X binary image
@@ -40,7 +43,7 @@ def class2color(C):
 #input: list of class image names
 #output: C x Y x X binary image specifying class/pixel membership
 #example: image2class(("class_coll.bmp", "class_epith.bmp"))
-def image2class(masks):
+def filenames2class(masks):
     #get num of mask file names
     num_masks = len(masks)
 
@@ -50,9 +53,11 @@ def image2class(masks):
         return
 
     classimages = []
-    for m in masks:
-        img = scipy.misc.imread(m, flatten=True).astype(numpy.bool)
+    bar = progressbar.ProgressBar(max_value=num_masks)
+    for m in range(0, num_masks):
+        img = scipy.misc.imread(masks[m], flatten=True).astype(numpy.bool)
         classimages.append(img)
+        bar.update(m+1)
 
     result = numpy.stack(classimages)
     sum_images = numpy.sum(result.astype(numpy.uint32), 0)
@@ -141,7 +146,24 @@ def random_mask(M, n):
     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
+def envi_batch_predict(E, C, batch=10000):
+
+    Fv = E.loadbatch(batch).transpose()
+    i = 0
+    Tv = []
+    plt.ion()
+    bar = progressbar.ProgressBar(max_value=numpy.count_nonzero(E.mask))
+    while not Fv == []:
+        if i == 0:
+            Tv = C.predict(Fv)
+        else:
+            Tv = numpy.concatenate((Tv, C.predict(Fv).transpose()), 0)
+        tempmask = E.batchmask()
+        Lv = spectral.unsift2(Tv, tempmask)
+        Cv = label2class(Lv.squeeze(), background=0)
+        RGB = class2color(Cv)
+        plt.imshow(RGB)
+        plt.pause(0.05)
+        Fv = E.loadbatch(batch).transpose()   
+        i = i + 1
+        bar.update(len(Tv))
\ No newline at end of file
diff --git a/python/envi.py b/python/envi.py
index 22c54ec..e624d60 100644
--- a/python/envi.py
+++ b/python/envi.py
@@ -10,6 +10,7 @@ import numpy
 import scipy
 import matplotlib.pyplot as plt
 import progressbar
+import sys
 
 class envi_header:
     def __init__(self, filename = ""):
@@ -245,7 +246,7 @@ class envi:
                 self.file.readinto(spectrum)
                 M[:, p] = spectrum
                 bar.update(p+1)
-        if self.header.interleave == "bsq":
+        elif self.header.interleave == "bsq":
             band = numpy.zeros(mask.shape, dtype=self.header.data_type)
             i = numpy.nonzero(mask)
             bar = progressbar.ProgressBar(max_value=B)
@@ -254,7 +255,7 @@ class envi:
                 self.file.readinto(band)
                 M[b, :] = band[i]
                 bar.update(b+1)
-        if self.header.interleave == "bil":
+        elif self.header.interleave == "bil":
             plane = numpy.zeros((B, X), dtype=self.header.data_type)
             p = 0
             bar = progressbar.ProgressBar(max_value=Y)
@@ -294,6 +295,7 @@ class envi:
         F = []
         T = []
         for c in range(0, C):
+            print("\nLoading class " + str(c+1) + "...")
             f = self.loadmask(classimages[c, :, :])            #load the feature matrix for class c
             t = numpy.ones((f.shape[1])) * (c+1)         #generate a target array                 
             F.append(f)
--
libgit2 0.21.4