changed the name of the spectral library (to hyperspectral) and fixed some digital staining bugs

David Mayerich
1 parent 296aa37d
Showing 4 changed files with 27 additions and 13 deletions Show diff stats
python/classify.py
python/digitalstain.py
python/envi.py
python/stim_spectral.py → python/hyperspectral.py
@@ -12,7 +12,7 @@ import sklearn.metrics
 import scipy
 import scipy.misc
 import envi
-import spectral
+import hyperspectral
 import random
 import progressbar
 import matplotlib.pyplot as plt
@@ -177,7 +177,7 @@ 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 = hyperspectral.unsift2(Tv, tempmask)
         Cv = label2class(Lv.squeeze(), background=0)
         RGB = class2color(Cv)
         plt.imshow(RGB)
@@ -5,7 +5,7 @@ Created on Tue Jul 25 16:28:37 2017
 @author: david
 """
-import spectral
+import hyperspectral
 import envi
 import classify
 import numpy
@@ -18,22 +18,36 @@ import glob
 import matplotlib.pyplot as plt
 import random
-def generate_stain(envifile, stainfile, N=5000, batch_size=10000, validate=True):
-    E = envi.envi(envifile)
+def generate_stain(envifile, stainfile, maskfile="", trainmask="", N=5000, batch_size=10000, validate=True):
+    if trainmask == "":
+        E = envi.envi(envifile)
+    else:
+        mask = scipy.misc.imread(trainmask, flatten=True)
+        E = envi.envi(envifile, mask=mask)
+        
     mask = classify.random_mask(E.mask, N)
+    scipy.misc.imsave("random.bmp", mask)
     Ft = E.loadmask(mask).transpose()
     stain = numpy.rollaxis(scipy.misc.imread(stainfile), 2)
-    Tt = spectral.sift2(stain, mask).transpose()
+    Tt = hyperspectral.sift2(stain, mask).transpose()
+    print("Training MLPRegressor...")
     CLASS = sklearn.neural_network.MLPRegressor(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(), random_state=1, verbose=True)
     CLASS.fit(Ft, Tt)
     if validate == False:
         return CLASS
-    plt.ion()
+    print("Validating Stain...")
+    plt.ion()    
+    if not maskfile == "":
+        E.close()                                                                  #close the ENVI file
+        mask = scipy.misc.imread(maskfile, flatten=True)
+        print(numpy.count_nonzero(mask))
+        E = envi.envi(envifile, mask=mask)
+    
     Fv = E.loadbatch(batch_size)                                                #load the first batch
     n = 0
     while not Fv == []:                                                         #loop until an empty batch is returned
@@ -41,7 +55,7 @@ def generate_stain(envifile, stainfile, N=5000, batch_size=10000, validate=True)
             Tv = CLASS.predict(Fv.transpose()).transpose()
         else:
             Tv = numpy.append(Tv, CLASS.predict(Fv.transpose()).transpose(), 1)                        #append the predicted labels from this batch to those of previous batches
-        COLORS = spectral.unsift2(Tv, E.batchmask())                                    #convert the matrix of class labels to a 2D array
+        COLORS = hyperspectral.unsift2(Tv, E.batchmask())                                    #convert the matrix of class labels to a 2D array
         RGB = numpy.rollaxis(COLORS, 0, 3).astype(numpy.ubyte)
         plt.imshow(RGB)                                                             #display it
         plt.pause(0.05)
@@ -100,11 +100,11 @@ class envi_header:
             #t = map(str.strip, t)               #strip all of the tokens in the token list
             #handle the simple conditions
-            if l[li].startswith("file type"):
-                if not l[li].strip().endswith("ENVI Standard"):
-                    print("ERROR: unsupported ENVI file format: " + l[li].strip())
-                    return
-            elif l[li].startswith("samples"):
+            #if l[li].startswith("file type"):
+            #    if not l[li].strip().endswith("ENVI Standard"):
+            #        print("ERROR: unsupported ENVI file format: " + l[li].strip())
+            #        return
+            if l[li].startswith("samples"):
                 self.samples = int(l[li].split()[-1])
             elif l[li].startswith("lines"):
                 self.lines = int(l[li].split()[-1])