From ac5ae422226145edf277c89f7e6fb6c2c73b08da Mon Sep 17 00:00:00 2001
From: David Mayerich <mayerich@uh.edu>
Date: Wed, 9 Aug 2017 17:37:46 -0500
Subject: [PATCH] added digital staining functions

---
 python/digitalstain.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+), 0 deletions(-)
 create mode 100644 python/digitalstain.py

diff --git a/python/digitalstain.py b/python/digitalstain.py
new file mode 100644
index 0000000..9406846
--- /dev/null
+++ b/python/digitalstain.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jul 25 16:28:37 2017
+
+@author: david
+"""
+
+import spectral
+import envi
+import classify
+import numpy
+import scipy
+import scipy.misc
+import sklearn
+import sklearn.naive_bayes
+import sklearn.neural_network
+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)
+    mask = classify.random_mask(E.mask, N)
+    
+    Ft = E.loadmask(mask).transpose()
+
+    stain = numpy.rollaxis(scipy.misc.imread(stainfile), 2)
+    Tt = spectral.sift2(stain, mask).transpose()
+
+    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()
+    Fv = E.loadbatch(batch_size)                                                #load the first batch
+    n = 0
+    while not Fv == []:                                                         #loop until an empty batch is returned
+        if n == 0:
+            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
+        RGB = numpy.rollaxis(COLORS, 0, 3).astype(numpy.ubyte)
+        plt.imshow(RGB)                                                             #display it
+        plt.pause(0.05)
+        Fv = E.loadbatch(batch_size)                                                         #load the next batch
+        n = n + 1
+    return CLASS, RGB
--
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