digitalstain.py
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# -*- 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