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# -*- coding: utf-8 -*-
"""
Created on Sun Jul 23 16:04:33 2017
@author: david
"""
import numpy
import colorsys
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import sklearn
import sklearn.metrics
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#generate a 2D color class map using a stack of binary class images
def classcolor2(C):
#determine the number of classes
nc = C.shape[-1]
#generate an RGB image
RGB = numpy.zeros((C.shape[0], C.shape[1], 3), dtype=numpy.ubyte)
#for each class
for c in range(0, nc):
hsv = (c * 1.0 / nc, 1, 1)
color = numpy.asarray(colorsys.hsv_to_rgb(hsv[0], hsv[1], hsv[2])) * 255
RGB[C[:, :, c], :] = color
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return RGB
#create a function that loads a set of class images as a stack of binary masks
#input: list of class image names
#output: X x Y x C stack of binary mask images
#example: image2class(("class_coll.bmp", "class_epith.bmp"))
#create a set of feature/target pairs for classification
#input: envi file object, stack of class masks, list of class names
#output: feature matrix (features x pixels), target matrix (1 x pixels)
#example: generate_training(("class_coll.bmp", "class_epith.bmp"), (1, 2))
#create a class mask stack from an X x Y x C probability image
#input: X x Y x C image giving the probability P(c |x,y)
#output: X x Y x C binary class image
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#calculate an ROC curve given a probability image and mask of "True" values
def image2roc(P, t_vals, mask=[]):
if not P.shape == t_vals.shape:
print("ERROR: the probability and mask images must be the same shape")
return
#if a mask image isn't provided, create one for the entire image
if mask == []:
mask = numpy.ones(t_vals.shape, dtype=numpy.bool)
#create masks for the positive and negative probability scores
mask_p = t_vals
mask_n = mask - mask * t_vals
#calculate the indices for the positive and negative scores
idx_p = numpy.nonzero(mask_p)
idx_n = numpy.nonzero(mask_n)
Pp = P[idx_p]
Pn = P[idx_n]
Lp = numpy.ones((Pp.shape), dtype=numpy.bool)
Ln = numpy.zeros((Pn.shape), dtype=numpy.bool)
scores = numpy.concatenate((Pp, Pn))
labels = numpy.concatenate((Lp, Ln))
return sklearn.metrics.roc_curve(labels, scores)
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