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KTCScoring.py
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KTCScoring.py
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import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as sps
def Otsu(image, nvals, figno):
# binary Otsu's method for finding the segmentation level for sigma
histogramCounts, x = np.histogram(image.ravel(), nvals)
# plt.figure(figno)
# plt.clf()
# plt.hist(image.ravel(), 256)
# plt.hold(True)
total = np.sum(histogramCounts)
top = 256
sumB = 0
wB = 0
maximum = 0.0
sum1 = np.dot(np.arange(top), histogramCounts)
for ii in range(1, top):
wF = total - wB
if wB > 0 and wF > 0:
mF = (sum1 - sumB) / wF
val = wB * wF * (((sumB / wB) - mF) ** 2)
if val >= maximum:
level = ii
maximum = val
wB = wB + histogramCounts[ii]
sumB = sumB + (ii - 1) * histogramCounts[ii]
# plt.plot([x[level]] * 2, [0, np.max(histogramCounts)], linewidth=2, color='r')
# plt.title('histogram of image pixels')
# plt.gcf().set_size_inches(9, 5)
# plt.show()
return level, x
def Otsu2(image, nvals, figno):
# three class Otsu's method to find the semgentation point of sigma
histogramCounts, tx = np.histogram(image.ravel(), nvals)
x = (tx[0:-1] + tx[1:])/2
# plt.figure(figno)
# plt.clf()
# plt.stairs(histogramCounts, tx)
# plt.hold(True)
#total = np.sum(histogramCounts)
#top = 256
maximum = 0.0
muT = np.dot(np.arange(1, nvals+1), histogramCounts) / np.sum(histogramCounts)
for ii in range(1, nvals):
for jj in range(1, ii):
w1 = np.sum(histogramCounts[:jj])
w2 = np.sum(histogramCounts[jj:ii])
w3 = np.sum(histogramCounts[ii:])
if w1 > 0 and w2 > 0 and w3 > 0:
mu1 = np.dot(np.arange(1, jj+1), histogramCounts[:jj]) / w1
mu2 = np.dot(np.arange(jj+1, ii+1), histogramCounts[jj:ii]) / w2
mu3 = np.dot(np.arange(ii+1, nvals+1), histogramCounts[ii:]) / w3
val = w1 * ((mu1 - muT) ** 2) + w2 * ((mu2 - muT) ** 2) + w3 * ((mu3 - muT) ** 2)
if val >= maximum:
level = [jj-1, ii-1]
maximum = val
# plt.plot([x[level[0]]] * 2, [0, np.max(histogramCounts)], linewidth=2, color='r')
# plt.plot([x[level[1]]] * 2, [0, np.max(histogramCounts)], linewidth=2, color='r')
# plt.title('histogram of image pixels')
# plt.gcf().set_size_inches(9, 5)
# plt.show()
return level, x
def scoringFunction(groundtruth, reconstruction):
if (np.any(groundtruth.shape != np.array([256, 256]))):
raise Exception('The shape of the given ground truth is not 256 x 256!')
if (np.any(reconstruction.shape != np.array([256, 256]))):
return 0
truth_c = np.zeros(groundtruth.shape)
truth_c[np.abs(groundtruth - 2) < 0.1] = 1
reco_c = np.zeros(reconstruction.shape)
reco_c[np.abs(reconstruction - 2) < 0.1] = 1
score_c = ssim(truth_c, reco_c)
truth_d = np.zeros(groundtruth.shape)
truth_d[np.abs(groundtruth - 1) < 0.1] = 1
reco_d = np.zeros(reconstruction.shape)
reco_d[np.abs(reconstruction - 1) < 0.1] = 1
score_d = ssim(truth_d, reco_d)
score = 0.5*(score_c + score_d)
return score
def ssim(truth, reco):
c1 = 1e-4
c2 = 9e-4
r = 80
ws = np.ceil(2*r)
wr = np.arange(-ws, ws+1)
X, Y = np.meshgrid(wr, wr)
ker = (1/np.sqrt(2*np.pi)) * np.exp(-0.5 * np.divide((np.square(X) + np.square(Y)), r**2))
correction = sps.convolve2d(np.ones(truth.shape), ker, mode='same')
gt = np.divide(sps.convolve2d(truth, ker, mode='same'), correction)
gr = np.divide(sps.convolve2d(reco, ker, mode='same'), correction)
mu_t2 = np.square(gt)
mu_r2 = np.square(gr)
mu_t_mu_r = np.multiply(gt, gr)
sigma_t2 = np.divide(sps.convolve2d(np.square(truth), ker, mode='same'), correction) - mu_t2
sigma_r2 = np.divide(sps.convolve2d(np.square(reco), ker, mode='same'), correction) - mu_r2
sigma_tr = np.divide(sps.convolve2d(np.multiply(truth, reco), ker, mode='same'), correction) - mu_t_mu_r;
num = np.multiply((2*mu_t_mu_r + c1), (2*sigma_tr + c2))
den = np.multiply((mu_t2 + mu_r2 + c1), (sigma_t2 + sigma_r2 + c2))
ssimimage = np.divide(num, den)
score = np.mean(ssimimage)
return score