def onViewHist(self):
self.I2 = self.Ilast
if self.I2.ndim==2 :
# calculate the histogram
h = myHist.imhist(self.I2)
# and show the histogram
markerline, stemlines, baseline = plt.stem(np.arange(0, 256), h, '-')
plt.setp(markerline, 'marker', '.', 'markerfacecolor', 'none')
plt.setp(stemlines, 'color', 'b')
plt.setp(baseline, 'color','black', 'linewidth', 3)
plt.plot(np.arange(0, 256), h, color = 'black', linewidth = 2)
plt.title("Histogram")
plt.xlabel("Intensity Values (8 bit) rk")
plt.ylabel("p(rk)")
plt.show()
else :
# calculate the histogram for each R, G and B components
hR = myHist.imhist( self.I2[:, :, 0])
hG = myHist.imhist( self.I2[:, :, 1])
hB = myHist.imhist( self.I2[:, :, 2])
# and show the output
plt.plot(np.arange(0, 256), hR, color = 'red', linewidth = 2, label = 'Red Intensities')
plt.plot(np.arange(0, 256), hG, color = 'green', linewidth = 2, label = 'Blue Intensities')
plt.plot(np.arange(0, 256), hB, color = 'blue', linewidth = 2, label = 'Green Intensities')
plt.legend(loc='upper left')
plt.title("Histogram")
plt.xlabel("Intensity Values (8 bit) rk")
plt.ylabel("p(rk)")
plt.show()
def gbThresh(im):
if im.ndim == 3:
im = gray(im)
h = myHist.imhist(im)
i = np.arange(0, 256) # all intensity values
globalMean = np.uint8(np.sum(h * i)) # global mean
T = globalMean # initialize threshold as globalMean
while True:
M1 = np.uint16(np.sum(h[0:T] * np.arange(0, T)) / np.sum(h[0:T]))
M2 = np.uint16(np.sum(h[T:255] * np.arange(T, 255)) / np.sum(h[T:255]))
T2 = (M1 + M2) / 2
if abs(T2 - T) <= 1:
break
T = T2
return T, h
def otsu(im):
if im.ndim == 3:
im = gray(im)
h = myHist.imhist(im)
cdf = np.array(myHist.cumsum(h)) # cumulative distribution function
i = np.arange(0, 256) # all intensity values
cumMean = np.array(myHist.cumsum(h * i)) # cumulative mean for all intnsities
globalMean = np.sum(h * i) # global mean
# calculate between class variance
varB = np.floor(np.power(globalMean * cdf - cumMean, 2) / (cdf * (1 - cdf)))
# threshold k* is the value of k in varB(k) for which varB(k) is maximum
thresh = np.where(varB == np.nanmax(varB))
thresh = np.mean(thresh)
return thresh, h
