Python + opencv: threshold segmentation

Machine vision 001 2020-11-16 01:29:29
python opencv threshold segmentation


Python+OpenCV: Threshold segmentation

Threshold segmentation

####################################################################################################
# Threshold segmentation
def lmc_cv_thresholding():
"""
The functionality : Threshold segmentation .
"""
# Read images
image = lmc_cv.imread('D:/99-Research/Python/Image/Gradient.png')
# Threshold segmentation
rows, cols, channel = image.shape
ret, thresh1_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_BINARY)
ret, thresh2_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_BINARY_INV)
ret, thresh3_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_TRUNC)
ret, thresh4_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_TOZERO)
ret, thresh5_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_TOZERO_INV)
# Display images
pyplot.figure('Image Display')
titles = ['Original', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [image, thresh1_image, thresh2_image, thresh3_image, thresh4_image, thresh5_image]
for i in range(6):
pyplot.subplot(2, 3, i + 1)
pyplot.imshow(images[i], 'gray', vmin=0, vmax=255)
pyplot.title(titles[i])
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close()
return

Adaptive threshold segmentation

####################################################################################################
# Adaptive threshold segmentation
def lmc_cv_adaptive_thresholding():
"""
The functionality : Adaptive threshold segmentation .
"""
# Read images
image = lmc_cv.imread('D:/99-Research/Python/Image/Lena.jpg')
image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2GRAY)
# Adaptive threshold segmentation
image = lmc_cv.medianBlur(image, 5)
ret, thresh1_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_BINARY)
thresh2_image = lmc_cv.adaptiveThreshold(image, 255, lmc_cv.ADAPTIVE_THRESH_MEAN_C,
lmc_cv.THRESH_BINARY, 11, 2)
thresh3_image = lmc_cv.adaptiveThreshold(image, 255, lmc_cv.ADAPTIVE_THRESH_GAUSSIAN_C,
lmc_cv.THRESH_BINARY, 11, 2)
# Display images
pyplot.figure('Image Display')
titles = ['Original Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [image, thresh1_image, thresh2_image, thresh3_image]
for i in range(4):
pyplot.subplot(2, 2, i + 1)
pyplot.imshow(images[i], 'gray')
pyplot.title(titles[i])
pyplot.xticks([])
pyplot.yticks([])
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close()
return

Dajin law (OTSU) Threshold segmentation

####################################################################################################
# Dajin law (OTSU) Threshold segmentation
def lmc_cv_otsu_thresholding():
"""
The functionality : Dajin law (OTSU) Threshold segmentation .
"""
# Read images
image = lmc_cv.imread('D:/99-Research/Python/Image/Noise.png')
image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2GRAY)
# Dajin law (OTSU) Threshold segmentation
sigma = 1
ksize = (sigma * 3) | 1
image = lmc_cv.GaussianBlur(image, (ksize, ksize), sigma)
# global thresholding
ret1, thresh1_image = lmc_cv.threshold(image, 127, 255, lmc_cv.THRESH_BINARY)
# Otsu's thresholding
ret2, thresh2_image = lmc_cv.threshold(image, 0, 255, lmc_cv.THRESH_BINARY + lmc_cv.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
blur = lmc_cv.GaussianBlur(image, (5, 5), 0)
ret3, thresh3_image = lmc_cv.threshold(blur, 0, 255, lmc_cv.THRESH_BINARY + lmc_cv.THRESH_OTSU)
# Display images
pyplot.figure('Image Display')
images = [image, 0, thresh1_image,
image, 0, thresh2_image,
blur, 0, thresh3_image]
titles = ['Original Noisy Image', 'Histogram', 'Global Thresholding (v=127)',
'Original Noisy Image', 'Histogram', "Otsu's Thresholding",
'Gaussian filtered Image', 'Histogram', "Otsu's Thresholding"]
for i in range(3):
pyplot.subplot(3, 3, i * 3 + 1)
pyplot.imshow(images[i * 3], 'gray')
pyplot.title(titles[i * 3])
pyplot.xticks([]), pyplot.yticks([])
pyplot.subplot(3, 3, i * 3 + 2)
pyplot.hist(images[i * 3].ravel(), 256)
pyplot.title(titles[i * 3 + 1])
pyplot.xticks([]), pyplot.yticks([])
pyplot.subplot(3, 3, i * 3 + 3)
pyplot.imshow(images[i * 3 + 2], 'gray')
pyplot.title(titles[i * 3 + 2])
pyplot.xticks([]), pyplot.yticks([])
pyplot.show()
# Save images based on user input
if ord("q") == (lmc_cv.waitKey(0) & 0xFF):
# Destruction of the window
pyplot.close()
return

Dajin law (OTSU) The specific implementation of threshold segmentation

This section will demonstrate the Dazu method (OTSU) Threshold segmented Python Realization , To show how it actually works .

Because we are dealing with bimodal images , Dajin law (OTSU) Trying to find a threshold (t), The threshold makes the weighted variance within the class minimum :

It actually found a t Value , It's between the two peaks , So that the variance in both classes is minimized . It can be Python The simple implementation is as follows :

img = cv.imread('noisy2.png',0)
blur = cv.GaussianBlur(img,(5,5),0)
# find normalized_histogram, and its cumulative distribution function
hist = cv.calcHist([blur],[0],None,[256],[0,256])
hist_norm = hist.ravel()/hist.sum()
Q = hist_norm.cumsum()
bins = np.arange(256)
fn_min = np.inf
thresh = -1
for i in xrange(1,256):
p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
if q1 < 1.e-6 or q2 < 1.e-6:
continue
b1,b2 = np.hsplit(bins,[i]) # weights
# finding means and variances
m1,m2 = np.sum(p1*b1)/q1, np.sum(p2*b2)/q2
v1,v2 = np.sum(((b1-m1)**2)*p1)/q1,np.sum(((b2-m2)**2)*p2)/q2
# calculates the minimization function
fn = v1*q1 + v2*q2
if fn < fn_min:
fn_min = fn
thresh = i
# find otsu's threshold value with OpenCV function
ret, otsu = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)
print( "{} {}".format(thresh,ret) )

 

 

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