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Merge pull request #19336 from kyshel:patch-1

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This commit is contained in:
Alexander Alekhin 2021-01-18 20:16:04 +00:00
commit 291dbdfbe6
4 changed files with 11 additions and 11 deletions
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10
doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.markdown
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@ -88,27 +88,27 @@ B = cv.imread('orange.jpg')
# generate Gaussian pyramid for A # generate Gaussian pyramid for A
G = A.copy() G = A.copy()
gpA = [G] gpA = [G]
for i in xrange(6): for i in range(6):
G = cv.pyrDown(G) G = cv.pyrDown(G)
gpA.append(G) gpA.append(G)
# generate Gaussian pyramid for B # generate Gaussian pyramid for B
G = B.copy() G = B.copy()
gpB = [G] gpB = [G]
for i in xrange(6): for i in range(6):
G = cv.pyrDown(G) G = cv.pyrDown(G)
gpB.append(G) gpB.append(G)
# generate Laplacian Pyramid for A # generate Laplacian Pyramid for A
lpA = [gpA[5]] lpA = [gpA[5]]
for i in xrange(5,0,-1): for i in range(5,0,-1):
GE = cv.pyrUp(gpA[i]) GE = cv.pyrUp(gpA[i])
L = cv.subtract(gpA[i-1],GE) L = cv.subtract(gpA[i-1],GE)
lpA.append(L) lpA.append(L)
# generate Laplacian Pyramid for B # generate Laplacian Pyramid for B
lpB = [gpB[5]] lpB = [gpB[5]]
for i in xrange(5,0,-1): for i in range(5,0,-1):
GE = cv.pyrUp(gpB[i]) GE = cv.pyrUp(gpB[i])
L = cv.subtract(gpB[i-1],GE) L = cv.subtract(gpB[i-1],GE)
lpB.append(L) lpB.append(L)
@ -122,7 +122,7 @@ for la,lb in zip(lpA,lpB):
# now reconstruct # now reconstruct
ls_ = LS[0] ls_ = LS[0]
for i in xrange(1,6): for i in range(1,6):
ls_ = cv.pyrUp(ls_) ls_ = cv.pyrUp(ls_)
ls_ = cv.add(ls_, LS[i]) ls_ = cv.add(ls_, LS[i])

8
doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
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@ -47,7 +47,7 @@ ret,thresh5 = cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)
titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV'] titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5] images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in xrange(6): for i in range(6):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255) plt.subplot(2,3,i+1),plt.imshow(images[i],'gray',vmin=0,vmax=255)
plt.title(titles[i]) plt.title(titles[i])
plt.xticks([]),plt.yticks([]) plt.xticks([]),plt.yticks([])
@ -98,7 +98,7 @@ titles = ['Original Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding'] 'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3] images = [img, th1, th2, th3]
for i in xrange(4): for i in range(4):
plt.subplot(2,2,i+1),plt.imshow(images[i],'gray') plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i]) plt.title(titles[i])
plt.xticks([]),plt.yticks([]) plt.xticks([]),plt.yticks([])
@ -153,7 +153,7 @@ titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
'Original Noisy Image','Histogram',"Otsu's Thresholding", 'Original Noisy Image','Histogram',"Otsu's Thresholding",
'Gaussian filtered Image','Histogram',"Otsu's Thresholding"] 'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
for i in xrange(3): for i in range(3):
plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray') plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
plt.title(titles[i*3]), plt.xticks([]), plt.yticks([]) plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256) plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
@ -196,7 +196,7 @@ bins = np.arange(256)
fn_min = np.inf fn_min = np.inf
thresh = -1 thresh = -1
for i in xrange(1,256): for i in range(1,256):
p1,p2 = np.hsplit(hist_norm,[i]) # probabilities p1,p2 = np.hsplit(hist_norm,[i]) # probabilities
q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes q1,q2 = Q[i],Q[255]-Q[i] # cum sum of classes
if q1 < 1.e-6 or q2 < 1.e-6: if q1 < 1.e-6 or q2 < 1.e-6:

2
doc/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.markdown
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@ -268,7 +268,7 @@ fft_filters = [np.fft.fft2(x) for x in filters]
fft_shift = [np.fft.fftshift(y) for y in fft_filters] fft_shift = [np.fft.fftshift(y) for y in fft_filters]
mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift] mag_spectrum = [np.log(np.abs(z)+1) for z in fft_shift]
for i in xrange(6): for i in range(6):
plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray') plt.subplot(2,3,i+1),plt.imshow(mag_spectrum[i],cmap = 'gray')
plt.title(filter_name[i]), plt.xticks([]), plt.yticks([]) plt.title(filter_name[i]), plt.xticks([]), plt.yticks([])

2
doc/py_tutorials/py_photo/py_non_local_means/py_non_local_means.markdown
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@ -108,7 +108,7 @@ from matplotlib import pyplot as plt
cap = cv.VideoCapture('vtest.avi') cap = cv.VideoCapture('vtest.avi')
# create a list of first 5 frames # create a list of first 5 frames
img = [cap.read()[1] for i in xrange(5)] img = [cap.read()[1] for i in range(5)]
# convert all to grayscale # convert all to grayscale
gray = [cv.cvtColor(i, cv.COLOR_BGR2GRAY) for i in img] gray = [cv.cvtColor(i, cv.COLOR_BGR2GRAY) for i in img]