def _octagon_kernel(mo, no, mi, ni):
outer = (mo + 2 * no) ** 2 - 2 * no * (no + 1)
inner = (mi + 2 * ni) ** 2 - 2 * ni * (ni + 1)
outer_weight = 1.0 / (outer - inner)
inner_weight = 1.0 / inner
c = ((mo + 2 * no) - (mi + 2 * ni)) // 2
outer_oct = octagon(mo, no)
inner_oct = np.zeros((mo + 2 * no, mo + 2 * no))
inner_oct[c: -c, c: -c] = octagon(mi, ni)
bfilter = (outer_weight * outer_oct -
(outer_weight + inner_weight) * inner_oct)
return bfilter
def _octagon_kernel(mo, no, mi, ni):
outer = (mo + 2 * no)**2 - 2 * no * (no + 1)
inner = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
outer_weight = 1.0 / (outer - inner)
inner_weight = 1.0 / inner
c = ((mo + 2 * no) - (mi + 2 * ni)) // 2
outer_oct = octagon(mo, no)
inner_oct = np.zeros((mo + 2 * no, mo + 2 * no))
inner_oct[c:-c, c:-c] = octagon(mi, ni)
bfilter = (outer_weight * outer_oct -
(outer_weight + inner_weight) * inner_oct)
return bfilter
def test_corner_orientations_lena():
img = rgb2gray(data.lena())
corners = corner_peaks(corner_fast(img, 11, 0.35))
expected = np.array([-1.9195897 , -3.03159624, -1.05991162, -2.89573739,
-2.61607644, 2.98660159])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def cornerDemo(fileName):
img = cv2.imread(fileName, cv2.CV_LOAD_IMAGE_COLOR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#C = skimage.feature.corner_peaks(skimage.feature.corner_fast(gray, 14), min_distance=1)
#C = skimage.feature.corner_peaks(skimage.feature.corner_harris(gray), min_distance=1)
w, q = skimage.feature.corner_foerstner(gray)
accuracy_thresh = 0.4
roundness_thresh = 0.2
foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w
C = skimage.feature.corner_peaks(foerstner, min_distance=1)
orientations = skimage.feature.corner_orientations(gray, C, octagon(3, 2))
corners = np.rad2deg(orientations)
AngleBins = np.arange(0, 360, 45)
AngleBinsOrientation = np.array([0, 1, 2, 1, 0, 1, 2, 1])
OrientationHist = np.zeros((3, 1))
for a in corners:
OrientationHist[AngleBinsOrientation[np.argmin(
np.abs(a - AngleBins))]] += 1
if OrientationHist.sum() > 0:
OrientationHist = OrientationHist / OrientationHist.sum()
else:
OrientationHist = -0.01 * np.ones((3, 1))
# plt.subplot(2,1,1)
plt.imshow(img)
plt.plot(C[:, 1], C[:, 0], '*')
for i in range(len(orientations)):
plt.text(C[i, 1], C[i, 0], "{0:.2f}".format(corners[i]), fontsize=8)
# plt.subplot(2,1,2)
# plt.hist(corners, 20)
plt.show()
print OrientationHist
def getCornerFeatures(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
corners = skimage.feature.corner_peaks(skimage.feature.corner_fast(
gray, 14),
min_distance=1)
orientations = skimage.feature.corner_orientations(gray, corners,
octagon(3, 2))
corners = np.rad2deg(orientations)
corners = np.array(corners)
AngleBins = np.arange(0, 360, 45)
AngleBinsOrientation = np.array([0, 1, 2, 1, 0, 1, 2, 1])
OrientationHist = np.zeros((3, 1))
for a in corners:
OrientationHist[AngleBinsOrientation[np.argmin(
np.abs(a - AngleBins))]] += 1
if OrientationHist.sum() > 0:
OrientationHist = OrientationHist / OrientationHist.sum()
else:
OrientationHist = -0.01 * np.ones((3, 1))
F = []
F.extend(OrientationHist[:, 0].tolist())
F.append(100.0 * float(len(corners)) / (gray.shape[0] * gray.shape[1]))
Fnames = ["Corners-Hor", "Corners-Diag", "Corners-Ver", "Corners-Percent"]
return (F, Fnames)
def test_corner_orientations_lena():
img = rgb2gray(data.lena())
corners = corner_peaks(corner_fast(img, 11, 0.35))
expected = np.array([-1.9195897 , -3.03159624, -1.05991162, -2.89573739,
-2.61607644, 2.98660159])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def detectOpticDisc(image):
kernel = octagon(10, 10)
thresh = threshold_otsu(image[:,:,1])
binary = image > thresh
print binary.dtype
luminance = convertToHLS(image)[:,:,2]
t = threshold_otsu(luminance)
t = erosion(luminance, kernel)
labels = segmentation.slic(image[:,:,1], n_segments = 3)
out = color.label2rgb(labels, image[:,:,1], kind='avg')
skio.imshow(out)
x, y = computeCentroid(t)
print x, y
rows, cols, _ = image.shape
p1 = closing(image[:,:,1],kernel)
p2 = opening(p1, kernel)
p3 = reconstruction(p2, p1, 'dilation')
p3 = p3.astype(np.uint8)
#g = dilation(p3, kernel)-erosion(p3, kernel)
#g = rank.gradient(p3, disk(5))
g = cv2.morphologyEx(p3, cv2.MORPH_GRADIENT, kernel)
#markers = rank.gradient(p3, disk(5)) < 10
markers = drawCircle(rows, cols, x, y, 85)
#markers = ndimage.label(markers)[0]
#skio.imshow(markers)
g = g.astype(np.uint8)
#g = cv2.cvtColor(g, cv2.COLOR_GRAY2RGB)
w = watershed(g, markers)
print np.max(w), np.min(w)
w = w.astype(np.uint8)
#skio.imshow(w)
return w
def octMask(maskImg, octagon_m, octagon_n):
boxsize = maskImg.get_xsize()
maskArray = EMNumPy.em2numpy(maskImg)
if (boxsize <= octagon_n * 2 or boxsize <= octagon_m):
print "ERROR: (2 * the slanted sides) or (the horizontal and vertical sides) is larger than the boxsize of particles."
sys.exit()
#from skimage.morphology import octagon
#Generates an octagon shaped structuring element with a given size of horizontal and vertical sides and a given height or width of slanted sides.
#The slanted sides are 45 or 135 degrees to the horizontal axis and hence the widths and heights are equal.
octArray = octagon(octagon_m, octagon_n, dtype=np.uint8)
m, n = octArray.shape
assert m==n
if (m%2 == 0):
pad_before = (boxsize - m)/2
pad_after = (boxsize - m)/2
else:
pad_before = (boxsize - m)/2
pad_after = (boxsize - m)/2+1
octArrayPad = np.pad(octArray, (pad_before, pad_after), mode='constant')
octImg = EMNumPy.numpy2em(octArrayPad)
return octImg
def test_corner_orientations_square():
square = np.zeros((12, 12))
square[3:9, 3:9] = 1
corners = corner_peaks(corner_fast(square, 9), min_distance=1)
actual_orientations = corner_orientations(square, corners, octagon(3, 2))
actual_orientations_degrees = np.rad2deg(actual_orientations)
expected_orientations_degree = np.array([45.0, 135.0, -45.0, -135.0])
assert_array_equal(actual_orientations_degrees, expected_orientations_degree)
def open_close(im, val):
val = int(val)
if 4 > val > 0:
k = morphology.octagon(val, val)
im = filters.gaussian(im, val)
im = morphology.erosion(im, k)
im = morphology.dilation(im, k)
if 8 > val >= 4:
k = morphology.octagon(val // 2 + 1, val // 2 + 1)
im = filters.gaussian(im, val)
im = filters.gaussian(im, val)
im = morphology.erosion(im, k)
im = morphology.erosion(im, k)
im = morphology.dilation(im, k)
im = morphology.dilation(im, k)
if val >= 8:
k = morphology.octagon(val // 4 + 1, val // 4 + 1)
im = filters.gaussian(im, val)
im = filters.gaussian(im, val)
im = filters.gaussian(im, val)
im = filters.gaussian(im, val)
im = morphology.erosion(im, k)
im = morphology.erosion(im, k)
im = morphology.erosion(im, k)
im = morphology.erosion(im, k)
im = morphology.dilation(im, k)
im = morphology.dilation(im, k)
im = morphology.dilation(im, k)
im = morphology.dilation(im, k)
return im
def test_corner_orientations_square():
square = np.zeros((12, 12))
square[3:9, 3:9] = 1
corners = corner_peaks(corner_fast(square, 9), min_distance=1)
actual_orientations = corner_orientations(square, corners, octagon(3, 2))
actual_orientations_degrees = np.rad2deg(actual_orientations)
expected_orientations_degree = np.array([ 45., 135., -45., -135.])
assert_array_equal(actual_orientations_degrees,
expected_orientations_degree)
def test_corner_orientations_square(dtype):
square = np.zeros((12, 12), dtype=dtype)
square[3:9, 3:9] = 1
corners = corner_peaks(corner_fast(square, 9),
min_distance=1,
threshold_rel=0)
actual_orientations = corner_orientations(square, corners, octagon(3, 2))
assert actual_orientations.dtype == _supported_float_type(dtype)
actual_orientations_degrees = np.rad2deg(actual_orientations)
expected_orientations_degree = np.array([45, 135, -45, -135])
assert_array_equal(actual_orientations_degrees,
expected_orientations_degree)
def fg_markers(im_cent, im_bin, val, edges):
local_maxi = peak_local_max(im_cent,
indices=False,
min_distance=int(val),
labels=im_bin,
exclude_border=int(edges))
k = morphology.octagon(2, 2)
local_maxi = morphology.dilation(local_maxi, selem=k)
markers = ndimage.label(local_maxi)[0]
markers[local_maxi] += 1
return markers
def test_corner_orientations_astronaut():
img = rgb2gray(data.astronaut())
corners = corner_peaks(corner_fast(img, 11, 0.35))
expected = np.array([
-1.75220190e+00, 2.01197383e+00, -2.01162417e+00, -1.88247204e-01,
1.19134149e+00, -6.61151410e-01, -2.99143370e+00, 2.17103132e+00,
-7.52950306e-04, 1.25854853e+00, 2.43573659e+00, -1.69230287e+00,
-9.88548213e-01, 1.47154532e+00, -1.65449964e+00, 1.09650167e+00,
1.07812134e+00, -1.68885773e+00, -1.64397304e+00, 3.09780364e+00,
-3.49561988e-01, -1.46554357e+00, -2.81524886e+00, 8.12701702e-01,
2.47305654e+00, -1.63869275e+00, 5.46905279e-02, -4.40598471e-01,
3.14918803e-01, -1.76069982e+00, 3.05330950e+00, 2.39291733e+00,
-1.22091334e-01, -3.09279990e-01, 1.45931342e+00
])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def test_corner_orientations_astronaut():
img = rgb2gray(data.astronaut())
corners = corner_peaks(corner_fast(img, 11, 0.35),
min_distance=10, threshold_abs=0, threshold_rel=0.1)
expected = np.array([-1.75220190e+00, 2.01197383e+00, -2.01162417e+00,
-1.88247204e-01, 1.19134149e+00, -6.61151410e-01,
-2.99143370e+00, 2.17103132e+00, -7.52950306e-04,
1.25854853e+00, 2.43573659e+00, -1.69230287e+00,
-9.88548213e-01, 1.47154532e+00, -1.65449964e+00,
1.09650167e+00, 1.07812134e+00, -1.68885773e+00,
-1.64397304e+00, 3.09780364e+00, -3.49561988e-01,
-1.46554357e+00, -2.81524886e+00, 8.12701702e-01,
2.47305654e+00, -1.63869275e+00, 5.46905279e-02,
-4.40598471e-01, 3.14918803e-01, -1.76069982e+00,
3.05330950e+00, 2.39291733e+00, -1.22091334e-01,
-3.09279990e-01, 1.45931342e+00])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def objects():
from skimage.morphology import (square, rectangle, diamond, disk, cube,
octahedron, ball, octagon, star)
from mpl_toolkits.mplot3d import Axes3D
# Generate 2D and 3D structuring elements.
struc_2d = {
"square(15)": square(15),
"rectangle(15, 10)": rectangle(15, 10),
"diamond(7)": diamond(7),
"disk(7)": disk(7),
"octagon(7, 4)": octagon(7, 4),
"star(5)": star(5)
}
struc_3d = {
"cube(11)": cube(11),
"octahedron(5)": octahedron(5),
"ball(5)": ball(5)
}
# Visualize the elements.
fig = plt.figure(figsize=(8, 8))
idx = 1
for title, struc in struc_2d.items():
ax = fig.add_subplot(3, 3, idx)
ax.imshow(struc, cmap="Paired", vmin=0, vmax=12)
for i in range(struc.shape[0]):
for j in range(struc.shape[1]):
ax.text(j, i, struc[i, j], ha="center", va="center", color="w")
ax.set_axis_off()
ax.set_title(title)
idx += 1
for title, struc in struc_3d.items():
ax = fig.add_subplot(3, 3, idx, projection=Axes3D.name)
ax.voxels(struc)
ax.set_title(title)
idx += 1
fig.tight_layout()
plt.show()
def task1():
img = mimg.imread("bw1.bmp")
new_img1 = binary_erosion(img, selem=square(width=30))
new_img2 = binary_erosion(img, selem=rectangle(width=30, height=20))
new_img3 = binary_erosion(img, selem=diamond(radius=5))
new_img4 = binary_erosion(img, selem=disk(radius=15))
new_img5 = binary_erosion(img, selem=star(a=10))
new_img6 = binary_erosion(img, selem=octagon(m=10, n=20))
new_img7 = binary_dilation(img, )
fig, ax = plt.subplots(1, 8)
show(ax[0], img, "original")
show(ax[1], new_img1, "BE square")
show(ax[2], new_img2, "BE rectangle")
show(ax[3], new_img3, "BE diamond")
show(ax[4], new_img4, "BE disk")
show(ax[5], new_img5, "BE star")
show(ax[6], new_img6, "BE octagon")
show(ax[7], new_img2, "binary_dilation")
plt.show()
def getCornerFeatures(img): gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) corners = skimage.feature.corner_peaks(skimage.feature.corner_fast(gray, 14), min_distance=1) orientations = skimage.feature.corner_orientations(gray, corners, octagon(3, 2)) corners = np.rad2deg(orientations) corners = np.array(corners) AngleBins = np.arange(0,360,45); AngleBinsOrientation = np.array([0, 1, 2, 1, 0, 1, 2, 1]) OrientationHist = np.zeros((3,1)) for a in corners: OrientationHist[AngleBinsOrientation[np.argmin(np.abs(a-AngleBins))]] += 1 if OrientationHist.sum()>0: OrientationHist = OrientationHist / OrientationHist.sum() else: OrientationHist = - 0.01*np.ones((3,1)) F = [] F.extend(OrientationHist[:,0].tolist()) F.append(100.0*float(len(corners)) / ( gray.shape[0] * gray.shape[1] ) ) Fnames = ["Corners-Hor", "Corners-Diag", "Corners-Ver", "Corners-Percent"] return (F, Fnames)
def watershed(markers, im_bin, im_edge, d_mat, val, edges):
k = morphology.octagon(2, 2)
im_bin = morphology.binary_dilation(im_bin, selem=k)
im_bin = morphology.binary_dilation(im_bin, selem=k)
im_bin = morphology.binary_dilation(im_bin, selem=k)
markers_temp = markers + np.logical_not(im_bin)
shed_im = (1 - val) * im_edge - val * d_mat
labels = morphology.watershed(image=shed_im, markers=markers_temp)
labels -= 1
if edges == 1:
edge_vec = np.hstack((labels[:, 0].flatten(), labels[:, -1].flatten(),
labels[0, :].flatten(), labels[-1, :].flatten()))
edge_val = np.unique(edge_vec)
for val in edge_val:
if not val == 0:
labels[labels == val] = 0
return labels
def cornerDemo(fileName):
img = cv2.imread(fileName, cv2.CV_LOAD_IMAGE_COLOR)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#C = skimage.feature.corner_peaks(skimage.feature.corner_fast(gray, 14), min_distance=1)
#C = skimage.feature.corner_peaks(skimage.feature.corner_harris(gray), min_distance=1)
w, q = skimage.feature.corner_foerstner(gray)
accuracy_thresh = 0.4
roundness_thresh = 0.2
foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w
C = skimage.feature.corner_peaks(foerstner, min_distance=1)
orientations = skimage.feature.corner_orientations(gray, C, octagon(3, 2))
corners = np.rad2deg(orientations)
AngleBins = np.arange(0,360,45);
AngleBinsOrientation = np.array([0, 1, 2, 1, 0, 1, 2, 1])
OrientationHist = np.zeros((3,1))
for a in corners:
OrientationHist[AngleBinsOrientation[np.argmin(np.abs(a-AngleBins))]] += 1
if OrientationHist.sum()>0:
OrientationHist = OrientationHist / OrientationHist.sum()
else:
OrientationHist = - 0.01*np.ones((3,1))
# plt.subplot(2,1,1)
plt.imshow(img)
plt.plot(C[:,1], C[:,0], '*')
for i in range(len(orientations)):
plt.text(C[i,1], C[i,0], "{0:.2f}".format(corners[i]), fontsize=8);
# plt.subplot(2,1,2)
# plt.hist(corners, 20)
plt.show()
print OrientationHist
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from skimage.morphology import (square, rectangle, diamond, disk, cube,
octahedron, ball, octagon, star)
# Generate 2D and 3D structuring elements.
struc_2d = {
"square(15)": square(15),
"rectangle(15, 10)": rectangle(15, 10),
"diamond(7)": diamond(7),
"disk(7)": disk(7),
"octagon(7, 4)": octagon(7, 4),
"star(5)": star(5)
}
struc_3d = {
"cube(11)": cube(11),
"octahedron(5)": octahedron(5),
"ball(5)": ball(5)
}
# Visualize the elements.
fig = plt.figure(figsize=(8, 8))
idx = 1
for title, struc in struc_2d.items():
ax = fig.add_subplot(3, 3, idx)
img = sk.io.imread('teeth.jpg', True)
grey = img.copy()
img_t = cv2.threshold(255*grey, 160, 170, cv2.THRESH_BINARY_INV)[1]
img_tmp = 255 -img_t
plt.axis('off')
plt.imshow(img_tmp, cmap='gray')
plt.show()
img_e = binary_dilation(img_t, rectangle(2,3))
img_tmp = 255- 255*img_e
plt.imshow(img_tmp, cmap='gray')
plt.show()
img_e = binary_closing(img_e, rectangle(8,2))
img_tmp = 255- 255*img_e
plt.imshow(img_tmp, cmap='gray')
plt.show()
img_e = binary_dilation(img_e, octagon(10,2))
img_tmp = 255- 255*img_e
plt.imshow(img_tmp, cmap='gray')
plt.show()
img_e = binary_dilation(img_e, star(8))
img_tmp = 255- 255*img_e
plt.imshow(img_tmp, cmap='gray')
plt.show()
for i in range(3):
img_e = binary_erosion(img_e, star(3 + i))
img_e = 255- 255*img_e
plt.imshow(img_e, cmap='gray')
plt.axis('off')
plt.show()
def element(self) -> np.array:
return morphology.octagon(self.m, self.n)
from skimage.morphology import disk, octagon, square, star, erosion, dilation
#Variables basicas
opcion = 3
tamaño = 10
img = imread('circunferencias.png')
noisy_image = rgb2gray(img)
binaria = 0.25 <= noisy_image
binaria = ~binaria
# Erosion y dilatación
if opcion == 0:
erosionada = erosion(binaria, disk(tamaño))
imagen_final = dilation(erosionada, disk(tamaño))
if opcion == 1:
erosionada = erosion(binaria, octagon(tamaño, int(tamaño / 2)))
imagen_final = dilation(erosionada, octagon(tamaño, int(tamaño / 2)))
if opcion == 2:
erosionada = erosion(binaria, square(tamaño))
imagen_final = dilation(erosionada, square(tamaño))
if opcion == 3:
erosionada = erosion(binaria, star(tamaño))
imagen_final = dilation(erosionada, star(tamaño))
#Gaurdado de imagenes
plt.imshow(binaria, cmap='gray', vmin=0, vmax=1)
plt.title('Binaria')
plt.show()
plt.imshow(erosionada, cmap='gray', vmin=0, vmax=1)
plt.title('Tratada')
plt.show()
# cval=0, order=None)#6个三通道的原图大小矩阵
hmd=feature.hessian_matrix_det(imgrey, sigma=1)#原图大小矩阵
# hme=feature.hessian_matrix_eigvals(hmf, Hxy=None, Hyy=None)
si=feature.shape_index(imgrey, sigma=1, mode='constant', cval=0)#原图大小矩阵
# ckr=feature.corner_kitchen_rosenfeld(image, mode='constant', cval=0) ##原图大小矩阵 三通道
# ch=feature.corner_harris(imgrey, method='k', k=0.05, eps=1e-06, sigma=1)#原图大小矩阵
# cht=feature.corner_shi_tomasi(imgrey, sigma=1)#原图大小矩阵
# cfs=feature.corner_foerstner(imgrey, sigma=1)#2个 #原图大小矩阵
# csb=feature.corner_subpix(image, ch, window_size=11, alpha=0.99)
cps=feature.corner_peaks(imgrey, min_distance=1, threshold_abs=None,
threshold_rel=0.1, exclude_border=True, indices=True,
footprint=None, labels=None)#一堆坐标值
# cmr=feature.corner_moravec(imgrey, window_size=1)#原图大小矩阵
# cft=feature.corner_fast(imgrey, n=12, threshold=0.15)#原图大小矩阵
corners = feature.corner_peaks(feature.corner_fast(imgrey, 9), min_distance=1)#一堆坐标
corts=feature.corner_orientations(imgrey, corners, octagon(3, 2))#一维矩阵长度不定
# mtem=feature.match_template(image, template, pad_input=False,
# mode='constant', constant_values=0)
# bldg=feature.blob_dog(imgrey, min_sigma=1, max_sigma=50,
# sigma_ratio=1.6, threshold=2.0, overlap=0.5)#不懂
# bldoh=feature.blob_doh(imgrey, min_sigma=1, max_sigma=30, num_sigma=10,
# threshold=0.01, overlap=0.5, log_scale=False)#不懂
# bllog=feature.blob_log(imgrey, min_sigma=1, max_sigma=50, num_sigma=10,
# threshold=0.2, overlap=0.5, log_scale=False)#不懂
zong.append([imname,
greycghg[0,0],greycghg[0,1],greycghg[0,2],greycghg[0,3],greycghg[0,4],
greycgcl[0,0],greycgcl[0,1],greycgcl[0,2],greycgcl[0,3],greycgcl[0,4],
greycgeg[0,0],greycgeg[0,1],greycgeg[0,2],greycgeg[0,3],greycgeg[0,4],
greycgasm[0,0],greycgasm[0,1],greycgasm[0,2],greycgasm[0,3],greycgasm[0,4],
greycgctt[0,0],greycgctt[0,1],greycgctt[0,2],greycgctt[0,3],greycgctt[0,4],
np.mean(lbp),np.std(lbp),len(plm)/(len(image[:,0,0])*len(image[0,:,0])),
import matplotlib.pyplot as plt
import cv2
from skimage.morphology import disk, star, opening, closing, diamond, octagon, rectangle, square, ball, erosion, dilation, binary_opening, binary_closing
img = 255 - sk.io.imread("bw2.bmp", True)
##img_e = erosion(img.copy(), rectangle(7,4))
##img_d = dilation(img_e.copy(), octagon(10,2))
##img_d = erosion(img_d.copy(), disk(3))
##sk.io.imshow(255-img, cmap = "gray")
##sk.io.show()
##img_d = 255 - np.array([[255 if img_d[i][j] > 140 else 0 for j in range(img.shape[1])] for i in range(img.shape[0])])
##sk.io.imshow(img_d, cmap = "gray")
##sk.io.show()
img_e = erosion(img.copy(), rectangle(7, 4))
img_d = dilation(img_e.copy(), octagon(10, 2))
img_d = erosion(img_d.copy(), disk(3))
img_d = 255 - np.array(
[[255 if img_d[i][j] > 140 else 0 for j in range(img.shape[1])]
for i in range(img.shape[0])])
out = np.hstack([255 - img, img_d])
plt.imshow(out, cmap="gray")
plt.title(
"Negacja->Erozja(prostokąt(7x4))->dylacja(ośmiokąt(10x2))->\nerozja(koło(3))->progowanie->Negacja",
{
'fontsize': 18,
'fontweight': 'bold',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'
})
areas = [rp.area for rp in rps] region_with_largest_area = np.argmax(areas) binary = binary_label == rps[region_with_largest_area].label #binary = convex_hull_image(binary) #binary = binary_dilation(binary, disk(15)) != 0 distance = distance_transform_edt(binary) local_maxi = peak_local_max(distance) # internal marker / center of [circle] <- (external marker) #################################### #################################### Gradient Image red_chan = gray_image io.imshow(red_chan) close = closing(red_chan, octagon(4,4)) io.imshow(close) opening = opening(close, octagon(6,6)) io.imshow(opening) rec = reconstruction(opening, close) rec = exposure.rescale_intensity(rec, out_range=(0,1)) gradient = gradient(rec, disk(5)) ######################################################## Apply watershed to gradient using imposed markers ### This is where I am having trouble. I have the gradient image and the internal marker location. I want ### to create a marker image with the internal marker (located at the center of the optic disk) and ### the external marker drawn as a circle around the optic disk so that the watershed algorithm can ### evaluate the specific contour of the disk when feeding in the gradient image and the marker image.
# Generate 2D and 3D structuring elements.
footprint_dict = {
"square(11) (separable)":
(square(11, decomposition=None), square(11, decomposition="separable")),
"square(11) (sequence)":
(square(11, decomposition=None), square(11, decomposition="sequence")),
"rectangle(7, 11) (separable)": (rectangle(7, 11, decomposition=None),
rectangle(7,
11,
decomposition="separable")),
"rectangle(7, 11) (sequence)": (rectangle(7, 11, decomposition=None),
rectangle(7, 11,
decomposition="sequence")),
"diamond(5) (sequence)": (diamond(5, decomposition=None),
diamond(5, decomposition="sequence")),
"octagon(7, 4) (sequence)": (octagon(7, 4, decomposition=None),
octagon(7, 4, decomposition="sequence")),
"cube(11) (separable)": (cube(11, decomposition=None),
cube(11, decomposition="separable")),
"cube(11) (sequence)": (cube(11, decomposition=None),
cube(11, decomposition="sequence")),
"octahedron(7) (sequence)": (octahedron(7, decomposition=None),
octahedron(7, decomposition="sequence")),
}
# Visualize the elements
# use a similar dark blue for the 2d plots as for the 3d voxel plots
cmap = colors.ListedColormap(['white', (0.1216, 0.4706, 0.70588)])
fontdict = dict(fontsize=16, fontweight='bold')
for title, (footprint, footprint_sequence) in footprint_dict.items():
img_e = erosion(img_e.copy(), diamond(5))
img_d = dilation(img_d.copy(), diamond(5))
plt.imshow(np.hstack([255 - img, 255 - img_e, 255 - img_d]), cmap='gray')
plt.title(
"diamond(5), iteration: " + str(i + 1), {
'fontsize': 28,
'fontweight': 'bold',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'
})
plt.axis('off')
plt.show()
img_e = img.copy()
img_d = img.copy()
for i in range(0, 3):
img_e = erosion(img_e.copy(), octagon(5, 2))
img_d = dilation(img_d.copy(), octagon(5, 2))
plt.imshow(np.hstack([255 - img, 255 - img_e, 255 - img_d]), cmap='gray')
plt.title(
"octagon(5,2), iteration: " + str(i + 1), {
'fontsize': 28,
'fontweight': 'bold',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'
})
plt.axis('off')
plt.show()
img_e = img.copy()
img_d = img.copy()
for i in range(0, 3):
img_e = erosion(img_e.copy(), rectangle(5, 2))
