def test_farid_vertical():
"""Farid on a vertical edge should be a vertical line."""
i, j = np.mgrid[-5:6, -5:6]
image = (j >= 0).astype(float)
result = filters.farid(image) * np.sqrt(2)
assert (np.all(result[j == 0] == result[j == 0][0]))
assert_allclose(result[np.abs(j) > 2], 0, atol=1e-10)
def test_farid_horizontal():
"""Farid on a horizontal edge should be a horizontal line."""
i, j = np.mgrid[-5:6, -5:6]
image = (i >= 0).astype(float)
result = filters.farid(image) * np.sqrt(2)
# Check if result match transform direction
assert (np.all(result[i == 0] == result[i == 0][0]))
assert_allclose(result[np.abs(i) > 2], 0, atol=1e-10)
def test_farid_horizontal():
"""Farid on a horizontal edge should be a horizontal line."""
i, j = np.mgrid[-5:6, -5:6]
image = (i >= 0).astype(float)
result = filters.farid(image) * np.sqrt(2)
# Fudge the eroded points
i[np.abs(j) == 5] = 10000
assert (np.all(result[i == 0] == result[i == 0][0]))
assert_allclose(result[np.abs(i) > 2], 0, atol=1e-10)
def test_farid_mask():
"""Farid on a masked array should be zero."""
result = filters.farid(np.random.uniform(size=(10, 10)),
np.zeros((10, 10), dtype=bool))
assert (np.all(result == 0))
def test_farid_zeros():
"""Farid on an array of all zeros."""
result = filters.farid(np.zeros((10, 10)), np.ones((10, 10), dtype=bool))
assert (np.all(result == 0))
def test_farid_mask(dtype):
"""Farid on a masked array should be zero."""
result = filters.farid(np.random.uniform(size=(10, 10)).astype(dtype),
mask=np.zeros((10, 10), dtype=bool))
assert result.dtype == _supported_float_type(dtype)
assert (np.all(result == 0))
def image_pre_process(image):
# Apply otsu filter to find the thresholds
print('starting with multi otsu filter')
thresholds = threshold_multiotsu(image, classes=THO_N + 1)
print('thresholds are', thresholds)
regions = np.digitize(image, bins=thresholds)
otsu_regions_img = img_as_ubyte(regions)
# Binary threshold methods
# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html
ret, img_binary = cv2.threshold(image, thresholds[0], 255,
cv2.THRESH_BINARY)
# Dilation and erosion
# https://www.youtube.com/watch?v=WQK_oOWW5Zo
kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(img_binary, kernel, iterations=1)
dilation = cv2.dilate(img_binary, kernel, iterations=1)
opening = cv2.morphologyEx(img_binary, cv2.MORPH_OPEN, kernel)
# Label image
# https://www.youtube.com/watch?v=u3nG5_EjfM0&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=119
#label_image = measure.label(otsu_regions_img, connectivity=img_binary.ndim)
label_image = measure.label(opening, connectivity=img_binary.ndim)
image_label_overlay = label2rgb(label_image, image=otsu_regions_img)
# Get different regions from the labeled image
# https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_label.html
i = 0
regions = []
bbx = []
for region in measure.regionprops(label_image):
i += 1
print('i is', i)
print('region is', region.image)
regions.append(region.image)
bbx.append(region.bbox)
figure = plt.figure()
plt.imshow(region.image)
plt.title('region ' + np.str(i))
plt.savefig(
'G:\\My Drive\\Project\\IntraOral Scanner Registration\\Results\\Segmentation test\\region '
+ np.str(i))
plt.close()
print('shape of regions is', np.shape(regions))
for bbx_ind in bbx:
#figure = plt.figure()
#plt.imshow(regions[-3])
#plt.title('label image check')
print('region bounding box is', bbx_ind)
# Edge detection
# https://www.youtube.com/watch?v=Oy4duAOGdWQ&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=105
robert_image = roberts(img_binary)
sobel_image = sobel(img_binary)
scharr_image = scharr(img_binary)
prewitt_image = prewitt(img_binary)
farid_image = farid(img_binary)
print('detected edge is', sobel_image)
return otsu_regions_img, image_label_overlay, sobel_image
kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(img_binary, kernel, iterations=1)
dilation = cv2.dilate(img_binary, kernel, iterations=1)
# Label image
# https://www.youtube.com/watch?v=u3nG5_EjfM0&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=119
label_image = measure.label(output, connectivity=img_binary.ndim)
image_label_overlay = label2rgb(label_image, image=output)
# Edge detection
# https://www.youtube.com/watch?v=Oy4duAOGdWQ&list=PLZsOBAyNTZwbIjGnolFydAN33gyyGP7lT&index=105
robert_image = roberts(img_binary)
sobel_image = sobel(img_binary)
scharr_image = scharr(img_binary)
prewitt_image = prewitt(img_binary)
farid_image = farid(img_binary)
#img_mutso = multiOtsu(3, img)
plt.figure()
plt.imshow(img_gray, cmap='gray')
plt.title("Grayscale original image")
plt.figure()
plt.imshow(output, cmap='gray')
plt.title("Ostu image")
plt.figure()
plt.imshow(img_segment, cmap='gray')
plt.title("Segment image")
plt.figure()
from skimage import io, filters, feature
import matplotlib.pyplot as plt
from skimage.color import rgb2gray
import cv2
import numpy as np
img = cv2.imread('images/sandstone.tif', 0)
#Edge detection
from skimage.filters import roberts, sobel, scharr, prewitt, farid
roberts_img = roberts(img)
sobel_img = sobel(img)
scharr_img = scharr(img)
prewitt_img = prewitt(img)
farid_img = farid(img)
cv2.imshow("Roberts", roberts_img)
cv2.imshow("Sobel", sobel_img)
cv2.imshow("Scharr", scharr_img)
cv2.imshow("Prewitt", prewitt_img)
cv2.imshow("Farid", farid_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
