def phog(image, bin, angle, L, roi):
""" phog -- Function that prepares data structures and histogram indexing and then
calls other functions to execute the pryamidal histogram of gradient code.
inputs: image -- String that names the file path to the image to compute on.
bin -- Number of components to use for each subwindow histogram
angle -- 180 or 360 if you want half-circle or whole-circle angles, respectively.
L -- Number of pyramid levels to use.
roi -- List with 4 entries, [top_y, bottom_y, left_x, right_x], that bounds the
image region for which you want to compute the HoG feature.
outputs: p -- A long vector that contains the pHoG descriptor.
"""
Img = imread(image)
if len(Img.shape) == 3:
G = clr.rgb2gray(Img)
else:
G = Img
if np.sum(G) > 100:
E = canny(G)
GradientX, GradientY = np.gradient(np.double(G))
Gr = np.sqrt(GradientX**2 + GradientY**2)
GradientX[GradientX == 0] = 1 * 10**(-5)
YX = np.divide(GradientY, GradientX)
if angle == 180:
A = (np.arctan(YX + np.pi / 2.0) * 180) / np.pi
if angle == 360:
A = (np.arctan2(GradientY, GradientX) + np.pi) * 180 / np.pi
# Function call
bh, bv = binMatrix(A, E, Gr, angle, bin)
else:
bh = np.zeros((G.shape[0], G.shape[1]))
bv = np.zeros((G.shape[0], G.shape[1]))
bh_roi = bh[roi[0]:roi[1], roi[2]:roi[3]]
bv_roi = bv[roi[0]:roi[1], roi[2]:roi[3]]
# Function call to actually extract the HoG vector.
p = phogDescriptor(bh_roi, bv_roi, L, bin)
return p
def phog(image, bin, angle, L, roi):
""" phog -- Function that prepares data structures and histogram indexing and then
calls other functions to execute the pryamidal histogram of gradient code.
inputs: image -- String that names the file path to the image to compute on.
bin -- Number of components to use for each subwindow histogram
angle -- 180 or 360 if you want half-circle or whole-circle angles, respectively.
L -- Number of pyramid levels to use.
roi -- List with 4 entries, [top_y, bottom_y, left_x, right_x], that bounds the
image region for which you want to compute the HoG feature.
outputs: p -- A long vector that contains the pHoG descriptor.
"""
Img = imread(image)
if len(Img.shape) == 3:
G = clr.rgb2gray(Img)
else:
G = Img
if np.sum(G) > 100:
E = canny(G)
GradientX, GradientY = np.gradient(np.double(G))
Gr = np.sqrt(GradientX**2 + GradientY**2)
GradientX[GradientX==0] = 1*10**(-5)
YX = np.divide(GradientY,GradientX)
if angle == 180:
A = (np.arctan(YX + np.pi/2.0)*180)/np.pi
if angle == 360:
A = (np.arctan2(GradientY,GradientX)+np.pi)*180/np.pi
# Function call
bh, bv = binMatrix(A,E,Gr,angle,bin)
else:
bh = np.zeros((G.shape[0],G.shape[1]))
bv = np.zeros((G.shape[0],G.shape[1]))
bh_roi = bh[roi[0]:roi[1], roi[2]:roi[3]]
bv_roi = bv[roi[0]:roi[1], roi[2]:roi[3]]
# Function call to actually extract the HoG vector.
p = phogDescriptor(bh_roi,bv_roi,L,bin)
return p
def test_01_02_circle_with_noise(self):
'''Test that the Canny filter finds the circle outlines in a noisy image'''
np.random.seed(0)
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
cf = c.astype(float) * .5 + np.random.uniform(size=c.shape) * .5
result = F.canny(cf, 4, .1, .2, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=4)
ce = binary_erosion(c, iterations=4)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_01_02_circle_with_noise(self):
'''Test that the Canny filter finds the circle outlines in a noisy image'''
np.random.seed(0)
i,j = np.mgrid[-200:200,-200:200].astype(float) / 200
c = np.abs(np.sqrt(i*i+j*j) - .5) < .02
cf = c.astype(float) * .5 + np.random.uniform(size=c.shape)*.5
result = F.canny(cf, 4, .1, .2,np.ones(c.shape,bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=4)
ce = binary_erosion(c,iterations=4)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_01_01_circle(self):
'''Test that the Canny filter finds the outlines of a circle'''
i, j = np.mgrid[-200:200, -200:200].astype(float) / 200
c = np.abs(np.sqrt(i * i + j * j) - .5) < .02
result = F.canny(c.astype(float), 4, 0, 0, np.ones(c.shape, bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=3)
ce = binary_erosion(c, iterations=3)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
#
# The circle has a radius of 100. There are two rings here, one
# for the inside edge and one for the outside. So that's 100 * 2 * 2 * 3
# for those places where pi is still 3. The edge contains both pixels
# if there's a tie, so we bump the count a little.
#
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_01_01_circle(self):
'''Test that the Canny filter finds the outlines of a circle'''
i,j = np.mgrid[-200:200,-200:200].astype(float) / 200
c = np.abs(np.sqrt(i*i+j*j) - .5) < .02
result = F.canny(c.astype(float), 4, 0, 0,np.ones(c.shape,bool))
#
# erode and dilate the circle to get rings that should contain the
# outlines
#
cd = binary_dilation(c, iterations=3)
ce = binary_erosion(c,iterations=3)
cde = np.logical_and(cd, np.logical_not(ce))
self.assertTrue(np.all(cde[result]))
#
# The circle has a radius of 100. There are two rings here, one
# for the inside edge and one for the outside. So that's 100 * 2 * 2 * 3
# for those places where pi is still 3. The edge contains both pixels
# if there's a tie, so we bump the count a little.
#
point_count = np.sum(result)
self.assertTrue(point_count > 1200)
self.assertTrue(point_count < 1600)
def test_00_00_zeros(self):
'''Test that the Canny filter finds no points for a blank field'''
result = F.canny(np.zeros((20, 20)), 4, 0, 0, np.ones((20, 20), bool))
self.assertFalse(np.any(result))
def test_00_01_zeros_mask(self):
'''Test that the Canny filter finds no points in a masked image'''
result = (F.canny(np.random.uniform(size=(20, 20)), 4, 0, 0,
np.zeros((20, 20), bool)))
self.assertFalse(np.any(result))
def test_00_00_zeros(self):
'''Test that the Canny filter finds no points for a blank field'''
result = F.canny(np.zeros((20,20)), 4, 0, 0, np.ones((20,20),bool))
self.assertFalse(np.any(result))
def test_00_01_zeros_mask(self):
'''Test that the Canny filter finds no points in a masked image'''
result = (F.canny(np.random.uniform(size=(20,20)), 4,0,0,
np.zeros((20,20),bool)))
self.assertFalse(np.any(result))
plt.title('Input image')
plt.subplot(122)
plt.imshow(np.log(1 + h),
extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
d[-1], d[0]],
cmap=plt.cm.gray, aspect=1/1.5)
plt.title('Hough transform')
plt.xlabel('Angles (degrees)')
plt.ylabel('Distance (pixels)')
# Line finding, using the Probabilistic Hough Transform
image = data.camera()
edges = canny(image, 2, 1, 25)
lines = probabilistic_hough(edges, threshold=10, line_length=5, line_gap=3)
plt.figure(figsize=(12, 4))
plt.subplot(131)
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Input image')
plt.subplot(132)
plt.imshow(edges, cmap=plt.cm.gray)
plt.title('Sobel edges')
plt.subplot(133)
plt.imshow(edges * 0)
