def visualize_go():
''' Visualize the gradient orientation responses. '''
img = sp.misc.imread('data/patterns.png', flatten=True)
go, go_m = gradient_orientation(img, 2.5, signed=True, fft=False)
imsave('go/patterns-go.png', go)
imsave('go/patterns-go_weighted.png', go*go_m)
go, go_m = gradient_orientation(img, 2.5, signed=False, fft=False)
imsave('go/patterns-go_unsigned.png', go)
def visualize_bif():
''' Visualize the shape index responses. '''
img = sp.misc.imread('data/camera.png', flatten=True)
bif_img = bif_colors(bif_response(img, 2, eps=0.02))
imsave('bif/lena-bif-sigma2.png', bif_img)
bif_img = bif_colors(bif_response(img, 4, eps=0.02))
imsave('bif/lena-bif-sigma4.png', bif_img)
bif_img = bif_colors(bif_response(img, 8, eps=0.02))
imsave('bif/lena-bif-sigma8.png', bif_img)
def visualize_si_fiducial_orientation():
''' Visualize the shape index orientation with a fiducial coordinate
system.'''
img = sp.misc.imread('data/rings.png', flatten=True)
si, si_c, si_o, si_om = shape_index(img, 2.5, orientations=True, fft=True)
# No fiducial coordinate system.
imsave('si/rings-si_o.png', si_o)
imsave('si/rings-si_o_weighted.png', si_o * si_om)
# Let each pixel have its own coordinate system where the origin is
# the image center and the first axis is the vector from the origin to
# the pixel.
h, w = img.shape[:2]
y = np.linspace(-h / 2., h / 2., h)
x = np.linspace(-w / 2., w / 2., w)
xv, yv = np.meshgrid(x, y)
offsets = np.arctan(yv / (xv + 1e-10))
si_o = np.mod(si_o + offsets, np.pi)
imsave('si/rings-si_o_fiducial.png', si_o)
imsave('si/rings-si_o_fiducial_weighted.png', si_o * si_om)
def visualize_si_fiducial_orientation():
''' Visualize the shape index orientation with a fiducial coordinate
system.'''
img = sp.misc.imread('data/rings.png', flatten=True)
si, si_c, si_o, si_om = shape_index(img, 2.5, orientations=True, fft=True)
# No fiducial coordinate system.
imsave('si/rings-si_o.png', si_o)
imsave('si/rings-si_o_weighted.png', si_o*si_om)
# Let each pixel have its own coordinate system where the origin is
# the image center and the first axis is the vector from the origin to
# the pixel.
h, w = img.shape[:2]
y = np.linspace(-h/2., h/2., h)
x = np.linspace(-w/2., w/2., w)
xv, yv = np.meshgrid(x, y)
offsets = np.arctan(yv/(xv+1e-10))
si_o = np.mod(si_o+offsets, np.pi)
imsave('si/rings-si_o_fiducial.png', si_o)
imsave('si/rings-si_o_fiducial_weighted.png', si_o*si_om)
def visualize():
''' Visualize the shape index responses. '''
img = sp.misc.imread('data/patterns.png', flatten=True)
go, go_m = gradient_orientation(img, 3)
iso_go = isophotes(go, 5, (-np.pi, np.pi), .5, 'gaussian')
imsave('isophotes/patterns-go.png', go*go_m)
for i in range(iso_go.shape[0]):
imsave('isophotes/patterns-go_iso_%i_gaussian.png'%i,
iso_go[i, ...]*go_m)
iso_go = isophotes(go, 5, (-np.pi, np.pi), .5, 'von_mises')
for i in range(iso_go.shape[0]):
imsave('isophotes/patterns-go_iso_%i_von_mises.png'%i,
iso_go[i, ...]*go_m)
def visualize():
''' Visualize the shape index responses. '''
img = sp.misc.imread('data/patterns.png', flatten=True)
go, go_m = gradient_orientation(img, 3)
iso_go = isophotes(go, 5, (-np.pi, np.pi), .5, 'gaussian')
imsave('isophotes/patterns-go.png', go * go_m)
for i in range(iso_go.shape[0]):
imsave('isophotes/patterns-go_iso_%i_gaussian.png' % i,
iso_go[i, ...] * go_m)
iso_go = isophotes(go, 5, (-np.pi, np.pi), .5, 'von_mises')
for i in range(iso_go.shape[0]):
imsave('isophotes/patterns-go_iso_%i_von_mises.png' % i,
iso_go[i, ...] * go_m)
def visualize_go_fiducial_orientation():
''' Visualize the shape index orientation with a fiducial coordinate
system.'''
img = sp.misc.imread('data/rings.png', flatten=True)
go, go_m = gradient_orientation(img, 2.5, signed=True, fft=False)
# No fiducial coordinate system.
imsave('go/rings-go.png', go)
# Let each pixel have its own coordinate system where the origin is
# the image center and the first axis is the vector from the origin to
# the pixel.
h, w = img.shape[:2]
y = np.linspace(-h/2., h/2., h)
x = np.linspace(-w/2., w/2., w)
xv, yv = np.meshgrid(x, y)
offsets = np.arctan2(yv, xv)
go = np.mod(go-offsets, 2*np.pi)
imsave('go/rings-go-fiducial.png', go)
imsave('go/rings-go-fiducial_weighted.png', go*go_m)
def visualize_donuts():
''' Visualize donut spatial weighs. '''
ds = donuts((100, 100), 3, 30, 8.0, 1.2)
for i, d in enumerate(ds):
imsave('donuts/donut%i.png'%i, d)
def visualize_si():
''' Visualize the shape index responses. '''
img = sp.misc.imread('data/patterns.png', flatten=True)
si, si_c, si_o, si_om = shape_index(img, 2.5, orientations=True, fft=True)
imsave('si/patterns-si.png', si)
imsave('si/patterns-si_c.png', si_c)
imsave('si/patterns-si_weighted.png', si * si_c)
imsave('si/patterns-si_o.png', si_o)
imsave('si/patterns-si_om.png', si_om)
imsave('si/patterns-si_o_weighted.png', si_o * si_om)
def visualize_donuts():
''' Visualize donut spatial weighs. '''
ds = donuts((100, 100), 3, 30, 8.0, 1.2)
for i, d in enumerate(ds):
imsave('donuts/donut%i.png' % i, d)
def visualize_si():
''' Visualize the shape index responses. '''
img = sp.misc.imread('data/patterns.png', flatten=True)
si, si_c, si_o, si_om = shape_index(img, 2.5, orientations=True, fft=True)
imsave('si/patterns-si.png', si)
imsave('si/patterns-si_c.png', si_c)
imsave('si/patterns-si_weighted.png', si*si_c)
imsave('si/patterns-si_o.png', si_o)
imsave('si/patterns-si_om.png', si_om)
imsave('si/patterns-si_o_weighted.png', si_o*si_om)
