def scharr(data, sliceId=2):
edges = np.zeros(data.shape)
if sliceId == 2:
for idx in range(data.shape[2]):
edges[:, :, idx] = skifil.scharr(data[:, :, idx])
elif sliceId == 0:
for idx in range(data.shape[0]):
edges[idx, :, :] = skifil.scharr(data[idx, :, :])
return edges
def test_scharr_vertical():
"""Scharr on a vertical edge should be a vertical line."""
i, j = np.mgrid[-5:6, -5:6]
image = (j >= 0).astype(float)
result = F.scharr(image) * np.sqrt(2)
j[np.abs(i) == 5] = 10000
assert_allclose(result[j == 0], 1)
assert (np.all(result[np.abs(j) > 1] == 0))
def test_scharr_vertical():
"""Scharr on a vertical edge should be a vertical line"""
i, j = np.mgrid[-5:6, -5:6]
image = (j >= 0).astype(float)
result = F.scharr(image)
j[np.abs(i) == 5] = 10000
assert (np.all(result[j == 0] == 1))
assert (np.all(result[np.abs(j) > 1] == 0))
def test_scharr_horizontal():
"""Scharr on an edge should be a horizontal line."""
i, j = np.mgrid[-5:6, -5:6]
image = (i >= 0).astype(float)
result = F.scharr(image) * np.sqrt(2)
# Fudge the eroded points
i[np.abs(j) == 5] = 10000
assert_allclose(result[i == 0], 1)
assert (np.all(result[np.abs(i) > 1] == 0))
def test_scharr_horizontal():
"""Scharr on an edge should be a horizontal line"""
i, j = np.mgrid[-5:6, -5:6]
image = (i >= 0).astype(float)
result = F.scharr(image)
# Fudge the eroded points
i[np.abs(j) == 5] = 10000
assert (np.all(result[i == 0] == 1))
assert (np.all(result[np.abs(i) > 1] == 0))
def delphi_scharr(image_roi, attrs={}, debug=False):
greyscale = rgb2gray(image_roi)
edge_magnitude = scharr(greyscale)
H = np.histogram(edge_magnitude, bins=8, range=(0,1), density=True)[0]
if debug:
print "=== Scharr Texture Histogram ==="
print H
print
attrs.update(('Scharr %d' % i, v) for i, v in enumerate(H))
return attrs
def delphi_scharr(image_roi, attrs={}, debug=False):
greyscale = rgb2gray(image_roi)
edge_magnitude = scharr(greyscale)
H = np.histogram(edge_magnitude, bins=8, range=(0, 1), density=True)[0]
if debug:
print "=== Scharr Texture Histogram ==="
print H
print
attrs.update(('Scharr %d' % i, v) for i, v in enumerate(H))
return attrs
def filter_function(img_grey, filt='canny'):
"""
Grayscales and apply edge detectors to image.
Returns the flattened filtered image array.
input: raw image 3d tensor
output: filtered image
filters: 'sobel', 'roberts', 'scharr'
default filter = 'canny'
"""
# grayscale filters:
if filt == 'sobel':
return sobel(img_grey)
elif filt == 'roberts':
return roberts(img_grey)
elif filt == 'canny':
return canny(img_grey)
elif filt == 'scharr':
return scharr(image_grey)
elif filt == ('canny', 'sobel'):
return canny(sobel(img_grey))
else:
raise Exception('No Such Filter!')
def test_scharr_mask():
"""Scharr on a masked array should be zero."""
np.random.seed(0)
result = F.scharr(np.random.uniform(size=(10, 10)),
np.zeros((10, 10), bool))
assert_allclose(result, 0)
def test_scharr_zeros():
"""Scharr on an array of all zeros."""
result = F.scharr(np.zeros((10, 10)), np.ones((10, 10), bool))
assert (np.all(result < 1e-16))
def edge_detect(pic):
edges = scharr(color.rgb2grey(pic))
#return edges
return to_rgb3a(edges)
import os
import matplotlib.pyplot as plt
get_ipython().magic(u'matplotlib inline')
path = '/Users/heymanhn/Virginia/Zipfian/Capstone_Project/Test_Output_Images/boots'
file_name = 'barneys_158585078.jpg'
image = io.imread('%s/%s' % (path, file_name))
plt.imshow(image)
image_grey = color.rgb2gray(image)
# In[58]:
edge_roberts = roberts(image_grey)
edge_canny = canny(image_grey)
edge_sobel = sobel(image_grey)
edge_scharr = scharr(image_grey)
# In[59]:
fig, ((ax0, ax1), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 7))
ax0.imshow(edge_roberts, cmap=plt.cm.gray)
ax0.set_title('Roberts Edge Detection')
ax0.axis('off')
ax1.imshow(edge_canny, cmap=plt.cm.gray)
ax1.set_title('Canny Edge Detection')
ax1.axis('off')
ax3.imshow(edge_sobel, cmap=plt.cm.gray)
ax3.set_title('Sobel Edge Detection')
def test_scharr_mask():
"""Scharr on a masked array should be zero"""
np.random.seed(0)
result = F.scharr(np.random.uniform(size=(10, 10)), np.zeros((10, 10),
bool))
assert (np.all(result == 0))
def test_scharr_zeros():
"""Scharr on an array of all zeros"""
result = F.scharr(np.zeros((10, 10)), np.ones((10, 10), bool))
assert (np.all(result == 0))
def edge_detect(pic):
edges = scharr(color.rgb2grey(pic))
#return edges
return to_rgb3a(edges)