def quickedge(filename, min_contrast=4, sys=None, cli=False, max_curvature=10):
i = Image.open(filename).convert("L")
img = np.asarray(i)
if sys is None:
sys = EdgeSystem(*img.shape)
ed, ori = sys.detect(img, min_contrast)
thinned_ed = mask(ed, thin_mask(ed))
thinned_ori = mask(ed, thin_mask(ed))
rgb_curv = curvature_rgb(curvature(thinned_ori), max_curvature=max_curvature)
f, ((ax11, ax12, ax13, ax14), (ax21, ax22, ax23, ax24)) = plt.subplots(2, 4, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
ax11.imshow(img, cmap='gray', interpolation='none')
ax11.set_title("Original\nimage")
ax12.imshow(ed, cmap='jet', interpolation='none')
ax12.set_title("Edge\nmeasurement")
ax13.imshow(thinned_ed, cmap='jet', interpolation='none')
ax13.set_title("Thinned edge\nmeasurement")
ax21.imshow(overlay(img, ed), interpolation='none')
ax21.set_title("Edge overlay")
ax22.imshow(ori, cmap=coshrem.util.image.cyclic_cmap(), interpolation='none')
ax22.set_title("Orientation\nmeasurement")
ax23.imshow(thinned_ori, cmap=coshrem.util.image.cyclic_cmap(), interpolation='none')
ax23.set_title("Thinned orientation\nmeasurement")
ax24.imshow(rgb_curv, cmap='jet', interpolation='none')
ax24.set_title("Local curvature\nmeasurement")
if(cli):
Image.fromarray(ed, mode="L").save("edges.jpg", "JPEG")
f.set_size_inches(11.692, 8.267)
f.set_dpi(300)
f.savefig("edge_example.jpg")
else:
plt.show()
def main():
filename = "../tests/resources/img/B0.png"
def output_filename(str):
basename, extension = os.path.splitext(os.path.basename(filename))
if not os.path.exists(basename):
os.makedirs(basename)
return basename + "/" + basename + "." + str + extension
# Open an image and read it in as numpy array
i = Image.open(filename).convert("L")
img = np.asarray(i)
# Create a shearlet system for ridge detection using default parameters
#
# The same system could be created via
#
# sys = RidgeSystem(*img.shape,
# wavelet_eff_supp=60,
# gaussian_eff_supp=20,
# scales_per_octave=4,
# shear_level=3,
# alpha=0.2,
# octaves=3.5
# )
sys = RidgeSystem(*img.shape)
# Perform ridge and ridge-orientation measurement
ridges, orientations = sys.detect(img, min_contrast=10)
# Use a mask on the array to thin ridges to single pixel width
thinned_ridges = mask(ridges, thin_mask(ridges))
thinned_orientations = mask(orientations, thin_mask(ridges))
# Perform curvature measurement on the thinned orientations and convert into rgb image
rgb_curv = curvature_rgb(curvature(thinned_orientations), max_curvature=10)
# Write images to disk
Image.fromarray(np.uint8(ridges * 255),
mode="L").save(output_filename("ridges"))
Image.fromarray(overlay(img, ridges),
mode="RGB").save(output_filename("overlay_ridges"))
Image.fromarray(np.uint8(thinned_ridges * 255),
mode="L").save(output_filename("thin_ridges"))
# Create overview diagram with matplotlib
f, ((ax11, ax12, ax13, ax14),
(ax21, ax22, ax23,
ax24)) = plt.subplots(2,
4,
sharex=True,
sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax11.imshow(img, cmap='gray', interpolation='none')
ax11.set_title("Original image")
ax12.imshow(ridges, cmap='jet', interpolation='none')
ax12.set_title("Ridge\nmeasurement")
ax13.imshow(thinned_ridges, cmap='jet', interpolation='none')
ax13.set_title("Thinned ridge\nmeasurement")
ax14.axis('off')
ax21.imshow(overlay(img, ridges), interpolation='none')
ax21.set_title("Ridge overlay")
ax22.imshow(orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax22.set_title("Orientation\nmeasurement")
ax23.imshow(thinned_orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax23.set_title("Thinned orientation\nmeasurement")
ax24.imshow(rgb_curv, interpolation='none')
ax24.set_title("Local curvature\nmeasurement")
f.set_tight_layout(True)
f.set_size_inches(16, 9)
f.set_dpi(1600)
f.savefig(output_filename("diagram"))
def main():
filename_edge = "../tests/resources/data/K05_OH_full.txt"
filename_ridge = "../tests/resources/data/K05_CH_full.txt"
# Open an image data from txtfile and read it in as numpy array
img_edges = np.loadtxt(filename_edge)
img_ridges = np.loadtxt(filename_ridge)
# Create a shearlet system for edge detection with tweaked parameters
edge_sys = EdgeSystem(*img_edges.shape,
wavelet_eff_supp=100,
gaussian_eff_supp=25,
scales_per_octave=2,
shear_level=3,
alpha=0.8,
octaves=4)
# Perform edge and edge-orientation measurement
edges, edge_orientations = edge_sys.detect(img_edges,
min_contrast=70,
pivoting_scales='lowest')
# Create a shearlet system for ridge detection with tweaked parameters
ridge_sys = RidgeSystem(*img_ridges.shape,
wavelet_eff_supp=60,
gaussian_eff_supp=20,
scales_per_octave=4,
shear_level=3,
alpha=0.2,
octaves=4)
# Perform ridge and ridge-orientation measurement
ridges, ridge_orientations = ridge_sys.detect(img_ridges, min_contrast=20)
# Use a mask on the array to thin ridges and ridges to single pixel width
thinned_ridges = mask(ridges, thin_mask(ridges))
thinned_ridge_orientations = mask(ridge_orientations, thin_mask(ridges))
thinned_edges = mask(edges, thin_mask(edges))
thinned_edge_orientations = mask(edge_orientations, thin_mask(edges))
# Perform curvature measurement on the thinned orientations and convert into rgb image
rgb_ridge_curv = curvature_rgb(curvature(thinned_ridge_orientations),
max_curvature=10)
rgb_edge_curv = curvature_rgb(curvature(thinned_edge_orientations),
max_curvature=10)
# Write images to disk
Image.fromarray(np.uint8(ridges * 255),
mode="L").save(output_filename("ridges", filename_ridge),
quality=100)
Image.fromarray(overlay(img_ridges, ridges),
mode="RGB").save(output_filename("overlay_ridges",
filename_ridge),
quality=100)
Image.fromarray(np.uint8(thinned_ridges * 255),
mode="L").save(output_filename("thin_ridges",
filename_ridge),
quality=100)
Image.fromarray(np.uint8(edges * 255),
mode="L").save(output_filename("edges", filename_edge),
quality=100)
Image.fromarray(overlay(img_edges, edges),
mode="RGB").save(output_filename("overlay_edges",
filename_edge),
quality=100)
Image.fromarray(np.uint8(thinned_edges * 255),
mode="L").save(output_filename("thin_edges",
filename_edge),
quality=100)
# Create overview diagram with matplotlib
f, ((ax11, ax12, ax13, ax14), (ax21, ax22, ax23, ax24), (ax31, ax32, ax33,
ax34),
(ax41, ax42, ax43,
ax44)) = plt.subplots(4,
4,
sharex=True,
sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax11.imshow(img_edges, cmap='gray', interpolation='none')
ax11.set_title("Original edge image")
ax12.imshow(edges, cmap='jet', interpolation='none')
ax12.set_title("Edge\nmeasurement")
ax13.imshow(thinned_edges, cmap='jet', interpolation='none')
ax13.set_title("Thinned edge\nmeasurement")
ax14.axis('off')
ax21.imshow(overlay(img_edges, edges), interpolation='none')
ax21.set_title("Edge overlay")
ax22.imshow(edge_orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax22.set_title("Edge orientation\nmeasurement")
ax23.imshow(thinned_edge_orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax23.set_title("Thinned orientation\nmeasurement of edges")
ax24.imshow(rgb_edge_curv, interpolation='none')
ax24.set_title("Local edge\ncurvature measurement")
ax31.imshow(img_ridges, cmap='gray', interpolation='none')
ax31.set_title("Original ridge image")
ax32.imshow(ridges, cmap='jet', interpolation='none')
ax32.set_title("Ridge\nmeasurement")
ax33.imshow(thinned_ridges, cmap='jet', interpolation='none')
ax33.set_title("Thinned ridge\nmeasurement")
ax34.axis('off')
ax41.imshow(overlay(img_ridges, ridges), interpolation='none')
ax41.set_title("Ridge overlay")
ax42.imshow(ridge_orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax42.set_title("Ridge orientation\nmeasurement")
ax43.imshow(thinned_ridge_orientations,
cmap=coshrem.util.image.cyclic_cmap(),
interpolation='none')
ax43.set_title("Thinned ridge\norientation measurement")
ax44.imshow(rgb_ridge_curv, interpolation='none')
ax44.set_title("Local ridge curvature\nmeasurement")
f.set_size_inches(16, 16)
f.set_dpi(1600)
f.savefig(output_filename("overview", filename_edge))