def test_size_fail(self):
for sz in (8, 13):
try:
fast.corner_detect(self.img0, size=sz)
except ValueError:
pass
else:
raise Exception("Expected ValueError")
def test_basic(self):
for n in range(9, 13):
xy = fast.corner_detect(self.img0, size=n)
height, width = self.img0.shape
assert len(xy) > 0
for x, y in xy:
assert ((x >= 0) and (x < width))
assert ((y >= 0) and (y < height))
import os
import numpy as np
from supreme.lib import fast
from supreme.config import data_path
from supreme.io import ImageCollection
import scipy as sp
ic = ImageCollection(os.path.join(data_path, 'toystory/toystory00*.png'),
grey=True)
xy0 = fast.corner_detect(ic[0], barrier=30)
xy1 = fast.corner_detect(ic[1], barrier=30)
def mark_feature(img,xy):
shape = list(img.shape) + [3]
rgb = np.zeros(shape,np.uint8)
for band in range(3):
rgb[:,:,band].flat = img
for x,y in xy:
rgb[y,x] = [255,0,0]
return rgb
img0 = mark_feature(ic[0],xy0)
img1 = mark_feature(ic[1],xy1)
import pylab as P
P.subplot(121)
P.imshow(img0)
P.subplot(122)
def test_input_image(self):
xy0 = fast.corner_detect(self.img0, size=9)
xy1 = fast.corner_detect(self.img1, size=9)
assert_array_equal(xy0, xy1)
def test_barrier(self):
xy0 = fast.corner_detect(self.img0, barrier=10)
xy1 = fast.corner_detect(self.img0, barrier=20)
assert (len(xy0) > len(xy1))
weight_mod, area_mod = dpt_feat(pulses_mod, img1.shape, win_size=window)
# Ignore features that occur right on boundary
set_boundary_zero(weight, 5)
set_boundary_zero(area, 5)
set_boundary_zero(weight_mod, 5)
set_boundary_zero(area_mod, 5)
feat_coord, feat_area = get_brightest_pulses(weight, area, N=dpt_feature_nr)
feat_mod_coord, feat_mod_area = get_brightest_pulses(weight_mod, area_mod,
N=dpt_feature_nr)
elif feature_method == 'fast':
from supreme.lib import fast
perm = np.random.permutation
feat_coord = perm(fast.corner_detect(img0,
barrier=fast_barrier))
print "FAST found %d features." % len(feat_coord)
feat_coord = [(i,j) for (j,i) in feat_coord]
feat_mod_coord = perm(fast.corner_detect(img1,
barrier=fast_barrier))
feat_area = np.ones(len(feat_coord)) * 4
feat_mod_coord = [(i,j) for (j,i) in feat_mod_coord]
feat_mod_area = np.ones(len(feat_mod_coord)) * 4
else:
raise ValueError("Invalid feature extractor specified.")
import matplotlib.pyplot as plt
if show_features:
plt.subplot(121)
import os
import numpy as np
from supreme.lib import fast
from supreme.config import data_path
from supreme.io import ImageCollection
import scipy as sp
ic = ImageCollection(os.path.join(data_path, 'toystory/toystory00*.png'),
grey=True)
xy0 = fast.corner_detect(ic[0], barrier=30)
xy1 = fast.corner_detect(ic[1], barrier=30)
def mark_feature(img, xy):
shape = list(img.shape) + [3]
rgb = np.zeros(shape, np.uint8)
for band in range(3):
rgb[:, :, band].flat = img
for x, y in xy:
rgb[y, x] = [255, 0, 0]
return rgb
img0 = mark_feature(ic[0], xy0)
img1 = mark_feature(ic[1], xy1)
import pylab as P
set_boundary_zero(weight, 5)
set_boundary_zero(area, 5)
set_boundary_zero(weight_mod, 5)
set_boundary_zero(area_mod, 5)
feat_coord, feat_area = get_brightest_pulses(weight,
area,
N=dpt_feature_nr)
feat_mod_coord, feat_mod_area = get_brightest_pulses(weight_mod,
area_mod,
N=dpt_feature_nr)
elif feature_method == 'fast':
from supreme.lib import fast
perm = np.random.permutation
feat_coord = perm(fast.corner_detect(img0, barrier=fast_barrier))
print "FAST found %d features." % len(feat_coord)
feat_coord = [(i, j) for (j, i) in feat_coord]
feat_mod_coord = perm(fast.corner_detect(img1, barrier=fast_barrier))
feat_area = np.ones(len(feat_coord)) * 4
feat_mod_coord = [(i, j) for (j, i) in feat_mod_coord]
feat_mod_area = np.ones(len(feat_mod_coord)) * 4
else:
raise ValueError("Invalid feature extractor specified.")
import matplotlib.pyplot as plt
if show_features:
plt.subplot(121)
plt.hold(True)
