def ncc_segment(self, segment1, segments2, tol):
shape = [tol, tol]
region1 = segment1.get_image_region()
scale = segment1.get_segmented_image().get_scale()
i1 = segments2.get_img().get_data()
i1 = nputils.zoom(i1, region1.get_center(), [2 * tol, 2 * tol])
i2 = region1.get_region()
mask = (i2 > 0)
if min(region1.get_region().shape) <= 3:
return None
if self.mode == 'ncc':
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
ncc[ncc < self.ncc_threshold] = 0
# ncc = nputils.weighted_norm_xcorr2(i1, i2, (i2 > 0), mode='same')
data = nputils.resize(ncc, shape)
elif self.mode == 'ncc_peaks':
data = np.zeros(shape)
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
ncc = nputils.resize(ncc, shape)
peaks = nputils.find_peaks(ncc,
4,
self.ncc_threshold,
fit_gaussian=False)
for peak in peaks[:]:
data += imgutils.gaussian(shape, width=8,
center=peak) * ncc[tuple(peak)]
return data
def ncc_segment(self, segment1, segments2, tol):
shape = [tol, tol]
region1 = segment1.get_image_region()
scale = segment1.get_segmented_image().get_scale()
i1 = segments2.get_img().get_data()
i1 = nputils.zoom(i1, region1.get_center(), [2 * tol, 2 * tol])
i2 = region1.get_region()
mask = (i2 > 0)
if min(region1.get_region().shape) <= 3:
return None
if self.mode == 'ncc':
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
ncc[ncc < self.ncc_threshold] = 0
# ncc = nputils.weighted_norm_xcorr2(i1, i2, (i2 > 0), mode='same')
data = nputils.resize(ncc, shape)
elif self.mode == 'ncc_peaks':
data = np.zeros(shape)
ncc = nputils.norm_xcorr2(i1, i2, mode='same')
ncc = nputils.resize(ncc, shape)
peaks = nputils.find_peaks(ncc, 4, self.ncc_threshold, fit_gaussian=False)
for peak in peaks[:]:
data += imgutils.gaussian(shape, width=8, center=peak) * ncc[tuple(peak)]
return data
def do_gaussien(exp, size, nsigma=None, width=None):
res = imgutils.gaussian(size, nsigma=nsigma, width=width)
return np.equal(exp, (res * 1000).astype(np.int)).all()
def test_gaussien_no_arg():
try:
imgutils.gaussian(5)
assert False
except ValueError:
pass
def test_region_image():
m2 = np.zeros([5, 5])
m2[2:3, 2:4] = 1
img1 = imgutils.gaussian(100, width=6, angle=-0.5, center=[70, 55])
img1[img1 < 0.1] = 0
seg1, index = nputils.crop_threshold(img1, output_index=True)
region1 = imgutils.ImageRegion(img1, index)
assert np.allclose(region1.get_region(), seg1)
assert np.allclose(region1.get_data(), img1)
assert region1.get_shape() == img1.shape
assert region1.get_shift() == [0, 0]
assert region1.get_index() == index
assert list(region1.get_center()) == [70, 55]
region1.set_shift([-5, -4])
img2 = imgutils.gaussian(100, width=6, angle=-0.5, center=[65, 51])
img2[img2 < 0.1] = 0
seg2, index2 = nputils.crop_threshold(img2, output_index=True)
assert np.allclose(region1.get_region(), seg2)
assert np.allclose(region1.get_data(), img2)
assert list(region1.get_center()) == [65, 51], region1.get_center()
region1.set_shift([-70, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[0, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [0 + 6 / 2, 65]
region1.set_shift([30, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[100, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [100 - 5 / 2, 65
], (region1.get_center(), seg3.shape)
region1.set_shift([20, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[90, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [90, 65]
region2 = imgutils.ImageRegion(img2, index2)
builder = imgutils.ImageBuilder()
builder.add(region1)
builder.add(region2)
res = builder.get()
seg4, index = nputils.crop_threshold(img2 + img3, output_index=True)
assert res.get_shape() == img1.shape
assert np.allclose(res.get_data(), img3 + img2)
assert np.allclose(res.get_region(), seg4)
assert res.get_index() == index
def test_gaussien_no_arg():
try:
imgutils.gaussian(5)
assert False
except ValueError:
pass
def test_region_image():
m2 = np.zeros([5, 5])
m2[2:3, 2:4] = 1
img1 = imgutils.gaussian(100, width=6, angle=-0.5, center=[70, 55])
img1[img1 < 0.1] = 0
seg1, index = nputils.crop_threshold(img1, output_index=True)
region1 = imgutils.ImageRegion(img1, index)
assert np.allclose(region1.get_region(), seg1)
assert np.allclose(region1.get_data(), img1)
assert region1.get_shape() == img1.shape
assert region1.get_shift() == [0, 0]
assert region1.get_index() == index
assert list(region1.get_center()) == [70, 55]
region1.set_shift([-5, -4])
img2 = imgutils.gaussian(100, width=6, angle=-0.5, center=[65, 51])
img2[img2 < 0.1] = 0
seg2, index2 = nputils.crop_threshold(img2, output_index=True)
assert np.allclose(region1.get_region(), seg2)
assert np.allclose(region1.get_data(), img2)
assert list(region1.get_center()) == [65, 51], region1.get_center()
region1.set_shift([-70, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[0, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [0 + 6 / 2, 65]
region1.set_shift([30, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[100, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [100 - 5 / 2, 65], (region1.get_center(), seg3.shape)
region1.set_shift([20, 10])
img3 = imgutils.gaussian(100, width=6, angle=-0.5, center=[90, 65])
img3[img3 < 0.1] = 0
seg3, index = nputils.crop_threshold(img3, output_index=True)
assert np.allclose(region1.get_region(), seg3)
assert np.allclose(region1.get_data(), img3)
assert list(region1.get_center()) == [90, 65]
region2 = imgutils.ImageRegion(img2, index2)
builder = imgutils.ImageBuilder()
builder.add(region1)
builder.add(region2)
res = builder.get()
seg4, index = nputils.crop_threshold(img2 + img3, output_index=True)
assert res.get_shape() == img1.shape
assert np.allclose(res.get_data(), img3 + img2)
assert np.allclose(res.get_region(), seg4)
assert res.get_index() == index
def do_gaussien(exp, size, nsigma=None, width=None):
res = imgutils.gaussian(size, nsigma=nsigma, width=width)
return np.equal(exp, (res * 1000).astype(np.int)).all()
