def test_01_03_adjacent_and_different(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5:6] = 1
expected = (image == 1)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
result = is_local_maximum(image, labels)
self.assertTrue(np.all(result == expected))
def test_01_03_adjacent_and_different(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5:6] = 1
expected = (image == 1)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
result = is_local_maximum(image, labels)
self.assertTrue(np.all(result == expected))
def test_03_01_disk_1(self):
'''regression test of img-1194, footprint = [1]
Test is_local_maximum when every point is a local maximum
'''
np.random.seed(31)
image = np.random.uniform(size=(10, 20))
footprint = np.array([[1]])
result = is_local_maximum(image, np.ones((10, 20)), footprint)
self.assertTrue(np.all(result))
result = is_local_maximum(image, footprint=footprint)
self.assertTrue(np.all(result))
def test_03_01_disk_1(self):
'''regression test of img-1194, footprint = [1]
Test is_local_maximum when every point is a local maximum
'''
np.random.seed(31)
image = np.random.uniform(size=(10, 20))
footprint = np.array([[1]])
result = is_local_maximum(image, np.ones((10, 20)), footprint)
self.assertTrue(np.all(result))
result = is_local_maximum(image, footprint=footprint)
self.assertTrue(np.all(result))
def test_01_02_adjacent_and_same(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5:6] = 1
labels[5, 5:6] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_01_one_point(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
labels[5, 5] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_02_adjacent_and_same(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5:6] = 1
labels[5, 5:6] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_01_one_point(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
labels[5, 5] = 1
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == (labels == 1)))
def test_01_06_adjacent_different_objects(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5] = 1
labels[5, 6] = 2
expected = (labels > 0)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
def test_01_06_adjacent_different_objects(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
image[5, 5] = 1
image[5, 6] = .5
labels[5, 5] = 1
labels[5, 6] = 2
expected = (labels > 0)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(result == expected))
def test_02_01_four_quadrants(self):
np.random.seed(21)
image = np.random.uniform(size=(40, 60))
i, j = np.mgrid[0:40, 0:60]
labels = 1 + (i >= 20) + (j >= 30) * 2
i, j = np.mgrid[-3:4, -3:4]
footprint = (i * i + j * j <= 9)
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = is_local_maximum(image, labels, footprint)
self.assertTrue(np.all(result == expected))
def test_02_01_four_quadrants(self):
np.random.seed(21)
image = np.random.uniform(size=(40, 60))
i, j = np.mgrid[0:40, 0:60]
labels = 1 + (i >= 20) + (j >= 30) * 2
i, j = np.mgrid[-3:4, -3:4]
footprint = (i * i + j * j <= 9)
expected = np.zeros(image.shape, float)
for imin, imax in ((0, 20), (20, 40)):
for jmin, jmax in ((0, 30), (30, 60)):
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
image[imin:imax, jmin:jmax], footprint=footprint)
expected = (expected == image)
result = is_local_maximum(image, labels, footprint)
self.assertTrue(np.all(result == expected))
def test_00_00_empty(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(~ result))
def test_00_00_empty(self):
image = np.zeros((10, 20))
labels = np.zeros((10, 20), int)
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
self.assertTrue(np.all(~result))
def _filter_test(self, image, frame, target, dim, cthreshold, mi, ma):
'''
if the convexity of the target is <threshold try to do a watershed segmentation
make black image with white polygon
do watershed segmentation
find polygon center
'''
def test(ti):
ctest = ti.convexity > cthreshold
centtest = self._near_center(ti.centroid, frame)
atest = ma > ti.area > mi
# print ma, ti.area, mi
# print ctest, centtest, atest
return ctest, centtest, atest
# find the label with the max area ie max of histogram
def get_limits(values, bins, width=1):
ind = argmax(values)
if ind == 0:
bil = bins[ind]
biu = bins[ind + width]
elif ind == len(bins) - width:
bil = bins[ind - width]
biu = bins[ind]
else:
bil = bins[ind - width]
biu = bins[ind + width]
return bil, biu, ind
ctest, centtest, atest = test(target)
if not ctest and (atest and centtest):
src = image.get_frame(0)
draw_polygons(src, [target.poly_points], color=(0, 255, 255))
wh = get_size(src)
# make image with polygon
im = zeros(wh)
points = asarray(target.poly_points)
points = asarray([(pi.x, pi.y) for pi in points])
rr, cc = polygon(points[:, 0], points[:, 1])
im[cc, rr] = 255
# do watershedding
distance = ndimage.distance_transform_edt(im)
local_maxi = is_local_maximum(distance, im)
markers, ns = ndimage.label(local_maxi)
wsrc = watershed(-distance, markers,
mask=im
)
# bins = 3 * number of labels. this allows you to precisely pick the value of the max area
values, bins = histogram(wsrc, bins=ns * 3)
bil, biu, ind = get_limits(values, bins)
if not bil:
values = delete(values, ind)
bins = delete(bins, (ind, ind + 1))
bil, biu, ind = get_limits(values, bins)
nimage = ones_like(wsrc, dtype='uint8') * 255
nimage[wsrc < bil] = 0
nimage[wsrc > biu] = 0
nimage = invert(nimage)
img = asMat(nimage)
# locate new polygon from the segmented image
tars = self._find_targets(image, img, dim, start=10, w=4, n=2)
# do_later(lambda: self.debug_show(im, distance, wsrc, nimage))
if tars:
target = tars[0]
ctest, centtest, atest = test(target)
else:
return None, False
return target, ctest and atest and centtest
