def roundtrip(self, dtype, x):
f = NamedTemporaryFile(suffix='.tif')
fname = f.name
f.close()
imsave(fname, x)
y = imread(fname)
assert_array_equal(x, y)
def test_all_negative_image():
im = np.array([-128, -1], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-128, 0))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_negative_overflow():
im = np.array([-1, 127], dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im)
assert_array_equal(bin_centers, np.arange(-1, 128))
assert frequencies[0] == 1
assert frequencies[-1] == 1
assert_array_equal(frequencies[1:-1], 0)
def test_img_as_gray_flatten():
img = imread(os.path.join(data_dir, 'color.png'), as_gray=True)
with expected_warnings(['deprecated']):
img_flat = imread(os.path.join(data_dir, 'color.png'), flatten=True)
assert_array_equal(img, img_flat)
def test_skeletonize_no_foreground(self):
im = np.zeros((5, 5))
result = skeletonize(im)
assert_array_equal(result, np.zeros((5, 5)))
def test_ellipse_generic():
img = np.zeros((4, 4), 'uint8')
rr, cc = ellipse(1.5, 1.5, 1.1, 1.7)
img[rr, cc] = 1
img_ = np.array([
[0, 0, 0, 0],
[1, 1, 1, 1],
[1, 1, 1, 1],
[0, 0, 0, 0],
])
assert_array_equal(img, img_)
img = np.zeros((5, 5), 'uint8')
rr, cc = ellipse(2, 2, 1.7, 1.7)
img[rr, cc] = 1
img_ = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0],
])
assert_array_equal(img, img_)
img = np.zeros((10, 10), 'uint8')
rr, cc = ellipse(5, 5, 3, 4)
img[rr, cc] = 1
img_ = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
assert_array_equal(img, img_)
img = np.zeros((10, 10), 'uint8')
rr, cc = ellipse(4.5, 5, 3.5, 4)
img[rr, cc] = 1
img_ = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
assert_array_equal(img, img_)
img = np.zeros((15, 15), 'uint8')
rr, cc = ellipse(7, 7, 3, 7)
img[rr, cc] = 1
img_ = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
assert_array_equal(img, img_)
def test_ellipse_perimeter_nzeroangle():
# angle != 0
img = np.zeros((30, 25), 'uint8')
rr, cc = ellipse_perimeter(15, 11, 12, 6, 1.1)
img[rr, cc] = 1
img_ = np.array([[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0
],
[
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0
],
[
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0
],
[
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0
],
[
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0
],
[
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0
],
[
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0
],
[
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0
],
[
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0
],
[
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0
],
[
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
]])
assert_array_equal(img, img_)
def test_filled_image():
img = regionprops(SAMPLE)[0].filled_image
assert_array_equal(img, SAMPLE)
def test_image():
img = regionprops(SAMPLE)[0].image
assert_array_equal(img, SAMPLE)
img = regionprops(SAMPLE_3D)[0].image
assert_array_equal(img, SAMPLE_3D[1:4, 1:3, 1:3])
def test_flat_int_range_dtype():
im = np.linspace(-128, 128, 256, dtype=np.int8)
frequencies, bin_centers = exposure.histogram(im, source_range='dtype')
assert_array_equal(bin_centers, np.arange(-128, 128))
assert_equal(frequencies.shape, (256,))
def test_basic():
x = np.array([[1, 1, 3], [0, 2, 0], [4, 3, 1]])
path, cost = spath.shortest_path(x)
assert_array_equal(path, [0, 0, 1])
assert_equal(cost, 1)
def test_non_square():
x = np.array([[1, 1, 1, 1, 5, 5, 5], [5, 0, 0, 5, 9, 1, 1],
[0, 5, 1, 0, 5, 5, 0], [6, 1, 1, 5, 0, 0, 1]])
path, cost = spath.shortest_path(x, reach=2)
assert_array_equal(path, [2, 1, 1, 2, 3, 3, 2])
assert_equal(cost, 0)
def test_reach():
x = np.array([[1, 1, 3], [0, 2, 0], [4, 3, 1]])
path, cost = spath.shortest_path(x, reach=2)
assert_array_equal(path, [0, 0, 2])
assert_equal(cost, 0)
def test_1d_erosion():
image = np.array([1, 2, 3, 2, 1])
expected = np.array([1, 1, 2, 1, 1])
eroded = gray.erosion(image)
testing.assert_array_equal(eroded, expected)
def _check_maps(ar, ar_relab, fw, inv):
assert_array_equal(fw[ar], ar_relab)
assert_array_equal(inv[ar_relab], ar)
def test_arraymap_call():
ar = np.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=np.intp)
relabeled, fw, inv = relabel_sequential(ar)
testing.assert_array_equal(relabeled, fw(ar))
testing.assert_array_equal(ar, inv(relabeled))
def test_adjust_gamma_one():
"""Same image should be returned for gamma equal to one"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 1)
assert_array_equal(result, image)
def test_adjust_gamma_zero():
"""White image should be returned for gamma equal to zero"""
image = np.random.uniform(0, 255, (8, 8))
result = exposure.adjust_gamma(image, 0)
dtype = image.dtype.type
assert_array_equal(result, dtype_range[dtype][1])
def test_consistent_2d_3d_hulls(images2d3d):
image, image3d = images2d3d
chimage = convex_hull_image(image)
chimage[8, 0] = True # correct for single point exactly on hull edge
chimage3d = convex_hull_image(image3d)
assert_array_equal(chimage3d[1], chimage)
def test_peak_float_out_of_range_image():
im = np.array([10, 100], dtype=np.float16)
frequencies, bin_centers = exposure.histogram(im, nbins=90)
# offset values by 0.5 for float...
assert_array_equal(bin_centers, np.arange(10, 100) + 0.5)
def test_empty_image():
image = np.zeros((6, 6), dtype=bool)
with expected_warnings(['entirely zero']):
assert_array_equal(convex_hull_image(image), image)
def test_label():
label = regionprops(SAMPLE)[0].label
assert_array_equal(label, 1)
label = regionprops(SAMPLE_3D)[0].label
assert_array_equal(label, 1)
def test_intensity_range_uint8(test_input, expected):
image = np.array([0, 1], dtype=np.uint8)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
def test_bilevel():
expected = np.zeros((10, 10))
expected[::2] = 255
img = imread(os.path.join(data_dir, 'checker_bilevel.png'))
assert_array_equal(img, expected)
def test_intensity_range_float(test_input, expected):
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values=test_input)
assert_array_equal(out, expected)
def test_bezier_segment_curved():
img = np.zeros((25, 25), 'uint8')
r0, c0 = 20, 20
r1, c1 = 20, 2
r2, c2 = 2, 2
rr, cc = _bezier_segment(r0, c0, r1, c1, r2, c2, 1)
img[rr, cc] = 1
img_ = np.array([[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
],
[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
]])
assert_equal(img[r0, c0], 1)
assert_equal(img[r2, c2], 1)
assert_array_equal(img, img_)
def test_intensity_range_clipped_float():
image = np.array([0.1, 0.2], dtype=np.float64)
out = intensity_range(image, range_values='dtype', clip_negative=True)
assert_array_equal(out, (0, 1))
def test_stack_basic():
x = np.arange(12).reshape(3, 4)
io.push(x)
assert_array_equal(io.pop(), x)
def test_lut_generation(self):
g123, g123p = _generate_thin_luts()
assert_array_equal(g123, G123_LUT)
assert_array_equal(g123p, G123P_LUT)
def test_rescale_no_overflow():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=np.uint8)
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == np.uint8
def test_skeletonize_single_point(self):
im = np.zeros((5, 5), np.uint8)
im[3, 3] = 1
result = skeletonize(im)
assert_array_equal(result, im)
def test_rescale_float_output():
image = np.array([-128, 0, 127], dtype=np.int8)
output_image = exposure.rescale_intensity(image, out_range=(0, 255))
testing.assert_array_equal(output_image, [0, 128, 255])
assert output_image.dtype == float
def test_bilevel():
expected = np.zeros((10, 10), bool)
expected[::2] = 1
img = imread(fetch('data/checker_bilevel.png'))
assert_array_equal(img.astype(bool), expected)
