def test_moments_coords():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu_image = moments(image)
coords = np.array([[r, c] for r in range(13, 17)
for c in range(13, 17)], dtype=np.double)
mu_coords = moments_coords(coords)
assert_almost_equal(mu_coords, mu_image)
def test_moments_coords():
image = np.zeros((20, 20), dtype=np.double)
image[13:17, 13:17] = 1
mu_image = moments(image)
coords = np.array([[r, c] for r in range(13, 17) for c in range(13, 17)],
dtype=np.double)
mu_coords = moments_coords(coords)
assert_almost_equal(mu_coords, mu_image)
def centroids(points):
"""
Calculate X and Y centroids for a polygon using OpenCV
input: list[tuple(x, y)]
output: (centroidx, centroidy)
"""
# Determine moments from points using OpenCV
im_moment = moments_coords(points, order=1)
# Use moments to determine centroids
cx = im_moment[1, 0] / im_moment[0, 0]
cy = im_moment[0, 1] / im_moment[0, 0]
return cx, cy
def test_moments_coords_dtype(dtype):
image = np.zeros((20, 20), dtype=dtype)
image[13:17, 13:17] = 1
expected_dtype = _supported_float_type(dtype)
mu_image = moments(image)
assert mu_image.dtype == expected_dtype
coords = np.array([[r, c] for r in range(13, 17) for c in range(13, 17)],
dtype=dtype)
mu_coords = moments_coords(coords)
assert mu_coords.dtype == expected_dtype
assert_almost_equal(mu_coords, mu_image)
def get_max_contour_colors(contour, image_shape):
"""Determine number of colors to fill a contour with gradient or random colors"""
# Determine contour perimeter
try:
rr, cc = draw.polygon_perimeter(contour[:, 0], contour[:, 1], shape=image_shape)
perimeter = np.column_stack((rr, cc))
M = measure.moments_coords(perimeter)
centroid = (M[1, 0] / M[0, 0], M[0, 1] / M[0, 0])
area_center = np.array([centroid])
# Determine distance of each point from the center
D = distance.cdist(perimeter, area_center, metric='euclidean')
# Get nunber of colors
num_colors = int(np.max(D)) + 1
return num_colors
except IndexError:
return None
def draw_contour_(contour_img,
contour,
color,
gradient_colors=("red", "blue"),
min_size=10,
filled=True,
compute_mask=False):
""" Draw a contour as optionally fill it with color.
Differs from draw_contour() in that in addition to contour_img and mask also
returns perimeter, area, contour center and distance of each pixel of area in a center"""
# Determine contour perimeter
mask, perimeter, area, centroid, D = None, None, None, None, None
try:
rr, cc = draw.polygon_perimeter(contour[:, 0], contour[:, 1], shape=contour_img.shape)
if max(rr.shape[0], cc.shape[0]) < min_size:
return
perimeter = np.column_stack((rr, cc))
except IndexError:
return None, None
if not filled:
# Contour not filled, draw it
color = ensure_numeric_color(color)
contour_img[rr, cc] = color
else:
# Get contour area
rr, cc = draw.polygon(contour[:, 0], contour[:, 1], shape=contour_img.shape)
if max(rr.shape[0], cc.shape[0]) < min_size:
return
area = np.column_stack((rr, cc))
if compute_mask:
mask = np.zeros((contour_img.shape[0], contour_img.shape[1]), dtype="uint8")
mask[rr, cc] = True
if color != "random" and color != "gradient" and type(color) != list:
# If a solid color provided, simply fill the polygon
color = ensure_numeric_color(color)
contour_img[rr, cc] = color
else:
# Gradient, random colors or list of colors specified
# Define contour center
M = measure.moments_coords(perimeter)
centroid = (M[1, 0] / M[0, 0], M[0, 1] / M[0, 0])
area_center = np.array([centroid])
# Determine distance of each area point from the center
D = distance.cdist(area, area_center, metric='euclidean')
# Define colors
num_colors = int(np.max(D)) + 1
colours = ensure_numeric_color(color, gradient_colors, num_colors)
# Fill polygon by colors specified by distance of each pixel from the center
for n, p in enumerate(area):
d = int(D[n])
c = colours[d] if len(colours) > 1 else colours[0]
contour_img[p[0], p[1]] = c
if compute_mask:
return contour_img, mask, perimeter, area, centroid, D
else:
return contour_img, None, perimeter, area, centroid, D
quoting=csv.QUOTE_MINIMAL)
post_writer.writerow([
'X center (um)', 'Y center (um)', 'Z minimum (um)', 'Z maximum (um)',
'Radius (um)'
])
for layer in range(0, num):
data = np.squeeze(bin_index[:, :, start + step * layer])
contours = measure.find_contours(data, 0.5)
post_data = []
post_idx = 0
for contour in contours:
M = measure.moments_coords(contour, order=1)
area = M[0, 0]
radius = np.sqrt(area / np.pi)
center_x = (M[1, 0] / M[0, 0]
) * device_size_lateral_um / device_voxels_lateral
center_y = (M[0, 1] / M[0, 0]
) * device_size_lateral_um / device_voxels_lateral
draw_circle = plt.Circle((center_x, center_y),
radius,
fill=False,
edgecolor='r',
linewidth=2)
z_start_um = layer * um_per_layer
z_end_um = z_start_um + um_per_layer
def find_centre(c):
moments = measure.moments_coords(c)
m0 = int(moments[1, 0] / moments[0, 0])
m1 = int(moments[0, 1] / moments[0, 0])
return (m0, m1)
def get_mu(coord):
"""return moments from coordinates"""
return measure.moments_coords(coord)
