def test_hough_ellipse_non_zero_angle():
img = np.zeros((20, 20), dtype=int)
a = 6
b = 9
x0 = 10
y0 = 10
angle = np.pi / 1.35
rr, cc = ellipse_perimeter(x0, x0, b, a, orientation=angle)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=15, accuracy=3)
assert_almost_equal(result[0][0] / 100., x0 / 100., decimal=1)
assert_almost_equal(result[0][1] / 100., y0 / 100., decimal=1)
assert_almost_equal(result[0][2] / 100., b / 100., decimal=1)
assert_almost_equal(result[0][3] / 100., a / 100., decimal=1)
assert_almost_equal(result[0][4], angle, decimal=1)
def test_hough_ellipse_zero_angle():
img = np.zeros((25, 25), dtype=int)
rx = 6
ry = 8
x0 = 12
y0 = 15
angle = 0
rr, cc = ellipse_perimeter(y0, x0, ry, rx)
img[rr, cc] = 1
result = transform.hough_ellipse(img, threshold=9)
best = result[-1]
assert_equal(best[1], y0)
assert_equal(best[2], x0)
assert_almost_equal(best[3], ry, decimal=1)
assert_almost_equal(best[4], rx, decimal=1)
assert_equal(best[5], angle)
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0,
x0,
int(best[3]),
int(best[4]),
orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def test_hough_ellipse_non_zero_posangle2():
# ry < rx, angle in [0:pi/2]
img = np.zeros((30, 24), dtype=int)
rx = 12
ry = 6
x0 = 10
y0 = 15
angle = np.pi / 1.35
rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=15, accuracy=3)
result.sort(order='accumulator')
best = result[-1]
assert_almost_equal(best[1] / 100., y0 / 100., decimal=1)
assert_almost_equal(best[2] / 100., x0 / 100., decimal=1)
assert_almost_equal(best[3] / 10., ry / 10., decimal=1)
assert_almost_equal(best[4] / 100., rx / 100., decimal=1)
assert_almost_equal(best[5], angle, decimal=1)
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0,
x0,
int(best[3]),
int(best[4]),
orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def test_ellipse_perimeter_shape():
img = np.zeros((15, 20), 'uint8')
rr, cc = ellipse_perimeter(7, 10, 9, 9, 0, shape=(15, 20))
img[rr, cc] = 1
shift = 5
img_ = np.zeros((15 + 2 * shift, 20), 'uint8')
rr, cc = ellipse_perimeter(7 + shift, 10, 9, 9, 0, shape=None)
img_[rr, cc] = 1
assert_array_equal(img, img_[shift:-shift, :])
def test_ellipse_perimeter_shape():
img = np.zeros((15, 20), 'uint8')
rr, cc = ellipse_perimeter(7, 10, 9, 9, 0, shape=(15, 20))
img[rr, cc] = 1
shift = 5
img_ = np.zeros((15 + 2 * shift, 20), 'uint8')
rr, cc = ellipse_perimeter(7 + shift, 10, 9, 9, 0, shape=None)
img_[rr, cc] = 1
assert_array_equal(img, img_[shift:-shift, :])
def enclosing_ellipse(beam1, beam2):
bmaj1, bmin1, bpa1 = beam1
bmaj2, bmin2, bpa2 = beam2
if bmaj1 <= bmin2:
return (bmaj2, bmin2, bpa2)
if bmaj2 <= bmin1:
return (bmaj1, bmin1, bpa1)
if np.logical_and.reduce(
(bmaj1 == bmaj2, bmin1 == bmin2, ((bpa1 - bpa2) % np.pi) == 0)):
return (bmaj1, bmin1, bpa1)
if bmaj2 < bmaj1:
tmp = bmaj2
bmaj2 = bmaj1
bmaj1 = tmp
tmp = bmin2
bmin2 = bmin1
bmin1 = tmp
tmp = bpa2
bpa2 = bpa1
bpa1 = tmp
dpa = bpa1
bpa1 -= dpa
bpa2 -= dpa
Y = bmaj1 / bmin1
A = r_e(bmaj2, bmin2, bpa2, t_m(bmaj2, bmin2, bpa2, Y), y=Y)
B = max([
r_e(bmaj2, bmin2, bpa2, t_m(bmaj2, bmin2, bpa2, Y) + 0.5 * np.pi, y=Y),
bmaj1
])
#(mx,my)= ellipse (bmaj2,bmin2,bpa2,t_m(bmaj2,bmin2,bpa2,Y);y=Y)
(mx, my) = ellipse_perimeter(0, 0, bmin2, bmaj2, orientation=bpa2)
P = np.arctan2(my, mx)
print(bmin2, bmaj2, bpa2)
print(A, B, P)
a = r_e(A, B, P, t_m(A, B, P, 1. / Y), y=1. / Y)
b = r_e(A, B, P, t_m(A, B, P, 1. / Y) + 0.5 * np.pi, y=1. / Y)
# (mx,my)= ellipse (A,B,P,t_m(A,B,P,1./Y),y=1./Y)
(mx, my) = ellipse_perimeter(0, 0, A, B, orientation=P)
p = np.arctan2(my, mx)
p += dpa
if b > a:
(a, b, p) = (b, a, p + 0.5 * np.pi
) # make sure that 'a' is the semimajor axis
return (a, b, p % np.pi)
def plot_segmap_ellpreds(image, seg_map, pupil_ellipse, iris_ellipse):
loc_iris = seg_map == 1
loc_pupil = seg_map == 2
out_image = np.stack([image] * 3, axis=2)
alphaBlend = 20
loc_image_non_sat = image < (255 - alphaBlend)
# Add green to iris
out_image[...,
1] = out_image[...,
1] + alphaBlend * loc_iris * loc_image_non_sat
# Add yellow to pupil
out_image[...,
0] = out_image[...,
0] + alphaBlend * loc_pupil * loc_image_non_sat
out_image[...,
1] = out_image[...,
1] + alphaBlend * loc_pupil * loc_image_non_sat
# Sketch iris ellipse
if not np.all(iris_ellipse == -1):
[rr_i, cc_i] = draw.ellipse_perimeter(int(iris_ellipse[1]),
int(iris_ellipse[0]),
int(iris_ellipse[3]),
int(iris_ellipse[2]),
orientation=iris_ellipse[4])
rr_i = rr_i.clip(6, image.shape[0] - 6)
cc_i = cc_i.clip(6, image.shape[1] - 6)
out_image[rr_i, cc_i, ...] = np.array([0, 0, 255])
if not np.all(pupil_ellipse == -1):
# Sketch pupil ellipse
[rr_p, cc_p] = draw.ellipse_perimeter(int(pupil_ellipse[1]),
int(pupil_ellipse[0]),
int(pupil_ellipse[3]),
int(pupil_ellipse[2]),
orientation=pupil_ellipse[4])
rr_p = rr_p.clip(6, image.shape[0] - 6)
cc_p = cc_p.clip(6, image.shape[1] - 6)
out_image[rr_p, cc_p, ...] = np.array([255, 0, 0])
return out_image
def animate(i):
plt.title('Frame %d' % i)
slice_data = preprocess_data(data[i + 100])
if np.count_nonzero(slice_data):
labeled_data, num_features = segment_data(slice_data)
stats = slice_stats(labeled_data)
stats = stats[(stats.area > 20) & ((stats.major_axis_length < frame_shape[0]) | (stats.major_axis_length < frame_shape[1]))]
stats = stats[stats.circularity > 0.5]
for index, row in stats.iterrows():
print 'Frame# %d, Circle# %d [circularity = %f]' % (i, row.label, row.circularity)
yc, xc = [int(round(x)) for x in row.centroid]
orientation = row.orientation
major_axis = int(round(row.major_axis_length/2.))
minor_axis = int(round(row.minor_axis_length/2.))
slice_data = ski.color.gray2rgb(slice_data)
cy, cx = ellipse_perimeter(yc, xc, minor_axis, major_axis, orientation)
slice_data[cy, cx] = (220, 20, 20)
rr, cc = circle(yc, xc, 2)
slice_data[rr, cc] = (220, 20, 20)
im.set_data(slice_data)
return im,
def random_walker():
plt.close("all")
p = Path(
'C:/Users/fshaw/Documents/PycharmProjects/tilapia/data/raw/padded_images/2015_00T2A6_A.JPG'
)
image = io.imread(p)
image_gray = color.rgb2gray(image)
image_labels = np.zeros(image_gray.shape, dtype=np.uint8)
points = circle_points(resolution=40, center=[250, 250], radius=160)[:-1]
# indices = draw.circle_perimeter(130, 250, 100)
indices = draw.ellipse_perimeter(250, 250, r_radius=30, c_radius=200)
image_labels[indices] = 1
image_labels[points[:, 1].astype(np.int), points[:, 0].astype(np.int)] = 2
image_show(image_labels)
image_segmented = seg.random_walker(image_gray,
image_labels,
mode='bf',
beta=200,
tol=1.e-3) # Check our results
fig, ax = image_show(image_gray)
ax.imshow(image_segmented == 1, alpha=0.3)
plt.show()
def elip(image):
image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
edges = canny(image_gray, sigma=2.0,low_threshold=0.55, high_threshold=0.8)
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250,
min_size=100, max_size=120)
result.sort(order='accumulator')
# Estimated parameters for the ellipse
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
image_rgb[cy, cx] = (0, 0, 255)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(img_as_ubyte(edges))
edges[cy, cx] = (250, 0, 0)
fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
sharey=True,
subplot_kw={'adjustable':'box-forced'})
ax1.set_title('Original picture')
ax1.imshow(image_rgb)
ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)
def draw_error_ellipse2d(image, mu, sigma, color="k"):
if image.shape[2] != 3:
print('The input image should has 3 channels')
else:
eigen_values, eigen_vecters = np.linalg.eigh(sigma)
angle = np.arctan2(eigen_vecters[1,1], eigen_vecters[0,0])
angle = angle/np.pi*180
# 95%---5.991 90%---4.605 99%---9.21
x_value = np.sqrt(eigen_values[0]*5.991)
y_value = np.sqrt(eigen_values[1]*5.991)
points = draw.ellipse_perimeter(int(mu[1]), int(mu[0]), int(y_value), int(x_value), angle)
pointsr = points[0]
pointsc = points[1]
(sizerow, sizecol, _) = image.shape
valid_points_r = []
valid_points_c = []
for i in range(len(pointsr)):
if pointsr[i] > 0 and pointsc[i] > 0 and pointsr[i] < sizerow and pointsc[i] < sizecol:
valid_points_r.append(pointsr[i])
valid_points_c.append(pointsc[i])
if color=='k':
image[valid_points_r, valid_points_c, 1] = 255
else: image[valid_points_r, valid_points_c, 1] = 0
return image
def reference_circle(coords, dimX, dimY, radius):
"""Draw the perimeter of an circle at a given position in the diffraction
pattern (e.g. to provide a reference for finding the direct beam center).
Parameters
----------
coords : np.array size n,2
size n,2 array of coordinates to draw the circle.
dimX : int
first dimension of the diffraction pattern (size)
dimY : int
second dimension of the diffraction pattern (size)
radius : int
radius of the circle to be drawn
Returns
-------
img: np.array
Array containing the circle at the position given in the coordinates.
"""
img = np.zeros((dimX, dimY))
for n in range(np.size(coords, 0)):
rr, cc = ellipse_perimeter(coords[n, 0], coords[n, 1], radius, radius)
img[rr, cc] = 1
return img
def outerEllipse(innerEllipseParam):
'''outer ellipse'''
outerBoundary = 5
outerEllipseParam = innerEllipseParam
outerEllipseParam[
'ellipseSemiY'] = innerEllipseParam['ellipseSemiY'] + outerBoundary
outerEllipseParam[
'ellipseSemiX'] = innerEllipseParam['ellipseSemiX'] + outerBoundary
rrOuter, ccOuter = ellipse_perimeter(
outerEllipseParam['ellipseCenterY'],
outerEllipseParam['ellipseCenterX'],
outerEllipseParam['ellipseSemiY'],
outerEllipseParam['ellipseSemiX'],
orientation=outerEllipseParam['orientation'])
outerEllipseVerts = np.dstack([rrOuter, ccOuter])[0]
edgePoints = []
points = [i for i in outerEllipseVerts if i[1] == 0] # y=0的两个点
maxXOuterEllipse = max(outerEllipseVerts, key=lambda x: x[0])[0]
minXOuterEllipse = min(outerEllipseVerts, key=lambda x: x[0])[0]
rightPoints = [i for i in outerEllipseVerts if i[0] == maxXOuterEllipse]
leftPoints = [i for i in outerEllipseVerts if i[0] == minXOuterEllipse]
rightPoints = np.asarray(rightPoints)
leftPoints = np.asarray(leftPoints)
edgePoints.append(np.mean(rightPoints, axis=0)) # 最左边和最右边的两个点
edgePoints.append(np.mean(leftPoints, axis=0))
edgePoints = edgePoints + points
return outerEllipseVerts, edgePoints
def _ellipses(self, color, size):
new_clusters = []
image_shape = self.cluster_masks[color].shape
for cluster in self.cluster_props[color]:
test_mask = np.zeros(image_shape, bool)
centroid = cluster.centroid
ycenter = int(round(centroid[0]))
xcenter = int(round(centroid[1]))
major_axis = int(round(cluster.major_axis_length/2 * size))
minor_axis = int(round(cluster.minor_axis_length/2 * size))
orientation = cluster.orientation
if (major_axis < 1 or minor_axis < 1):
major_axis = 1
minor_axis = 1
rr, cc = ellipse_perimeter(ycenter,xcenter,major_axis,minor_axis,orientation,image_shape)
test_mask[rr,cc] = True
test_mask = ndimage.morphology.binary_fill_holes(test_mask)
test_mask = np.where(test_mask == True, 1, 0)
new_cluster = measure.regionprops(test_mask, coordinates='xy')
new_clusters += new_cluster
self.cluster_props[color] = new_clusters
def ellipse_perimeter(r, c, r_radius, c_radius, orientation=0., shape=None):
""" see New version scikit-image v0.14
:param int r: center position in rows
:param int c: center position in columns
:param int r_radius: ellipse diam in rows
:param int c_radius: ellipse diam in columns
:param float orientation: ellipse orientation
:param (int, int) shape: size of output mask
:return ([int], [int]): indexes of filled positions
>>> img = np.zeros((14, 20), dtype=int)
>>> rr, cc = ellipse_perimeter(7, 10, 3, 9, np.deg2rad(30), img.shape)
>>> img[rr, cc] = 1
>>> img
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, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 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, 1, 1, 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, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 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, 1, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 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]])
"""
rr, cc = draw.ellipse_perimeter(r, c, r_radius, c_radius,
orientation=-orientation, shape=shape)
return rr, cc
def draw_ell(img, x, y, rmin, rmaj, posang):
#returns rr,cc: index px that belong to the ellipse perimeter.
rr, cc = ellipse_perimeter(
x, y, rmin, rmaj, orientation=posang
) #(x,y) center coord., posang major axis orientation clockwise [rad]
img[rr, cc] = 1
return img
def img_generate_counter(img, bboxes):
subimages, bboxes = cropImage(img, bboxes)
y_points = np.array([])
x_points = np.array([])
defect_img_List = list()
for subim, bbox in zip(subimages, bboxes):
defects_Dict = dict()
defects_X_List = list()
defects_Y_List = list()
region1 = flood_fitting(subim)
result = (int(region1['centroid'][0] + bbox[0]),
int(region1['centroid'][1] + bbox[1]),
int(region1['minor_axis_length'] / 2),
int(region1['major_axis_length'] / 2),
-region1['orientation'])
rr, cc = draw.ellipse_perimeter(*result)
# rr is row which is x in pixel
# cc is column which is y in pixel
# (rr,cc) represents one data points and there may be redundent data points
assert len(rr) == len(cc)
for i in range(len(cc)):
print(cc[i])
y_points = np.concatenate((y_points, rr))
x_points = np.concatenate((x_points, cc))
fig = plt.figure(figsize=(10, 10))
plt.imshow(img[0, :, :], cmap='gray')
plt.scatter(x_points, y_points, s=(1 * 72. / fig.dpi)**2, alpha=0.5)
def test_ellipse_perimeter_dot_nzeroangle():
# dot, angle != 0
img = np.zeros((30, 15), 'uint8')
rr, cc = ellipse_perimeter(15, 7, 0, 0, 1)
img[rr, cc] = 1
assert (np.sum(img) == 1)
assert (img[15][7] == 1)
def hough_ellipse_finder(I_sub):
"""
Uses the ellipse hough transform to find ellipses. Use if the bw theshold method fails.
:param I_sub: The ROI image from which to extract the ellipses
:return: innner,outer ellipse points.
"""
edges = feature.canny(I_sub, sigma=2.0)
result = transform.hough_ellipse(edges,
accuracy=20,
threshold=250,
min_size=100)
result.sort(order='accumulator')
for ii in range(len(result)):
ellip = list(result[-ii])
yc, xc, a, b = [int(round(x)) for x in ellip[1:5]]
orientation = ellip[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
cy = cy[cy < I_sub.shape[0]]
cx = cx[cx < I_sub.shape[1]]
# Draw the edge (white) and the resulting ellipse (red)
Itemp = color.gray2rgb(I_sub)
Itemp[cy, cx] = (250, 0, 0)
plt.imshow(Itemp)
plt.pause(1)
def gen_ellipse_contour_perim(perim, color="r"):
# Vertices
input_points = np.where(perim == 1)
if (np.unique(input_points[0]).shape[0]) < 6 or (np.unique(
input_points[1]).shape[0] < 6):
return None, None, None, None, None, None, None
else:
try:
vertices = np.array([input_points[0], input_points[1]]).T
# Contour
fitted = LSqEllipse()
fitted.fit([vertices[:, 1], vertices[:, 0]])
center, w, h, radian = fitted.parameters()
ell = mpl.patches.Ellipse(xy=[center[0], center[1]],
width=w * 2,
height=h * 2,
angle=np.rad2deg(radian),
fill=False,
color=color)
# Because of the np indexing of y-axis, orientation needs to be minus
rr, cc = ellipse_perimeter(int(np.round(center[0])),
int(np.round(center[1])),
int(np.round(w)), int(np.round(h)),
-radian)
return rr, cc, center, w, h, radian, ell
except:
return None, None, None, None, None, None, None
def test_ellipse_perimeter_dot_zeroangle():
# dot, angle == 0
img = np.zeros((30, 15), "uint8")
rr, cc = ellipse_perimeter(15, 7, 0, 0, 0)
img[rr, cc] = 1
assert np.sum(img) == 1
assert img[15][7] == 1
def test_ellipse_perimeter_dot_nzeroangle():
# dot, angle != 0
img = np.zeros((30, 15), 'uint8')
rr, cc = ellipse_perimeter(15, 7, 0, 0, 1)
img[rr, cc] = 1
assert(np.sum(img) == 1)
assert(img[15][7] == 1)
def ellipse(self, row: int, col: int, radius_r: float, radius_c: float,
color: ColorType, fill: bool=False, alpha: float=1.0) -> 'Layer':
"""
Draw an ellipse centered on the specified row and column,
with the given radiuses.
:param row: Center row of ellipse
:param col: Center column of ellipse
:param radius_r: Radius of ellipse on y axis
:param radius_c: Radius of ellipse on x axis
:param color: Color to draw with
:param fill: True if the circle should be filled
:return: This frame instance
"""
if fill:
rr, cc = draw.ellipse(row, col, math.floor(radius_r), math.floor(radius_c),
shape=self.matrix.shape)
self._draw(rr, cc, color, alpha)
else:
rr, cc = draw.ellipse_perimeter(row, col, math.floor(radius_r), math.floor(radius_c),
shape=self.matrix.shape)
self._draw(rr, cc, color, alpha)
return self
def draw_ellipse(ellipse, on_img, with_color=None):
with_color = with_color or utils.make_n_colors(1)[0]
_, yc, xc, a, b, orientation = ellipse
py, px = ellipse_perimeter(to_int(yc), to_int(xc), to_int(a), to_int(b), orientation)
on_img[py, px] = with_color
# draw axes
draw_ellipse_axes(on_img, yc, xc, a, b, orientation, with_color)
def make_image():
im = (np.ones((201, 201, 3)) * 255).astype(np.uint8)
rr, cc = draw.ellipse_perimeter(100,
100,
30,
70,
orientation=math.radians(17))
im[rr, cc] = (0, 0, 0)
return im
def test_hough_ellipse_non_zero_negangle4():
# ry < rx, angle in [-pi:-3pi/4]
img = np.zeros((30, 24), dtype=int)
rx = 12
ry = 6
x0 = 10
y0 = 15
angle = -np.pi / 1.35 - np.pi
rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=15, accuracy=3)
result.sort(order="accumulator")
best = result[-1]
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def test_ellipse_perimeter_flat_zeroangle():
# flat ellipse
img = np.zeros((20, 18), 'uint8')
img_ = np.zeros((20, 18), 'uint8')
rr, cc = ellipse_perimeter(6, 7, 0, 5, 0)
img[rr, cc] = 1
rr, cc = line(6, 2, 6, 12)
img_[rr, cc] = 1
assert_array_equal(img, img_)
def test_ellipse_perimeter_flat_zeroangle():
# flat ellipse
img = np.zeros((20, 18), 'uint8')
img_ = np.zeros((20, 18), 'uint8')
rr, cc = ellipse_perimeter(6, 7, 0, 5, 0)
img[rr, cc] = 1
rr, cc = line(6, 2, 6, 12)
img_[rr, cc] = 1
assert_array_equal(img, img_)
def test_ellipse_perimeter():
img = np.zeros((30, 15), 'uint8')
rr, cc = ellipse_perimeter(15, 7, 0, 0)
img[rr, cc] = 1
assert(np.sum(img) == 1)
assert(img[15][7] == 1)
img = np.zeros((30, 15), 'uint8')
rr, cc = ellipse_perimeter(15, 7, 14, 6)
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, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]]
)
assert_array_equal(img, img_)
def test_hough_ellipse_non_zero_negangle4():
# ry < rx, angle in [-pi:-3pi/4]
img = np.zeros((30, 24), dtype=int)
rx = 12
ry = 6
x0 = 10
y0 = 15
angle = - np.pi / 1.35 - np.pi
rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=15, accuracy=3)
result.sort(order='accumulator')
best = result[-1]
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]),
orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def ONH_Segmentation(im_orig, model, max_ind_0, max_ind_1, ONH_box_size):
numSegments = 200
im = im_orig[:, :, 1]
im_red = im_orig[:, :, 0]
im_blue = im_orig[:, :, 2]
feature_mat_norm_test, segments = FMC.create_feature_mat_classify(
im_orig, im_red, im, im_blue, numSegments)
decision_results = model.decision_function(feature_mat_norm_test)
segments2 = segments.copy().astype('float')
for i in range(numSegments):
segments2[segments == i] = decision_results[i]
segment_result1 = cv2.blur(segments2, (9, 9))
bin_result = segment_result1 > 0
ret, labels = cv2.connectedComponents(bin_result.astype(np.uint8))
regions = regionprops(labels)
max_region_area = 0
for region in regions:
if region.area > max_region_area:
max_region_label = region.label
max_region_area = region.area
labels = labels == max_region_label
selem = disk(27)
label_close = closing(labels + 0, selem=selem)
edges = canny(label_close,
sigma=2.0,
low_threshold=0.55,
high_threshold=0.8)
result = hough_ellipse(edges,
accuracy=40,
threshold=40,
min_size=40,
max_size=60)
result.sort(order='accumulator')
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
im_orig[cy, cx] = (0, 0, 255)
cx_real_axes = cx + max_ind_1 - ONH_box_size
cy_real_axes = cy + max_ind_0 - ONH_box_size
return cx_real_axes, cy_real_axes
def main(argv):
if len(argv) == 0:
print("Please specify input and output image files.")
if len(argv) == 1:
print("Please specify an output image file.")
input_image_path = argv[0]
output_image_path = argv[1]
# Used for shape matching
ellipse_image = make_ellipse_image(50, 100, 40, 80)
threshold_ellipse = canny_threshold(ellipse_image, 40, 2)
ellipse_image_contours, contours, hierarchy = cv2.findContours(threshold_ellipse, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
ellipse_contour = contours[1]
# Input image
image = cv2.imread(input_image_path)
height, width = image.shape[:2]
threshold_image = canny_threshold(image, 40, 2)
kernel = np.ones((5, 5), np.uint8)
threshold_image = cv2.morphologyEx(threshold_image, cv2.MORPH_DILATE, kernel)
image_contours, contours, hierarchy = cv2.findContours(threshold_image, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
major_axis, minor_axis = get_axes(contour)
# If contour too small
if not is_good_distance(contour, 10, 10):
continue
# If shape too large
if major_axis > width * 0.70 or minor_axis > height * 0.70:
continue
# Match the current contour with ellipse shape contour
ret = cv2.matchShapes(ellipse_contour, contour, 2, 0)
# Key parameter
if ret > 0.75:
continue
# Draw an ellipse in the image
most_left, most_right, most_top, most_bottom = corners(contour)
center_y = int((most_top[1] + most_bottom[1]) / 2)
center_x = int((most_right[0] + most_left[0]) / 2)
cy, cx = ellipse_perimeter(center_y, center_x, int(major_axis / 2), int(minor_axis / 2), math.radians(90))
image[cy, cx] = (0, 255, 0)
cv2.imwrite(output_image_path, image)
def show_fitted_ellipse(img):
"""
Show fitted ellipse on the image
:param img: img in CHW format
:return: plot ellipse on top of the image
"""
region1 = flood_fitting(img)
rr, cc = draw.ellipse_perimeter(int(region1['centroid'][0]), int(region1['centroid'][1]),
int(region1['minor_axis_length'] / 2),
int(region1['major_axis_length'] / 2), -region1['orientation'], img.shape[1:])
plt.imshow(img[1,:,:], cmap='gray')
plt.plot(cc, rr, '.')
def test_hough_ellipse_zero_angle():
img = np.zeros((25, 25), dtype=int)
rx = 6
ry = 8
x0 = 12
y0 = 15
angle = 0
rr, cc = ellipse_perimeter(y0, x0, ry, rx)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=9)
best = result[-1]
assert_equal(best[1], y0)
assert_equal(best[2], x0)
assert_almost_equal(best[3], ry, decimal=1)
assert_almost_equal(best[4], rx, decimal=1)
assert_equal(best[5], angle)
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def draw_female(self,
color=(255, 0, 0),
thickness=2,
show_head_location=True):
"""Draws an ellipse perimeter around a female.
Parameters
----------
color : 3-tuple
Color of ellipse outline.
thickness : int
Thickness of ellipse outline.
show_head_location : bool (optional, default=True)
If true, will draw an arrow from female's rear->head.
Returns
-------
image : 3D np.ndarray of shape [arena.background_image.shape]
Background image with female surrounded by ellipse.
"""
image = self.arena.background_image.copy()
# convert to color
image = gray2rgb(image)
assert image.dtype == np.uint8, "image is not of type uint8"
if self.maj_ax_rad <= 1:
return image
rr, cc = ellipse_perimeter(
self.center[0],
self.center[1],
self.min_ax_rad,
self.maj_ax_rad,
orientation=(self.orientation * np.pi /
180), # Negating this value
# gives good results in test_trk_female.py
# but leads to improperly oriented ellipse in
# the gui display.
shape=self.arena.background_image.shape)
ellipse_outline_mask = np.zeros_like(self.arena.background_image)
ellipse_outline_mask[rr, cc] = 1
dilated_ellipse_outline = dilation(ellipse_outline_mask,
disk(thickness))
rr, cc = np.where(dilated_ellipse_outline)
image[rr, cc, :] = color
if show_head_location:
rr, cc = line(self.center[0], self.center[1], self.head[0],
self.head[1])
image[rr, cc] = color
return image
def plot_segmap_ellpreds(image, seg_map, pupil_ellipse, iris_ellipse):
loc_iris = seg_map == 1
loc_pupil = seg_map == 2
out_image = np.stack([image] * 3, axis=2)
loc_image_non_sat = image < (255 - 100)
# Add green to iris
out_image[..., 1] = out_image[..., 1] + 100 * loc_iris * loc_image_non_sat
# Add yellow to pupil
out_image[..., 0] = out_image[..., 0] + 100 * loc_pupil * loc_image_non_sat
out_image[..., 1] = out_image[..., 1] + 100 * loc_pupil * loc_image_non_sat
# Sketch iris ellipse
[rr_i, cc_i] = draw.ellipse_perimeter(round(iris_ellipse[1]),
round(iris_ellipse[0]),
round(iris_ellipse[3]),
round(iris_ellipse[2]),
orientation=iris_ellipse[4])
# Sketch pupil ellipse
[rr_p, cc_p] = draw.ellipse_perimeter(round(pupil_ellipse[1]),
round(pupil_ellipse[0]),
round(pupil_ellipse[3]),
round(pupil_ellipse[2]),
orientation=pupil_ellipse[4])
# Clip the perimeter display incase it goes outside bounds
rr_i = rr_i.clip(6, image.shape[0] - 6)
rr_p = rr_p.clip(6, image.shape[0] - 6)
cc_i = cc_i.clip(6, image.shape[1] - 6)
cc_p = cc_p.clip(6, image.shape[1] - 6)
out_image[rr_i, cc_i, ...] = np.array([0, 0, 255])
out_image[rr_p, cc_p, ...] = np.array([255, 0, 0])
return out_image.astype(np.uint8)
def find_plate(path):
# Load picture, convert to grayscale and detect edges
image_rgb = io.imread(path)
image_rgb = rescale(image_rgb, 0.05)
image_gray = color.rgb2gray(image_rgb)
edges = canny(image_gray, sigma=2.0, low_threshold=0.2, high_threshold=0.8)
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges)
# plt.imshow(edges)
# plt.show()
# print (0)
# result = hough_ellipse(edges)
print(result.size)
if result.size == 0:
print(path + "\t non detected")
return
print(path + "\t detected")
result.sort(order='accumulator')
# Estimated parameters for the ellipse
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
try:
image_rgb[cy, cx] = (0, 0, 255)
edges = color.gray2rgb(img_as_ubyte(edges))
edges[cy, cx] = (250, 0, 0)
except IndexError as e:
print(e)
# Draw the edge (white) and the resulting ellipse (red)
fig2, (ax1, ax2) = plt.subplots(ncols=2,
nrows=1,
figsize=(8, 4),
sharex=True,
sharey=True)
ax1.set_title('Original picture')
ax1.imshow(image_rgb)
ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)
plt.show()
def intensity(self, size=None, t=None, smoothing_window=None, substract_noise=True):
"""
Mean intensity at the position of the particle along time.
Arguments:
size <int, float, 2-tuple>: radius of the ellipse to consider for intensity, in pixels. Centered at the center of the particle. If None, uses the mean sx and sy values of the particle.
t <2,3-tuple, slice, int>: the timepoints at which to collect the intensity information. As compatible with cats.utils.slicify. If 'None', uses the whole length of the dataset.
smoothing_window <int>: The number of datapoints to average to smooth the output
substract_noise <bool>: Whether to substract the surrounding value of noise from the intensity.
"""
noise_distance = 2 # Extra diameter to get noise close to content
T, I = list(), list()
# Treat time
ts = slicify(t, slice(0, self.source.length, 1))
# Treat size
if size is None:
sx, sy = int(ceil(np.mean(self.sx))), int(ceil(np.mean(self.sy)))
elif type(size) in (int, float):
sx, sy = int(ceil(size)), int(ceil(size))
elif '__iter__' in dir(size):
sx, sy = int(ceil(size[0])), int(ceil(size[1]))
# Get data
for t in range(ts.start, ts.stop, ts.step):
# Get the exact or closest position for given time
if t not in self.t:
index = np.where(self.t == get_closest_value(t, self.t))[0]
else:
index = np.where(self.t == t)[0]
# Get the xy position and data at exact or closest time
roi = draw.ellipse(int(self.y[index]), int(self.x[index]), sy, sx, shape=self.source.shape)
spot = self.source.get(t)[roi]
intensity = spot.mean()
# Remove noise
if substract_noise == True:
noise = draw.ellipse_perimeter(int(self.y[index]), int(self.x[index]), sy + noise_distance, sx + noise_distance, shape=self.source.shape)
noise = self.source.get(t)[noise]
intensity -= noise.mean()
T.append(t)
I.append(intensity)
if smoothing_window is not None and smoothing_window > 1:
for t in range(0, len(I)):
j, k = max(t - smoothing_window, 0), min(t + smoothing_window, len(I))
I[t] = np.median(I[j:k])
return T, I
def detect(d, i,**kwargs):
image = d.read(d.images[i])
global rgb
rgb = d.read(d.images[i], flatten=False)
pupil = find_pupil(image)[0]
img, points, ellipse = find_iris(image, pupil, **kwargs)
x, y = circle_perimeter(pupil[0], pupil[1], pupil[2])
rgb[x,y] = (220, 40, 40)
ex, ey = ellipse.center
major, minor = ellipse.axes
orientation = ellipse.orientation
x, y = ellipse_perimeter(int(ex), int(ey), int(major), int(minor), orientation)
rgb[x,y] = (220, 40, 40)
imshow(rgb)
def test_hough_ellipse_non_zero_posangle2():
# ry < rx, angle in [0:pi/2]
img = np.zeros((30, 24), dtype=int)
rx = 12
ry = 6
x0 = 10
y0 = 15
angle = np.pi / 1.35
rr, cc = ellipse_perimeter(y0, x0, ry, rx, orientation=angle)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=15, accuracy=3)
result.sort(order="accumulator")
best = result[-1]
assert_almost_equal(best[1] / 100.0, y0 / 100.0, decimal=1)
assert_almost_equal(best[2] / 100.0, x0 / 100.0, decimal=1)
assert_almost_equal(best[3] / 10.0, ry / 10.0, decimal=1)
assert_almost_equal(best[4] / 100.0, rx / 100.0, decimal=1)
assert_almost_equal(best[5], angle, decimal=1)
# Check if I re-draw the ellipse, points are the same!
# ie check API compatibility between hough_ellipse and ellipse_perimeter
rr2, cc2 = ellipse_perimeter(y0, x0, int(best[3]), int(best[4]), orientation=best[5])
assert_equal(rr, rr2)
assert_equal(cc, cc2)
def draw_ellipses(slice_data, ellipse, color=(220, 20, 20)):
yc, xc = [int(round(x)) for x in ellipse.centroid]
orientation = ellipse.orientation
major_axis = int(round(ellipse.major_axis_length/2.))
minor_axis = int(round(ellipse.minor_axis_length/2.))
image = ski.color.gray2rgb(slice_data)
cy, cx = ellipse_perimeter(yc, xc, minor_axis, major_axis, -orientation)
image[cy, cx] = color
rr, cc = circle(yc, xc, 2)
image[rr, cc] = color
return image
def test_hough_ellipse_zero_angle():
img = np.zeros((25, 25), dtype=int)
a = 6
b = 8
x0 = 12
y0 = 12
angle = 0
rr, cc = ellipse_perimeter(x0, x0, b, a)
img[rr, cc] = 1
result = tf.hough_ellipse(img, threshold=9)
assert_equal(result[0][0], x0)
assert_equal(result[0][1], y0)
assert_almost_equal(result[0][2], b, decimal=1)
assert_almost_equal(result[0][3], a, decimal=1)
assert_equal(result[0][4], angle)
def draw_ellipse(self, i=0):
data = self._results_flat
j = _np.argmax(data['count_density'][i])
x, y = _skdraw.ellipse_perimeter(
cy = data['yc'][i][j].astype('int'),
cx = data['xc'][i][j].astype('int'),
yradius = data['a'][i][j].astype('int'),
xradius = data['b'][i][j].astype('int'),
orientation = data['orientation'][i][j]
)
bounds_int = self._bounds[i].astype('int')
bounds_int[x, y] = 2
_ss.matplotlib.Imshow_Slider(bounds_int)
def main():
# Load picture, convert to grayscale and detect edges
camera = cv2.VideoCapture(0)
(ret, img) = camera.read()
cv2.imshow("blah",img)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cp = gray.copy()
edges = cv2.Canny(gray,50,150,apertureSize = 3)
print "hi"
edges = img_as_float(edges)
print "hi"
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250, min_size=100, max_size=120)
print "hi"
result.sort(order='accumulator')
print result
# Estimated parameters for the ellipse
if (len(result) > 0):
print "apple"
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
image_rgb[cy, cx] = (0, 0, 255)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(edges)
edges[cy, cx] = (25, 0, 255)
# fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
# sharey=True,
# subplot_kw={'adjustable':'box-forced'})
# ax1.set_title('Original picture')
cv2.imshow("apple",img_as_ubyte(edges))
def test_ellipse_perimeter_zeroangle():
# angle == 0
img = np.zeros((30, 15), "uint8")
rr, cc = ellipse_perimeter(15, 7, 14, 6, 0)
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, 1, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0],
]
)
assert_array_equal(img, img_)
def redraw():
global points
global ellipse_params
global in_image
global plt
global fig
display_image = np.copy(in_image)
from skimage.draw import line
r_index, c_index = line(points[0][0], points[0][1], points[1][0], points[1][1])
display_image[r_index, c_index] = np.array([200, 0, 0])
if ellipse_params is not None:
r_index, c_index = ellipse_perimeter(ellipse_params[0], ellipse_params[1],
ellipse_params[2], ellipse_params[3],
ellipse_params[4])
display_image[r_index, c_index] = np.array([0, 0, 200])
pltcop.set_data(display_image)
fig.canvas.draw()
def test_detect_by_stats():
data = np.memmap("E:\\guts_tracking\\data\\fish202_aligned_masked_8bit_150x200x440.raw", dtype='uint8', shape=(440,200,150)).copy()
data_slice = data[68]
slice_data = preprocess_data(data_slice)
labeled_data, num_features = segment_data(slice_data)
#plt.imshow(labeled_data == 4, cmap='gray')
#plt.show()
stats = slice_stats(labeled_data)
#stats = stats[(stats.area > 20) & (stats.area < np.pi * min(data_slice.shape)**2)]
#stats = stats[(stats.area > 20) & (stats.area < 20000)]
stats = stats[(stats.area > 2) & (stats.area < 20000)]
#print stats
#stats = stats[stats.circularity > 0.2]
image = data_slice
for index, row in stats.iterrows():
print row
yc, xc = [int(round(x)) for x in row.centroid]
orientation = row.orientation
major_axis = int(round(row.major_axis_length/2.))
minor_axis = int(round(row.minor_axis_length/2.))
image = ski.color.gray2rgb(image)
cy, cx = ellipse_perimeter(yc, xc, minor_axis, major_axis, -orientation)
image[cy, cx] = (220, 20, 20)
rr, cc = circle(yc, xc, 2)
image[rr, cc] = (220, 20, 20)
plt.imshow(image, cmap='gray')
plt.show()
print stats
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_)
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
accum = hough_ellipse(edges, accuracy=10, threshold=170, min_size=50)
accum.sort(key=lambda x:x[5])
# Estimated parameters for the ellipse
center_y = int(accum[-1][0])
center_x = int(accum[-1][1])
xradius = int(accum[-1][2])
yradius = int(accum[-1][3])
angle = np.pi - accum[-1][4]
# Draw the ellipse on the original image
cx, cy = ellipse_perimeter(center_y, center_x,
yradius, xradius, orientation=angle)
image_rgb[cy, cx] = (0, 0, 1)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(edges)
edges[cy, cx] = (250, 0, 0)
fig = plt.subplots(figsize=(10, 6))
plt.subplot(1, 2, 1)
plt.title('Original picture')
plt.imshow(image_rgb)
plt.subplot(1, 2, 2)
plt.title('Edge (white) and result (red)')
plt.imshow(edges)
plt.show()
def annotate_ellipse(input_image, filename, output_filename, tag, params=None, save=True):
from skimage.draw import ellipse_perimeter
import matplotlib.pyplot as plt
global in_image
global in_filename
in_image = input_image
in_filename = filename
display_image = np.copy(input_image)
if params:
r_index, c_index = ellipse_perimeter(params[0], params[1],
params[2], params[3],
float(params[4]))
display_image[r_index, c_index] = np.array([0, 0, 200])
global fig
global plt
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(8, 4, forward=True)
plt.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)
pltcop = ax.imshow(display_image)
ax.set_xticks(())
ax.set_yticks(())
global click_counter
global is_select_axis
global points
global semi_minor
global ellipse_params
click_counter = 0
points = [[0, 0], [0, 0]]
is_select_axis = True
semi_minor = 5
ellipse_params = [0, 0, 0, 0, 0]
def redraw():
global points
global ellipse_params
global in_image
global plt
global fig
display_image = np.copy(in_image)
from skimage.draw import line
r_index, c_index = line(points[0][0], points[0][1], points[1][0], points[1][1])
display_image[r_index, c_index] = np.array([200, 0, 0])
if ellipse_params is not None:
r_index, c_index = ellipse_perimeter(ellipse_params[0], ellipse_params[1],
ellipse_params[2], ellipse_params[3],
ellipse_params[4])
display_image[r_index, c_index] = np.array([0, 0, 200])
pltcop.set_data(display_image)
fig.canvas.draw()
def click(event):
global click_counter
global points
global is_select_axis
global semi_minor
global ellipse_params
def find_length(y1, y2, x1, x2):
from math import sqrt, pow
return sqrt(pow(y1 - y2, 2) + pow(x1 - x2, 2))
def find_center(y1, y2, x1, x2):
return [(y1 + y2) / 2, (x1 + x2) / 2]
def find_rad(y1, y2, x1, x2):
from math import radians
if x1 == x2: return radians(90)
else:
return float(y1 - y2) / float(x1 - x2)
if event.button == 3:
is_select_axis = not is_select_axis
elif event.button == 1:
if(event.xdata is None): return
x = int(event.xdata)
y = int(event.ydata)
if is_select_axis:
points[click_counter] = [y, x]
click_counter += 1
if click_counter >= len(points):
click_counter = 0
center_y, center_x = find_center(points[0][0], points[1][0], points[0][1], points[1][1])
if not is_select_axis:
semi_minor = find_length(center_y, y, center_x, x)
semi_major = find_length(points[0][0], points[1][0], points[0][1], points[1][1]) / 2
from math import atan
radi = atan(find_rad(points[0][0], points[1][0], points[0][1], points[1][1]))
ellipse_params = [int(center_y), int(center_x), int(semi_minor), int(semi_major), radi]
redraw()
def on_key(event):
global points
global ellipse_params
global in_filename
if event.key == "enter" or event.key == "shift":
if save:
f = open(combined_dir + output_filename , 'a')
f.write(in_filename + ",")
for e_param in ellipse_params:
f.write(str(e_param) + ",")
f.write(tag + "\n")
f.close()
print "%s:%s" % (in_filename, str(ellipse_params))
plt.close('all')
if params:
cid = fig.canvas.mpl_connect('button_press_event', click)
cid = fig.canvas.mpl_connect('key_press_event', on_key)
mng = plt.get_current_fig_manager()
mng.resize(*mng.window.maxsize())
plt.show()
low_threshold=0.55, high_threshold=0.8)
# Perform a Hough Transform
# The accuracy corresponds to the bin size of a major axis.
# The value is chosen in order to get a single high accumulator.
# The threshold eliminates low accumulators
result = hough_ellipse(edges, accuracy=20, threshold=250,
min_size=100, max_size=120)
result.sort(order='accumulator')
# Estimated parameters for the ellipse
best = list(result[-1])
yc, xc, a, b = [int(round(x)) for x in best[1:5]]
orientation = best[5]
# Draw the ellipse on the original image
cy, cx = ellipse_perimeter(yc, xc, a, b, orientation)
image_rgb[cy, cx] = (0, 0, 255)
# Draw the edge (white) and the resulting ellipse (red)
edges = color.gray2rgb(edges)
edges[cy, cx] = (250, 0, 0)
fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True,
sharey=True,
subplot_kw={'adjustable':'box-forced'})
ax1.set_title('Original picture')
ax1.imshow(image_rgb)
ax2.set_title('Edge (white) and result (red)')
ax2.imshow(edges)
import random
import imp
ksvd = imp.load_source('ksvd', '../Source/ksvd.py')
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import orthogonal_mp
from skimage.draw import circle_perimeter, ellipse_perimeter, polygon, line
################# COLLECTION OF SHAPES #######################
cercle = np.zeros((10, 10), np.uint8)
cercle[circle_perimeter(4, 4, 3)] = 1
ellipse = np.zeros((10, 10), np.uint8)
ellipse[ellipse_perimeter(4, 4, 3, 5)] = 1
square = np.zeros((10, 10), np.uint8)
square[polygon(np.array([1, 4, 4, 1]), np.array([1, 1, 4, 4]))] = 1
dline = np.zeros((10, 10), np.uint8)
dline[line(1, 1, 8, 8)] = 1
shapes = [cercle, ellipse, square, dline]
################ GENERATING DICTIONARY ####################
D = np.zeros((100, 4))
for i in range(len(shapes)):
(480, 320),
(380, 430),
(220, 590),
(300, 300),
))
rr, cc = polygon(poly[:,0], poly[:,1], img.shape)
img[rr,cc,1] = 255
# fill circle
rr, cc = circle(200, 200, 100, img.shape)
img[rr,cc,:] = (255, 255, 0)
# fill ellipse
rr, cc = ellipse(300, 300, 100, 200, img.shape)
img[rr,cc,2] = 255
# circle
rr, cc = circle_perimeter(120, 400, 15)
img[rr, cc, :] = (255, 0, 0)
# ellipses
rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 4.)
img[rr, cc, :] = (255, 0, 255)
rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=-math.pi / 4.)
img[rr, cc, :] = (0, 0, 255)
rr, cc = ellipse_perimeter(120, 400, 60, 20, orientation=math.pi / 2.)
img[rr, cc, :] = (255, 255, 255)
plt.imshow(img)
plt.show()
