def autoDetectContour(point, cnt, start, end, delta):
self.new_points = npy.append(self.new_points, point, 0)
points = point[:, :-2]
count = 0
for j in range(start + delta, end + delta, delta):
center = calCentroidFromContour(points).reshape(2)
image = dataset['fix'].getData()[j, :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) -
npy.min(image)) * 255
result = ac_segmentation(center, image)
a1 = ac_area(points.transpose(), image.shape)
a2 = ac_area(result, image.shape)
rate = a2 * 1.0 / a1
if rate >= min(1.5 + count * 0.2, 2.1) or rate <= 0.7:
temp_array = points.copy()
if cnt != 1 and rate > 0.7:
count += 1
else:
temp_array = result.transpose().copy()
count = 0
points = temp_array.copy()
temp_array = npy.insert(temp_array, 2, [[j], [cnt]], 1)
self.new_points = npy.append(self.new_points, temp_array, 0)
sys.stdout.write(str(j) + ',')
sys.stdout.flush()
print ' '
def autoDetectContour(point, cnt, start, end, delta, res, type):
self.new_points = npy.append(self.new_points, point, 0)
points = point[:, :-2]
d = 20
count = 0
for i in range(start + delta, end + delta, delta):
center = calCentroidFromContour(points).reshape(2)
image = dataset[type].getData()[i, :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) -
npy.min(image)) * 255
down = npy.max([npy.ceil(center[0] - d / res[0]), 0])
up = npy.min(
[npy.floor(center[0] + d / res[0]), image.shape[0]])
left = npy.max([npy.ceil(center[1] - d / res[1]), 0])
right = npy.min(
[npy.floor(center[1] + d / res[1]), image.shape[1]])
crop_image = image[down:up, left:right]
center -= [down, left]
result = ac_segmentation(center, crop_image)
a1 = ac_area(points.transpose(), image.shape)
a2 = ac_area(result, crop_image.shape)
rate = a2 * 1.0 / a1
if rate >= min(1.5 + count * 0.2, 2.1) or rate <= 0.7:
temp_array = points.copy()
if cnt != 1 and rate > 0.7:
count += 1
else:
temp_array = result.transpose().copy()
temp_array[:, :2] += [down, left]
count = 0
points = temp_array.copy()
temp_array = npy.insert(temp_array, 2, [[i], [cnt]], 1)
self.new_points = npy.append(self.new_points, temp_array, 0)
sys.stdout.write(str(i) + ',')
sys.stdout.flush()
print ' '
def autoDetectContour(point, cnt, start, end, delta, res):
self.new_points = npy.append(self.new_points, point, 0)
points = point[:, :-2]
d = 20
count = 0
for i in range(start + delta, end + delta, delta):
center = calCentroidFromContour(points).reshape(2)
image = dataset['fix'].getData()[i, :, :].transpose().copy()
image = (image - npy.min(image)) / (npy.max(image) - npy.min(image)) * 255
down = npy.max([npy.ceil(center[0] - d / res[0]), 0])
up = npy.min([npy.floor(center[0] + d / res[0]), image.shape[0]])
left = npy.max([npy.ceil(center[1] - d / res[1]), 0])
right = npy.min([npy.floor(center[1] + d / res[1]), image.shape[1]])
crop_image = image[down : up, left : right]
center -= [down, left]
result = ac_segmentation(center, crop_image)
a1 = ac_area(points.transpose(), image.shape)
a2 = ac_area(result, crop_image.shape)
rate = a2 * 1.0 / a1
if rate >= min(1.5 + count * 0.2, 2.1) or rate <= 0.7:
temp_array = points.copy()
if cnt != 1 and rate > 0.7:
count += 1
else:
temp_array = result.transpose().copy()
temp_array[:, :2] += [down, left]
count = 0
points = temp_array.copy()
temp_array = npy.insert(temp_array, 2, [[i], [cnt]], 1)
self.new_points = npy.append(self.new_points, temp_array, 0)
sys.stdout.write(str(i) + ',')
sys.stdout.flush()
print ' '
def analysis(self, data, point_data_fix=None, all=False):
if point_data_fix is None:
point_data_fix = self.gui.dataModel[
data.getFixedIndex()].getPointSet('Contour').copy()
point_data_result = data.getPointSet('Contour').copy()
self.spacing = data.getResolution().tolist()
self.spacing[
2] = 1.0 # The resolution of z axis is nothing to do with the analysis
point_data_fix[:, :3] *= self.spacing[:3]
point_data_result[:, :3] *= self.spacing[:3]
cnt_num = npy.array([0, 0, 0])
mean_dis = npy.array([0.0, 0.0, 0.0])
max_dis = npy.array([0.0, 0.0, 0.0])
square_sum_dis = npy.array([0.0, 0.0, 0.0])
if all:
result = [{}, {}, {}]
bif = db.getBifurcation(point_data_fix)
for cnt in range(3):
temp_result = point_data_result[npy.where(
npy.round(point_data_result[:, -1]) == cnt)]
temp_fix = point_data_fix[npy.where(
npy.round(point_data_fix[:, -1]) == cnt)]
if not temp_result.shape[0] or not temp_fix.shape[0]:
continue
zmin = int(
npy.max([npy.min(temp_result[:, 2]),
npy.min(temp_fix[:, 2])]) + 0.5)
zmax = int(
npy.min([npy.max(temp_result[:, 2]),
npy.max(temp_fix[:, 2])]) + 0.5)
for z in range(zmin, zmax + 1):
data_fix = temp_fix[npy.where(npy.round(temp_fix[:, 2]) == z)]
data_result = temp_result[npy.where(
npy.round(temp_result[:, 2]) == z)]
if data_fix is not None and data_result is not None:
if data_fix.shape[0] == 0 or data_result.shape[0] == 0:
continue
cnt_num[cnt] += 1
#center_fix = npy.mean(data_fix[:, :2], axis = 0)
center_fix = calCentroidFromContour(data_fix[:, :2])[0]
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result = calCentroidFromContour(
data_result[:, :2])[0]
points_fix = getPointsOntheSpline(data_fix, center_fix,
900)
points_result = getPointsOntheSpline(
data_result, center_result, 900)
i = j = 0
for k in range(-44, 46):
angle = k * 4 / 180.0 * npy.pi
while i < 900 and points_fix[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_fix[i - 1, 2]
< points_fix[i, 2] - angle):
ind_fix = i - 1
else:
ind_fix = i
while j < 900 and points_result[j, 2] < angle:
j += 1
if j == 900 or (j > 0
and angle - points_result[j - 1, 2] <
points_result[j, 2] - angle):
ind_result = j - 1
else:
ind_result = j
temp_dis = npy.hypot(
points_fix[ind_fix, 0] -
points_result[ind_result, 0],
points_fix[ind_fix, 1] -
points_result[ind_result, 1])
max_dis[cnt] = npy.max([max_dis[cnt], temp_dis])
mean_dis[cnt] += temp_dis
square_sum_dis[cnt] += temp_dis**2
if all:
result[cnt][z - bif] = result[cnt].get(
z - bif, 0) + temp_dis
cnt_total = npy.sum(cnt_num)
sd = npy.sqrt(
npy.max([(square_sum_dis - mean_dis**2 / (90 * cnt_num)) /
(90 * cnt_num - 1), [0, 0, 0]],
axis=0))
sd[sd != sd] = 0
sd_all = npy.sqrt(
npy.max([(npy.sum(square_sum_dis) - npy.sum(mean_dis)**2 /
(90 * cnt_total)) / (90 * cnt_total - 1), 0]))
mean_dis /= 90
mean_whole = npy.sum(mean_dis)
mean_dis /= cnt_num
mean_dis[
mean_dis != mean_dis] = 0 # Replace the NAN in the mean distance
if self.gui is not None:
message = "Error on Vessel 0: %0.2fmm (SD = %0.2fmm, Total %d slices)\nError on Vessel 1: %0.2fmm (SD = %0.2fmm, Total %d slices)\nError on Vessel 2: %0.2fmm (SD = %0.2fmm, Total %d slices)\nWhole Error: %0.2fmm (SD = %0.2fmm, Total %d slices)\n" \
% (mean_dis[0], sd[0], cnt_num[0], mean_dis[1], sd[1], cnt_num[1], mean_dis[2], sd[2], cnt_num[2], mean_whole / cnt_total, sd_all, cnt_total) + \
"-----------------------------------------------------------------------------\n" + \
"Max Error on Vessel 0: %0.2fmm\nMax Error on Vessel 1: %0.2fmm\nMax Error on Vessel 2: %0.2fmm\nTotal Max Error: %0.2fmm" \
% (max_dis[0], max_dis[1], max_dis[2], npy.max(max_dis))
self.gui.showErrorMessage("Contour Registration Error", message)
if not all:
return mean_dis, mean_whole / cnt_total, max_dis, npy.max(max_dis)
else:
for cnt in range(3):
for x in result[cnt].keys():
result[cnt][x] /= 90
return mean_dis, mean_whole / cnt_total, max_dis, npy.max(
max_dis), result
def analysis(self, data, point_data_fix=None, area=False):
if point_data_fix is None:
point_data_fix = self.gui.dataModel[
data.getFixedIndex()].getPointSet('Contour').copy()
point_data_result = data.getPointSet('Contour').copy()
image_size = image = data.getData()[0, :, :].transpose().shape
self.spacing = data.getResolution().tolist()
self.spacing[
2] = 1.0 # The resolution of z axis is nothing to do with the analysis
point_data_fix[:, :3] *= self.spacing[:3]
point_data_result[:, :3] *= self.spacing[:3]
cnt_num = npy.array([0, 0, 0])
union_area = npy.array([0.0, 0.0, 0.0])
total_area = npy.array([0.0, 0.0, 0.0])
if area:
mr_area = [0.0, 0.0, 0.0, 0.0]
us_area = [0.0, 0.0, 0.0, 0.0]
for cnt in range(3):
temp_result = point_data_result[npy.where(
npy.round(point_data_result[:, -1]) == cnt)]
temp_fix = point_data_fix[npy.where(
npy.round(point_data_fix[:, -1]) == cnt)]
if not temp_result.shape[0] or not temp_fix.shape[0]:
continue
zmin = int(
npy.min([npy.min(temp_result[:, 2]),
npy.min(temp_fix[:, 2])]) + 0.5)
zmax = int(
npy.max([npy.max(temp_result[:, 2]),
npy.max(temp_fix[:, 2])]) + 0.5)
for z in range(zmin, zmax + 1):
data_fix = temp_fix[npy.where(npy.round(temp_fix[:, 2]) == z)]
data_result = temp_result[npy.where(
npy.round(temp_result[:, 2]) == z)]
if data_fix is not None and data_result is not None:
if data_fix.shape[0] == 0 or data_result.shape[0] == 0:
continue
cnt_num[cnt] += 1
#center_fix = npy.mean(data_fix[:, :2], axis = 0)
center_fix = calCentroidFromContour(data_fix[:, :2])[0]
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result = calCentroidFromContour(
data_result[:, :2])[0]
points_fix = getPointsOntheSpline(
data_fix, center_fix, data_fix.shape[0] * 10)[:, :2]
points_result = getPointsOntheSpline(
data_result, center_result,
data_result.shape[0] * 10)[:, :2]
fix_mask = ac_mask(points_fix.transpose(), image_size)
result_mask = ac_mask(points_result.transpose(),
image_size)
temp_fix_area = npy.sum(fix_mask)
temp_result_area = npy.sum(result_mask)
total_area[cnt] += temp_fix_area + temp_result_area
union_area[cnt] += npy.sum(fix_mask | result_mask)
if area:
mr_area[cnt] += temp_fix_area
mr_area[3] += temp_fix_area
us_area[cnt] += temp_result_area
us_area[3] += temp_result_area
intersect_area = total_area - union_area
jaccard_index = intersect_area / union_area
dice_index = 2 * intersect_area / total_area
jaccard_index_all = npy.sum(intersect_area) / npy.sum(union_area)
dice_index_all = 2 * npy.sum(intersect_area) / npy.sum(total_area)
# Replace the NAN
jaccard_index[jaccard_index != jaccard_index] = 0
dice_index[dice_index != dice_index] = 0
if self.gui is not None:
message = "Jaccard Index on Vessel 0: %0.3f\nJaccard Index on Vessel 1: %0.3f\nJaccard Index on Vessel 2: %0.3f\nTotal Jaccard Index: %0.3f\n" \
% (jaccard_index[0], jaccard_index[1], jaccard_index[2], jaccard_index_all) + \
"-------------------------------------------------------\n" + \
"Dice Index on Vessel 0: %0.3f\nDice Index on Vessel 1: %0.3f\nDice Index on Vessel 2: %0.3f\nTotal Dice Index: %0.3f" \
% (dice_index[0], dice_index[1], dice_index[2], dice_index_all)
self.gui.showErrorMessage("Registration Area Index", message)
if not area:
return dice_index, dice_index_all
else:
for i in range(3):
mr_area[i] /= cnt_num[i]
us_area[i] /= cnt_num[i]
mr_area[3] /= npy.sum(cnt_num)
us_area[3] /= npy.sum(cnt_num)
return dice_index, dice_index_all, mr_area, us_area
def analysis(self, data, point_data_fix = None, area = False):
if point_data_fix is None:
point_data_fix = self.gui.dataModel[data.getFixedIndex()].getPointSet('Contour').copy()
point_data_result = data.getPointSet('Contour').copy()
self.spacing = data.getResolution().tolist()
point_data_fix[:, :2] *= self.spacing[:2]
point_data_result[:, :2] *= self.spacing[:2]
center_data = calCenterlineFromContour({'Contour': point_data_fix})
ind = center_data[:, 2].argsort()
center_data = center_data[ind]
center_data_z = center_data[:, 2].copy()
fixed_bif = db.getBifurcation(center_data)
bif_point = center_data[npy.round(center_data[:, 2]) == fixed_bif - 1]
center_data[:, :3] *= self.spacing[:3]
bif_point[:, :3] *= self.spacing[:3]
spline = [None, None, None]
for cnt in range(3):
spline[cnt] = [None, None, None]
xx = center_data_z[npy.round(center_data[:, -1]) == cnt]
yy = center_data[npy.round(center_data[:, -1]) == cnt]
if cnt > 0:
xx = npy.append(fixed_bif - 1, xx)
yy = npy.append(bif_point, yy, axis = 0)
for i in range(3):
spline[cnt][i] = itp.InterpolatedUnivariateSpline(xx, yy[:, i])
cnt_num = npy.array([0, 0, 0])
mean_dis = npy.array([0.0, 0.0, 0.0])
max_dis = npy.array([0.0, 0.0, 0.0])
square_sum_dis = npy.array([0.0, 0.0, 0.0])
if area:
area_mr = npy.array([0.0, 0.0, 0.0])
area_us = npy.array([0.0, 0.0, 0.0])
for cnt in range(3):
temp_result = point_data_result[npy.where(npy.round(point_data_result[:, -1]) == cnt)]
temp_fix = point_data_fix[npy.where(npy.round(point_data_fix[:, -1]) == cnt)]
if not temp_result.shape[0] or not temp_fix.shape[0]:
continue
zmin = int(npy.max([npy.min(temp_result[:, 2]), npy.min(temp_fix[:, 2])]) + 0.5)
zmax = int(npy.min([npy.max(temp_result[:, 2]), npy.max(temp_fix[:, 2])]) + 0.5)
for z in range(zmin, zmax + 1):
data_fix = temp_fix[npy.where(npy.round(temp_fix[:, 2]) == z)]
data_result = temp_result[npy.where(npy.round(temp_result[:, 2]) == z)]
if data_fix is not None and data_result is not None:
if data_fix.shape[0] == 0 or data_result.shape[0] == 0:
continue
cnt_num[cnt] += 1
#center_fix = npy.mean(data_fix[:, :2], axis = 0)
center_fix, area_fix = calCentroidFromContour(data_fix[:, :2], True)
center_fix = center_fix[0]
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result, area_result = calCentroidFromContour(data_result[:, :2], True)
center_result = center_result[0]
if area:
area_mr[cnt] += area_fix
area_us[cnt] += area_result
points_fix = getPointsOntheSpline(data_fix, center_fix, 900)
points_result = getPointsOntheSpline(data_result, center_result, 900)
normal = npy.array([None, None, None])
for i in range(3):
normal[i] = spline[cnt][i].derivatives(z)[1]
w1 = normal[2] ** 2 / npy.sum(normal ** 2) # cos(alpha) ^ 2
theta0 = npy.arctan2(normal[1], normal[0])
i = j = 0
for k in range(-44, 46):
angle = k * 4 / 180.0 * npy.pi
while i < 900 and points_fix[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_fix[i - 1, 2] < points_fix[i, 2] - angle):
ind_fix = i - 1
else:
ind_fix = i
while j < 900 and points_result[j, 2] < angle:
j += 1
if j == 900 or (j > 0 and angle - points_result[j - 1, 2] < points_result[j, 2] - angle):
ind_result = j - 1
else:
ind_result = j
weigh = npy.sqrt(npy.sin(angle - theta0) ** 2 + npy.cos(angle - theta0) ** 2 / w1)
temp_dis = npy.hypot(points_fix[ind_fix, 0] - points_result[ind_result, 0], points_fix[ind_fix, 1] - points_result[ind_result, 1]) / weigh
#if area:
# temp_dis /= max([area_result / area_fix, area_fix / area_result])
max_dis[cnt] = npy.max([max_dis[cnt], temp_dis])
mean_dis[cnt] += temp_dis
cnt_total = npy.sum(cnt_num)
mean_dis /= 90
if area:
for cnt in range(3):
if area_mr[cnt] < area_us[cnt]:
rate = area_us[cnt] / area_mr[cnt]
else:
rate = area_us[cnt] / area_mr[cnt]
mean_dis[cnt] /= rate
mean_whole = npy.sum(mean_dis)
mean_dis /= cnt_num
mean_dis[mean_dis != mean_dis] = 0 # Replace the NAN in the mean distance
if self.gui is not None:
message = "Error on Vessel 0: %0.2fmm (Total %d slices)\nError on Vessel 1: %0.2fmm (Total %d slices)\nError on Vessel 2: %0.2fmm (Total %d slices)\nWhole Error: %0.2fmm (Total %d slices)\n" \
% (mean_dis[0], cnt_num[0], mean_dis[1], cnt_num[1], mean_dis[2], cnt_num[2], mean_whole / cnt_total, cnt_total) + \
"-----------------------------------------------------------------------------\n" + \
"Max Error on Vessel 0: %0.2fmm\nMax Error on Vessel 1: %0.2fmm\nMax Error on Vessel 2: %0.2fmm\nTotal Max Error: %0.2fmm" \
% (max_dis[0], max_dis[1], max_dis[2], npy.max(max_dis));
self.gui.showErrorMessage("Weighted Contour Registration Error", message)
return mean_dis, mean_whole / cnt_total, max_dis, npy.max(max_dis)
def augmentPointset(ori_points, multiple, opt_size, bif, nn=-1):
if multiple <= 1:
return ori_points
if nn < 0:
zmin = int(npy.min(ori_points[:, 2]) + 0.5)
zmax = int(npy.max(ori_points[:, 2]) + 0.5)
nn = int((opt_size - ori_points.shape[0]) / ((2 * zmax - zmin - bif)) /
(multiple - 1) + 0.5) + 1
#print nn
new_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
zmin = [0, 0, 0]
zmax = [0, 0, 0]
resampled_points = [None, None, None]
for cnt in range(3):
temp_result = ori_points[npy.where(
npy.round(ori_points[:, -1]) == cnt)]
if not temp_result.shape[0]:
continue
zmin[cnt] = int(npy.min(temp_result[:, 2]) + 0.5)
zmax[cnt] = int(npy.max(temp_result[:, 2]) + 0.5)
resampled_points[cnt] = npy.zeros(
[(zmax[cnt] - zmin[cnt] + 1) * nn, 4], dtype=npy.float32)
resampled_index = 0
for z in range(zmin[cnt], zmax[cnt] + 1):
data_result = temp_result[npy.where(
npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result = calCentroidFromContour(data_result[:, :2])[0]
points_result = getPointsOntheSpline(data_result,
center_result, 900)
i = 0
for k in range(-nn / 2 + 1, nn / 2 + 1):
angle = k * 2 * npy.pi / nn
while i < 900 and points_result[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_result[i - 1, 2] <
points_result[i, 2] - angle):
ind_result = i - 1
else:
ind_result = i
resampled_points[cnt][resampled_index, :2] = points_result[
ind_result, :2]
resampled_points[cnt][resampled_index, 2] = z
resampled_points[cnt][resampled_index, 3] = k + 4
resampled_index += 1
trans_points = npy.array(ori_points)
for cnt in range(3):
for k in range(0, nn):
data = resampled_points[cnt][npy.where(
npy.round(resampled_points[cnt][:, -1]) == k)]
count = data.shape[0]
if count == 0:
continue
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, data[i, 0], data[i, 1], data[i, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
deltaz = 1.0 / multiple
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax[cnt] - zmin[cnt] + 1) * 10)
i = 0
for z in range(zmin[cnt], zmax[cnt] + 1):
znow = float(z)
for dd in range(1, multiple):
znow += deltaz
while i < numberOfOutputPoints:
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
trans_points = npy.append(
trans_points,
[[new_point[0], new_point[1], znow, cnt]],
axis=0)
old_pt = pt
break
i += 1
return trans_points
def resliceTheResultPoints(moving_points,
moving_center,
nn,
moving_res,
fixed_res,
discard,
R,
T,
C=npy.asmatrix([0, 0, 0]).T):
resampled_points = [None, None, None]
nearbif_points = [None, None, None]
nearbif_center = [None, None]
bif_slice = 0
for cnt in range(3):
temp_result = moving_points[npy.where(
npy.round(moving_points[:, -1]) == cnt)].copy()
if not temp_result.shape[0]:
continue
zmin = int(npy.min(temp_result[:, 2]) + 0.5)
zmax = int(npy.max(temp_result[:, 2]) + 0.5)
resampled_points[cnt] = npy.zeros([(zmax - zmin + 1) * nn, 4],
dtype=npy.float32)
resampled_index = 0
for z in range(zmin, zmax + 1):
data_result = temp_result[npy.where(
npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
center_result = calCentroidFromContour(data_result[:, :2])[0]
points_result = getPointsOntheSpline(data_result,
center_result, 900)
if cnt > 0 and z == zmin:
nearbif_points[cnt] = points_result
nearbif_center[cnt - 1] = center_result
bif_slice = z
elif cnt == 0 and z == zmax - 1:
nearbif_points[cnt] = points_result
i = 0
for k in range(-nn / 2 + 1, nn / 2 + 1):
angle = k * 360 / nn / 180.0 * npy.pi
while i < 900 and points_result[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_result[i - 1, 2] <
points_result[i, 2] - angle):
ind_result = i - 1
else:
ind_result = i
resampled_points[cnt][resampled_index, :2] = points_result[
ind_result, :2]
resampled_points[cnt][resampled_index, 2] = z
resampled_points[cnt][resampled_index, 3] = k + nn / 2 - 1
resampled_index += 1
nearbif_angle = [
npy.arctan2(nearbif_center[1][1] - nearbif_center[0][1],
nearbif_center[1][0] - nearbif_center[0][0]),
npy.arctan2(nearbif_center[0][1] - nearbif_center[1][1],
nearbif_center[0][0] - nearbif_center[1][0])
]
point_near_bif = npy.zeros([2, 2], dtype=npy.float32)
for cnt in range(2):
ind = npy.argmin(
npy.abs(nearbif_points[cnt + 1][:, 2] - nearbif_angle[cnt]))
point_near_bif[cnt, :] = nearbif_points[cnt + 1][ind, :2]
bif_points = npy.zeros([3, 3], dtype=npy.float32)
bif_points[0, :2] = npy.mean(point_near_bif, axis=0)
bif_points[0, 2] = bif_slice
nearbif_angle = [
npy.arctan2(nearbif_center[1][0] - nearbif_center[0][0],
nearbif_center[0][1] - nearbif_center[1][1]),
npy.arctan2(nearbif_center[0][0] - nearbif_center[1][0],
nearbif_center[1][1] - nearbif_center[0][1])
]
nearbif_points[0][:, 2] = npy.arctan2(
nearbif_points[0][:, 1] - bif_points[0, 1],
nearbif_points[0][:, 0] - bif_points[0, 0])
for cnt in range(1, 3):
ind = npy.argmin(
npy.abs(nearbif_points[0][:, 2] - nearbif_angle[cnt - 1]))
bif_points[cnt, :2] = nearbif_points[0][ind, :2]
bif_points[1:, 2] = bif_slice - 1
# Apply the transformation on the resampled points
for cnt in range(3):
resampled_points[cnt][:, :3] = applyTransformForPoints(
resampled_points[cnt][:, :3], moving_res, fixed_res, R, T, C)
bif_points = applyTransformForPoints(bif_points, moving_res, fixed_res, R,
T, C)
# Resample the points near the bifurcation
points = vtk.vtkPoints()
points.InsertPoint(0, bif_points[1, 0], bif_points[1, 1], bif_points[1, 2])
points.InsertPoint(1, bif_points[0, 0], bif_points[0, 1], bif_points[0, 2])
points.InsertPoint(2, bif_points[2, 0], bif_points[2, 1], bif_points[2, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
numberOfOutputPoints = 6
new_bif_points = npy.zeros([numberOfOutputPoints + 1, 4],
dtype=npy.float32)
for i in range(0, numberOfOutputPoints + 1):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
new_bif_points[i, :3] = pt
new_bif_points[:, 3] = 0
# Reslice the result points
trans_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
bif_slice = int(npy.ceil(bif_points[0, 2]))
for cnt in range(3):
zmin = int(npy.ceil(npy.max(resampled_points[cnt][:nn, 2])))
zmax = int(npy.min(resampled_points[cnt][-nn:, 2]))
if not discard:
if cnt == 0:
zmax = bif_slice
else:
zmin = bif_slice
for k in range(0, nn):
data = resampled_points[cnt][npy.where(
npy.round(resampled_points[cnt][:, -1]) == k)]
if not discard:
if cnt == 0:
dis1 = npy.hypot(
data[-1, 0] - resampled_points[1][nn:nn * 2, 0],
data[-1, 1] - resampled_points[1][nn:nn * 2, 1])
#dis1 = npy.hypot(data[-1, 0] - nearbif_points[1][:, 0], data[-1, 1] - nearbif_points[1][:, 1])
ind1 = npy.argmin(dis1)
dis2 = npy.hypot(
data[-1, 0] - resampled_points[2][nn:nn * 2, 0],
data[-1, 1] - resampled_points[2][nn:nn * 2, 1])
ind2 = npy.argmin(dis2)
if dis1[ind1] < dis2[ind2]:
data_add = resampled_points[1][npy.where(
(npy.round(resampled_points[1][:, -1]) == ind1)
& (resampled_points[1][:, 2] <= zmax + 1))]
else:
data_add = resampled_points[2][npy.where(
(npy.round(resampled_points[2][:, -1]) == ind2)
& (resampled_points[2][:, 2] <= zmax + 1))]
data = npy.append(data, data_add[1:, :], axis=0)
else:
dis1 = npy.hypot(
data[0, 0] - resampled_points[0][-nn * 2:-nn, 0],
data[0, 1] - resampled_points[0][-nn * 2:-nn, 1])
ind1 = npy.argmin(dis1)
dis2 = npy.sqrt(
npy.sum((new_bif_points[:, :3] - data[0, :3])**2,
axis=1))
ind2 = npy.argmin(dis2)
if dis1[ind1] < dis2[ind2]:
data_add = resampled_points[0][npy.where(
(npy.round(resampled_points[0][:, -1]) == ind1)
& (resampled_points[0][:, 2] >= zmin - 1))]
else:
data_add = new_bif_points[ind2, :].reshape(1, -1)
data = npy.append(data_add[:-1, :], data, axis=0)
count = data.shape[0]
if count == 0:
continue
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, data[i, 0], data[i, 1], data[i, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
znow = zmin
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax - zmin + 3) * nn)
for i in range(0, numberOfOutputPoints):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
trans_points = npy.append(
trans_points,
[[new_point[0], new_point[1], znow, cnt]],
axis=0)
znow += 1
if znow > zmax:
break
old_pt = pt
new_trans_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
for cnt in range(3):
temp_result = trans_points[npy.where(
npy.round(trans_points[:, -1]) == cnt)]
if not temp_result.shape[0]:
continue
zmin = int(npy.min(temp_result[:, 2]) + 0.5)
zmax = int(npy.max(temp_result[:, 2]) + 0.5)
for z in range(zmin, zmax + 1):
data_result = temp_result[npy.where(
npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
ind = sortContourPoints(data_result)
data_result[:, :] = data_result[ind, :]
for x in data_result:
if isDifferent(new_trans_points[-1, :], x):
new_trans_points = npy.append(new_trans_points,
x.reshape(1, -1),
axis=0)
result_center_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
if moving_center is not None and moving_center.shape[0] > 1:
result_center = moving_center[npy.where(moving_center[:, 0] >= 0)]
result_center[:, :3] = applyTransformForPoints(result_center[:, :3],
moving_res, fixed_res,
R, T, C)
ind = result_center[:, 2].argsort()
result_center = result_center[ind]
result_center_points = npy.array([[-1, -1, -1, -1]], dtype=npy.float32)
for cnt in range(3):
resampled_points = result_center[npy.where(
npy.round(result_center[:, -1]) == cnt)]
zmin = int(npy.ceil(resampled_points[0, 2]))
zmax = int(resampled_points[-1, 2])
count = resampled_points.shape[0]
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, resampled_points[i, 0],
resampled_points[i, 1], resampled_points[i,
2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
znow = zmin
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax - zmin + 1) * 10)
for i in range(0, numberOfOutputPoints):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
result_center_points = npy.append(
result_center_points,
[[new_point[0], new_point[1], znow, cnt]],
axis=0)
znow += 1
if znow > zmax:
break
old_pt = pt
return new_trans_points, result_center_points
def augmentPointset(ori_points, multiple, opt_size, bif, nn = -1):
if multiple <= 1:
return ori_points
if nn < 0:
zmin = int(npy.min(ori_points[:, 2]) + 0.5)
zmax = int(npy.max(ori_points[:, 2]) + 0.5)
nn = int((opt_size - ori_points.shape[0]) / ((2 * zmax - zmin - bif)) / (multiple - 1) + 0.5) + 1
#print nn
new_points = npy.array([[-1, -1, -1, -1]], dtype = npy.float32)
zmin = [0, 0, 0]
zmax = [0, 0, 0]
resampled_points = [None, None, None]
for cnt in range(3):
temp_result = ori_points[npy.where(npy.round(ori_points[:, -1]) == cnt)]
if not temp_result.shape[0]:
continue
zmin[cnt] = int(npy.min(temp_result[:, 2]) + 0.5)
zmax[cnt] = int(npy.max(temp_result[:, 2]) + 0.5)
resampled_points[cnt] = npy.zeros([(zmax[cnt] - zmin[cnt] + 1) * nn, 4], dtype = npy.float32)
resampled_index = 0
for z in range(zmin[cnt], zmax[cnt] + 1):
data_result = temp_result[npy.where(npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result = calCentroidFromContour(data_result[:, :2])[0]
points_result = getPointsOntheSpline(data_result, center_result, 900)
i = 0
for k in range(-nn / 2 + 1, nn / 2 + 1):
angle = k * 2 * npy.pi / nn
while i < 900 and points_result[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_result[i - 1, 2] < points_result[i, 2] - angle):
ind_result = i - 1
else:
ind_result = i
resampled_points[cnt][resampled_index, :2] = points_result[ind_result, :2]
resampled_points[cnt][resampled_index, 2] = z
resampled_points[cnt][resampled_index, 3] = k + 4
resampled_index += 1
trans_points = npy.array(ori_points)
for cnt in range(3):
for k in range(0, nn):
data = resampled_points[cnt][npy.where(npy.round(resampled_points[cnt][:, -1]) == k)]
count = data.shape[0]
if count == 0:
continue
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, data[i, 0], data[i, 1], data[i, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
deltaz = 1.0 / multiple
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax[cnt] - zmin[cnt] + 1) * 10)
i = 0
for z in range(zmin[cnt], zmax[cnt] + 1):
znow = float(z)
for dd in range(1, multiple):
znow += deltaz
while i < numberOfOutputPoints:
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
trans_points = npy.append(trans_points, [[new_point[0], new_point[1], znow, cnt]], axis = 0)
old_pt = pt
break
i += 1
return trans_points
def resliceTheResultPoints(moving_points, moving_center, nn, moving_res, fixed_res, discard, R, T, C = npy.asmatrix([0, 0, 0]).T):
resampled_points = [None, None, None]
nearbif_points = [None, None, None]
nearbif_center = [None, None]
bif_slice = 0
for cnt in range(3):
temp_result = moving_points[npy.where(npy.round(moving_points[:, -1]) == cnt)].copy()
if not temp_result.shape[0]:
continue
zmin = int(npy.min(temp_result[:, 2]) + 0.5)
zmax = int(npy.max(temp_result[:, 2]) + 0.5)
resampled_points[cnt] = npy.zeros([(zmax - zmin + 1) * nn, 4], dtype = npy.float32)
resampled_index = 0
for z in range(zmin, zmax + 1):
data_result = temp_result[npy.where(npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
center_result = calCentroidFromContour(data_result[:, :2])[0]
points_result = getPointsOntheSpline(data_result, center_result, 900)
if cnt > 0 and z == zmin:
nearbif_points[cnt] = points_result
nearbif_center[cnt - 1] = center_result
bif_slice = z
elif cnt == 0 and z == zmax - 1:
nearbif_points[cnt] = points_result
i = 0
for k in range(- nn / 2 + 1, nn / 2 + 1):
angle = k * 360 / nn / 180.0 * npy.pi
while i < 900 and points_result[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_result[i - 1, 2] < points_result[i, 2] - angle):
ind_result = i - 1
else:
ind_result = i
resampled_points[cnt][resampled_index, :2] = points_result[ind_result, :2]
resampled_points[cnt][resampled_index, 2] = z
resampled_points[cnt][resampled_index, 3] = k + nn / 2 - 1
resampled_index += 1
nearbif_angle = [npy.arctan2(nearbif_center[1][1] - nearbif_center[0][1], nearbif_center[1][0] - nearbif_center[0][0]),
npy.arctan2(nearbif_center[0][1] - nearbif_center[1][1], nearbif_center[0][0] - nearbif_center[1][0])]
point_near_bif = npy.zeros([2, 2], dtype = npy.float32)
for cnt in range(2):
ind = npy.argmin(npy.abs(nearbif_points[cnt + 1][:, 2] - nearbif_angle[cnt]))
point_near_bif[cnt, :] = nearbif_points[cnt + 1][ind, :2]
bif_points = npy.zeros([3, 3], dtype = npy.float32)
bif_points[0, :2] = npy.mean(point_near_bif, axis = 0)
bif_points[0, 2] = bif_slice
nearbif_angle = [npy.arctan2(nearbif_center[1][0] - nearbif_center[0][0], nearbif_center[0][1] - nearbif_center[1][1]),
npy.arctan2(nearbif_center[0][0] - nearbif_center[1][0], nearbif_center[1][1] - nearbif_center[0][1])]
nearbif_points[0][:, 2] = npy.arctan2(nearbif_points[0][:, 1] - bif_points[0, 1], nearbif_points[0][:, 0] - bif_points[0, 0])
for cnt in range(1, 3):
ind = npy.argmin(npy.abs(nearbif_points[0][:, 2] - nearbif_angle[cnt - 1]))
bif_points[cnt, :2] = nearbif_points[0][ind, :2]
bif_points[1:, 2] = bif_slice - 1
# Apply the transformation on the resampled points
for cnt in range(3):
resampled_points[cnt][:, :3] = applyTransformForPoints(resampled_points[cnt][:, :3], moving_res, fixed_res, R, T, C)
bif_points = applyTransformForPoints(bif_points, moving_res, fixed_res, R, T, C)
# Resample the points near the bifurcation
points = vtk.vtkPoints()
points.InsertPoint(0, bif_points[1, 0], bif_points[1, 1], bif_points[1, 2])
points.InsertPoint(1, bif_points[0, 0], bif_points[0, 1], bif_points[0, 2])
points.InsertPoint(2, bif_points[2, 0], bif_points[2, 1], bif_points[2, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
numberOfOutputPoints = 6
new_bif_points = npy.zeros([numberOfOutputPoints + 1, 4], dtype = npy.float32)
for i in range(0, numberOfOutputPoints + 1):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
new_bif_points[i, :3] = pt
new_bif_points[:, 3] = 0
# Reslice the result points
trans_points = npy.array([[-1, -1, -1, -1]], dtype = npy.float32)
bif_slice = int(npy.ceil(bif_points[0, 2]))
for cnt in range(3):
zmin = int(npy.ceil(npy.max(resampled_points[cnt][:nn, 2])))
zmax = int(npy.min(resampled_points[cnt][-nn:, 2]))
if not discard:
if cnt == 0:
zmax = bif_slice
else:
zmin = bif_slice
for k in range(0, nn):
data = resampled_points[cnt][npy.where(npy.round(resampled_points[cnt][:, -1]) == k)]
if not discard:
if cnt == 0:
dis1 = npy.hypot(data[-1, 0] - resampled_points[1][nn:nn * 2, 0], data[-1, 1] - resampled_points[1][nn:nn * 2, 1])
#dis1 = npy.hypot(data[-1, 0] - nearbif_points[1][:, 0], data[-1, 1] - nearbif_points[1][:, 1])
ind1 = npy.argmin(dis1)
dis2 = npy.hypot(data[-1, 0] - resampled_points[2][nn:nn * 2, 0], data[-1, 1] - resampled_points[2][nn:nn * 2, 1])
ind2 = npy.argmin(dis2)
if dis1[ind1] < dis2[ind2]:
data_add = resampled_points[1][npy.where((npy.round(resampled_points[1][:, -1]) == ind1) & (resampled_points[1][:, 2] <= zmax + 1))]
else:
data_add = resampled_points[2][npy.where((npy.round(resampled_points[2][:, -1]) == ind2) & (resampled_points[2][:, 2] <= zmax + 1))]
data = npy.append(data, data_add[1:, :], axis = 0)
else:
dis1 = npy.hypot(data[0, 0] - resampled_points[0][-nn * 2:-nn, 0], data[0, 1] - resampled_points[0][-nn * 2:-nn, 1])
ind1 = npy.argmin(dis1)
dis2 = npy.sqrt(npy.sum((new_bif_points[:, :3] - data[0, :3]) ** 2, axis = 1))
ind2 = npy.argmin(dis2)
if dis1[ind1] < dis2[ind2]:
data_add = resampled_points[0][npy.where((npy.round(resampled_points[0][:, -1]) == ind1) & (resampled_points[0][:, 2] >= zmin - 1))]
else:
data_add = new_bif_points[ind2, :].reshape(1, -1)
data = npy.append(data_add[:-1, :], data, axis = 0)
count = data.shape[0]
if count == 0:
continue
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, data[i, 0], data[i, 1], data[i, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
znow = zmin
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax - zmin + 3) * nn)
for i in range(0, numberOfOutputPoints):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
trans_points = npy.append(trans_points, [[new_point[0], new_point[1], znow, cnt]], axis = 0)
znow += 1
if znow > zmax:
break
old_pt = pt
new_trans_points = npy.array([[-1, -1, -1, -1]], dtype = npy.float32)
for cnt in range(3):
temp_result = trans_points[npy.where(npy.round(trans_points[:, -1]) == cnt)]
if not temp_result.shape[0]:
continue
zmin = int(npy.min(temp_result[:, 2]) + 0.5)
zmax = int(npy.max(temp_result[:, 2]) + 0.5)
for z in range(zmin, zmax + 1):
data_result = temp_result[npy.where(npy.round(temp_result[:, 2]) == z)]
if data_result is not None:
if data_result.shape[0] == 0:
continue
ind = sortContourPoints(data_result)
data_result[:, :] = data_result[ind, :]
for x in data_result:
if isDifferent(new_trans_points[-1, :], x):
new_trans_points = npy.append(new_trans_points, x.reshape(1, -1), axis = 0)
result_center_points = npy.array([[-1, -1, -1, -1]], dtype = npy.float32)
if moving_center is not None and moving_center.shape[0] > 1:
result_center = moving_center[npy.where(moving_center[:, 0] >= 0)]
result_center[:, :3] = applyTransformForPoints(result_center[:, :3], moving_res, fixed_res, R, T, C)
ind = result_center[:, 2].argsort()
result_center = result_center[ind]
result_center_points = npy.array([[-1, -1, -1, -1]], dtype = npy.float32)
for cnt in range(3):
resampled_points = result_center[npy.where(npy.round(result_center[:, -1]) == cnt)]
zmin = int(npy.ceil(resampled_points[0, 2]))
zmax = int(resampled_points[-1, 2])
count = resampled_points.shape[0]
points = vtk.vtkPoints()
for i in range(count):
points.InsertPoint(i, resampled_points[i, 0], resampled_points[i, 1], resampled_points[i, 2])
para_spline = vtk.vtkParametricSpline()
para_spline.SetPoints(points)
para_spline.ClosedOff()
znow = zmin
old_pt = [0.0, 0.0, 0.0]
numberOfOutputPoints = int((zmax - zmin + 1) * 10)
for i in range(0, numberOfOutputPoints):
t = i * 1.0 / numberOfOutputPoints
pt = [0.0, 0.0, 0.0]
para_spline.Evaluate([t, t, t], pt, [0] * 9)
if pt[2] >= znow:
if pt[2] - znow < znow - old_pt[2]:
new_point = pt
else:
new_point = old_pt
result_center_points = npy.append(result_center_points, [[new_point[0], new_point[1], znow, cnt]], axis = 0)
znow += 1
if znow > zmax:
break
old_pt = pt
return new_trans_points, result_center_points
def analysis(self, data, point_data_fix = None, all = False):
if point_data_fix is None:
point_data_fix = self.gui.dataModel[data.getFixedIndex()].getPointSet('Contour').copy()
point_data_result = data.getPointSet('Contour').copy()
self.spacing = data.getResolution().tolist()
self.spacing[2] = 1.0 # The resolution of z axis is nothing to do with the analysis
point_data_fix[:, :3] *= self.spacing[:3]
point_data_result[:, :3] *= self.spacing[:3]
cnt_num = npy.array([0, 0, 0])
mean_dis = npy.array([0.0, 0.0, 0.0])
max_dis = npy.array([0.0, 0.0, 0.0])
square_sum_dis = npy.array([0.0, 0.0, 0.0])
if all:
result = [{}, {}, {}]
bif = db.getBifurcation(point_data_fix)
for cnt in range(3):
temp_result = point_data_result[npy.where(npy.round(point_data_result[:, -1]) == cnt)]
temp_fix = point_data_fix[npy.where(npy.round(point_data_fix[:, -1]) == cnt)]
if not temp_result.shape[0] or not temp_fix.shape[0]:
continue
zmin = int(npy.max([npy.min(temp_result[:, 2]), npy.min(temp_fix[:, 2])]) + 0.5)
zmax = int(npy.min([npy.max(temp_result[:, 2]), npy.max(temp_fix[:, 2])]) + 0.5)
for z in range(zmin, zmax + 1):
data_fix = temp_fix[npy.where(npy.round(temp_fix[:, 2]) == z)]
data_result = temp_result[npy.where(npy.round(temp_result[:, 2]) == z)]
if data_fix is not None and data_result is not None:
if data_fix.shape[0] == 0 or data_result.shape[0] == 0:
continue
cnt_num[cnt] += 1
#center_fix = npy.mean(data_fix[:, :2], axis = 0)
center_fix = calCentroidFromContour(data_fix[:, :2])[0]
#center_result = npy.mean(data_result[:, :2], axis = 0)
center_result = calCentroidFromContour(data_result[:, :2])[0]
points_fix = getPointsOntheSpline(data_fix, center_fix, 900)
points_result = getPointsOntheSpline(data_result, center_result, 900)
i = j = 0
for k in range(-44, 46):
angle = k * 4 / 180.0 * npy.pi
while i < 900 and points_fix[i, 2] < angle:
i += 1
if i == 900 or (i > 0 and angle - points_fix[i - 1, 2] < points_fix[i, 2] - angle):
ind_fix = i - 1
else:
ind_fix = i
while j < 900 and points_result[j, 2] < angle:
j += 1
if j == 900 or (j > 0 and angle - points_result[j - 1, 2] < points_result[j, 2] - angle):
ind_result = j - 1
else:
ind_result = j
temp_dis = npy.hypot(points_fix[ind_fix, 0] - points_result[ind_result, 0], points_fix[ind_fix, 1] - points_result[ind_result, 1])
max_dis[cnt] = npy.max([max_dis[cnt], temp_dis])
mean_dis[cnt] += temp_dis
square_sum_dis[cnt] += temp_dis ** 2
if all:
result[cnt][z - bif] = result[cnt].get(z - bif, 0) + temp_dis
cnt_total = npy.sum(cnt_num)
sd = npy.sqrt(npy.max([(square_sum_dis - mean_dis ** 2 / (90 * cnt_num)) / (90 * cnt_num - 1), [0, 0, 0]], axis = 0))
sd[sd != sd] = 0
sd_all = npy.sqrt(npy.max([(npy.sum(square_sum_dis) - npy.sum(mean_dis) ** 2 / (90 * cnt_total)) / (90 * cnt_total - 1), 0]))
mean_dis /= 90
mean_whole = npy.sum(mean_dis)
mean_dis /= cnt_num
mean_dis[mean_dis != mean_dis] = 0 # Replace the NAN in the mean distance
if self.gui is not None:
message = "Error on Vessel 0: %0.2fmm (SD = %0.2fmm, Total %d slices)\nError on Vessel 1: %0.2fmm (SD = %0.2fmm, Total %d slices)\nError on Vessel 2: %0.2fmm (SD = %0.2fmm, Total %d slices)\nWhole Error: %0.2fmm (SD = %0.2fmm, Total %d slices)\n" \
% (mean_dis[0], sd[0], cnt_num[0], mean_dis[1], sd[1], cnt_num[1], mean_dis[2], sd[2], cnt_num[2], mean_whole / cnt_total, sd_all, cnt_total) + \
"-----------------------------------------------------------------------------\n" + \
"Max Error on Vessel 0: %0.2fmm\nMax Error on Vessel 1: %0.2fmm\nMax Error on Vessel 2: %0.2fmm\nTotal Max Error: %0.2fmm" \
% (max_dis[0], max_dis[1], max_dis[2], npy.max(max_dis));
self.gui.showErrorMessage("Contour Registration Error", message)
if not all:
return mean_dis, mean_whole / cnt_total, max_dis, npy.max(max_dis)
else:
for cnt in range(3):
for x in result[cnt].keys():
result[cnt][x] /= 90
return mean_dis, mean_whole / cnt_total, max_dis, npy.max(max_dis), result