def preprocess(trainingSamples):
global MS, IS
XS = l.create_partial_XS(trainingSamples)
MS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
IS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
for j in range(c.get_nb_teeth()):
S = XS[j, :, :]
M, Y = pa.PA(S)
MS[j, :] = M
mtx = mty = ms = mtheta = 0
n = S.shape[0]
for i in range(n):
tx, ty, s, theta = mu.full_align_params(
M, fu.original_to_cropped(S[i, :]))
mtx += tx
mty += ty
ms += s
mtheta += theta
n = float(n)
mtx /= n
mty /= n
ms /= n
mtheta /= n
IS[j, :] = mu.full_align(M, mtx, mty, ms, mtheta)
def preprocess(trainingSamples):
'''
Creates MS, EWS and fs, used by the fitting procedure
* MS contains for each tooth, the tooth model (in the model coordinate frame)
* EWS contains for each tooth, a (sqrt(Eigenvalues), Eigenvectors) pair (in the model coordinate frame)
* fitting function for each tooth, for each landmark.
'''
global MS, EWS, fns, fts
XS = l.create_partial_XS(trainingSamples)
MS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
for j in range(c.get_nb_teeth()):
S = XS[j, :, :]
M, Y = pa.PA(S)
MS[j, :] = M
E, W, MU = pca.pca_percentage(Y)
EWS.append((np.sqrt(E), W))
GNS, GTS = ff.create_partial_GS_for_multiple_levels(trainingSamples,
XS,
MS, (max_level + 1),
offsetX=fu.offsetX,
offsetY=fu.offsetY,
k=k,
method=method)
fns, fts = ff.create_fitting_functions_for_multiple_levels(GNS, GTS)
def create_average_models(trainingSamples, method=''):
XS = l.create_partial_XS(trainingSamples)
MS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
IS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
for j in range(c.get_nb_teeth()):
S = XS[j,:,:]
M, Y = pa.PA(S)
MS[j,:] = M
mtx = mty = ms = mtheta = 0
n = S.shape[0]
for i in range(n):
tx, ty, s, theta = mu.full_align_params(M, fu.original_to_cropped(S[i,:]))
mtx += tx
mty += ty
ms += s
mtheta += theta
n = float(n)
IS[j,:] = mu.full_align(M, (mtx / n), (mty / n), (ms / n), (mtheta / n))
def create_partial_XS(trainingSamples):
'''
Creates an array that contains all the training samples
corresponding to the given training samples and corresponding to all the teeth.
@param trainingSamples: the training samples
@return np.array, shape=(nb of teeth, nb of training samples, nb of dimensions)
'''
XS = np.zeros(np.array([c.get_nb_teeth(), len(trainingSamples), c.get_nb_dim()]))
for j in range(c.get_nb_teeth()):
XS[j,:,:] = create_partial_X(trainingSamples, nr_tooth=(j+1))
return XS
def store_plotted_vary_mode_param(closed_curve=False):
'''
Stores the plots of the effects of varying one mode parameter / shape parameter
in -3 s.d. / -2 s.d. / -1 s.d. / M / +1 s.d. / +2 s.d. / +3 s.d.
in the model coordinate frame for all the teeth.
@param closed_curve: must the curve be closed
'''
for t in c.get_teeth_range():
M, Y = pa.PA(l.create_full_X(nr_tooth=t))
E, W, MU = pca.pca_percentage(Y)
YS = np.zeros(np.array([E.shape[0], 7, c.get_nb_dim()]))
for e in range(YS.shape[0]):
se = math.sqrt(E[e])
C = np.zeros(W.shape[1])
C[e] = 1
j = 0
for i in range(-3, 4):
YS[e, j, :] = pca.reconstruct(W, C * i * se, M)
j += 1
(ymin, ymax, xmin, xmax) = pre.learn_offsets(YS)
ymin -= 0.01
ymax += 0.01
xmin -= 0.01
xmax += 0.01
for e in range(YS.shape[0]):
pyplot.figure()
for j in range(1, YS.shape[1] + 1):
xCoords, yCoords = mu.extract_coordinates(YS[e, (j - 1), :])
if (closed_curve):
xCoords = mu.make_circular(xCoords)
yCoords = mu.make_circular(yCoords)
# x coordinates , y coordinates
pyplot.subplot(1, 7, j)
pyplot.plot(xCoords, yCoords, '-+r')
pyplot.axis([xmin, xmax, ymin, ymax])
pyplot.gca().set_aspect('equal', adjustable='box')
if j == 1:
pyplot.ylabel('y\'')
if j == 4:
pyplot.title('Tooth nr: ' + str(t) + ' | ' +
'Eigenvalue: ' + str(E[e]))
pyplot.xlabel('x\'')
frame = pyplot.gca()
if j % 2 == 0:
frame.axes.get_xaxis().set_ticks([])
else:
frame.axes.get_xaxis().set_ticks([xmin, xmax])
pyplot.gca().invert_yaxis()
#pyplot.subplots_adjust(right=1)
fname = c.get_fname_vis_pca(t, (e + 1))
pyplot.savefig(fname, bbox_inches='tight')
pyplot.close()
def preprocess():
'''
Creates XS and MS, used by the drawing functions.
* XS contains for each tooth, for each training sample, all landmarks (in the image coordinate frame)
* MS contains for each tooth, the tooth model (in the model coordinate frame)
'''
global XS, MS
XS = l.create_full_XS()
MS = np.zeros((c.get_nb_teeth(), c.get_nb_dim()))
for j in range(c.get_nb_teeth()):
M, Y = pa.PA(l.create_full_X(j+1))
MS[j,:] = M
def test2_combined():
Results = np.zeros(
(c.get_nb_trainingSamples(), 3 * c.get_nb_teeth(), c.get_nb_dim()))
color_lines = np.array([
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
])
for i in c.get_trainingSamples_range():
trainingSamples = c.get_trainingSamples_range()
trainingSamples.remove(i)
preprocess(trainingSamples)
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
for f in range(2):
for j in range(c.get_nb_teeth()):
fname = c.get_fname_original_landmark(i, (j + 1))
P = fu.original_to_cropped(
np.fromfile(fname, dtype=float, count=-1, sep=' '))
if f == 0: Results[(i - 1), j, :] = P
Results[(i - 1), (f + 1) * c.get_nb_teeth() +
j, :] = multi_resolution_search(img,
P,
j,
fitting_function=f)
fname = str(i) + 'm.png'
cv2.imwrite(
fname,
fu.mark_results(np.copy(img), Results[(i - 1), :], color_lines))
def create_partial_X(trainingSamples, nr_tooth=1):
'''
Creates an array that contains all the training samples
corresponding to the given training samples and tooth number.
@param trainingSamples: the training samples
@param nrTooth: the number of the tooth
@return np.array, shape=(nb of training samples, nb of dimensions)
'''
X = np.zeros((len(trainingSamples), c.get_nb_dim()))
index = 0
for i in trainingSamples:
fname = c.get_fname_original_landmark(i, nr_tooth)
print("loaded: " + fname)
X[index,:] = np.fromfile(fname, dtype=float, count=-1, sep=' ')
index += 1
return X
def test3_combined():
BS = cu.create_bboxes(method)
Avg = cu.get_average_size(method)
Results = np.zeros(
(c.get_nb_trainingSamples(), 3 * c.get_nb_teeth(), c.get_nb_dim()))
color_lines = np.array([
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 0, 255]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([0, 255, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
np.array([255, 0, 0]),
])
for i in c.get_trainingSamples_range():
trainingSamples = c.get_trainingSamples_range()
trainingSamples.remove(i)
preprocess(trainingSamples)
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
Params = cu.get_average_params(trainingSamples, method)
x_min = BS[(i - 1), 0]
x_max = BS[(i - 1), 1]
y_min = BS[(i - 1), 2]
y_max = BS[(i - 1), 3]
ty = y_min + (y_max - y_min) / 2
for j in range(c.get_nb_teeth() / 2):
if j == 0: tx = x_min + Avg[0, 0] / 2.0
if j == 1: tx = x_min + Avg[0, 0] + Avg[1, 0] / 2.0
if j == 2: tx = x_max - Avg[3, 0] - Avg[2, 0] / 2.0
if j == 3: tx = x_max - Avg[3, 0] / 2.0
P = limit(
img, mu.full_align(MS[j, :], tx, ty, Params[j, 2], Params[j,
3]))
fname = c.get_fname_original_landmark(i, (j + 1))
I = fu.original_to_cropped(
np.fromfile(fname, dtype=float, count=-1, sep=' '))
Results[(i - 1), j, :] = I
Results[(i - 1), c.get_nb_teeth() + j, :] = limit(
img,
multi_resolution_search(img, P,
j)) #only limit for i=9: gigantic fail
Results[(i - 1), 2 * c.get_nb_teeth() + j, :] = P
x_min = BS[(i - 1), 4]
x_max = BS[(i - 1), 5]
y_min = BS[(i - 1), 6]
y_max = BS[(i - 1), 7]
ty = y_min + (y_max - y_min) / 2
for j in range(c.get_nb_teeth() / 2, c.get_nb_teeth()):
if j == 4: tx = x_min + Avg[4, 0] / 2.0
if j == 5: tx = x_min + Avg[4, 0] + Avg[5, 0] / 2.0
if j == 6: tx = x_max - Avg[7, 0] - Avg[6, 0] / 2.0
if j == 7: tx = x_max - Avg[7, 0] / 2.0
P = limit(
img, mu.full_align(MS[j, :], tx, ty, Params[j, 2], Params[j,
3]))
fname = c.get_fname_original_landmark(i, (j + 1))
I = fu.original_to_cropped(
np.fromfile(fname, dtype=float, count=-1, sep=' '))
Results[(i - 1), j, :] = I
Results[(i - 1), c.get_nb_teeth() + j, :] = limit(
img,
multi_resolution_search(img, P,
j)) #only limit for i=9: gigantic fail
Results[(i - 1), 2 * c.get_nb_teeth() + j, :] = P
fname = str(i) + 'c.png'
cv2.imwrite(
fname,
fu.mark_results(np.copy(img), Results[(i - 1), :], color_lines))
def init_params(fname):
global img, count, landmarks
count = 0
landmarks = np.zeros((c.get_nb_dim()))
img = cv2.imread(fname)
import cv2
import numpy as np
import configuration as c
img = None
count = 0
landmarks = np.zeros((c.get_nb_dim()))
def draw_landmark(event, x, y, flags, param):
if (event == cv2.EVENT_LBUTTONDOWN and count < c.get_nb_landmarks()):
global img, count, landmarks
landmarks[(2 * count)] = float(x)
landmarks[(2 * count + 1)] = float(y)
if count > 0:
xp = int(landmarks[(2 * count - 2)])
yp = int(landmarks[(2 * count - 1)])
cv2.line(img, (xp, yp), (x, y), (0, 0, 255))
cv2.circle(img, (x, y), 2, (0, 0, 255), -1)
count += 1
if count == c.get_nb_landmarks():
xp = int(landmarks[0])
yp = int(landmarks[1])
cv2.line(img, (x, y), (xp, yp), (0, 0, 255))
def init_params(fname):
global img, count, landmarks
count = 0
landmarks = np.zeros((c.get_nb_dim()))
img = cv2.imread(fname)
