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_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 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 classify_positives(method=''):
XS = l.create_full_XS()
for s in c.get_trainingSamples_range():
trainingSamples = c.get_trainingSamples_range()
trainingSamples.remove(s)
try:
info_name_upper = c.get_dir_prefix() + 'data/Visualizations/Classified Samples/info' + method + str(s) + '-u' + '.txt'
info_name_lower = c.get_dir_prefix() + 'data/Visualizations/Classified Samples/info' + method + str(s) + '-l' + '.txt'
info_file_upper = open(info_name_upper, "w")
info_file_lower = open(info_name_lower, "w")
for i in trainingSamples:
s = ''
if (i < 10):
s = '0'
img_name = s + str(i) + '.png'
min_y = min_x = float("inf")
max_y = max_x = 0
for j in range(0, c.get_nb_teeth()/2):
x_coords, y_coords = mu.extract_coordinates(XS[j, i-1, :])
for k in range(c.get_nb_landmarks()):
if x_coords[k] < min_x: min_x = x_coords[k]
if x_coords[k] > max_x: max_x = x_coords[k]
if y_coords[k] < min_y: min_y = y_coords[k]
if y_coords[k] > max_y: max_y = y_coords[k]
line = 'rawdata/' + method + img_name + ' 1 ' + str(int(min_x - fu.offsetX)) + ' ' + str(int(min_y - fu.offsetY)) + ' ' + str(int(max_x - min_x)) + ' ' + str(int(max_y - min_y)) + '\n'
info_file_upper.write(line)
min_y = min_x = float("inf")
max_y = max_x = 0
for j in range(c.get_nb_teeth()/2, c.get_nb_teeth()):
x_coords, y_coords = mu.extract_coordinates(XS[j, i-1, :])
for k in range(c.get_nb_landmarks()):
if x_coords[k] < min_x: min_x = x_coords[k]
if x_coords[k] > max_x: max_x = x_coords[k]
if y_coords[k] < min_y: min_y = y_coords[k]
if y_coords[k] > max_y: max_y = y_coords[k]
line = 'rawdata/' + method + img_name + ' 1 ' + str(int(min_x - fu.offsetX)) + ' ' + str(int(min_y - fu.offsetY)) + ' ' + str(int(max_x - min_x)) + ' ' + str(int(max_y - min_y)) + '\n'
info_file_lower.write(line)
finally:
info_file_upper.close()
info_file_lower.close()
def create_fitting_functions(GNS, GTS):
'''
Creates the fitting function for each tooth, for each landmark.
@param GNS: the matrix GNS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through that landmark)
@param GTS: the matrix GTS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through that landmark)
@return The fitting functions for each tooth, for each landmark.
'''
fns = [[
get_fitting_function(tooth, landmark, GNS)
for landmark in range(c.get_nb_landmarks())
] for tooth in range(c.get_nb_teeth())]
fts = [[
get_fitting_function(tooth, landmark, GTS)
for landmark in range(c.get_nb_landmarks())
] for tooth in range(c.get_nb_teeth())]
return fns, fts
def create_fitting_functions_for_multiple_levels(L_GNS, L_GTS):
'''
Creates the fitting function for each level, for each tooth, for each landmark.
@param L_GNS: the matrix L_GNS which contains for each level, for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through that landmark)
@param L_GTS: the matrix L_GTS which contains for each level, for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through that landmark)
@return The fitting functions for each level, for each tooth, for each landmark.
'''
l_fns = [[[
get_fitting_function(tooth, landmark, L_GNS[level, :])
for landmark in range(c.get_nb_landmarks())
] for tooth in range(c.get_nb_teeth())] for level in range(L_GNS.shape[0])]
l_fts = [[[
get_fitting_function(tooth, landmark, L_GTS[level, :])
for landmark in range(c.get_nb_landmarks())
] for tooth in range(c.get_nb_teeth())] for level in range(L_GTS.shape[0])]
return l_fns, l_fts
def create_partial_GS(trainingSamples,
XS,
MS,
level=0,
offsetX=0,
offsetY=0,
k=5,
method=''):
'''
Creates the matrix GNS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through the landmarks).
Creates the matrix GTS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through the landmarks).
@param trainingSamples: the number of the training samples (not the test training samples!)
@param XS: contains for each tooth, for each training sample, all landmarks (in the image coordinate frame)
@param MS: contains for each tooth, the tooth model (in the model coordinate frame)
@param level: the current level
@param offsetX: the possible offset in x direction (used when working with cropped images and non-cropped landmarks)
@param offsetY: the possible offset in y direction (used when working with cropped images and non-cropped landmarks)
@param k: the number of pixels to sample either side for each of the model points along the profile normal
@param method: the method used for preprocessing
@return The matrix GNS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through that landmark).
The matrix GTS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through that landmark).
'''
GNS = np.zeros((c.get_nb_teeth(), len(trainingSamples),
c.get_nb_landmarks(), 2 * k + 1))
GTS = np.zeros((c.get_nb_teeth(), len(trainingSamples),
c.get_nb_landmarks(), 2 * k + 1))
for j in range(c.get_nb_teeth()):
index = 0
for i in trainingSamples:
# model of tooth j from model coordinate frame to image coordinate frame
xs, ys = mu.extract_coordinates(
mu.full_align_with(MS[j], XS[j, index, :]))
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
pyramid = gip.get_gaussian_pyramid_at(img, level)
GN, GT = create_G(pyramid, k, xs, ys, offsetX, offsetY)
GNS[j, index, :] = GN
GTS[j, index, :] = GT
index += 1
return GNS, GTS
def create_partial_GS_for_multiple_levels(trainingSamples,
XS,
MS,
nb_levels=1,
offsetX=0,
offsetY=0,
k=5,
method=''):
'''
Creates the matrix L_GNS which contains for each level, for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through the landmarks).
Creates the matrix L_GTS which contains for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through the landmarks).
@param trainingSamples: the number of the training samples (not the test training samples!)
@param XS: contains for each tooth, for each training sample, all landmarks (in the image coordinate frame)
@param MS: contains for each tooth, the tooth model (in the model coordinate frame)
@param nb_levels: the number of levels
@param offsetX: the possible offset in x direction (used when working with cropped images and non-cropped landmarks)
@param offsetY: the possible offset in y direction (used when working with cropped images and non-cropped landmarks)
@param k: the number of pixels to sample either side for each of the model points along the profile normal
@param method: the method used for preprocessing
@return The matrix L_GNS which contains for each level, for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile normal through that landmark).
The matrix L_GTS which contains for each level, for each tooth, for each of the given training samples,
for each landmark, a normalized sample (along the profile tangent through that landmark).
'''
L_GNS = np.zeros((nb_levels, c.get_nb_teeth(), len(trainingSamples),
c.get_nb_landmarks(), 2 * k + 1))
L_GTS = np.zeros((nb_levels, c.get_nb_teeth(), len(trainingSamples),
c.get_nb_landmarks(), 2 * k + 1))
for i in range(nb_levels):
GNS, GTS = create_partial_GS(trainingSamples,
np.around(np.divide(XS, 2**i)),
MS,
i,
offsetX=round(float(offsetX) / 2**i),
offsetY=round(float(offsetY) / 2**i),
k=k,
method=method)
L_GNS[i, :] = GNS
L_GTS[i, :] = GTS
return L_GNS, L_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 get_average_size(method=''):
IBS = create_individual_bboxes(method)
Avg = np.zeros((c.get_nb_teeth(), 2))
for j in range(IBS.shape[0]):
x = y = 0
for i in range(IBS.shape[1]):
x += IBS[j,i,1] - IBS[j,i,0]
y += IBS[j,i,3] - IBS[j,i,2]
Avg[j,0] = x / float(IBS.shape[1])
Avg[j,1] = y / float(IBS.shape[1])
return Avg
def create_landmarks_and_models_images(color_init=np.array([0,255,255]), color_mid=np.array([255,0,255]), color_end=np.array([255,255,0]), color_line=np.array([0,0,255]), color_model_line=np.array([255,0,0]), method=''):
'''
Stores all the preprocessed images corresponding to the given method with the landmarks
of the training samples and models (transformed to the image coordinate system) marked.
@param color_init: the BGR color for the first landmark
@param color_mid: the BGR color for all landmarks except the first and last landmark
@param color_end: the BGR color for the last landmark
@param color_line: the BGR color for the line between two consecutive landmarks of the training samples
@param color_model_line: the BGR color for the line between two consecutive landmarks of the models
@param method: the method used for preproccesing
'''
for i in c.get_trainingSamples_range():
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
for j in range(c.get_nb_teeth()):
xs, ys = mu.extract_coordinates(XS[j,(i-1),:])
mxs, mys = mu.extract_coordinates(mu.full_align_with(MS[j], XS[j,(i-1),:]))
for k in range(c.get_nb_landmarks()):
x = int(xs[k] - offsetX)
y = int(ys[k] - offsetY)
mx = int(mxs[k] - offsetX)
my = int(mys[k] - offsetY)
if (k == c.get_nb_landmarks()-1):
x_succ = int(xs[0] - offsetX)
y_succ = int(ys[0] - offsetY)
mx_succ = int(mxs[0] - offsetX)
my_succ = int(mys[0] - offsetY)
else:
x_succ = int(xs[(k+1)] - offsetX)
y_succ = int(ys[(k+1)] - offsetY)
mx_succ = int(mxs[(k+1)] - offsetX)
my_succ = int(mys[(k+1)] - offsetY)
cv2.line(img, (x,y), (x_succ,y_succ), color_line)
cv2.line(img, (mx,my), (mx_succ,my_succ), color_model_line)
for k in range(c.get_nb_landmarks()):
x = int(xs[k] - offsetX)
y = int(ys[k] - offsetY)
mx = int(mxs[k] - offsetX)
my = int(mys[k] - offsetY)
if (k == 0):
img[y,x] = color_init
img[my,mx] = color_init
elif (k == c.get_nb_landmarks()-1):
img[y,x] = color_end
img[my,mx] = color_end
else:
img[y,x] = color_mid
img[my,mx] = color_mid
fname = c.get_fname_vis_ff_landmarks_and_models(i, method)
cv2.imwrite(fname, img)
def create_profile_normals_images(k=5, color_init=np.array([0,255,255]), color_mid=np.array([255,0,255]), color_end=np.array([255,255,0]), color_line=np.array([255,0,0]), color_profile_point=np.array([0,255,0]), method=''):
'''
Stores all the preprocessed images corresponding to the given method with the landmarks
of the models (transformed to the image coordinate system) and the points along the profile
normals marked.
@param k: the number of profile points along either side
of the profile normal and profile tangent
@param color_init: the BGR color for the first landmark
@param color_mid: the BGR color for all landmarks except the first and last landmark
@param color_end: the BGR color for the last landmark
@param color_line: the BGR color for the line between two consecutive landmarks
@param color_profile_point: the BGR color for the profile points
@param method: the method used for preproccesing
'''
for i in c.get_trainingSamples_range():
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
for j in range(c.get_nb_teeth()):
xs, ys = mu.extract_coordinates(mu.full_align_with(MS[j], XS[j,(i-1),:]))
for h in range(c.get_nb_landmarks()):
x = int(xs[h] - offsetX)
y = int(ys[h] - offsetY)
if (h == c.get_nb_landmarks()-1):
x_succ = int(xs[0] - offsetX)
y_succ = int(ys[0] - offsetY)
else:
x_succ = int(xs[(h+1)] - offsetX)
y_succ = int(ys[(h+1)] - offsetY)
cv2.line(img, (x,y), (x_succ,y_succ), color_line)
for h in range(c.get_nb_landmarks()):
x = int(xs[h] - offsetX)
y = int(ys[h] - offsetY)
tx, ty, nx, ny = ff.create_ricos(img, h, xs, ys)
for n in range(-k, k+1):
kx = round(x + n * nx)
ky = round(y + n * ny)
img[ky, kx] = color_profile_point
for t in range(-k, k+1):
kx = round(x + t * tx)
ky = round(y + t * ty)
img[ky, kx] = color_profile_point
if (h == 0):
img[y,x] = color_init
elif (h == c.get_nb_landmarks()-1):
img[y,x] = color_end
else:
img[y,x] = color_mid
fname = c.get_fname_vis_ff_profile_normals(i, method)
cv2.imwrite(fname, img)
def create_negatives(method=''):
XS = l.create_full_XS()
for i in c.get_trainingSamples_range():
fname = c.get_fname_vis_pre(i, method)
img = cv2.imread(fname)
s = ''
if (i < 10): s = '0'
min_y = min_x = float("inf")
max_y = max_x = 0
for j in range(0, c.get_nb_teeth()/2):
x_coords, y_coords = mu.extract_coordinates(XS[j, i-1, :])
for k in range(c.get_nb_landmarks()):
if x_coords[k] < min_x: min_x = x_coords[k]
if x_coords[k] > max_x: max_x = x_coords[k]
if y_coords[k] < min_y: min_y = y_coords[k]
if y_coords[k] > max_y: max_y = y_coords[k]
fname = c.get_dir_prefix() + 'data/Visualizations/Classified Samples/' + method + str(s) + str(i) + '-l' + '.png'
cv2.imwrite(fname, img[max_y-fu.offsetY+1:,:])
min_y = min_x = float("inf")
max_y = max_x = 0
for j in range(c.get_nb_teeth()/2, c.get_nb_teeth()):
x_coords, y_coords = mu.extract_coordinates(XS[j, i-1, :])
for k in range(c.get_nb_landmarks()):
if x_coords[k] < min_x: min_x = x_coords[k]
if x_coords[k] > max_x: max_x = x_coords[k]
if y_coords[k] < min_y: min_y = y_coords[k]
if y_coords[k] > max_y: max_y = y_coords[k]
fname = c.get_dir_prefix() + 'data/Visualizations/Classified Samples/' + method + str(s) + str(i) + '-u' + '.png'
cv2.imwrite(fname, img[:min_y-fu.offsetY,:])
def test():
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 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=' '))
fname = str(i) + '-' + str((j + 1)) + '.png'
cv2.imwrite(fname,
fu.show_iteration(np.copy(img), 10000, P, IS[j, :]))
def test1():
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 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=' '))
R = multi_resolution_search(img, P, j)
fname = str(i) + '-' + str((j + 1)) + '.png'
cv2.imwrite(fname, fu.mark_results(np.copy(img), np.array([P, R])))
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))
