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 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 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 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 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))