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 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_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 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_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 vis():
for m in ['l', 'u']:
for i in c.get_trainingSamples_range():
cascade = cv2.CascadeClassifier(
"CV/data/Training/training/cascadesSCD" + str(i) + '-' + m +
"/cascade.xml")
fname = c.get_fname_vis_pre(i, 'SCD')
img = cv2.imread(fname)
rects = cascade.detectMultiScale(img,
scaleFactor=1.3,
minNeighbors=1,
minSize=(100, 100))
# result is an array of x coordinate, y coordinate, weight, height for each rectangle
rects[:, 2:] += rects[:, :2]
# result is an array of x coordinate, y coordinate, x + weight, y + height for each rectangle (opposite corners)
for r in range(rects.shape[0]):
cv2.rectangle(img, (rects[r, 0], rects[r, 1]),
(rects[r, 2], rects[r, 3]), (0, 255, 0), 2)
fname = 'test' + str(i) + '-' + m + '.png'
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 preproccess():
'''
Preproccess all the radiographs.
'''
XS = l.create_full_XS()
(ymin, ymax, xmin, xmax) = learn_offsets_safe(XS)
print("Preproccess learned offsets:")
print(" * ymin: " + str(ymin)) #ymin: 497.0
print(" * ymax: " + str(ymax)) #ymax: 1362.0
print(" * xmin: " + str(xmin)) #xmin: 1234.0
print(" * xmax: " + str(xmax)) #xmax: 1773.0
for i in c.get_trainingSamples_range():
# read -> crop -> convert to grey scale
grey_image = cv2.cvtColor(crop_by_diagonal(cv2.imread(c.get_fname_radiograph(i)), ymin, ymax, xmin, xmax), cv2.COLOR_BGR2GRAY)
grey_image_denoised = cv2.fastNlMeansDenoising(grey_image)
cv2.imwrite(c.get_fname_vis_pre(i, method='O'), grey_image)
cv2.imwrite(c.get_fname_vis_pre(i, method='D'), grey_image_denoised)
cv2.imwrite(c.get_fname_vis_pre(i, method='EH'), cv2.equalizeHist(grey_image))
cv2.imwrite(c.get_fname_vis_pre(i, method='EHD'), cv2.equalizeHist(grey_image_denoised))
cv2.imwrite(c.get_fname_vis_pre(i, method='SC'), stretch_contrast(grey_image))
cv2.imwrite(c.get_fname_vis_pre(i, method='SCD'), stretch_contrast(grey_image_denoised))
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 draw_and_write(nr_trainingSample, nr_tooth, method=''):
selected = draw_landmarks(c.get_fname_vis_pre(nr_trainingSample, method))
fname = c.get_fname_fitting_manual_landmark(nr_trainingSample, nr_tooth)
selected.tofile(fname, sep=" ", format="%s")
