def userinit(x,img):
global polygon
w,h = 1360,768
img = rg.resize(img,w,h)
imgw = img.shape[1]
imgh = img.shape[0]
canvasimg = np.array(img)
minx = abs(min(x[:len(x)/2]))
miny = abs(min(x[len(x)/2:]))
# transform model points to image coord
pts = zip(x[:len(x)/2],x[len(x)/2:])
pts = [((point[0]+minx)/3.2,(point[1]+miny)/3.2) for point in pts]
polygon = pts
pinimg = [(int(y[0]*imgw),int(y[1]*imgh)) for y in pts]
pinimg = np.array(np.array(pinimg).tolist(), np.int32)
pinimg = pinimg.reshape((-1,1,2))
cv2.polylines(img,[pinimg],True,(0,0,255))
#show gui
cv2.imshow('choose',img)
cv.SetMouseCallback( 'choose', mouse, canvasimg)
cv2.waitKey(0)
return [el for tple in zip(*polygon) for el in tple]
def LOOFIT(k,m,inciset,enhancedgraphs, colors):
for i in range(14):
print "INCISOR: "+str(inciset[0]) + " LEFTOUT: "+str(i)
trainingset = range(14)
trainingset.remove(i)
mask = segment(k,m,inciset,trainingset,enhancedgraphs, colors, i, 1)
# save mask
cv2.imwrite("fits/fit-incis"+str(inciset[0])+"-leftout"+str(i)+".png",rg.resize(mask,3023,1597))
def plotinit(est, img, colors, imgidx):
colors = [(0,0,255),(50,50,255),(100,100,255),(150,150,255),(255,0,0),(255,50,50),(255,100,100),(255,150,150)]
dw,dh = 1360,768
image = rg.resize(cv2.cvtColor(img,cv.CV_GRAY2RGB),dw,dh)
sx = (image.shape[1])
sy = (image.shape[0])
for i in range(len(est)):
p = [[int(q[0]*(sx)),int(q[1]*(sy))] for q in zip(est[i][:len(est[i])/2],est[i][len(est[i])/2:])]
cv2.polylines(image,np.int32(np.array([p])),True,colors[i])
cv2.imwrite("init/"+str(imgidx)+".png",image)
def init(landmarks, img):
"""Allows the user to provide an initial fit for the given model in the
given image by dragging the mean shape to the right position on a dental
radiograph.
Args:
landmarks (Landmark): A model.
img (nparray): An image to fit the model on.
Returns:
The centroid of the manual fit.
The landmark points, adapted to the position chosen by the user and the
scale of the image.
"""
global tooth
oimgh = img.shape[0]
img, scale = rg.resize(img, 1200, 800)
imgh = img.shape[0]
canvasimg = np.array(img)
# transform model points to image coord
points = landmarks.as_matrix()
min_x = abs(points[:, 0].min())
min_y = abs(points[:, 1].min())
points = [((point[0] + min_x) * scale, (point[1] + min_y) * scale)
for point in points]
tooth = points
pimg = np.array([(int(p[0] * imgh), int(p[1] * imgh)) for p in points])
cv2.polylines(img, [pimg], True, (0, 255, 0))
# show gui
cv2.imshow('choose', img)
cv.SetMouseCallback('choose', __mouse, canvasimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
centroid = np.mean(tooth, axis=0)
return centroid, Landmarks(
np.array([[point[0] * oimgh, point[1] * oimgh] for point in tooth]))
def init(landmarks, img):
"""Allows the user to provide an initial fit for the given model in the
given image by dragging the mean shape to the right position on a dental
radiograph.
Args:
landmarks (Landmark): A model.
img (nparray): An image to fit the model on.
Returns:
The centroid of the manual fit.
The landmark points, adapted to the position chosen by the user and the
scale of the image.
"""
global tooth
oimgh = img.shape[0]
img, scale = rg.resize(img, 1200, 800)
imgh = img.shape[0]
canvasimg = np.array(img)
# transform model points to image coord
points = landmarks.as_matrix()
min_x = abs(points[:, 0].min())
min_y = abs(points[:, 1].min())
points = [((point[0]+min_x)*scale, (point[1]+min_y)*scale) for point in points]
tooth = points
pimg = np.array([(int(p[0]*imgh), int(p[1]*imgh)) for p in points])
cv2.polylines(img, [pimg], True, (0, 255, 0))
# show gui
cv2.imshow('choose', img)
cv.SetMouseCallback('choose', __mouse, canvasimg)
cv2.waitKey(0)
cv2.destroyAllWindows()
centroid = np.mean(tooth, axis=0)
return centroid, Landmarks(np.array([[point[0]*oimgh, point[1]*oimgh] for point in tooth]))
def create_database(radiographs):
"""Interactively select rectangle ROIs for the four upper/lower incisors
and store list of bboxes.
Args:
radiographs: A list with dental radiographs.
.. _Based on:
http://stackoverflow.com/a/36642507
"""
def draw_rect_roi(event, x, y, flags, param):
"""Mouse callback function"""
# grab references to the global variables
global rect_bbox, rect_endpoint_tmp, drawing, bbox
# if the left mouse button was clicked, record the starting
# (x, y) coordinates and indicate that drawing is being
# performed. set rect_endpoint_tmp empty list.
if event == cv2.EVENT_LBUTTONDOWN:
rect_endpoint_tmp = []
rect_bbox = [(x, y)]
drawing = True
# check to see if the left mouse button was released
elif event == cv2.EVENT_LBUTTONUP:
# record the ending (x, y) coordinates and indicate that
# drawing operation is finished
rect_bbox.append((x, y))
drawing = False
# for bbox find upper left and bottom right points
p_1, p_2 = rect_bbox
p_1x, p_1y = p_1
p_2x, p_2y = p_2
lx = min(p_1x, p_2x)
ty = min(p_1y, p_2y)
rx = max(p_1x, p_2x)
by = max(p_1y, p_2y)
# add bbox to list if both points are different
if (lx, ty) != (rx, by):
bbox = [(lx, ty), (rx, by)]
# if mouse is drawing set tmp rectangle endpoint to (x,y)
elif event == cv2.EVENT_MOUSEMOVE and drawing:
rect_endpoint_tmp = [(x, y)]
# First do the upper incisors, next the upper incisors
for is_lower in range(0, 2):
if is_lower:
print 'Select the region of the four lower incisors for each radiograph\n', \
'and press the c key when done or the d key to ignore the example.'
else:
print 'Select the region of the four upper incisors for each radiograph\n', \
'and press the c key when done or the d key to ignore the example.'
bbox_list = []
for ind, img in enumerate(radiographs):
if is_lower:
windowtitle = "Lower incisors [%d/%d]" % (
ind + 1,
len(radiographs),
)
else:
windowtitle = "Upper incisors [%d/%d]" % (
ind + 1,
len(radiographs),
)
# clone image and setup the mouse callback function
canvasimg = img.copy()
# scale image to fit on screen
canvasimg, scale = rg.resize(canvasimg, 1200, 800)
cv2.namedWindow(windowtitle)
cv2.setMouseCallback(windowtitle, draw_rect_roi)
# keep looping until the 'c' key is pressed
while True:
# display the image and wait for a keypress
rect_cpy = canvasimg.copy()
if not drawing:
if bbox:
start_point = bbox[0]
end_point_tmp = bbox[1]
cv2.rectangle(rect_cpy, start_point, end_point_tmp,
(0, 255, 0), 1)
cv2.imshow(windowtitle, rect_cpy)
else:
cv2.imshow(windowtitle, canvasimg)
elif drawing and rect_endpoint_tmp:
start_point = rect_bbox[0]
end_point_tmp = rect_endpoint_tmp[0]
cv2.rectangle(rect_cpy, start_point, end_point_tmp,
(0, 255, 0), 1)
cv2.imshow(windowtitle, rect_cpy)
key = cv2.waitKey(1) & 0xFF
# if the 'c' key is pressed, break from the loop, and store the example
if key == ord('c'):
bbox_list.append(bbox)
break
# if the 'd' key is pressed, break from the loop and ignore the example
if key == ord('d'):
break
# close all open windows
cv2.destroyAllWindows()
# rescale bounding boxes
bbox_list = np.array([[(int(p[0] / scale), int(p[1] / scale))
for p in bb] for bb in bbox_list])
# print results summary
bbs = [bb[1] - bb[0] for bb in bbox_list]
avg_width, avg_height = np.mean(bbs, axis=0)
print 'Avg. height: ' + str(avg_height)
print 'Avg. width: ' + str(avg_width)
# save the results
if is_lower:
np.save('Models/lower_incisor_model', bbox_list)
else:
np.save('Models/upper_incisor_model', bbox_list)
def create_database(radiographs):
"""Interactively select rectangle ROIs for the four upper/lower incisors
and store list of bboxes.
Args:
radiographs: A list with dental radiographs.
.. _Based on:
http://stackoverflow.com/a/36642507
"""
def draw_rect_roi(event, x, y, flags, param):
"""Mouse callback function"""
# grab references to the global variables
global rect_bbox, rect_endpoint_tmp, drawing, bbox
# if the left mouse button was clicked, record the starting
# (x, y) coordinates and indicate that drawing is being
# performed. set rect_endpoint_tmp empty list.
if event == cv2.EVENT_LBUTTONDOWN:
rect_endpoint_tmp = []
rect_bbox = [(x, y)]
drawing = True
# check to see if the left mouse button was released
elif event == cv2.EVENT_LBUTTONUP:
# record the ending (x, y) coordinates and indicate that
# drawing operation is finished
rect_bbox.append((x, y))
drawing = False
# for bbox find upper left and bottom right points
p_1, p_2 = rect_bbox
p_1x, p_1y = p_1
p_2x, p_2y = p_2
lx = min(p_1x, p_2x)
ty = min(p_1y, p_2y)
rx = max(p_1x, p_2x)
by = max(p_1y, p_2y)
# add bbox to list if both points are different
if (lx, ty) != (rx, by):
bbox = [(lx, ty), (rx, by)]
# if mouse is drawing set tmp rectangle endpoint to (x,y)
elif event == cv2.EVENT_MOUSEMOVE and drawing:
rect_endpoint_tmp = [(x, y)]
# First do the upper incisors, next the upper incisors
for is_lower in range(0, 2):
if is_lower:
print 'Select the region of the four lower incisors for each radiograph\n', \
'and press the c key when done or the d key to ignore the example.'
else:
print 'Select the region of the four upper incisors for each radiograph\n', \
'and press the c key when done or the d key to ignore the example.'
bbox_list = []
for ind, img in enumerate(radiographs):
if is_lower:
windowtitle = "Lower incisors [%d/%d]" % (ind+1, len(radiographs),)
else:
windowtitle = "Upper incisors [%d/%d]" % (ind+1, len(radiographs),)
# clone image and setup the mouse callback function
canvasimg = img.copy()
# scale image to fit on screen
canvasimg, scale = rg.resize(canvasimg, 1200, 800)
cv2.namedWindow(windowtitle)
cv2.setMouseCallback(windowtitle, draw_rect_roi)
# keep looping until the 'c' key is pressed
while True:
# display the image and wait for a keypress
rect_cpy = canvasimg.copy()
if not drawing:
if bbox:
start_point = bbox[0]
end_point_tmp = bbox[1]
cv2.rectangle(rect_cpy, start_point, end_point_tmp, (0, 255, 0), 1)
cv2.imshow(windowtitle, rect_cpy)
else:
cv2.imshow(windowtitle, canvasimg)
elif drawing and rect_endpoint_tmp:
start_point = rect_bbox[0]
end_point_tmp = rect_endpoint_tmp[0]
cv2.rectangle(rect_cpy, start_point, end_point_tmp, (0, 255, 0), 1)
cv2.imshow(windowtitle, rect_cpy)
key = cv2.waitKey(1) & 0xFF
# if the 'c' key is pressed, break from the loop, and store the example
if key == ord('c'):
bbox_list.append(bbox)
break
# if the 'd' key is pressed, break from the loop and ignore the example
if key == ord('d'):
break
# close all open windows
cv2.destroyAllWindows()
# rescale bounding boxes
bbox_list = np.array([[(int(p[0]/scale), int(p[1]/scale))
for p in bb]
for bb in bbox_list])
# print results summary
bbs = [bb[1] - bb[0] for bb in bbox_list]
avg_width, avg_height = np.mean(bbs, axis=0)
print 'Avg. height: ' + str(avg_height)
print 'Avg. width: ' + str(avg_width)
# save the results
if is_lower:
np.save('Models/lower_incisor_model', bbox_list)
else:
np.save('Models/upper_incisor_model', bbox_list)