def findfits(X,gmods,img,k,m):
wdth = len(img[0])
hgth = len(img)
X = np.append(np.array(X[:len(X)/2])*wdth,np.array(X[len(X)/2:])*hgth)
X = np.trunc(X).astype(int)
Xn = zip(X[:len(X)/2],X[len(X)/2:])
fits = np.zeros(shape=(len(X)))
for i in range(len(Xn)):
co,s = gmods[i]
# get profile to be sampled btwn current point i and its neighbour
# also returns normal perpendicular to profile
profile, n = prfl.get(Xn,i)
# During search we sample a profile m pixels either side of the current point
# 2(m − k) + 1 possible positions
candidates = prfl.getpositionsalong(profile[0],n,(m-k))
# We then test the quality of fit of the corresponding grey-level model
# at each of the 2(m − k) + 1 possible positions along the sample (Figure 10) and
# choose the one which gives the best match (lowest value of f(gs)).
# where f(gs) = mahalanobis distance
dmin, best = np.inf, 0
for j in range(2*(m-k)+1):
# get distance from candidate to greymodel (co,s)
d = prfl.greylvldistancemetric(candidates[j], s, co, k ,n,img)
# assign new best fit
if d < dmin:
dmin = d
best = j
fits[i], fits[len(fits)/2+i] = candidates[best,0]/float(wdth), candidates[best,1]/float(hgth)
return fits
def autoinit(mean, marks, trainimgs, testimg, k, m, gmod):
imgw, imgh = testimg.shape[1],testimg.shape[0]
# get appearance model
a_mean,mean_shape = appearance(mean,marks,trainimgs)
# get TL model (mean and standard dev) for top-left corners of landmarks
TL, TLmin, TLmax = TLmodel(marks,trainimgs, imgw, imgh)
# get radiograph as gradient image (also equalizes for better edges)
gradimg = rg.togradient(testimg)
#
xmean, ymean = mean[:len(mean)/2], mean[len(mean)/2:]
min_x,min_y = np.min(xmean),np.min(ymean)
initshape = zip([(x-min_x)*imgw for x in xmean],[(y-min_y)*imgh for y in ymean])
min_x,min_y = np.min(mean[:len(mean)/2]),np.min(mean[len(mean)/2:])
pts = zip(mean[:len(mean)/2],mean[len(mean)/2:])
pts = [((point[0]-min_x),(point[1]-min_y)) for point in pts]
initshape = [(int(y[0]*imgw),int(y[1]*imgh)) for y in pts]
# set of angles and scales to nudge new shapes in
angles = np.array(range(-8,9,1))/2.0
scales = range(8,-1,-1)
# to start, use template equal to bounding box around mean shape of appearance model
template = a_mean.reshape((mean_shape[0],mean_shape[1]))
temph, tempw = template.shape
chosenangle = 0
chosenscalex = 0
chosenscaley = 0
chosenlocation = (0,0)
dmin = np.inf
# scale shape over select amount of scaling factors- x axis
for sx in [0.05*s+0.1 for s in scales]:
xtempl = cv2.resize(template,(int(tempw*sx),temph))
# scale shape over select amount of scaling factors - y axis
for sy in [0.05*s+0.1 for s in scales]:
X,Y,dTL = gettemplateparam(xtempl,sy, TLmin, TLmax, TL, gradimg)
# rotate shape over select amount of rotations, find best fit
for a in angles:
angle = a*0.1
# get a new shape by 'nudging' the current shape
# = scale, rotate, translate
nudgedshape = nudgeshape(sx,sy,angle,X,Y,initshape,1.0,1.0)
points = np.array(zip(*nudgedshape)).flatten().tolist()
d = 0
if not(np.max(points[:len(points)/2])+k > imgw-1 or np.max(points[len(points)/2:])+k > imgh-1):
for l in range(len(nudgedshape)):
# get model, gradients, normal and profile for point l
profile, n = prfl.get(nudgedshape,l)
cov,mean = gmod[l]
# Get mahalanobis distance from profile[0]'s greylvl model to provided greymodel (co,mean)
d = d + prfl.greylvldistancemetric(profile[0], mean, cov, k ,n,testimg)
# calculate full metric
d = d/len(nudgedshape)
dtot = d+0.01*np.linalg.norm(dTL)
# update
if dtot < dmin:
dmin = dtot
chosenlocation = X,Y
chosenscalex = sx
chosenscaley = sy
chosenangle = angle
print "Estimate found:"
print "Location = ("+ ",".join([str(c).split(".")[0] for c in chosenlocation]) + ")"
print "Angle = " + str(chosenangle)
print "Scale X = " + str(chosenscalex)
print "Scale Y = " + str(chosenscaley)
# nudge initial shape by chosen angle, scale and position
result = nudgeshape(chosenscalex,chosenscaley,chosenangle,chosenlocation[0],chosenlocation[1],initshape,(1.0/float(imgw)),(1.0/float(imgh)))
return [el for point in zip(*result) for el in point]
