def __build_descriptors(self):
for k in range(0, len(self.__features)):
(x, y, s, _) = self.__features[k]
sigma = SIGMA[s]
l = int(0.5*sigma)
theta = self.__orientation[k]
cosT, sinT = np.cos(theta), np.sin(theta)
descriptor = np.zeros((64))
k = 0
for (i, j) in itertools.product(range(0, 4), range(0, 4)):
sum_resp_x = sum_resp_y = sum_abs_resp_x = sum_abs_resp_y = 0
for (m, n) in itertools.product(range(0, 5), range(0, 5)):
X = cv.Round(x + sigma*(cosT*(5*(i-2) + m) - sinT*(5*(j-2) + n)))
Y = cv.Round(y + sigma*(sinT*(5*(i-2) + m) + cosT*(5*(j-2) + n)))
gauss = mt.gaussian(X-x, Y-y, 3.3*sigma)
resp_x = self.__integralImage.haarX(X, Y, l)
resp_y = self.__integralImage.haarY(X, Y, l)
respX = gauss*(-sinT*resp_x + cosT*resp_y)
respY = gauss*(cosT*resp_x + sinT*resp_y)
sum_resp_x += respX
sum_resp_y += respY
sum_abs_resp_x += np.abs(respX)
sum_abs_resp_y += np.abs(respY)
descriptor[k] = sum_resp_x
descriptor[k+1] = sum_resp_y
descriptor[k+2] = sum_abs_resp_x
descriptor[k+3] = sum_abs_resp_y
k = k + 4
norm = np.linalg.norm(descriptor)
self.__descriptors.append(descriptor / norm)
def __find_orientation(self):
for (x, y, s, _) in self.__features:
sigma = SIGMA[s]
l = int(sigma)
haar_respX = {}
haar_respY = {}
angle = {}
indexes = set(itertools.product(range(-6, 7), range(-6, 7)))
for (i, j) in [elem for elem in indexes if (elem[0]**2 + elem[1]**2) <= 36]:
X, Y = cv.Round(x + i * sigma), cv.Round(y + j * sigma)
gauss = mt.gaussian(X-x, Y-y, 2*sigma)
haar_respX[(i, j)] = self.__integralImage.haarX(X, Y, l) * gauss
haar_respY[(i, j)] = self.__integralImage.haarY(X, Y, l) * gauss
angle[(i, j)] = cv.FastArctan(haar_respY[(i, j)], haar_respX[(i, j)])
max_length = 0
max_resp_x = max_resp_y = 0
for theta in range(Surf.STARTING_ANGLE, Surf.LAST_ANGLE):
resp_x = resp_y = 0
for p in [index for index in angle.keys() if (theta - 30 <= angle[index] and
angle[index] <= theta + 30)]:
resp_x += haar_respX[p]
resp_y += haar_respY[p]
resp_len = resp_x**2 + resp_y**2
if resp_len > max_length:
max_resp_x, max_resp_y = resp_x, resp_y
max_length = resp_len
self.__orientation.append(cv.FastArctan(max_resp_y, max_resp_x) * mt.TO_RADS_RATIO)
