def __build_descriptors(self): for k in range(0, len(self.__features)): (x, y, s, l_sign) = self.__features[k] sigma = mt.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.__box_integral(0, l, l, l, X, Y) - self.__box_integral(l, 0, l, l, X, Y)) resp_y = (self.__box_integral(l, l, l, 0, X, Y) - self.__box_integral(l, l, 0, l, X, Y)) 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, l_sign) in self.__features:
sigma = mt.SIGMA[s]
l = cv.Round(2*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.__box_integral(0, l, l, l, X, Y) -
self.__box_integral(l, 0, l, l, X, Y)) * gauss
haar_respY[(i, j)] = (self.__box_integral(l, l, l, 0, X, Y) -
self.__box_integral(l, l, 0, l, X, Y)) * 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)
