def __init__(self, filename, thresh): self.__filename = filename self.__thresh = thresh self.__image = cv.LoadImage(filename, cv.CV_LOAD_IMAGE_UNCHANGED) gray = mt.normalize_image(cv.LoadImage(filename, cv.CV_LOAD_IMAGE_GRAYSCALE)) self.__grayscale = cv.CreateImage(cv.GetSize(gray), cv.IPL_DEPTH_8U, 1) cv.Convert(gray, self.__grayscale) self.__integralImage = mt.integral_image(self.__grayscale) self.__responses = [] self.__features = [] self.__traces = [] self.__orientation = [] self.__descriptors = []
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)
def __scale_space_refinement(self): responses = self.__responses fake_maxima = [] for i in range(0, len(self.__features)): (x, y, s, l_sign) = self.__features[i] k = Surf.REFINEMENT_ITERATION_COUNT while k >= 0 and self.__is_not_outlier(x, y, s): k = k - 1 dx = mt.du_dx(responses, x, y, s) dy = mt.du_dy(responses, x, y, s) ds = mt.du_ds(responses, x, y, s) dxx = mt.du2_dx2(responses, x, y, s) dyy = mt.du2_dy2(responses, x, y, s) dxy = mt.du2_dxdy(responses, x, y, s) dss = mt.du2_ds2(responses, x, y, s) dxds = mt.du2_dxds(responses, x, y, s) dyds = mt.du2_dyds(responses, x, y, s) detH = (2*dxds*dxy*dyds - (dxds**2)*dyy - (dxy**2)*dss + dxx*dyy*dss - dxx*(dyds**2)) if detH <> 0: delta_x = -(dx*(dyy*dss - dyds**2) + dy*(dxds*dyds - dss*dxy) + ds*(dxy*dyds - dxds*dyy)) / detH delta_y = -(dx*(dxy*dss - dxds*dyds) + dy*(dxds**2 - dxx*dss) + ds*(dxx*dyds - dxds*dxy)) / detH delta_s = -(dx*(dxy*dyds - dxds*dyy) + dy*(dxds*dxy - dxx*dyds) + ds*(dxx*dyy - dxy**2)) / detH if (np.abs(delta_x) < 1 and np.abs(delta_y) < 1 and np.abs(delta_s) < 0.4*(2**Surf.OCTAVES[s])): break else: (x, y, s) = (int(x + delta_x + 0.5), int(y + delta_y + 0.5), int(s + delta_s/(0.4*(2**Surf.OCTAVES[s])) + 0.5)) else: break if (k == -1 or not self.__is_not_outlier(x, y, s) or np.abs(x - self.__features[i][0]) > Surf.REFINEMENT_ITERATION_COUNT or np.abs(y - self.__features[i][1]) > Surf.REFINEMENT_ITERATION_COUNT): fake_maxima.append(self.__features[i]) else: self.__features[i] = (x, y, s, l_sign) self.__features = mt.list_subtraction(self.__features, fake_maxima)
def __find_features(self): self.__build_resp_layers() resp_layers = self.__responses for i in range(0, len(Surf.FILTER_MAP)): for j in range(1, 3): b = resp_layers[Surf.FILTER_MAP[i, j-1]] indM = Surf.FILTER_MAP[i, j] m = resp_layers[indM] t = resp_layers[Surf.FILTER_MAP[i, j+1]] candidates = mt.nms_2d(m, m.width, m.height, Surf.N) for (k, n) in candidates: if np.abs(m[k, n]) <= self.__thresh: continue failed = False for (x, y) in itertools.product(range(k-1, k+2), range(n-1, n+2)): if failed: break # > or >= if b[x, y] >= m[k, n] or t[x, y] >= m[k, n]: failed = True if not failed and self.__is_not_outlier(n, k, indM): self.__features.append((n, k, indM, self.__laplacian_sign(n, k, indM)))
