def tangentCoordinates(N_32F): N = N_32F.reshape(-1, 3) v_z = np.array([0.0, 0.0, 1.0]) A = np.cross(N, v_z) sin_theta = normVectors(A) cos_theta = np.dot(N, v_z) v_x = np.array([1.0, 0.0, 0.0]) v_y = np.array([0.0, 1.0, 0.0]) T = np.zeros_like(N) T[:, :] = np.array([1.0, 0.0, 0.0]) B = np.zeros_like(N) B[:, :] = np.array([0.0, 1.0, 0.0]) sin_valid = sin_theta > 1e-4 T[sin_valid, :] = rotateVectors(A, T[sin_valid, :], cos_theta[sin_valid], sin_theta[sin_valid]) B[sin_valid, :] = rotateVectors(A, B[sin_valid, :], cos_theta[sin_valid], sin_theta[sin_valid]) return T, B
def shadingError(self): C_diff = self._C_32F - self._C0_32F h, w = C_diff.shape[:2] C_diff = C_diff.reshape(-1, 3) C_diff = normVectors(C_diff) C_diff = C_diff.reshape(h, w) return C_diff
def _illuminationFromColorDifference(self, Cs): I0s = np.average(Cs, axis=1) I0_ids = self._I_ids(I0s) C_map = np.zeros((self._map_size, Cs.shape[1])) hist = np.zeros((self._map_size)) C_map[I0_ids, :] += Cs[:, :] hist[I0_ids] += 1.0 hist_positive = hist > 0 for ci in xrange(3): C_map[hist_positive, ci] *= 1.0 / hist[hist_positive] I_map = np.zeros((self._map_size)) I_min, I_max = self._Iminmax Ims = np.linspace(I_min, I_max, num=self._map_size) for ci in xrange(3): C_map[:, ci] = Rbf(Ims[hist_positive], C_map[hist_positive, ci], smooth=0.0005)(Ims) sigma = 0.02 for mi in xrange(self._map_size): c = C_map[mi] dc = normVectors(self._map - c) dc_i = np.argmin(dc) wc = np.exp(-(dc**2) / (sigma**2)) I = np.dot(wc, Ims) / np.sum(wc) #I_map[mi] = I_min + (I_max - I_min) * dc_i / float(self._map_size - 1) I_map[mi] = I Im_max = 0.0 for mi in xrange(self._map_size): Im_max = max(I_map[mi], Im_max) I_map[mi] = Im_max I_map = Rbf(Ims, I_map, smooth=0.0005)(Ims) # I_map[np.max(I0_ids):] = I_max return I_map[I0_ids]
def _selectConstraint(self, p): if self._normal_constraints.empty(): return False ps = self._normal_constraints.positions() dP = normVectors(ps - p) p_min = np.argmin(dP) if dP[p_min] < 5: self._selected_constraint = self._normal_constraints.constraint(p_min) self._n_old = self._selected_constraint.normal() return True return False
def computeErrors(L, C0_32F, C_32F, Ng_32F, N_32F, A_8U): h, w = A_8U.shape N_error = angleErros(N_32F.reshape(-1, 3), Ng_32F.reshape(-1, 3)).reshape(h, w) N_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 N_error = trim(N_error, A_8U) C_error = normVectors(C0_32F.reshape(-1, 3) - C_32F.reshape(-1, 3)).reshape(h, w) C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 C_error = trim(C_error, A_8U) I_32F = np.clip(LdotN(L, N_32F), 0.0, 1.0) Ig_32F = np.clip(LdotN(L, Ng_32F), 0.0, 1.0) I_error = np.abs(I_32F - Ig_32F) I_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 I_error = trim(I_error, A_8U) return C_error, N_error, I_error
def _illuminationFromColorDifference(self, Cs): I0s = np.average(Cs, axis=1) I0_ids = self._I_ids(I0s) C_map = np.zeros((self._map_size, Cs.shape[1])) hist = np.zeros((self._map_size)) C_map[I0_ids, :] += Cs[:, :] hist[I0_ids] += 1.0 hist_positive = hist > 0 for ci in xrange(3): C_map[hist_positive, ci] *= 1.0 / hist[hist_positive] I_map = np.zeros((self._map_size)) I_min, I_max = self._Iminmax Ims = np.linspace(I_min, I_max, num=self._map_size) for ci in xrange(3): C_map[:, ci] = Rbf(Ims[hist_positive], C_map[hist_positive, ci], smooth=0.0005)(Ims) sigma = 0.02 for mi in xrange(self._map_size): c = C_map[mi] dc = normVectors(self._map - c) dc_i = np.argmin(dc) wc = np.exp(-(dc ** 2) / (sigma ** 2)) I = np.dot(wc, Ims) / np.sum(wc) # I_map[mi] = I_min + (I_max - I_min) * dc_i / float(self._map_size - 1) I_map[mi] = I Im_max = 0.0 for mi in xrange(self._map_size): Im_max = max(I_map[mi], Im_max) I_map[mi] = Im_max I_map = Rbf(Ims, I_map, smooth=0.0005)(Ims) # I_map[np.max(I0_ids):] = I_max return I_map[I0_ids]
def errorTable(): colormap_files = colorMapFiles() num_colormap_files = len(colormap_files) M_errors = np.zeros((num_colormap_files)) for mi, colormap_file in enumerate(colormap_files): M_32F = loadColorMap(colormap_file) C_32F = M_32F.reshape(1, len(M_32F), 3) I_32F = np.linspace(0.0, 1.0, len(M_32F)) I_32F = I_32F.reshape(C_32F.shape[:2]) reflectance = LambertReflectanceEstimation(C_32F, I_32F) Ml = reflectance.shading(I_32F)[0, :, :] I0_32F = luminance(C_32F) IL_32F = luminance(Ml.reshape(1, -1, 3)) I_min, I_max = np.min(I0_32F), np.max(I0_32F) M_error = normVectors(M_32F - Ml) #M_errors[mi] = np.mean(M_error) / (I_max - I_min) # M_errors[mi] = computeGradientDistance(M_32F, Ml) / (I_max - I_min) #M_errors[mi] = np.linalg.norm(I0_32F - IL_32F) / (I_max - I_min) M_errors[mi] = np.mean(M_error) / (I_max - I_min) # M_errors[mi] = np.linalg.norm(M_32F - Ml) / (I_max - I_min) # M_errors[mi] = compareHist(M_32F, Ml) file_path = shapeResultFile("ReflectanceEstimation", "ReflectanceError", file_ext=".npy") np.save(file_path, M_errors) plt.plot(M_errors) plt.show()
def reflectanceEstimationFigure(): errorTable() M_errors = loadReflectanceErrorTable() M_error_orders = np.argsort(M_errors) print M_errors[M_error_orders] colormap_files = colorMapFiles() colormap_files = [colormap_files[M_error_order] for M_error_order in M_error_orders] colormap_files = colormap_files[0:-1:3] Ms = [] MLs = [] for colormap_file in colormap_files: M_32F = loadColorMap(colormap_file) Ms.append(M_32F) C_32F = M_32F.reshape(1, len(M_32F), 3) I_32F = np.linspace(0.0, 1.0, len(M_32F)) I_32F = I_32F.reshape(C_32F.shape[:2]) reflectance = LambertReflectanceEstimation(C_32F, I_32F) Ml = reflectance.shading(I_32F) MLs.append(Ml[0, :, :]) num_rows = 3 num_cols = len(colormap_files) w = 10 h = w * num_rows / num_cols fig, axes = plt.subplots(figsize=(w, h)) font_size = 15 fig.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, hspace=0.05, wspace=0.05) fig.suptitle("", fontsize=font_size) N_sphere, A_32F = normalSphere(h=512, w=512) Lg = normalizeVector(np.array([-0.2, 0.3, 0.6])) plot_grid = SubplotGrid(num_rows, num_cols) mi = 1 for M, Ml in zip(Ms, MLs): CM_32F = ColorMapShader(M).diffuseShading(Lg, N_sphere) CL_32F = ColorMapShader(Ml).diffuseShading(Lg, N_sphere) C_error = normVectors((CM_32F - CL_32F).reshape(-1, 3)).reshape(CL_32F.shape[:2]) C_error[A_32F < 0.5 * np.max(A_32F)] = 0.0 plot_grid.setPlot(1, mi) plot_grid.showImage(setAlpha(CM_32F, A_32F), "") plot_grid.setPlot(2, mi) plot_grid.showImage(setAlpha(CL_32F, A_32F), "") plot_grid.setPlot(3, mi) plot_grid.showColorMap(C_error, "", v_min=0.0, v_max=0.3, with_colorbar=False) mi += 1 file_path = shapeResultFile("ReflectanceEstimation", "ReflectanceEstimationError") fig.savefig(file_path, transparent=True)