def _runImp(self): normal_data = loadNormal(self._data_file) if normal_data is None: return N0_32F, A_8U = normal_data # N0_32F = cv2.resize(N0_32F, (64, 64)) # A_8U = cv2.resize(A_8U, N0_32F.shape[:2]) A_32F = to32F(A_8U) L = normalizeVector(np.array([-0.2, 0.3, 0.7])) # C0_32F = ToonShader().diffuseShading(L, N0_32F) C0_32F = LambertShader().diffuseShading(L, N0_32F) sfs_method = Wu08SFS(L, C0_32F, A_8U) sfs_method.run() N_32F = sfs_method.normal() saveNormal(self.resultFile(self._data_file_name, result_name="Wu08"), N_32F, A_8U) C_error = sfs_method.shadingError() I_32F = sfs_method.brightness() I_32F = gray2rgb(I_32F) C_32F = sfs_method.shading() N0_32F = trim(N0_32F, A_8U) C0_32F = trim(C0_32F, A_8U) C_32F = trim(C_32F, A_8U) N_32F = trim(N_32F, A_8U) C_error = trim(C_error, A_8U) I_32F = trim(I_32F, A_8U) A_32F = trim(A_32F, A_8U) A_8U = trim(A_8U, A_8U) h, w = N_32F.shape[:2] N_error = angleErros(N_32F.reshape(-1, 3), N0_32F.reshape(-1, 3)).reshape(h, w) N_error[A_8U < np.max(A_8U)] = 0.0 fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05) fig.suptitle(self.name(), fontsize=font_size) num_rows = 2 num_cols = 3 plot_grid = SubplotGrid(num_rows, num_cols) plot_grid.showImage(normalToColor(N0_32F, A_8U), r"Ground Truth Normal: $N_g$") plot_grid.showImage(normalToColor(N_32F, A_8U), r"Estimated Normal: $N$") plot_grid.showColorMap(N_error, r"Angle Error: $N_g, N$", v_min=0, v_max=30.0) plot_grid.showImage(setAlpha(C0_32F, A_32F), r"Shading: $C$") plot_grid.showImage(setAlpha(C_32F, A_32F), r"Estimated Shading: $C$") plot_grid.showColorMap(C_error, r"Shading Error: $C_g, C$", v_min=0, v_max=0.1) showMaximize()
def _runImp(self): normal_data = loadNormal(self._data_file) if normal_data is None: return N0_32F, A_8U = normal_data #N0_32F = cv2.resize(N0_32F, (64, 64)) #A_8U = cv2.resize(A_8U, N0_32F.shape[:2]) A_32F = to32F(A_8U) L = normalizeVector(np.array([-0.2, 0.3, 0.7])) # C0_32F = ToonShader().diffuseShading(L, N0_32F) C0_32F = LambertShader().diffuseShading(L, N0_32F) sfs_method = Wu08SFS(L, C0_32F, A_8U) sfs_method.run() N_32F = sfs_method.normal() saveNormal(self.resultFile(self._data_file_name, result_name="Wu08"), N_32F, A_8U) C_error = sfs_method.shadingError() I_32F = sfs_method.brightness() I_32F = gray2rgb(I_32F) C_32F = sfs_method.shading() N0_32F = trim(N0_32F, A_8U) C0_32F = trim(C0_32F, A_8U) C_32F = trim(C_32F, A_8U) N_32F = trim(N_32F, A_8U) C_error = trim(C_error, A_8U) I_32F = trim(I_32F, A_8U) A_32F = trim(A_32F, A_8U) A_8U = trim(A_8U, A_8U) h, w = N_32F.shape[:2] N_error = angleErros(N_32F.reshape(-1, 3), N0_32F.reshape(-1, 3)).reshape(h, w) N_error[A_8U < np.max(A_8U)] = 0.0 fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05) fig.suptitle(self.name(), fontsize=font_size) num_rows = 2 num_cols = 3 plot_grid = SubplotGrid(num_rows, num_cols) plot_grid.showImage(normalToColor(N0_32F, A_8U), r'Ground Truth Normal: $N_g$') plot_grid.showImage(normalToColor(N_32F, A_8U), r'Estimated Normal: $N$') plot_grid.showColorMap(N_error, r'Angle Error: $N_g, N$', v_min=0, v_max=30.0) plot_grid.showImage(setAlpha(C0_32F, A_32F), r'Shading: $C$') plot_grid.showImage(setAlpha(C_32F, A_32F), r'Estimated Shading: $C$') plot_grid.showColorMap(C_error, r'Shading Error: $C_g, C$', v_min=0, v_max=0.1) showMaximize()
def _runImp(self): normal_data = loadNormal(self._data_file) if normal_data is None: return N0_32F, A_8U = normal_data A_32F = to32F(A_8U) L = normalizeVector(np.array([-0.2, 0.4, 0.7])) C0_32F = LambertShader().diffuseShading(L, N0_32F) I_32F = luminance(C0_32F) br_field = BrightnessField(I_32F, sigma=5.0) I_smooth_32F = br_field.smoothBrightness() dI = br_field.brightnessDifference() gx, gy = br_field.gradients() N_32F = br_field.field() fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05) fig.suptitle(self.name(), fontsize=font_size) num_rows = 2 num_cols = 4 plot_grid = SubplotGrid(num_rows, num_cols) plot_grid.showImage(I_32F, r'$I$') plot_grid.showColorMap(dI, r'$dI$') plot_grid.showColorMap(gx, r'$gx$') plot_grid.showColorMap(gy, r'$gy$') plot_grid.showImage(normalToColor(N_32F, A_8U), r'$N$') plot_grid.showImage(N_32F[:, :, 2], r'$N_z$') showMaximize()
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)
def methodComparisonFigure(shape_name="ThreeBox", cmap_id=10): num_methods = 3 num_rows = 3 num_cols = 2 * num_methods + 1 w = 20 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=0.96, bottom=0.04, hspace=0.15, wspace=0.1) fig.suptitle("", fontsize=font_size) plot_grid = SubplotGrid(num_rows, num_cols) L = normalizeVector(np.array([-0.4, 0.5, 0.6])) Ng_data = shapeFile(shape_name) Ng_data = loadNormal(Ng_data) Ng_32F, A_8U = Ng_data N0_file = shapeResultFile(result_name="InitialNormal", data_name=shape_name) N0_data = loadNormal(N0_file) N0_32F, A_8U = N0_data A_8U = cv2.bilateralFilter(A_8U, 0, 5, 2) colormap_file = colorMapFile(cmap_id) M_32F = loadColorMap(colormap_file) C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F) toon_sfs = ToonSFS(L, C0_32F, A_8U) toon_sfs.setInitialNormal(N0_32F) toon_sfs.setNumIterations(iterations=100) toon_sfs.setWeights(w_lap=0.1) toon_sfs.run() N_toon = toon_sfs.normal() C_toon = toon_sfs.shading() C_lumo, N_lumo = lumoSFS(C0_32F, L, N0_32F, A_8U) C_wu, N_wu = wuSFS(C0_32F, L, N0_32F, A_8U) C_error_toon, N_error_toon, I_error_toon = computeErrors( L, C0_32F, C_toon, Ng_32F, N_toon, A_8U) C_error_lumo, N_error_lumo, I_error_lumo = computeErrors( L, C0_32F, C_lumo, Ng_32F, N_lumo, A_8U) C_error_wu, N_error_wu, I_error_wu = computeErrors(L, C0_32F, C_wu, Ng_32F, N_wu, A_8U) plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), "Ground-truth") title = "" plot_grid.showImage(setAlpha(C_lumo, to32F(A_8U)), "Lumo") plot_grid.showColorMap(C_error_lumo, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(setAlpha(C_wu, to32F(A_8U)), "Lambert assumption") plot_grid.showColorMap(C_error_wu, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(setAlpha(C_toon, to32F(A_8U)), "Our result") plot_grid.showColorMap(C_error_toon, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(normalToColor(Ng_32F, A_8U), title) plot_grid.showImage(normalToColor(N_lumo, A_8U), title) plot_grid.showColorMap(N_error_lumo, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(normalToColor(N_wu, A_8U), title) plot_grid.showColorMap(N_error_wu, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(normalToColor(N_toon, A_8U), title) plot_grid.showColorMap(N_error_toon, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(computeIllumination(L, Ng_32F, A_8U), title) plot_grid.showImage(computeIllumination(L, N_lumo, A_8U), title) plot_grid.showColorMap(I_error_lumo, title, v_min=0, v_max=0.2, with_colorbar=True) plot_grid.showImage(computeIllumination(L, N_wu, A_8U), title) plot_grid.showColorMap(I_error_wu, title, v_min=0, v_max=0.2, with_colorbar=True) plot_grid.showImage(computeIllumination(L, N_toon, A_8U), title) plot_grid.showColorMap(I_error_toon, title, v_min=0, v_max=0.2, with_colorbar=True) # showMaximize() file_path = shapeResultFile("ShapeEstimation", "Comparison_%s_%s" % (shape_name, cmap_id), file_ext=".png") fig.savefig(file_path, transparent=True)
def materialShapeVariationFigure(): target_colormaps = [23, 3, 12] #target_colormaps = [3, 17] colormap_files = [colorMapFile(cmap_id) for cmap_id in target_colormaps] target_shapes = ["Blob1", "ThreeBox"] shape_names = target_shapes #shape_names = shapeNames()[4:5] num_shapes = len(shape_names) num_colormaps = len(colormap_files) num_rows = num_colormaps num_cols = 6 w = 20 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=0.96, bottom=0.02, hspace=0.15, wspace=0.1) fig.suptitle("", fontsize=font_size) plot_grid = SubplotGrid(num_rows, num_cols) L = normalizeVector(np.array([-0.4, 0.5, 0.6])) shape_name = "Blob1" Ng_data = shapeFile(shape_name) Ng_data = loadNormal(Ng_data) Ng_32F, A_8U = Ng_data N0_file = shapeResultFile(result_name="InitialNormal", data_name=shape_name) N0_data = loadNormal(N0_file) N0_32F, A_8U = N0_data for mi, colormap_file in enumerate(colormap_files): M_32F = loadColorMap(colormap_file) C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F) sfs_method = ToonSFS(L, C0_32F, A_8U) sfs_method.setInitialNormal(N0_32F) sfs_method.setNumIterations(iterations=40) sfs_method.setWeights(w_lap=5.0) sfs_method.run() N_32F = sfs_method.normal() C_32F = sfs_method.shading() #C_32F = cv2.bilateralFilter(C_32F, 0, 0.1, 3) C_error = sfs_method.shadingError() C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 C_error = trim(C_error, 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) title = "" if mi == 0: title = "Ground-truth" plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title) title = "" if mi == 0: title = "Our result" plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title) title = "" if mi == 0: title = "Error (shading)" plot_grid.showColorMap(C_error, title, v_min=0, v_max=0.1, with_colorbar=True) title = "" if mi == 0: title = "Ground-truth" plot_grid.showImage(normalToColor(Ng_32F, A_8U), title) title = "" if mi == 0: title = "Our result" plot_grid.showImage(normalToColor(N_32F, A_8U), title) title = "" if mi == 0: title = "Error (shape)" plot_grid.showColorMap(N_error, title, v_min=0, v_max=50.0, with_colorbar=True) #showMaximize() file_path = shapeResultFile("ShapeEstimation", "MaterialShapeEvaluation", file_ext=".pdf") fig.savefig(file_path, transparent=False)
def LambertShadingFigure(): target_shapes = [ "Sphere", "Cone", "Blob1", "Man", "Cone", "OctaFlower", "Pulley", "Grog", "Lucy", "Raptor" ] target_shapes = ["Blob1", "Pulley", "Lucy"] shape_names = target_shapes num_rows = len(shape_names) num_cols = 6 w = 20 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=0.96, bottom=0.02, hspace=0.15, wspace=0.1) fig.suptitle("", fontsize=font_size) plot_grid = SubplotGrid(num_rows, num_cols) L = normalizeVector(np.array([-0.2, 0.3, 0.6])) for si, shape_name in enumerate(shape_names): Ng_data = shapeFile(shape_name) Ng_data = loadNormal(Ng_data) Ng_32F, A_8U = Ng_data N0_file = shapeResultFile(result_name="InitialNormal", data_name=shape_name) N0_data = loadNormal(N0_file) N0_32F, A_8U = N0_data C0_32F = LambertShader().diffuseShading(L, Ng_32F) sfs_method = ToonSFS(L, C0_32F, A_8U) sfs_method.setInitialNormal(N0_32F) sfs_method.setNumIterations(iterations=70) sfs_method.setWeights(w_lap=1.0) sfs_method.run() N_32F = sfs_method.normal() C_32F = sfs_method.shading() C_error = sfs_method.shadingError() C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 C_error = trim(C_error, 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) title = "" if si == 0: title = "Ground-truth" plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title) title = "" if si == 0: title = "Our result" plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title) title = "" if si == 0: title = "Error (shading)" plot_grid.showColorMap(C_error, title, v_min=0, v_max=0.1, with_colorbar=True) title = "" if si == 0: title = "Ground-truth" plot_grid.showImage(normalToColor(Ng_32F, A_8U), title) title = "" if si == 0: title = "Our result" plot_grid.showImage(normalToColor(N_32F, A_8U), title) title = "" if si == 0: title = "Error (shape)" plot_grid.showColorMap(N_error, title, v_min=0, v_max=30.0, with_colorbar=True) file_path = shapeResultFile("ShapeEstimation", "LambertEstimationError", file_ext=".pdf") fig.savefig(file_path, transparent=True)
def _runImp(self): normal_data = loadNormal(self._data_file) if normal_data is None: return N0_32F, A_8U = normal_data A_32F = to32F(A_8U) N0_32F = trim(N0_32F, A_8U) A_32F = trim(A_32F, A_8U) A_8U = trim(A_8U, A_8U) L = normalizeVector(np.array([0.5, -0.2, 0.7])) C0_32F = ToonShader().diffuseShading(L, N0_32F) I0_32F = luminance(C0_32F) I_min, I_max = np.min(I0_32F), np.max(I0_32F) I_scale = (I0_32F - I_min) / (I_max - I_min) I_L = cv2.Laplacian(cv2.GaussianBlur(I_scale, (0, 0), 31.0), cv2.CV_32F, ksize=1) I_L_avg = np.average(np.abs(I_L)) Ix = cv2.Sobel(I0_32F, cv2.CV_64F, 1, 0, ksize=1) Ix = cv2.GaussianBlur(Ix, (0, 0), 3.0) Ixx = cv2.Sobel(Ix, cv2.CV_64F, 1, 0, ksize=1) Ixx = cv2.GaussianBlur(Ixx, (0, 0), 5.0) Iy = -cv2.Sobel(I0_32F, cv2.CV_64F, 0, 1, ksize=1) Iy = cv2.GaussianBlur(Iy, (0, 0), 3.0) Iyy = -cv2.Sobel(Iy, cv2.CV_64F, 0, 1, ksize=1) Iyy = cv2.GaussianBlur(Iyy, (0, 0), 5.0) fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05) fig.suptitle(self.name(), fontsize=font_size) num_rows = 2 num_cols = 5 plot_grid = SubplotGrid(num_rows, num_cols) Nx = cv2.Sobel(N0_32F[:, :, 0], cv2.CV_64F, 1, 0, ksize=1) Nx = cv2.GaussianBlur(Nx, (0, 0), 3.0) Nxx = cv2.Sobel(Nx, cv2.CV_64F, 1, 0, ksize=1) Nxx = cv2.GaussianBlur(Nxx, (0, 0), 5.0) Ny = -cv2.Sobel(N0_32F[:, :, 1], cv2.CV_64F, 0, 1, ksize=1) Ny = cv2.GaussianBlur(Ny, (0, 0), 3.0) Nyy = -cv2.Sobel(Ny, cv2.CV_64F, 0, 1, ksize=1) Nyy = cv2.GaussianBlur(Nyy, (0, 0), 5.0) Nz_L = cv2.Laplacian(cv2.GaussianBlur(N0_32F[:, :, 2], (0, 0), 5.0), cv2.CV_32F, ksize=5) Nz_L_avg = np.average(np.abs(Nz_L)) Nz_L *= 1.0 / Nz_L_avg I_L *= 1.0 / I_L_avg print I_L_avg, Nz_L_avg Nz_L = np.clip(Nz_L, -5.0, 5.0) I_L = np.clip(I_L, -5.0, 5.0) plot_grid.showColorMap(N0_32F[:, :, 0], r"$N_{x}$", v_min=-0.01, v_max=0.01) plot_grid.showColorMap(N0_32F[:, :, 1], r"$N_{y}$", v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Nx, r"$N_{xx}$", v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Ny, r"$N_{yy}$", v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Nz_L, r"$Nz_L$") # plot_grid.showColorMap(Nx + Ny, r'$N_{xx} + N_{yy}$', v_min=-0.01, v_max=0.01) # Ixx[Ixx>0] = 1.0 # Ixx[Ixx<0] = -1.0 # Iyy[Iyy>0] = 1.0 # Iyy[Iyy<0] = -1.0 plot_grid.showColorMap(-Ix, r"$I_{x}$", v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Iy, r"$I_{y}$", v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Ixx, r"$I_{xx}$", v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Iyy, r"$I_{yy}$", v_min=-0.001, v_max=0.001) plot_grid.showColorMap(I_L, r"$I_L$") # plot_grid.showColorMap(-Ixx - Iyy, r'$I_{xx} + I_{yy}$', v_min=-0.01, v_max=0.01) # plot_grid.showColorMap(Iy, r'$I_{y}$') showMaximize()
def _runImp(self): normal_data = loadNormal(self._data_file) if normal_data is None: return N0_32F, A_8U = normal_data A_32F = to32F(A_8U) N0_32F = trim(N0_32F, A_8U) A_32F = trim(A_32F, A_8U) A_8U = trim(A_8U, A_8U) L = normalizeVector(np.array([0.5, -0.2, 0.7])) C0_32F = ToonShader().diffuseShading(L, N0_32F) I0_32F = luminance(C0_32F) I_min, I_max = np.min(I0_32F), np.max(I0_32F) I_scale = (I0_32F - I_min) / (I_max - I_min) I_L = cv2.Laplacian(cv2.GaussianBlur(I_scale, (0, 0), 31.0), cv2.CV_32F, ksize=1) I_L_avg = np.average(np.abs(I_L)) Ix = cv2.Sobel(I0_32F, cv2.CV_64F, 1, 0, ksize=1) Ix = cv2.GaussianBlur(Ix, (0, 0), 3.0) Ixx = cv2.Sobel(Ix, cv2.CV_64F, 1, 0, ksize=1) Ixx = cv2.GaussianBlur(Ixx, (0, 0), 5.0) Iy = -cv2.Sobel(I0_32F, cv2.CV_64F, 0, 1, ksize=1) Iy = cv2.GaussianBlur(Iy, (0, 0), 3.0) Iyy = -cv2.Sobel(Iy, cv2.CV_64F, 0, 1, ksize=1) Iyy = cv2.GaussianBlur(Iyy, (0, 0), 5.0) fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05) fig.suptitle(self.name(), fontsize=font_size) num_rows = 2 num_cols = 5 plot_grid = SubplotGrid(num_rows, num_cols) Nx = cv2.Sobel(N0_32F[:, :, 0], cv2.CV_64F, 1, 0, ksize=1) Nx = cv2.GaussianBlur(Nx, (0, 0), 3.0) Nxx = cv2.Sobel(Nx, cv2.CV_64F, 1, 0, ksize=1) Nxx = cv2.GaussianBlur(Nxx, (0, 0), 5.0) Ny = -cv2.Sobel(N0_32F[:, :, 1], cv2.CV_64F, 0, 1, ksize=1) Ny = cv2.GaussianBlur(Ny, (0, 0), 3.0) Nyy = -cv2.Sobel(Ny, cv2.CV_64F, 0, 1, ksize=1) Nyy = cv2.GaussianBlur(Nyy, (0, 0), 5.0) Nz_L = cv2.Laplacian(cv2.GaussianBlur(N0_32F[:, :, 2], (0, 0), 5.0), cv2.CV_32F, ksize=5) Nz_L_avg = np.average(np.abs(Nz_L)) Nz_L *= 1.0 / Nz_L_avg I_L *= 1.0 / I_L_avg print I_L_avg, Nz_L_avg Nz_L = np.clip(Nz_L, -5.0, 5.0) I_L = np.clip(I_L, -5.0, 5.0) plot_grid.showColorMap(N0_32F[:, :, 0], r'$N_{x}$', v_min=-0.01, v_max=0.01) plot_grid.showColorMap(N0_32F[:, :, 1], r'$N_{y}$', v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Nx, r'$N_{xx}$', v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Ny, r'$N_{yy}$', v_min=-0.01, v_max=0.01) plot_grid.showColorMap(Nz_L, r'$Nz_L$') #plot_grid.showColorMap(Nx + Ny, r'$N_{xx} + N_{yy}$', v_min=-0.01, v_max=0.01) # Ixx[Ixx>0] = 1.0 # Ixx[Ixx<0] = -1.0 # Iyy[Iyy>0] = 1.0 # Iyy[Iyy<0] = -1.0 plot_grid.showColorMap(-Ix, r'$I_{x}$', v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Iy, r'$I_{y}$', v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Ixx, r'$I_{xx}$', v_min=-0.001, v_max=0.001) plot_grid.showColorMap(-Iyy, r'$I_{yy}$', v_min=-0.001, v_max=0.001) plot_grid.showColorMap(I_L, r'$I_L$') #plot_grid.showColorMap(-Ixx - Iyy, r'$I_{xx} + I_{yy}$', v_min=-0.01, v_max=0.01) #plot_grid.showColorMap(Iy, r'$I_{y}$') showMaximize()
def methodComparisonFigure(shape_name="ThreeBox", cmap_id=10): num_methods = 3 num_rows = 3 num_cols = 2 * num_methods + 1 w = 20 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=0.96, bottom=0.04, hspace=0.15, wspace=0.1) fig.suptitle("", fontsize=font_size) plot_grid = SubplotGrid(num_rows, num_cols) L = normalizeVector(np.array([-0.4, 0.5, 0.6])) Ng_data = shapeFile(shape_name) Ng_data = loadNormal(Ng_data) Ng_32F, A_8U = Ng_data N0_file = shapeResultFile(result_name="InitialNormal", data_name=shape_name) N0_data = loadNormal(N0_file) N0_32F, A_8U = N0_data A_8U = cv2.bilateralFilter(A_8U, 0, 5, 2) colormap_file = colorMapFile(cmap_id) M_32F = loadColorMap(colormap_file) C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F) toon_sfs = ToonSFS(L, C0_32F, A_8U) toon_sfs.setInitialNormal(N0_32F) toon_sfs.setNumIterations(iterations=100) toon_sfs.setWeights(w_lap=0.1) toon_sfs.run() N_toon = toon_sfs.normal() C_toon = toon_sfs.shading() C_lumo, N_lumo = lumoSFS(C0_32F, L, N0_32F, A_8U) C_wu, N_wu = wuSFS(C0_32F, L, N0_32F, A_8U) C_error_toon, N_error_toon, I_error_toon = computeErrors(L, C0_32F, C_toon, Ng_32F, N_toon, A_8U) C_error_lumo, N_error_lumo, I_error_lumo = computeErrors(L, C0_32F, C_lumo, Ng_32F, N_lumo, A_8U) C_error_wu, N_error_wu, I_error_wu = computeErrors(L, C0_32F, C_wu, Ng_32F, N_wu, A_8U) plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), "Ground-truth") title = "" plot_grid.showImage(setAlpha(C_lumo, to32F(A_8U)), "Lumo") plot_grid.showColorMap(C_error_lumo, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(setAlpha(C_wu, to32F(A_8U)), "Lambert assumption") plot_grid.showColorMap(C_error_wu, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(setAlpha(C_toon, to32F(A_8U)), "Our result") plot_grid.showColorMap(C_error_toon, title, v_min=0, v_max=0.1, with_colorbar=True) plot_grid.showImage(normalToColor(Ng_32F, A_8U), title) plot_grid.showImage(normalToColor(N_lumo, A_8U), title) plot_grid.showColorMap(N_error_lumo, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(normalToColor(N_wu, A_8U), title) plot_grid.showColorMap(N_error_wu, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(normalToColor(N_toon, A_8U), title) plot_grid.showColorMap(N_error_toon, title, v_min=0, v_max=50.0, with_colorbar=True) plot_grid.showImage(computeIllumination(L, Ng_32F, A_8U), title) plot_grid.showImage(computeIllumination(L, N_lumo, A_8U), title) plot_grid.showColorMap(I_error_lumo, title, v_min=0, v_max=0.2, with_colorbar=True) plot_grid.showImage(computeIllumination(L, N_wu, A_8U), title) plot_grid.showColorMap(I_error_wu, title, v_min=0, v_max=0.2, with_colorbar=True) plot_grid.showImage(computeIllumination(L, N_toon, A_8U), title) plot_grid.showColorMap(I_error_toon, title, v_min=0, v_max=0.2, with_colorbar=True) # showMaximize() file_path = shapeResultFile("ShapeEstimation", "Comparison_%s_%s" %(shape_name, cmap_id), file_ext=".png") fig.savefig(file_path, transparent=True)
def LambertShadingFigure(): target_shapes = ["Sphere", "Cone", "Blob1", "Man", "Cone", "OctaFlower", "Pulley", "Grog", "Lucy", "Raptor"] target_shapes = ["Blob1", "Pulley", "Lucy"] shape_names = target_shapes num_rows = len(shape_names) num_cols = 6 w = 20 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=0.96, bottom=0.02, hspace=0.15, wspace=0.1) fig.suptitle("", fontsize=font_size) plot_grid = SubplotGrid(num_rows, num_cols) L = normalizeVector(np.array([-0.2, 0.3, 0.6])) for si, shape_name in enumerate(shape_names): Ng_data = shapeFile(shape_name) Ng_data = loadNormal(Ng_data) Ng_32F, A_8U = Ng_data N0_file = shapeResultFile(result_name="InitialNormal", data_name=shape_name) N0_data = loadNormal(N0_file) N0_32F, A_8U = N0_data C0_32F = LambertShader().diffuseShading(L, Ng_32F) sfs_method = ToonSFS(L, C0_32F, A_8U) sfs_method.setInitialNormal(N0_32F) sfs_method.setNumIterations(iterations=70) sfs_method.setWeights(w_lap=1.0) sfs_method.run() N_32F = sfs_method.normal() C_32F = sfs_method.shading() C_error = sfs_method.shadingError() C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0 C_error = trim(C_error, 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) title = "" if si == 0: title = "Ground-truth" plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title) title = "" if si == 0: title = "Our result" plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title) title = "" if si == 0: title = "Error (shading)" plot_grid.showColorMap(C_error, title, v_min=0, v_max=0.1, with_colorbar=True) title = "" if si == 0: title = "Ground-truth" plot_grid.showImage(normalToColor(Ng_32F, A_8U), title) title = "" if si == 0: title = "Our result" plot_grid.showImage(normalToColor(N_32F, A_8U), title) title = "" if si == 0: title = "Error (shape)" plot_grid.showColorMap(N_error, title, v_min=0, v_max=30.0, with_colorbar=True) file_path = shapeResultFile("ShapeEstimation", "LambertEstimationError", file_ext=".pdf") fig.savefig(file_path, transparent=True)