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): # file_path = self._data_file file_path = self.resultFile(self._data_file_name, result_name="Wu08") normal_data = loadNormal(file_path) if normal_data is None: return N0_32F, A_8U = normal_data D_32F = depthFromNormal(N0_32F, A_8U) fig, axes = plt.subplots(figsize=(11, 5)) font_size = 15 fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.05, wspace=0.05) fig.suptitle("Depth From Normal", fontsize=font_size) plt.subplot(1, 4, 1) plt.title(r'Initial Normal: $N_0$') plt.imshow(normalToColor(N0_32F, A_8U)) plt.axis('off') plt.subplot(1, 4, 2) plt.title(r'Estimated Depth: $D$') plt.gray() plt.imshow(D_32F) plt.axis('off') N_32F = depthToNormal(D_32F) plt.subplot(1, 4, 3) plt.title(r'Recovered Normal: $N$') plt.imshow(normalToColor(N_32F, A_8U)) plt.axis('off') h, w = N_32F.shape[:2] N_diff = angleErros(N_32F.reshape(-1, 3), N0_32F.reshape(-1, 3)).reshape(h, w) N_diff[A_8U < np.max(A_8U)] = 0.0 plt.subplot(1, 4, 4) plt.title(r'Angle Error: $N, N_0$') plt.imshow(N_diff, cmap=plt.cm.jet, vmin=0, vmax=30.0) plt.axis('off') plt.colorbar() #out_file_path = self.resultFile(self._data_file_name) out_file_path = self.resultFile(self._data_file_name, result_name="Wu08Depth") plt.savefig(out_file_path)
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 computeLambertShadingError(): shape_names = shapeNames() L = normalizeVector(np.array([-0.2, 0.3, 0.6])) C_errors = np.zeros(len(shape_names)) N_errors = np.zeros(len(shape_names)) 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=60) 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_errors[si] = np.average(C_error[A_8U > 0.5 * np.max(A_8U)]) h, w = A_8U.shape[:2] 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_errors[si] = np.average(N_error[A_8U > 0.5 * np.max(A_8U)]) file_path = shapeResultFile("ShapeEstimation", "ShadingError", file_ext=".npy") np.save(file_path, C_errors) file_path = shapeResultFile("ShapeEstimation", "NormalError", file_ext=".npy") np.save(file_path, N_errors)
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 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)