def _runSFS(self, C0_32F, A_8U, N0_32F, AN_8U):
I0_32F = luminance(C0_32F)
L = lightEstimation(I0_32F, N0_32F, A_8U)
LdN = LdotN(L, N0_32F)
layer_area = A_8U > 0.5 * np.max(A_8U)
Cs = C0_32F[layer_area].reshape(-1, 3)
Is = LdN[layer_area].flatten()
M = ColorMapEstimation(Cs, Is)
I_const = M.illumination(Cs)
I_32F = np.array(I0_32F)
I_32F[layer_area] = I_const
I_32F[A_8U < 0.5 * np.max(A_8U)] = np.min(I_const)
toon_sfs = ToonSFS(L, C0_32F, A_8U)
toon_sfs.setInitialNormal(N0_32F)
toon_sfs.run()
N_32F = toon_sfs.normal()
LdN_recovered = LdotN(L, N_32F)
LdN_recovered[A_8U < 0.5 * np.max(A_8U)] = np.min(LdN_recovered)
Is = LdN_recovered[layer_area].flatten()
Cs_recovered = M.shading(Is)
C_32F = np.array(C0_32F)
C_32F[layer_area, :] = Cs_recovered
return N_32F, L, C_32F, M
def _runColorMap(self, colormap_file, Ng_32F, N0_32F, A_8U):
M_32F = loadColorMap(colormap_file)
L0 = normalizeVector(np.array([-0.2, 0.3, 0.6]))
L0_img = lightSphere(L0)
L0_txt = 0.01 * np.int32(100 * L0)
C0_32F = ColorMapShader(M_32F).diffuseShading(L0, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
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 = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(setAlpha(C0_32F, A_8U), r'Input image: $\mathbf{c}$', font_size=font_size)
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'Initial normal: $\mathbf{N}_0$')
plot_grid.showImage(L0_img, r'Ground trugh light: $L_g = (%s, %s, %s)$' %(L0_txt[0], L0_txt[1], L0_txt[2]))
plot_grid.showImage(L_img, r'Estimated light: $L = (%s, %s, %s)$' %(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runSFS(self, C0_32F, A_8U, N0_32F, AN_8U):
I0_32F = luminance(C0_32F)
L = lightEstimation(I0_32F, N0_32F, A_8U)
LdN = LdotN(L, N0_32F)
layer_area = A_8U > 0.5 * np.max(A_8U)
Cs = C0_32F[layer_area].reshape(-1, 3)
Is = LdN[layer_area].flatten()
M = ColorMapEstimation(Cs, Is)
I_const = M.illumination(Cs)
I_32F = np.array(I0_32F)
I_32F[layer_area] = I_const
I_32F[A_8U < 0.5 * np.max(A_8U)] = np.min(I_const)
toon_sfs = ToonSFS(L, C0_32F, A_8U)
toon_sfs.setInitialNormal(N0_32F)
toon_sfs.run()
N_32F = toon_sfs.normal()
LdN_recovered = LdotN(L, N_32F)
LdN_recovered[A_8U < 0.5 * np.max(A_8U)] = np.min(LdN_recovered)
Is = LdN_recovered[layer_area].flatten()
Cs_recovered = M.shading(Is)
C_32F = np.array(C0_32F)
C_32F[layer_area, :] = Cs_recovered
return N_32F, L, C_32F, M
def computeErrorTables(Lg=normalizeVector(np.array([-0.2, 0.3, 0.6]))):
colormap_files = colorMapFiles()
shape_names = shapeNames()
num_materials = len(colormap_files)
num_shapes = len(shape_names)
L_errors = np.zeros((num_shapes, num_materials))
Ms = []
for colormap_file in colormap_files:
M_32F = loadColorMap(colormap_file)
Ms.append(M_32F)
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
for mi, M_32F in enumerate(Ms):
C0_32F = ColorMapShader(M_32F).diffuseShading(Lg, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_errors[si, mi] = angleError(Lg, L)
file_path = shapeResultFile("LightEstimation", "LightEstimationError", file_ext=".npy")
np.save(file_path, L_errors)
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
N0_32F, A_8U = loadNormal(
self.characterResultFile("N0_d.png",
data_name="BaseDetailSepration"))
Nd_32F, A_8U = loadNormal(
self.characterResultFile("N_d_smooth.png",
data_name="BaseDetailSepration"))
Nb_32F, A_8U = loadNormal(
self.characterResultFile("N_b_smooth.png",
data_name="BaseDetailSepration"))
W_32F = np.array(Nb_32F[:, :, 2])
W_32F = W_32F
W_32F[W_32F < 0.95] = 0.0
L = lightEstimation(I_32F, N0_32F, A_8U)
# L = lightEstimationByVoting(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
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 = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'$N$')
plot_grid.showImage(setAlpha(C0_32F, W_32F), r'$Nd_z$')
plot_grid.showImage(
L_img, r'$L: [%s, %s, %s]$' % (L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
N0_32F, A_8U = loadNormal(self.characterResultFile("N0_d.png", data_name="BaseDetailSepration"))
Nd_32F, A_8U = loadNormal(self.characterResultFile("N_d_smooth.png", data_name="BaseDetailSepration"))
Nb_32F, A_8U = loadNormal(self.characterResultFile("N_b_smooth.png", data_name="BaseDetailSepration"))
W_32F = np.array(Nb_32F[:, :, 2])
W_32F = W_32F
W_32F[W_32F < 0.95] = 0.0
L = lightEstimation(I_32F, N0_32F, A_8U)
# L = lightEstimationByVoting(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
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 = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'$N$')
plot_grid.showImage(setAlpha(C0_32F, W_32F), r'$Nd_z$')
plot_grid.showImage(L_img, r'$L: [%s, %s, %s]$' %(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runColorMap(self, colormap_file, Ng_32F, N0_32F, A_8U):
M_32F = loadColorMap(colormap_file)
L0 = normalizeVector(np.array([-0.2, 0.3, 0.6]))
L0_img = lightSphere(L0)
L0_txt = 0.01 * np.int32(100 * L0)
C0_32F = ColorMapShader(M_32F).diffuseShading(L0, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
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 = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(setAlpha(C0_32F, A_8U),
r'Input image: $\mathbf{c}$',
font_size=font_size)
plot_grid.showImage(normalToColor(N0_32F, A_8U),
r'Initial normal: $\mathbf{N}_0$')
plot_grid.showImage(
L0_img, r'Ground trugh light: $L_g = (%s, %s, %s)$' %
(L0_txt[0], L0_txt[1], L0_txt[2]))
plot_grid.showImage(
L_img, r'Estimated light: $L = (%s, %s, %s)$' %
(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def lightEstimationFigure():
target_colormaps = [23, 0, 6, 22, 4, 12]
target_shapes = ["Raptor", "Man", "Blob1", "OctaFlower", "Pulley", "Cone"]
colormap_files = [colorMapFile(cmap_id) for cmap_id in target_colormaps]
shape_names = target_shapes
num_rows = len(shape_names) + 1
num_cols = len(colormap_files) + 1
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)
plot_grid = SubplotGrid(num_rows, num_cols)
Lg = normalizeVector(np.array([-0.2, 0.3, 0.6]))
Lg_img = lightSphere(Lg)
plot_grid.showImage(Lg_img, "")
Ms = []
for colormap_file in colormap_files:
M_32F = loadColorMap(colormap_file)
Cs_32F = colorMapSphere(Lg, M_32F)
plot_grid.showImage(Cs_32F, "")
Ms.append(M_32F)
L_errors = np.zeros((num_rows, num_cols))
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
plot_grid.showImage(normalToColor(Ng_32F, A_8U), "")
for mi, M_32F in enumerate(Ms):
C0_32F = ColorMapShader(M_32F).diffuseShading(Lg, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_errors[si, mi] = angleError(Lg, L)
L_img = lightSphereColorMap(L, v=L_errors[si, mi], v_min=0, v_max=40)
plot_grid.showImage(L_img, "")
L_error_min, L_error_max = np.min(L_errors), np.max(L_errors)
file_path = shapeResultFile("LightEstimation", "LightEstimationError")
fig.savefig(file_path, transparent=True)
