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 _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 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)
