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 _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 _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))
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
sfs_method = Wu08SFS(L, C0_32F, A_8U)
sfs_method.run()
N_32F = sfs_method.normal()
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 = 2
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'Shading: $C$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'Estimated Normal: $N$')
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 _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))
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
sfs_method = Wu08SFS(L, C0_32F, A_8U)
sfs_method.run()
N_32F = sfs_method.normal()
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 = 2
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r"Shading: $C$")
plot_grid.showImage(normalToColor(N_32F, A_8U), r"Estimated Normal: $N$")
showMaximize()
def _computeInitialDetailNormal(self):
bump_scale = self._parameters["bumpScale"].value()
self._N_b[:, :, :2] *= bump_scale
self._N_b = normalizeImage(self._N_b, th=1.0)
self._N_d[:, :, :2] *= bump_scale
self._N_d = normalizeImage(self._N_d, th=1.0)
self._view.render(normalToColor(self._N_b))
def _computeInitialDetailNormal(self):
bump_scale = self._parameters["bumpScale"].value()
self._N_b[:, :, :2] *= bump_scale
self._N_b = normalizeImage(self._N_b, th=1.0)
self._N_d[:, :, :2] *= bump_scale
self._N_d = normalizeImage(self._N_d, th=1.0)
self._view.render(normalToColor(self._N_b))
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 keyPressEvent(self, e):
if e.key() == Qt.Key_0:
self._view.render(self._image)
if e.key() == Qt.Key_1:
if self._N_32F is None:
self._interpolateNormal()
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
self._view.render(normalToColor(self._N_32F, A_8U))
if e.key() == Qt.Key_2:
self._interpolateNormal()
if self._N_32F is None:
self._interpolateNormal()
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
self._view.render(normalToColor(self._N_32F, A_8U))
if e.key() == Qt.Key_Delete:
self._normal_constraints.clear()
self._view.update()
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N_32F, A_8U = normal_data
N_32F = trim(N_32F, A_8U)
A_8U = trim(A_8U, A_8U)
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
I_half = half_lambert.diffuse(N_32F, L)
I_half = setAlpha(gray2rgb(I_half), A_32F)
I_lambert = lambert.diffuse(N_32F, L)
I_lambert = setAlpha(gray2rgb(I_lambert), A_32F)
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'Normal: $N$')
plt.imshow(normalToColor(N_32F, A_8U))
plt.axis('off')
plt.subplot(1, 4, 2)
plt.title(r'Half Lambert: $I_h$')
plt.imshow(I_half)
plt.axis('off')
plt.subplot(1, 4, 3)
plt.title(r'Lambert: $I_l$')
plt.imshow(I_lambert)
plt.axis('off')
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 _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N_32F, A_8U = normal_data
N_32F = trim(N_32F, A_8U)
A_8U = trim(A_8U, A_8U)
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
I_half = half_lambert.diffuse(N_32F, L)
I_half = setAlpha(gray2rgb(I_half), A_32F)
I_lambert = lambert.diffuse(N_32F, L)
I_lambert = setAlpha(gray2rgb(I_lambert), A_32F)
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'Normal: $N$')
plt.imshow(normalToColor(N_32F, A_8U))
plt.axis('off')
plt.subplot(1, 4, 2)
plt.title(r'Half Lambert: $I_h$')
plt.imshow(I_half)
plt.axis('off')
plt.subplot(1, 4, 3)
plt.title(r'Lambert: $I_l$')
plt.imshow(I_lambert)
plt.axis('off')
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 _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 _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 _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
A_32F = to32F(A_8U)
C0_32F = to32F(rgb(C0_8U))
I0_32F = luminance(C0_32F)
initial_normals = ["N_lumo.png", "N0_d.png"]
layer_name = os.path.splitext(os.path.basename(layer_file))[0]
for initial_normal in initial_normals:
N0_32F, AN_8U = loadNormal(
self.characterResultFile(initial_normal,
data_name="BaseDetailSepration"))
N_32F, L, C_32F, M = self._runSFS(C0_32F, A_8U, N0_32F, AN_8U)
L_img = lightSphere(L)
M_img = M.mapImage()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.02,
right=0.98,
top=0.9,
hspace=0.12,
wspace=0.02)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(normalToColor(N0_32F, A_8U),
r'Initial Normal: $N_0$')
plot_grid.showImage(normalToColor(N_32F, A_8U),
r'Estimated Normal: $N$')
plot_grid.showImage(C0_8U, r'Shading: $C_0$')
plot_grid.showImage(setAlpha(C_32F, A_32F),
r'Recovered Shading: $C$')
out_file_path = self.characterResultFile("ToonSFS" +
initial_normal,
layer_name=layer_name)
plt.savefig(out_file_path)
N_trim = trim(N_32F, A_8U)
N0_trim = trim(N0_32F, A_8U)
C0_trim = trim(C0_32F, A_8U)
A_trim = trim(A_8U, A_8U)
out_file_path = self.characterResultFile(initial_normal,
layer_name=layer_name)
saveNormal(out_file_path, N_trim, A_trim)
images = self._relightingImages(N_trim, A_trim, M)
initial_normal_name = os.path.splitext(initial_normal)[0]
video_name = "Relighting" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name,
layer_name=layer_name)
saveVideo(out_file_path, images)
images = self._relightingOffsetImages(L, C0_trim, N0_trim, A_trim,
M)
video_name = "RelightingOffset" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name,
layer_name=layer_name)
saveVideo(out_file_path, images)
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 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 saveNormal(file_path, N_32F, A_8U=None):
C_8U = normalToColor(N_32F, A_8U)
saveImage(file_path, C_8U)
def normalColor(self):
A_8U = alpha(self._image)
return normalToColor(self._normal, A_8U)
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 _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
A_32F = to32F(A_8U)
C0_32F = to32F(rgb(C0_8U))
I0_32F = luminance(C0_32F)
initial_normals = ["N_lumo.png", "N0_d.png"]
layer_name = os.path.splitext(os.path.basename(layer_file))[0]
for initial_normal in initial_normals:
N0_32F, AN_8U = loadNormal(self.characterResultFile(initial_normal, data_name="BaseDetailSepration"))
N_32F, L, C_32F, M = self._runSFS(C0_32F, A_8U, N0_32F, AN_8U)
L_img = lightSphere(L)
M_img = M.mapImage()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.02, right=0.98, top=0.9, hspace=0.12, wspace=0.02)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'Initial Normal: $N_0$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'Estimated Normal: $N$')
plot_grid.showImage(C0_8U, r'Shading: $C_0$')
plot_grid.showImage(setAlpha(C_32F, A_32F), r'Recovered Shading: $C$')
out_file_path = self.characterResultFile("ToonSFS" + initial_normal, layer_name=layer_name)
plt.savefig(out_file_path)
N_trim = trim(N_32F, A_8U)
N0_trim = trim(N0_32F, A_8U)
C0_trim = trim(C0_32F, A_8U)
A_trim = trim(A_8U, A_8U)
out_file_path = self.characterResultFile(initial_normal, layer_name=layer_name)
saveNormal(out_file_path, N_trim, A_trim)
images = self._relightingImages(N_trim, A_trim, M)
initial_normal_name = os.path.splitext(initial_normal)[0]
video_name = "Relighting" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name, layer_name=layer_name)
saveVideo(out_file_path, images)
images = self._relightingOffsetImages(L, C0_trim, N0_trim, A_trim, M)
video_name = "RelightingOffset" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name, layer_name=layer_name)
saveVideo(out_file_path, images)
def overviewFigure():
cmap_id = 10
colormap_file = colorMapFile(cmap_id)
num_rows = 1
num_cols = 5
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=0.96, bottom=0.02, hspace=0.05, wspace=0.05)
fig.suptitle("", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
L = normalizeVector(np.array([-0.4, 0.6, 0.6]))
L_img = lightSphere(L)
shape_name = "ThreeBox"
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
M_32F = loadColorMap(colormap_file)
Cg_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
borders=[0.6, 0.8, 0.92]
colors = [np.array([0.2, 0.2, 0.4]),
np.array([0.3, 0.3, 0.6]),
np.array([0.4, 0.4, 0.8]),
np.array([0.5, 0.5, 1.0])]
#Cg_32F = ToonShader(borders, colors).diffuseShading(L, Ng_32F)
#Cg_32F = cv2.GaussianBlur(Cg_32F, (0,0), 2.0)
sfs_method = ToonSFS(L, Cg_32F, A_8U)
sfs_method.setInitialNormal(N0_32F)
sfs_method.setNumIterations(iterations=40)
sfs_method.setWeights(w_lap=10.0)
sfs_method.run()
N_32F = sfs_method.normal()
I_32F = np.float32(np.clip(LdotN(L, N_32F), 0.0, 1.0))
I0_32F = np.float32(np.clip(LdotN(L, N0_32F), 0.0, 1.0))
C_32F = sfs_method.shading()
C0_32F = sfs_method.initialShading()
M_32F = sfs_method.colorMap().mapImage()
L1 = normalizeVector(np.array([0.0, 0.6, 0.6]))
L1_img = lightSphere(L1)
C1_32F = sfs_method.relighting(L1)
L2 = normalizeVector(np.array([0.5, 0.8, 0.6]))
L2_img = lightSphere(L2)
C2_32F = sfs_method.relighting(L2)
N_sil = silhouetteNormal(A_8U, sigma=7.0)
N_sil[:, :, 2] = N_sil[:, :, 2] ** 10.0
N_sil = normalizeImage(N_sil)
A_sil = 1.0 - N_sil[:, :, 2]
A_sil = to8U(A_sil)
N_xy = N_sil[:, :, 0] ** 2 + N_sil[:, :, 1] ** 2
A_sil[N_xy < 0.1] = 0
title = ""
plot_grid.showImage(setAlpha(Cg_32F, to32F(A_8U)), title)
plot_grid.showImage(normalToColor(N0_32F, A_8U), title)
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title)
plot_grid.showImage(normalToColor(N_32F, A_8U), title)
plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title)
# plot_grid.showImage(normalToColor(Ng_32F, A_8U), title)
#showMaximize()
file_path = shapeResultFile("Overview", "Overview")
fig.savefig(file_path, transparent=True)
file_path = shapeResultFile("Overview", "Cg")
saveRGBA(file_path, setAlpha(Cg_32F, to32F(A_8U)))
file_path = shapeResultFile("Overview", "L")
saveRGB(file_path, gray2rgb(to8U(L_img)))
file_path = shapeResultFile("Overview", "L1")
saveRGB(file_path, gray2rgb(to8U(L1_img)))
file_path = shapeResultFile("Overview", "L2")
saveRGB(file_path, gray2rgb(to8U(L2_img)))
file_path = shapeResultFile("Overview", "N0")
saveNormal(file_path, N0_32F, A_8U)
file_path = shapeResultFile("Overview", "N_sil")
saveNormal(file_path, N_sil, A_sil)
file_path = shapeResultFile("Overview", "N")
saveNormal(file_path, N_32F, A_8U)
file_path = shapeResultFile("Overview", "C0")
saveRGBA(file_path, setAlpha(C0_32F, to32F(A_8U)))
file_path = shapeResultFile("Overview", "C")
saveRGBA(file_path, setAlpha(C_32F, to32F(A_8U)))
file_path = shapeResultFile("Overview", "C1")
saveRGBA(file_path, setAlpha(C1_32F, to32F(A_8U)))
file_path = shapeResultFile("Overview", "C2")
saveRGBA(file_path, setAlpha(C2_32F, to32F(A_8U)))
file_path = shapeResultFile("Overview", "I")
saveRGBA(file_path, setAlpha(gray2rgb(I_32F), to32F(A_8U)))
file_path = shapeResultFile("Overview", "I0")
saveRGBA(file_path, setAlpha(gray2rgb(I0_32F), to32F(A_8U)))
file_path = shapeResultFile("Overview", "M")
saveRGB(file_path, M_32F)
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)
def saveNormal(file_path, N_32F, A_8U=None):
C_8U = normalToColor(N_32F, A_8U)
saveImage(file_path, C_8U)
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)
