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 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 _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 _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 _computeBaseDetalSeparation(self):
sigma_space, sigma_range = self._parameters["sigmaSpace"], self._parameters["sigmaRange"]
C0_32F = self._C0_32F
I_32F = luminance(C0_32F)
B_b, D_b = baseDetailSeparationBilateral(I_32F, sigma_space=sigma_space.value(), sigma_range=sigma_range.value())
self._D = D_b
self._N_b = bumpNormal(B_b, scale=1.0, sigma=1.0)
self._N_d = bumpNormal(D_b, scale=1.0, sigma=1.0)
self._view.render(D_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)
Lab_32F = rgb2Lab(C0_32F)
th_specular = 0.2
th_contour = 0.02
th_material = 0.1
E_32F = DoG(I_32F, sigma=2.0)
contour = th_contour * np.min(E_32F) - E_32F
contour *= 1.0 / np.max(contour)
contour = np.clip(contour, 0.0, 1.0)
specular = E_32F - th_specular * np.max(E_32F)
specular *= 1.0 / np.max(specular)
specular = np.clip(specular, 0.0, 1.0)
material = rgb(C0_8U)
# edge_mask = np.zeros(I_32F.shape, dtype=np.uint8)
# edge_mask[contour > 0.0] = 1.0
# material = cv2.inpaint(material, edge_mask, 3, cv2.INPAINT_TELEA)
for i in xrange(1):
material = cv2.medianBlur(material, ksize=7)
# material = th_material * np.max(np.abs(E_32F)) - np.abs(E_32F)
# material *= 1.0 / np.max(material)
# material = np.clip(material, 0.0, 1.0)
# material[material > 0.0] = 1.0
E_32F[E_32F < 0.0] = 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 = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
#plot_grid.showImage(setAlpha(C0_32F, material), r'$Material$')
plot_grid.showImage(setAlpha(material, A_8U), r'$Material$')
plot_grid.showImage(setAlpha(C0_32F, contour), r'$Contour$')
plot_grid.showImage(setAlpha(C0_32F, specular), r'$Specular$')
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 __init__(self, C_32F, I_32F=None):
self._C_32F = C_32F
I0_32F = luminance(C_32F)
self._I0_32F = I0_32F
if I_32F is None:
I_32F = np.array(I0_32F)
self._I_32F = I_32F
self._compute()
def _computeBaseDetalSeparation(self):
sigma_space, sigma_range = self._parameters[
"sigmaSpace"], self._parameters["sigmaRange"]
C0_32F = self._C0_32F
I_32F = luminance(C0_32F)
B_b, D_b = baseDetailSeparationBilateral(
I_32F,
sigma_space=sigma_space.value(),
sigma_range=sigma_range.value())
self._D = D_b
self._N_b = bumpNormal(B_b, scale=1.0, sigma=1.0)
self._N_d = bumpNormal(D_b, scale=1.0, sigma=1.0)
self._view.render(D_b)
def errorTable():
colormap_files = colorMapFiles()
num_colormap_files = len(colormap_files)
M_errors = np.zeros((num_colormap_files))
for mi, colormap_file in enumerate(colormap_files):
M_32F = loadColorMap(colormap_file)
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)[0, :, :]
I0_32F = luminance(C_32F)
IL_32F = luminance(Ml.reshape(1, -1, 3))
I_min, I_max = np.min(I0_32F), np.max(I0_32F)
M_error = normVectors(M_32F - Ml)
#M_errors[mi] = np.mean(M_error) / (I_max - I_min)
# M_errors[mi] = computeGradientDistance(M_32F, Ml) / (I_max - I_min)
#M_errors[mi] = np.linalg.norm(I0_32F - IL_32F) / (I_max - I_min)
M_errors[mi] = np.mean(M_error) / (I_max - I_min)
# M_errors[mi] = np.linalg.norm(M_32F - Ml) / (I_max - I_min)
# M_errors[mi] = compareHist(M_32F, Ml)
file_path = shapeResultFile("ReflectanceEstimation", "ReflectanceError", file_ext=".npy")
np.save(file_path, M_errors)
plt.plot(M_errors)
plt.show()
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
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 _compute(self):
C0_8U = self._image
C0_32F = to32F(rgb(C0_8U))
Lab_32F = rgb2Lab(C0_32F)
I_32F = luminance(C0_32F)
h, w = I_32F.shape[:2]
edge_mask = np.zeros((h, w), dtype=np.uint8)
#E_32F = DoG(Lab_32F, sigma=7.0)
# E_32F[E_32F > 0.0] = 0.0
# E_32F = - E_32F
# E_32F /= np.max(E_32F)
# th_contour = 0.05
# for ci in xrange(3):
# edge_area = E_32F[:, :, ci] > th_contour
# edge_mask[edge_area] = 255
E_32F = DoG(I_32F, sigma=3.0)
h, w = E_32F.shape[:2]
E_norm = -np.array(E_32F)
E_norm[E_norm < 0.0] = 0.0
th_contour = 0.1
edge_area = E_norm > th_contour * np.max(E_norm)
edge_mask[edge_area] = 255
self._edge_mask = edge_mask
labels = np.array(edge_mask)
mask = np.ones((h + 2, w + 2), dtype=np.uint8)
mask[1:-1, 1:-1] = self._edge_mask
for label in xrange(1, 3):
regionSeeds = np.where(self._edge_mask == 0)
if len(regionSeeds[0]) == 0:
break
p = (regionSeeds[1][0], regionSeeds[0][0])
cv2.floodFill(labels, mask, p, label)
self._edge_mask[labels == label] = label
self._labels = labels
def _compute(self):
C0_8U = self._image
C0_32F = to32F(rgb(C0_8U))
Lab_32F = rgb2Lab(C0_32F)
I_32F = luminance(C0_32F)
h, w = I_32F.shape[:2]
edge_mask = np.zeros((h, w), dtype=np.uint8)
# E_32F = DoG(Lab_32F, sigma=7.0)
# E_32F[E_32F > 0.0] = 0.0
# E_32F = - E_32F
# E_32F /= np.max(E_32F)
# th_contour = 0.05
# for ci in xrange(3):
# edge_area = E_32F[:, :, ci] > th_contour
# edge_mask[edge_area] = 255
E_32F = DoG(I_32F, sigma=3.0)
h, w = E_32F.shape[:2]
E_norm = -np.array(E_32F)
E_norm[E_norm < 0.0] = 0.0
th_contour = 0.1
edge_area = E_norm > th_contour * np.max(E_norm)
edge_mask[edge_area] = 255
self._edge_mask = edge_mask
labels = np.array(edge_mask)
mask = np.ones((h + 2, w + 2), dtype=np.uint8)
mask[1:-1, 1:-1] = self._edge_mask
for label in xrange(1, 3):
regionSeeds = np.where(self._edge_mask == 0)
if len(regionSeeds[0]) == 0:
break
p = (regionSeeds[1][0], regionSeeds[0][0])
cv2.floodFill(labels, mask, p, label)
self._edge_mask[labels == label] = label
self._labels = labels
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 _interpolateNormalImage(self, N0_32F, W_32F, A_8U):
constraints = []
constraints.append(image_constraints.laplacianConstraints(w_c=0.1))
constraints.append(image_constraints.normalConstraints(W_32F, N0_32F, w_c=3.0))
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
I_32F = luminance(to32F(rgb(self._image)))
I_min, I_max = np.min(I_32F), np.max(I_32F)
I_32F = (I_32F - I_min) / (I_max - I_min)
# constraints.append(image_constraints.brightnessConstraints(L, I_32F, w_c=0.5))
constraints.append(image_constraints.silhouetteConstraints(A_8U, w_c=0.8))
solver_iter = image_solver.solveIterator(constraints,
[postNormalize(th=0.0)])
N_32F = np.array(N0_32F)
N_32F = image_solver.solveMG(N_32F, solver_iter, iterations=10)
N_32F = image_constraints.NxyToNz(N_32F)
return N_32F
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, 0.3, 0.7]))
#C0_32F = ToonShader().diffuseShading(L, N0_32F)
C0_32F = LambertShader().diffuseShading(L, N0_32F)
I0_32F = luminance(C0_32F)
I0_low_32F = cv2.resize(I0_32F, (256, 256))
A_low_8U = cv2.resize(A_8U, I0_low_32F.shape)
D_32F = depthFromGradient(I0_low_32F, A_low_8U)
D_32F = cv2.resize(D_32F, I0_32F.shape)
N_32F = depthToNormal(D_32F)
self._view.setRGBAD(setAlpha(C0_32F, A_32F), D_32F)
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, 0.3, 0.7]))
#C0_32F = ToonShader().diffuseShading(L, N0_32F)
C0_32F = LambertShader().diffuseShading(L, N0_32F)
I0_32F = luminance(C0_32F)
I0_low_32F = cv2.resize(I0_32F, (256, 256))
A_low_8U = cv2.resize(A_8U, I0_low_32F.shape)
D_32F = depthFromGradient(I0_low_32F, A_low_8U)
D_32F = cv2.resize(D_32F, I0_32F.shape)
N_32F = depthToNormal(D_32F)
self._view.setRGBAD(setAlpha(C0_32F, A_32F), D_32F)
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 _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 _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 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 _computeBrightness(self, C_32F):
return luminance(np.float32(C_32F))
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 _loadImage(self):
C0_32F = self._C0_32F
I_32F = luminance(C0_32F)
self._view.render(I_32F)
return
def _loadImage(self):
C0_32F = self._C0_32F
I_32F = luminance(C0_32F)
self._view.render(I_32F)
return
