def materialList():
shape_name = "ThreeBox"
num_rows = 1
num_cols = len(colorMapFiles())
w = 24
h = w * num_rows / num_cols
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
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=0.1, wspace=0.05)
fig.suptitle("", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
Ng_data = shapeFile(shape_name)
Ng_data = loadNormal(Ng_data)
Ng_32F, A_8U = Ng_data
for colormap_file in colorMapFilesSortedReflectanceError():
M_32F = loadColorMap(colormap_file)
C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), "")
file_path = shapeResultFile("ShapeEstimation", "MaterialList")
fig.savefig(file_path, transparent=True)
def shapeList():
num_rows = 1
num_cols = len(shapeNames())
w = 20
h = w * num_rows / num_cols
cmap_id = 10
colormap_file = colorMapFile(cmap_id)
M_32F = loadColorMap(colormap_file)
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
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)
for shape_name in shapeNames():
Ng_data = shapeFile(shape_name)
Ng_data = loadNormal(Ng_data)
Ng_32F, A_8U = Ng_data
C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), "")
file_path = shapeResultFile("ShapeEstimation", "ShapeList")
fig.savefig(file_path, transparent=True)
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 _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 _compute(self):
image = self._image
img_32F = to32F(self._image)
self._A_32F = alpha(img_32F)
sigma_xy = 100.0
xy = positionFeatures(img_32F) / sigma_xy
Lab = LabFeatures(img_32F)
sigma_L = 1.0
Lab[:, 0] /= sigma_L
foreground = foreGroundFeatures(img_32F)
Labxy = np.concatenate((Lab, xy), axis=1)
Labxy_samples = shuffle(Labxy[foreground, :], random_state=0)[:1000]
kmeans = sklearn.cluster.KMeans(n_clusters=self._num_colors, random_state=0).fit(Labxy_samples)
self._centers = kmeans.cluster_centers_
self._centers[:, 0] *= sigma_L
labels = kmeans.predict(Labxy)
self._labels = labels.reshape(image.shape[:2])
def _runImp(self):
image = self._scene.image()
I_32F = to32F(rgb2gray(rgb(image)))
N_32F, D_32F = estimateNormal(I_32F)
self._scene.setNormal(N_32F)
self._scene.setDepth(D_32F)
self._scene.setDisplayMode(Scene.DisplayNormal)
def _strokeEdited(self, stroke_sets):
image = self._scene.image()
C_32F = to32F(rgb(image))
for stroke_set in stroke_sets.strokeSets():
for stroke in stroke_set.strokes():
if stroke.empty():
continue
mask = np.zeros(image.shape[:2], dtype=np.uint8)
points = stroke.points()
points = np.int32(points)
brush_size = int(stroke.brushSize())
cv2.polylines(mask, [points], 0, 255, brush_size)
Cs = C_32F[mask > 0, :]
Is = np.arange(len(Cs), dtype=np.float32)
Is = np.array(Is)
Cs = np.array(Cs)
M = ColorMapEstimation(Cs, Is, num_samples=1000)
M_img = M.mapImage(image_size=(256, 32))
def plotFunc():
plt.imshow(M_img)
self._matplot_view.drawPlots(plotFunc)
saveImage(self._currentColorMapFile(), M_img)
self._newColorMapID()
def __init__(self, I, radius=5, epsilon=0.4):
radius = int(radius)
I_32F = to32F(I)
if _isGray(I):
self._guided_filter = GuidedFilterGray(I_32F, radius, epsilon)
else:
self._guided_filter = GuidedFilterColor(I_32F, radius, epsilon)
def baseDetailSeparationMedian(I_32F, ksize=5):
ksize = 2 * (ksize / 2) + 1
B = to32F(cv2.medianBlur(to8U(I_32F), ksize))
D = I_32F - B
return B, D
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 slicSegmentation(image, num_segments=600, sigma=5):
C_8U = rgb(image)
C_32F = to32F(C_8U)
segments = slic(C_32F, n_segments=num_segments, sigma=sigma)
fig = plt.figure("Superpixels -- %d segments" % (num_segments))
ax = fig.add_subplot(1, 1, 1)
ax.imshow(mark_boundaries(C_32F, segments))
plt.axis("off")
plt.show()
def filter(self, p):
p_32F = to32F(p)
if _isGray(p_32F):
return self._filterGray(p_32F)
cs = p.shape[2]
q = np.array(p_32F)
for ci in range(cs):
q[:, :, ci] = self._filterGray(p_32F[:, :, ci])
return q
def HSVFeatures(image, w_H=1.0, w_S=1.0, w_V=1.0):
img_32F = to32F(image)
rgb_32F = img_32F[:, :, :3]
hsv_32F = rgb2hsv(rgb_32F)
h, w, cs = image.shape
features = hsv_32F.reshape(h * w, -1)
features[:, 0] *= w_H
features[:, 1] *= w_S
features[:, 2] *= w_V
return features
def LabFeatures(image, w_L=1.0, w_a=1.0, w_b=1.0):
img_32F = to32F(image)
rgb_32F = img_32F[:, :, :3]
Lab_32F = rgb2Lab(rgb_32F)
h, w, cs = image.shape
features = Lab_32F.reshape(h * w, -1)
features[:, 0] *= w_L
features[:, 1] *= w_a
features[:, 2] *= w_b
return features
def colorToNormal(C_8U, fill_background=False):
rgb_8U = rgb(C_8U)
A_8U = alpha(C_8U)
C_32F = to32F(rgb_8U)
N_32F = 2.0 * C_32F - 1.0
if fill_background:
N_32F[A_8U < 10, :] = np.array([0.0, 0.0, 0.0])
N_32F_normalized = normalizeImage(N_32F)
return N_32F_normalized
def __init__(self, I, radius=5, epsilon=0.4, scale=4):
I_32F = to32F(I)
self._I = I_32F
h, w = I.shape[:2]
I_sub = _downSample(I_32F, scale)
self._I_sub = I_sub
radius = int(radius / scale)
if _isGray(I):
self._guided_filter = GuidedFilterGray(I_sub, radius, epsilon)
else:
self._guided_filter = GuidedFilterColor(I_sub, radius, epsilon)
def filter(self, p):
p_32F = to32F(p)
shape_original = p.shape[:2]
p_sub = _downSample(p_32F, shape=self._I_sub.shape[:2])
if _isGray(p_sub):
return self._filterGray(p_sub, shape_original)
cs = p.shape[2]
q = np.array(p_32F)
for ci in range(cs):
q[:, :, ci] = self._filterGray(p_sub[:, :, ci], shape_original)
return q
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)
self._C0_32F = C0_32F
self._loadImage()
def _runLayer(self, layer_file):
if layer_file is None:
return
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
C0_8U = loadRGB(layer_file)
if C0_8U is None:
return
h, w = C0_8U.shape[:2]
w_low = 1024
h_low = w_low * h / w
# C0_8U = cv2.resize(C0_8U, (w_low, h_low))
A_8U = alpha(C0_8U)
self._A_8U = A_8U
C0_32F = to32F(rgb(C0_8U))
if A_8U is not None:
C0_32F[A_8U < 0.9 * np.max(A_8U), :] = np.array([0, 0, 0])
self._C0_32F = C0_32F
self._loadImage()
self._computeBaseDetalSeparation()
self._computeInitialDetailNormal()
self.computeDetailNormal()
self._computeLumoNormal()
self.computeInitialNormal()
# plt.savefig(self.characterResultFile("BumpNormal.png"))
if self._N_b_smooth is not None:
self.cleanCharacterResultDir()
saveNormal(self.characterResultFile("N_b.png"), self._N_b, A_8U)
saveNormal(self.characterResultFile("N_b_smooth.png"), self._N_b_smooth, A_8U)
saveNormal(self.characterResultFile("N_d.png"), self._N_d, A_8U)
saveNormal(self.characterResultFile("N_d_smooth.png"), self._N_d_smooth, A_8U)
saveNormal(self.characterResultFile("N_lumo.png"), self._N_lumo, A_8U)
saveNormal(self.characterResultFile("N0_b.png"), self._N0_b_32F, A_8U)
saveNormal(self.characterResultFile("N0_d.png"), self._N0_d_32F, A_8U)
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 = LambertShader().diffuseShading(L, N0_32F)
self._normal_constraint.clear()
if os.path.exists(self.constraintFile()):
self._normal_constraint.load(self.constraintFile())
self._tool.setImage(setAlpha(C0_32F, A_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
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 _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 _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 stylizedShadingFigure():
target_shapes = ["Man", "Ogre", "Grog", "Vase"]
target_shapes = [shapeFile(shape_name) for shape_name in target_shapes]
target_colormaps = [1, 5, 10, 3]
target_colormaps = [colorMapFile(cmap_id) for cmap_id in target_colormaps]
fig, axes = plt.subplots(figsize=(12, 4))
font_size = 15
fig.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, hspace=0.1, wspace=0.1)
fig.suptitle("", fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
Ls = []
Ls.append(normalizeVector(np.array([-0.5, 0.3, 0.7])))
Ls.append(normalizeVector(np.array([0.2, -0.35, 0.4])))
Ls.append(normalizeVector(np.array([-0.2, 0.6, 0.3])))
Ls.append(normalizeVector(np.array([-0.2, 0.6, 0.3])))
for shape_file, colormap_file, L in zip(target_shapes, target_colormaps, Ls):
N_32F, A_8U = loadNormal(shape_file)
M_32F = loadColorMap(colormap_file)
C_32F = ColorMapShader(M_32F).diffuseShading(L, N_32F)
# C_32F = trim(C_32F, A_8U)
# A_8U = trim(A_8U, A_8U)
C_32F = setAlpha(C_32F, to32F(A_8U))
# h, w = C_32F.shape[:2]
#
# h_t = 512
# w_t = w * h_t / h
# C_32F = cv2.resize(C_32F, (w_t, h_t))
plot_grid.showImage(C_32F, "", alpha_clip=True)
file_path = os.path.join(shapeResultsDir(), "StylizedShading.png")
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)
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
#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)
def _runCharacterImp(self):
print self.fullLayerFile()
C0_8U = loadRGBA(self.fullLayerFile())
if C0_8U is None:
return
C0_32F = to32F(C0_8U)
print C0_32F
h, w = C0_32F.shape[:2]
w_low = 512
h_low = w_low * h / w
C0_32F = cv2.resize(C0_32F, (w_low, h_low))
self._normal_constraint.clear()
if os.path.exists(self.characterConstraintFile()):
self._normal_constraint.load(self.characterConstraintFile())
self._tool.setImage(C0_32F)
def slicSampling(image, num_segments=4000, sigma=5):
h, w = image.shape[:2]
C_32F = to32F(rgb(image))
segments = slic(C_32F, n_segments=num_segments, sigma=sigma)
print segments.shape
print np.max(segments)
num_centers = np.max(segments) + 1
hist_centers = np.zeros((num_centers), dtype=np.float32)
centers = np.zeros((num_centers, 3), dtype=np.float32)
hist_centers[segments[:, :]] += 1.0
centers[segments[:, :], :] += C_32F[:, :, :3]
hist_positive = hist_centers > 0.0
print np.count_nonzero(hist_positive)
for ci in xrange(3):
centers[hist_positive, ci] /= hist_centers[hist_positive]
map_rgb = to8U(centers[segments[:, :], :].reshape(h, w, -1))
# map_rgb = to8U(mark_boundaries(C_32F, segments))
map_image = setAlpha(map_rgb, alpha(image))
return map_image
def relightingFigure(shape_name="Vase", cmap_id=3):
num_methods = 3
num_lights = 2
num_rows = num_lights + 1
num_cols = num_methods + 2
w = 15
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)
Lg = normalizeVector(np.array([-0.2, 0.3, 0.5]))
Lg_img = lightSphere(Lg)
L1 = normalizeVector(np.array([0.0, 0.7, 0.6]))
L2 = normalizeVector(np.array([0.3, 0.5, 0.6]))
# Ls = [normalizeVector(Lg * (1.0 - t) + t * L1) for t in np.linspace(0.0, 1.0, num_lights) ]
Ls = [L1, L2]
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(Lg, Ng_32F)
toon_sfs = ToonSFS(Lg, C0_32F, A_8U)
toon_sfs.setInitialNormal(N0_32F)
toon_sfs.setNumIterations(iterations=50)
toon_sfs.setWeights(w_lap=0.2)
toon_sfs.run()
N_toon = toon_sfs.normal()
C_toon = toon_sfs.shading()
C_lumo, N_lumo = lumoSFS(C0_32F, Lg, N0_32F, A_8U)
C_wu, N_wu = wuSFS(C0_32F, Lg, N0_32F, A_8U)
M_lumo = estimatedReflectance(C0_32F, Lg, N_lumo, A_8U)
M_wu = estimatedReflectance(C0_32F, Lg, N_wu, A_8U)
plot_grid.showImage(Lg_img, "Light direction")
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)
for L in Ls:
C1 = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
C1_lumo = M_lumo.shading(LdotN(L,
N_lumo).flatten()).reshape(C0_32F.shape)
C1_wu = M_wu.shading(LdotN(L, N_wu).flatten()).reshape(C0_32F.shape)
C1_toon = toon_sfs.relighting(L)
plot_grid.showImage(lightSphere(L), "")
plot_grid.showImage(setAlpha(C1, to32F(A_8U)), "")
title = ""
plot_grid.showImage(setAlpha(C1_lumo, to32F(A_8U)), "")
#plot_grid.showColorMap(C_error_lumo, title, v_min=0, v_max=0.1, with_colorbar=True)
plot_grid.showImage(setAlpha(C1_wu, to32F(A_8U)), "")
#plot_grid.showColorMap(C_error_wu, title, v_min=0, v_max=0.1, with_colorbar=True)
plot_grid.showImage(setAlpha(C1_toon, to32F(A_8U)), "")
# showMaximize()
file_path = shapeResultFile("Relighting",
"RelightingComparison",
file_ext=".png")
fig.savefig(file_path, transparent=True)
def relightingVideo(shape_name="Ogre", cmap_id=3):
num_methods = 3
num_rows = 1
num_cols = num_methods + 2
num_lights = 120
w = 10
h = 5
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("Shading Analysis", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
Lg = normalizeVector(np.array([-0.2, 0.3, 0.5]))
Lg_img = lightSphere(Lg)
L1 = normalizeVector(np.array([0.5, 0.5, 0.6]))
Ls = [
normalizeVector(Lg * (1.0 - t) + t * L1)
for t in np.linspace(0.0, 1.0, num_lights)
]
# Ls = [normalizeVector(Lg + 1.0 * np.cos(t) * np.array([1, 0, 0]) + 1.0 * np.sin(t) * np.array([0, 1, 0])) for t in np.linspace(0.0, 1.0, num_lights) ]
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(Lg, Ng_32F)
toon_sfs = ToonSFS(Lg, C0_32F, A_8U)
toon_sfs.setInitialNormal(N0_32F)
toon_sfs.setNumIterations(iterations=100)
toon_sfs.setWeights(w_lap=0.2)
toon_sfs.run()
N_toon = toon_sfs.normal()
C_toon = toon_sfs.shading()
C_lumo, N_lumo = lumoSFS(C0_32F, Lg, N0_32F, A_8U)
C_wu, N_wu = wuSFS(C0_32F, Lg, N0_32F, A_8U)
M_lumo = estimatedReflectance(C0_32F, Lg, N_lumo, A_8U)
M_wu = estimatedReflectance(C0_32F, Lg, N_wu, A_8U)
plot_grid.showImage(Lg_img, "Light direction")
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)
images = []
for i in xrange(48):
images.append(figure2numpy(fig))
for li, L in enumerate(Ls):
print li
fig.clear()
fig.suptitle("Relighting", fontsize=font_size)
plot_grid.setPlot(1, 1)
C1 = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
C1_lumo = M_lumo.shading(LdotN(L,
N_lumo).flatten()).reshape(C0_32F.shape)
C1_wu = M_wu.shading(LdotN(L, N_wu).flatten()).reshape(C0_32F.shape)
C1_toon = toon_sfs.relighting(L)
plot_grid.showImage(lightSphere(L), "Light direction")
plot_grid.showImage(setAlpha(C1, to32F(A_8U)), "Ground-truth")
title = ""
plot_grid.showImage(setAlpha(C1_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(C1_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(C1_toon, to32F(A_8U)), "Our result")
images.append(figure2numpy(fig))
file_path = shapeResultFile("Relighting",
"Relighting_%s_%s" % (shape_name, cmap_id),
file_ext=".wmv")
saveVideo(file_path, images)
def rgbFeatures(image):
img_32F = to32F(image)
features = img_32F[:, :, :3].reshape(-1, 3)
return features
def _computeSegmentaiton(self, C0_8U):
C_32F = to32F(rgb(C0_8U))
label0 = np.zeros(C_32F.shape[:3], dtype=np.uint8)
colors = []
for stroke_set in self._stroke_sets.strokeSets():
print stroke_set.color()
color = np.array(stroke_set.color())[:3]
colors.append(color)
color = np.int32(255 * color)
print type(color[0])
for stroke in stroke_set.strokes():
if stroke.empty():
continue
points = stroke.points()
points = np.int32(points)
brush_size = int(stroke.brushSize())
print color
cv2.polylines(label0, [points], 0, (color[0], color[1], color[2]), brush_size)
colors = np.array(colors)
# h, w = label0.shape[:2]
#
# w_low = 512
# h_low = w_low * h / w
#
# gauide_filter = GuidedFilter(cv2.resize(C_32F, (w_low, h_low)), radius=11, epsilon=0.05)
#
# label0 = cv2.resize(label0, (w_low, h_low))
# h, w = label0.shape[:2]
# label = np.array(label0)
#
# dc = np.zeros((len(colors), h * w), dtype=np.float32)
#
# for i in xrange(5):
# label = gauide_filter.filter(label)
# label[label0 > 0] = label0[label0 > 0]
#
# label_flat = label.reshape(-1, 3)
#
# for ci, color in enumerate(colors):
# dc[ci, :] = normVectors(label_flat - color)
#
# centers = np.argmin(dc, axis=0)
#
# label_flat = colors[centers]
# label = label_flat.reshape(h, w, 3)
# kmeans = KMeans(C0_8U, num_colors=20)
# centerImage = kmeans.centerImage()
# self._view.render(centerImage)
#
# histImage = kmeans.histImage()
#
# plt.imshow(histImage)
# plt.show()
edgeSeg = EdgeBasedSegmentaiton(C0_8U)
labels = edgeSeg.labels()
plt.imshow(labels)
plt.show()
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 _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)
