def colorMapFigure():
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
N_32F, A_32F = normalSphere(h=512, w=512)
fig, axes = plt.subplots(figsize=(6, 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 = 4
num_cols = 6
plot_grid = SubplotGrid(num_rows, num_cols)
for colormap_file in colorMapFiles():
M_32F = loadColorMap(colormap_file)
C_32F = ColorMapShader(M_32F).diffuseShading(L, N_32F)
plot_grid.showImage(setAlpha(C_32F, A_32F), "")
file_path = os.path.join(colorMapResultsDir(), "ColorMapMaterials.png")
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 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 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 materialErrorTable():
shape_name = "ThreeBox"
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
num_materials = len(colorMapFiles())
C_errors = np.zeros((num_materials, 3))
N_errors = np.zeros((num_materials, 3))
I_errors = np.zeros((num_materials, 3))
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)
for mi, color_map_file in enumerate(colorMapFiles()):
M_32F = loadColorMap(color_map_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=20)
toon_sfs.setWeights(w_lap=9.0)
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)
C_errors[mi, 0] = np.mean(C_error_toon)
C_errors[mi, 1] = np.mean(C_error_lumo)
C_errors[mi, 2] = np.mean(C_error_wu)
I_errors[mi, 0] = np.mean(I_error_toon)
I_errors[mi, 1] = np.mean(I_error_lumo)
I_errors[mi, 2] = np.mean(I_error_wu)
N_errors[mi, 0] = np.mean(N_error_toon)
N_errors[mi, 1] = np.mean(N_error_lumo)
N_errors[mi, 2] = np.mean(N_error_wu)
file_path = shapeResultFile("ShapeEstimation", "MaterialShadingError", file_ext=".npy")
np.save(file_path, C_errors)
file_path = shapeResultFile("ShapeEstimation", "MaterialNormalError", file_ext=".npy")
np.save(file_path, N_errors)
file_path = shapeResultFile("ShapeEstimation", "MaterialIlluminationError", file_ext=".npy")
np.save(file_path, I_errors)
def lightEstimation(I_32F, N_32F, A_8U=None):
I = I_32F.flatten()
N = N_32F.reshape(-1, 3)
if A_8U is not None:
I = I_32F[A_8U > 0.5 * np.max(A_8U)]
N = N_32F[A_8U > 0.5 * np.max(A_8U), :]
num_samples = 2000
sample_ids = np.random.randint(len(I) - 1, size=num_samples)
I = I[sample_ids]
N = N[sample_ids]
I_avg = np.average(I)
N_avg = np.average(N, axis=0)
#I = I - 0.8 * I_avg
#N = N - 0.8 * N_avg
A = np.dot(N.T, N)
b = np.dot(N.T, I)
L = np.linalg.solve(A, b)
L = normalizeVector(L)
return L
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 lightSphere(L, h=256, w=256):
L = normalizeVector(L)
N_32F, A_32F = normalSphere(h, w)
I_32F = diffuse(N_32F, L)
I_32F = I_32F * A_32F
return I_32F
def LdotN(L, N_32F):
L = normalizeVector(L)
h, w = N_32F.shape[:2]
N_flat = N_32F.reshape((-1, 3))
LdN_flat = np.dot(N_flat, L)
LdN = LdN_flat.reshape(h, w)
return LdN
def lightEstimationByVoting(W_32F, N_32F, A_8U=None):
W = W_32F.flatten()
N = N_32F.reshape(-1, 3)
if A_8U is not None:
W = W_32F[A_8U > 0.9 * np.max(A_8U)]
N = N_32F[A_8U > 0.9 * np.max(A_8U), :]
num_samples = 2000
sample_ids = np.random.randint(len(W) - 1, size=num_samples)
W = W[sample_ids]
N = N[sample_ids, :]
N_min = np.min(N[:, 2])
N_max = np.max(N[:, 2])
xy_samples = N[:, 2] > N_min + 0.1 * (N_max - N_min)
W = W[xy_samples]
N = N[xy_samples, :]
xy_samples = N[:, 2] < N_min + 0.3 * (N_max - N_min)
W = W[xy_samples]
N = N[xy_samples, :]
print np.min(W), np.max(W)
W /= np.max(W)
W = W**1.0
L = np.dot(N.T, W)
L = normalizeVector(L)
return L
def lightEstimation(I_32F, N_32F, A_8U=None):
I = I_32F.flatten()
N = N_32F.reshape(-1, 3)
if A_8U is not None:
I = I_32F[A_8U > 0.5 * np.max(A_8U)]
N = N_32F[A_8U > 0.5 * np.max(A_8U), :]
num_samples = 2000
sample_ids = np.random.randint(len(I) - 1, size=num_samples)
I = I[sample_ids]
N = N[sample_ids]
I_avg = np.average(I)
N_avg = np.average(N, axis=0)
#I = I - 0.8 * I_avg
#N = N - 0.8 * N_avg
A = np.dot(N.T, N)
b = np.dot(N.T, I)
L = np.linalg.solve(A, b)
L = normalizeVector(L)
return L
def lightEstimationByVoting(W_32F, N_32F, A_8U=None):
W = W_32F.flatten()
N = N_32F.reshape(-1, 3)
if A_8U is not None:
W = W_32F[A_8U > 0.9 * np.max(A_8U)]
N = N_32F[A_8U > 0.9 * np.max(A_8U), :]
num_samples = 2000
sample_ids = np.random.randint(len(W) - 1, size=num_samples)
W = W[sample_ids]
N = N[sample_ids, :]
N_min = np.min(N[:, 2])
N_max = np.max(N[:, 2])
xy_samples = N[:, 2] > N_min + 0.1 * (N_max - N_min)
W = W[xy_samples]
N = N[xy_samples, :]
xy_samples = N[:, 2] < N_min + 0.3 * (N_max - N_min)
W = W[xy_samples]
N = N[xy_samples, :]
print np.min(W), np.max(W)
W /= np.max(W)
W = W ** 1.0
L = np.dot(N.T, W)
L = normalizeVector(L)
return L
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 _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 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 diffuse(N_32F, L):
L = normalizeVector(L)
h, w, cs = N_32F.shape
N_flat = N_32F.reshape((-1, 3))
I_flat = np.dot(N_flat, L)
I_32F = I_flat.reshape((h, w))
I_32F = np.clip(I_32F, 0.0, 1.0)
return np.float32(I_32F)
def _lightAnimation(self):
ts = np.linspace(0.0, 1.0, 120)
L_start = np.array([-0.6, 0.3, 0.7])
L_end = np.array([0.6, 0.2, 0.7])
Ls = []
for t in ts:
L = normalizeVector((1.0 - t) * L_start + t * L_end)
Ls.append(L)
return Ls
def _lightAnimation(self):
ts = np.linspace(0.0, 1.0, 120)
L_start = np.array([-0.6, 0.3, 0.7])
L_end = np.array([0.6, 0.2, 0.7])
Ls = []
for t in ts:
L = normalizeVector((1.0 - t) * L_start + t * L_end)
Ls.append(L)
return Ls
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 lightSphereWithBG(L, h=256, w=256, bg_color=np.array([1.0, 0.0, 0.0])):
L = normalizeVector(L)
N_32F, A_32F = normalSphere(h, w)
I_32F = diffuse(N_32F, L)
C_32F = np.zeros(N_32F.shape)
C_32F[:, :] = bg_color
for ci in xrange(3):
C_32F[:, :, ci] = I_32F * A_32F + (1.0 - A_32F) * C_32F[:, :, ci]
return np.clip(C_32F, 0.0, 1.0)
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 computeLambertShadingError():
shape_names = shapeNames()
L = normalizeVector(np.array([-0.2, 0.3, 0.6]))
C_errors = np.zeros(len(shape_names))
N_errors = np.zeros(len(shape_names))
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=60)
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_errors[si] = np.average(C_error[A_8U > 0.5 * np.max(A_8U)])
h, w = A_8U.shape[:2]
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_errors[si] = np.average(N_error[A_8U > 0.5 * np.max(A_8U)])
file_path = shapeResultFile("ShapeEstimation",
"ShadingError",
file_ext=".npy")
np.save(file_path, C_errors)
file_path = shapeResultFile("ShapeEstimation",
"NormalError",
file_ext=".npy")
np.save(file_path, N_errors)
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 _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 _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 _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 _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 colorMapFigure():
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
N_32F, A_32F = normalSphere(h=512, w=512)
fig, axes = plt.subplots(figsize=(6, 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 = 4
num_cols = 6
plot_grid = SubplotGrid(num_rows, num_cols)
for colormap_file in colorMapFiles():
M_32F = loadColorMap(colormap_file)
C_32F = ColorMapShader(M_32F).diffuseShading(L, N_32F)
plot_grid.showImage(setAlpha(C_32F, A_32F), "")
file_path = os.path.join(colorMapResultsDir(), "ColorMapMaterials.png")
fig.savefig(file_path, transparent=True)
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 computeLambertShadingError():
shape_names = shapeNames()
L = normalizeVector(np.array([-0.2, 0.3, 0.6]))
C_errors = np.zeros(len(shape_names))
N_errors = np.zeros(len(shape_names))
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=60)
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_errors[si] = np.average(C_error[A_8U > 0.5 * np.max(A_8U)])
h, w = A_8U.shape[:2]
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_errors[si] = np.average(N_error[A_8U > 0.5 * np.max(A_8U)])
file_path = shapeResultFile("ShapeEstimation", "ShadingError", file_ext=".npy")
np.save(file_path, C_errors)
file_path = shapeResultFile("ShapeEstimation", "NormalError", file_ext=".npy")
np.save(file_path, N_errors)
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
#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 _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 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 _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 diffuseTerm(self, L, N_32F):
L = normalizeVector(L)
LdN = LdotN(L, N_32F)
I_32F = np.clip(LdN, 0.0, 1.0)
return np.float32(I_32F)
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 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 reflectanceEstimationFigure():
errorTable()
M_errors = loadReflectanceErrorTable()
M_error_orders = np.argsort(M_errors)
print M_errors[M_error_orders]
colormap_files = colorMapFiles()
colormap_files = [colormap_files[M_error_order] for M_error_order in M_error_orders]
colormap_files = colormap_files[0:-1:3]
Ms = []
MLs = []
for colormap_file in colormap_files:
M_32F = loadColorMap(colormap_file)
Ms.append(M_32F)
C_32F = M_32F.reshape(1, len(M_32F), 3)
I_32F = np.linspace(0.0, 1.0, len(M_32F))
I_32F = I_32F.reshape(C_32F.shape[:2])
reflectance = LambertReflectanceEstimation(C_32F, I_32F)
Ml = reflectance.shading(I_32F)
MLs.append(Ml[0, :, :])
num_rows = 3
num_cols = len(colormap_files)
w = 10
h = w * num_rows / num_cols
fig, axes = plt.subplots(figsize=(w, h))
font_size = 15
fig.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, hspace=0.05, wspace=0.05)
fig.suptitle("", fontsize=font_size)
N_sphere, A_32F = normalSphere(h=512, w=512)
Lg = normalizeVector(np.array([-0.2, 0.3, 0.6]))
plot_grid = SubplotGrid(num_rows, num_cols)
mi = 1
for M, Ml in zip(Ms, MLs):
CM_32F = ColorMapShader(M).diffuseShading(Lg, N_sphere)
CL_32F = ColorMapShader(Ml).diffuseShading(Lg, N_sphere)
C_error = normVectors((CM_32F - CL_32F).reshape(-1, 3)).reshape(CL_32F.shape[:2])
C_error[A_32F < 0.5 * np.max(A_32F)] = 0.0
plot_grid.setPlot(1, mi)
plot_grid.showImage(setAlpha(CM_32F, A_32F), "")
plot_grid.setPlot(2, mi)
plot_grid.showImage(setAlpha(CL_32F, A_32F), "")
plot_grid.setPlot(3, mi)
plot_grid.showColorMap(C_error, "", v_min=0.0, v_max=0.3, with_colorbar=False)
mi += 1
file_path = shapeResultFile("ReflectanceEstimation", "ReflectanceEstimationError")
fig.savefig(file_path, transparent=True)
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
A_32F = to32F(A_8U)
N0_32F = trim(N0_32F, A_8U)
A_32F = trim(A_32F, A_8U)
A_8U = trim(A_8U, A_8U)
L = normalizeVector(np.array([0.5, -0.2, 0.7]))
C0_32F = ToonShader().diffuseShading(L, N0_32F)
I0_32F = luminance(C0_32F)
I_min, I_max = np.min(I0_32F), np.max(I0_32F)
I_scale = (I0_32F - I_min) / (I_max - I_min)
I_L = cv2.Laplacian(cv2.GaussianBlur(I_scale, (0, 0), 31.0), cv2.CV_32F, ksize=1)
I_L_avg = np.average(np.abs(I_L))
Ix = cv2.Sobel(I0_32F, cv2.CV_64F, 1, 0, ksize=1)
Ix = cv2.GaussianBlur(Ix, (0, 0), 3.0)
Ixx = cv2.Sobel(Ix, cv2.CV_64F, 1, 0, ksize=1)
Ixx = cv2.GaussianBlur(Ixx, (0, 0), 5.0)
Iy = -cv2.Sobel(I0_32F, cv2.CV_64F, 0, 1, ksize=1)
Iy = cv2.GaussianBlur(Iy, (0, 0), 3.0)
Iyy = -cv2.Sobel(Iy, cv2.CV_64F, 0, 1, ksize=1)
Iyy = cv2.GaussianBlur(Iyy, (0, 0), 5.0)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 2
num_cols = 5
plot_grid = SubplotGrid(num_rows, num_cols)
Nx = cv2.Sobel(N0_32F[:, :, 0], cv2.CV_64F, 1, 0, ksize=1)
Nx = cv2.GaussianBlur(Nx, (0, 0), 3.0)
Nxx = cv2.Sobel(Nx, cv2.CV_64F, 1, 0, ksize=1)
Nxx = cv2.GaussianBlur(Nxx, (0, 0), 5.0)
Ny = -cv2.Sobel(N0_32F[:, :, 1], cv2.CV_64F, 0, 1, ksize=1)
Ny = cv2.GaussianBlur(Ny, (0, 0), 3.0)
Nyy = -cv2.Sobel(Ny, cv2.CV_64F, 0, 1, ksize=1)
Nyy = cv2.GaussianBlur(Nyy, (0, 0), 5.0)
Nz_L = cv2.Laplacian(cv2.GaussianBlur(N0_32F[:, :, 2], (0, 0), 5.0), cv2.CV_32F, ksize=5)
Nz_L_avg = np.average(np.abs(Nz_L))
Nz_L *= 1.0 / Nz_L_avg
I_L *= 1.0 / I_L_avg
print I_L_avg, Nz_L_avg
Nz_L = np.clip(Nz_L, -5.0, 5.0)
I_L = np.clip(I_L, -5.0, 5.0)
plot_grid.showColorMap(N0_32F[:, :, 0], r'$N_{x}$', v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(N0_32F[:, :, 1], r'$N_{y}$', v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Nx, r'$N_{xx}$', v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Ny, r'$N_{yy}$', v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Nz_L, r'$Nz_L$')
#plot_grid.showColorMap(Nx + Ny, r'$N_{xx} + N_{yy}$', v_min=-0.01, v_max=0.01)
# Ixx[Ixx>0] = 1.0
# Ixx[Ixx<0] = -1.0
# Iyy[Iyy>0] = 1.0
# Iyy[Iyy<0] = -1.0
plot_grid.showColorMap(-Ix, r'$I_{x}$', v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Iy, r'$I_{y}$', v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Ixx, r'$I_{xx}$', v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Iyy, r'$I_{yy}$', v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(I_L, r'$I_L$')
#plot_grid.showColorMap(-Ixx - Iyy, r'$I_{xx} + I_{yy}$', v_min=-0.01, v_max=0.01)
#plot_grid.showColorMap(Iy, r'$I_{y}$')
showMaximize()
def methodComparisonFigure(shape_name="ThreeBox", cmap_id=10):
num_methods = 3
num_rows = 3
num_cols = 2 * num_methods + 1
w = 20
h = w * num_rows / num_cols
fig, axes = plt.subplots(figsize=(w, h))
font_size = 15
fig.subplots_adjust(left=0.02,
right=0.98,
top=0.96,
bottom=0.04,
hspace=0.15,
wspace=0.1)
fig.suptitle("", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
Ng_data = shapeFile(shape_name)
Ng_data = loadNormal(Ng_data)
Ng_32F, A_8U = Ng_data
N0_file = shapeResultFile(result_name="InitialNormal",
data_name=shape_name)
N0_data = loadNormal(N0_file)
N0_32F, A_8U = N0_data
A_8U = cv2.bilateralFilter(A_8U, 0, 5, 2)
colormap_file = colorMapFile(cmap_id)
M_32F = loadColorMap(colormap_file)
C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
toon_sfs = ToonSFS(L, C0_32F, A_8U)
toon_sfs.setInitialNormal(N0_32F)
toon_sfs.setNumIterations(iterations=100)
toon_sfs.setWeights(w_lap=0.1)
toon_sfs.run()
N_toon = toon_sfs.normal()
C_toon = toon_sfs.shading()
C_lumo, N_lumo = lumoSFS(C0_32F, L, N0_32F, A_8U)
C_wu, N_wu = wuSFS(C0_32F, L, N0_32F, A_8U)
C_error_toon, N_error_toon, I_error_toon = computeErrors(
L, C0_32F, C_toon, Ng_32F, N_toon, A_8U)
C_error_lumo, N_error_lumo, I_error_lumo = computeErrors(
L, C0_32F, C_lumo, Ng_32F, N_lumo, A_8U)
C_error_wu, N_error_wu, I_error_wu = computeErrors(L, C0_32F, C_wu, Ng_32F,
N_wu, A_8U)
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), "Ground-truth")
title = ""
plot_grid.showImage(setAlpha(C_lumo, to32F(A_8U)), "Lumo")
plot_grid.showColorMap(C_error_lumo,
title,
v_min=0,
v_max=0.1,
with_colorbar=True)
plot_grid.showImage(setAlpha(C_wu, to32F(A_8U)), "Lambert assumption")
plot_grid.showColorMap(C_error_wu,
title,
v_min=0,
v_max=0.1,
with_colorbar=True)
plot_grid.showImage(setAlpha(C_toon, to32F(A_8U)), "Our result")
plot_grid.showColorMap(C_error_toon,
title,
v_min=0,
v_max=0.1,
with_colorbar=True)
plot_grid.showImage(normalToColor(Ng_32F, A_8U), title)
plot_grid.showImage(normalToColor(N_lumo, A_8U), title)
plot_grid.showColorMap(N_error_lumo,
title,
v_min=0,
v_max=50.0,
with_colorbar=True)
plot_grid.showImage(normalToColor(N_wu, A_8U), title)
plot_grid.showColorMap(N_error_wu,
title,
v_min=0,
v_max=50.0,
with_colorbar=True)
plot_grid.showImage(normalToColor(N_toon, A_8U), title)
plot_grid.showColorMap(N_error_toon,
title,
v_min=0,
v_max=50.0,
with_colorbar=True)
plot_grid.showImage(computeIllumination(L, Ng_32F, A_8U), title)
plot_grid.showImage(computeIllumination(L, N_lumo, A_8U), title)
plot_grid.showColorMap(I_error_lumo,
title,
v_min=0,
v_max=0.2,
with_colorbar=True)
plot_grid.showImage(computeIllumination(L, N_wu, A_8U), title)
plot_grid.showColorMap(I_error_wu,
title,
v_min=0,
v_max=0.2,
with_colorbar=True)
plot_grid.showImage(computeIllumination(L, N_toon, A_8U), title)
plot_grid.showColorMap(I_error_toon,
title,
v_min=0,
v_max=0.2,
with_colorbar=True)
# showMaximize()
file_path = shapeResultFile("ShapeEstimation",
"Comparison_%s_%s" % (shape_name, cmap_id),
file_ext=".png")
fig.savefig(file_path, transparent=True)
def shapeErrorTable():
cmap_id = 10
colormap_file = colorMapFile(cmap_id)
M_32F = loadColorMap(colormap_file)
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
num_shapes = len(shapeNames())
C_errors = np.zeros((num_shapes, 3))
N_errors = np.zeros((num_shapes, 3))
I_errors = np.zeros((num_shapes, 3))
for si, shape_name in enumerate(shapeNames()):
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)
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=20)
toon_sfs.setWeights(w_lap=9.0)
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)
C_errors[si, 0] = np.mean(C_error_toon)
C_errors[si, 1] = np.mean(C_error_lumo)
C_errors[si, 2] = np.mean(C_error_wu)
I_errors[si, 0] = np.mean(I_error_toon)
I_errors[si, 1] = np.mean(I_error_lumo)
I_errors[si, 2] = np.mean(I_error_wu)
N_errors[si, 0] = np.mean(N_error_toon)
N_errors[si, 1] = np.mean(N_error_lumo)
N_errors[si, 2] = np.mean(N_error_wu)
file_path = shapeResultFile("ShapeEstimation",
"ShapeShadingError",
file_ext=".npy")
np.save(file_path, C_errors)
file_path = shapeResultFile("ShapeEstimation",
"ShapeNormalError",
file_ext=".npy")
np.save(file_path, N_errors)
file_path = shapeResultFile("ShapeEstimation",
"ShapeIlluminationErrorShape",
file_ext=".npy")
np.save(file_path, I_errors)
showShapeErrorTable()
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 materialShapeVariationFigure():
target_colormaps = [23, 3, 12]
#target_colormaps = [3, 17]
colormap_files = [colorMapFile(cmap_id) for cmap_id in target_colormaps]
target_shapes = ["Blob1", "ThreeBox"]
shape_names = target_shapes
#shape_names = shapeNames()[4:5]
num_shapes = len(shape_names)
num_colormaps = len(colormap_files)
num_rows = num_colormaps
num_cols = 6
w = 20
h = w * num_rows / num_cols
fig, axes = plt.subplots(figsize=(w, h))
font_size = 15
fig.subplots_adjust(left=0.02,
right=0.98,
top=0.96,
bottom=0.02,
hspace=0.15,
wspace=0.1)
fig.suptitle("", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
L = normalizeVector(np.array([-0.4, 0.5, 0.6]))
shape_name = "Blob1"
Ng_data = shapeFile(shape_name)
Ng_data = loadNormal(Ng_data)
Ng_32F, A_8U = Ng_data
N0_file = shapeResultFile(result_name="InitialNormal",
data_name=shape_name)
N0_data = loadNormal(N0_file)
N0_32F, A_8U = N0_data
for mi, colormap_file in enumerate(colormap_files):
M_32F = loadColorMap(colormap_file)
C0_32F = ColorMapShader(M_32F).diffuseShading(L, Ng_32F)
sfs_method = ToonSFS(L, C0_32F, A_8U)
sfs_method.setInitialNormal(N0_32F)
sfs_method.setNumIterations(iterations=40)
sfs_method.setWeights(w_lap=5.0)
sfs_method.run()
N_32F = sfs_method.normal()
C_32F = sfs_method.shading()
#C_32F = cv2.bilateralFilter(C_32F, 0, 0.1, 3)
C_error = sfs_method.shadingError()
C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
C_error = trim(C_error, A_8U)
h, w = A_8U.shape
N_error = angleErros(N_32F.reshape(-1, 3),
Ng_32F.reshape(-1, 3)).reshape(h, w)
N_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
N_error = trim(N_error, A_8U)
title = ""
if mi == 0:
title = "Ground-truth"
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title)
title = ""
if mi == 0:
title = "Our result"
plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title)
title = ""
if mi == 0:
title = "Error (shading)"
plot_grid.showColorMap(C_error,
title,
v_min=0,
v_max=0.1,
with_colorbar=True)
title = ""
if mi == 0:
title = "Ground-truth"
plot_grid.showImage(normalToColor(Ng_32F, A_8U), title)
title = ""
if mi == 0:
title = "Our result"
plot_grid.showImage(normalToColor(N_32F, A_8U), title)
title = ""
if mi == 0:
title = "Error (shape)"
plot_grid.showColorMap(N_error,
title,
v_min=0,
v_max=50.0,
with_colorbar=True)
#showMaximize()
file_path = shapeResultFile("ShapeEstimation",
"MaterialShapeEvaluation",
file_ext=".pdf")
fig.savefig(file_path, transparent=False)
def LambertShadingFigure():
target_shapes = [
"Sphere", "Cone", "Blob1", "Man", "Cone", "OctaFlower", "Pulley",
"Grog", "Lucy", "Raptor"
]
target_shapes = ["Blob1", "Pulley", "Lucy"]
shape_names = target_shapes
num_rows = len(shape_names)
num_cols = 6
w = 20
h = w * num_rows / num_cols
fig, axes = plt.subplots(figsize=(w, h))
font_size = 15
fig.subplots_adjust(left=0.02,
right=0.98,
top=0.96,
bottom=0.02,
hspace=0.15,
wspace=0.1)
fig.suptitle("", fontsize=font_size)
plot_grid = SubplotGrid(num_rows, num_cols)
L = normalizeVector(np.array([-0.2, 0.3, 0.6]))
for si, shape_name in enumerate(shape_names):
Ng_data = shapeFile(shape_name)
Ng_data = loadNormal(Ng_data)
Ng_32F, A_8U = Ng_data
N0_file = shapeResultFile(result_name="InitialNormal",
data_name=shape_name)
N0_data = loadNormal(N0_file)
N0_32F, A_8U = N0_data
C0_32F = LambertShader().diffuseShading(L, Ng_32F)
sfs_method = ToonSFS(L, C0_32F, A_8U)
sfs_method.setInitialNormal(N0_32F)
sfs_method.setNumIterations(iterations=70)
sfs_method.setWeights(w_lap=1.0)
sfs_method.run()
N_32F = sfs_method.normal()
C_32F = sfs_method.shading()
C_error = sfs_method.shadingError()
C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
C_error = trim(C_error, A_8U)
h, w = A_8U.shape
N_error = angleErros(N_32F.reshape(-1, 3),
Ng_32F.reshape(-1, 3)).reshape(h, w)
N_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
N_error = trim(N_error, A_8U)
title = ""
if si == 0:
title = "Ground-truth"
plot_grid.showImage(setAlpha(C0_32F, to32F(A_8U)), title)
title = ""
if si == 0:
title = "Our result"
plot_grid.showImage(setAlpha(C_32F, to32F(A_8U)), title)
title = ""
if si == 0:
title = "Error (shading)"
plot_grid.showColorMap(C_error,
title,
v_min=0,
v_max=0.1,
with_colorbar=True)
title = ""
if si == 0:
title = "Ground-truth"
plot_grid.showImage(normalToColor(Ng_32F, A_8U), title)
title = ""
if si == 0:
title = "Our result"
plot_grid.showImage(normalToColor(N_32F, A_8U), title)
title = ""
if si == 0:
title = "Error (shape)"
plot_grid.showColorMap(N_error,
title,
v_min=0,
v_max=30.0,
with_colorbar=True)
file_path = shapeResultFile("ShapeEstimation",
"LambertEstimationError",
file_ext=".pdf")
fig.savefig(file_path, transparent=True)
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
A_32F = to32F(A_8U)
N0_32F = trim(N0_32F, A_8U)
A_32F = trim(A_32F, A_8U)
A_8U = trim(A_8U, A_8U)
L = normalizeVector(np.array([0.5, -0.2, 0.7]))
C0_32F = ToonShader().diffuseShading(L, N0_32F)
I0_32F = luminance(C0_32F)
I_min, I_max = np.min(I0_32F), np.max(I0_32F)
I_scale = (I0_32F - I_min) / (I_max - I_min)
I_L = cv2.Laplacian(cv2.GaussianBlur(I_scale, (0, 0), 31.0), cv2.CV_32F, ksize=1)
I_L_avg = np.average(np.abs(I_L))
Ix = cv2.Sobel(I0_32F, cv2.CV_64F, 1, 0, ksize=1)
Ix = cv2.GaussianBlur(Ix, (0, 0), 3.0)
Ixx = cv2.Sobel(Ix, cv2.CV_64F, 1, 0, ksize=1)
Ixx = cv2.GaussianBlur(Ixx, (0, 0), 5.0)
Iy = -cv2.Sobel(I0_32F, cv2.CV_64F, 0, 1, ksize=1)
Iy = cv2.GaussianBlur(Iy, (0, 0), 3.0)
Iyy = -cv2.Sobel(Iy, cv2.CV_64F, 0, 1, ksize=1)
Iyy = cv2.GaussianBlur(Iyy, (0, 0), 5.0)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 2
num_cols = 5
plot_grid = SubplotGrid(num_rows, num_cols)
Nx = cv2.Sobel(N0_32F[:, :, 0], cv2.CV_64F, 1, 0, ksize=1)
Nx = cv2.GaussianBlur(Nx, (0, 0), 3.0)
Nxx = cv2.Sobel(Nx, cv2.CV_64F, 1, 0, ksize=1)
Nxx = cv2.GaussianBlur(Nxx, (0, 0), 5.0)
Ny = -cv2.Sobel(N0_32F[:, :, 1], cv2.CV_64F, 0, 1, ksize=1)
Ny = cv2.GaussianBlur(Ny, (0, 0), 3.0)
Nyy = -cv2.Sobel(Ny, cv2.CV_64F, 0, 1, ksize=1)
Nyy = cv2.GaussianBlur(Nyy, (0, 0), 5.0)
Nz_L = cv2.Laplacian(cv2.GaussianBlur(N0_32F[:, :, 2], (0, 0), 5.0), cv2.CV_32F, ksize=5)
Nz_L_avg = np.average(np.abs(Nz_L))
Nz_L *= 1.0 / Nz_L_avg
I_L *= 1.0 / I_L_avg
print I_L_avg, Nz_L_avg
Nz_L = np.clip(Nz_L, -5.0, 5.0)
I_L = np.clip(I_L, -5.0, 5.0)
plot_grid.showColorMap(N0_32F[:, :, 0], r"$N_{x}$", v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(N0_32F[:, :, 1], r"$N_{y}$", v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Nx, r"$N_{xx}$", v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Ny, r"$N_{yy}$", v_min=-0.01, v_max=0.01)
plot_grid.showColorMap(Nz_L, r"$Nz_L$")
# plot_grid.showColorMap(Nx + Ny, r'$N_{xx} + N_{yy}$', v_min=-0.01, v_max=0.01)
# Ixx[Ixx>0] = 1.0
# Ixx[Ixx<0] = -1.0
# Iyy[Iyy>0] = 1.0
# Iyy[Iyy<0] = -1.0
plot_grid.showColorMap(-Ix, r"$I_{x}$", v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Iy, r"$I_{y}$", v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Ixx, r"$I_{xx}$", v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(-Iyy, r"$I_{yy}$", v_min=-0.001, v_max=0.001)
plot_grid.showColorMap(I_L, r"$I_L$")
# plot_grid.showColorMap(-Ixx - Iyy, r'$I_{xx} + I_{yy}$', v_min=-0.01, v_max=0.01)
# plot_grid.showColorMap(Iy, r'$I_{y}$')
showMaximize()
def 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)