def _runImp(self):
image = self._scene.image()
A_8U = alpha(image)
N_32F = self._scene.normal()
if N_32F is None:
return
h_high, w_high = image.shape[:2]
w_low = min(256, w_high)
h_low = w_low * h_high / w_high
N_32F = cv2.resize(N_32F, (w_low, h_low))
if A_8U is not None:
A_8U = cv2.resize(A_8U, (w_low, h_low))
D_32F = depthFromNormal(N_32F, A_8U)
d_scale = w_high / float(w_low)
D_32F = d_scale * D_32F
D_32F = cv2.resize(D_32F, (w_high, h_high))
self._scene.setDepth(D_32F)
self._scene.setDisplayMode(Scene.DisplayDepth)
self._output_info = "(%s, %s)" %(np.min(D_32F), np.max(D_32F))
print self._output_info
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 _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 bilateralSmoothing(image):
sigma_xy = 0.1
xy = positionFeatures(image) / sigma_xy
Lab = LabFeatures(image)
foreground = foreGroundFeatures(image)
Labxy = np.concatenate((Lab, xy), axis=1)[foreground, :]
sigma_L = 1.0
Labxy[:, 0] = Labxy[:, 0] / sigma_L
Labxy_sparse = shuffle(Labxy, random_state=0)[:1000]
Lab_smooth = np.array(Lab)
smooth = 0.0
L_rbf = Rbf(Labxy_sparse[:, 0], Labxy_sparse[:, 1], Labxy_sparse[:, 2],
Labxy_sparse[:, 3], Labxy_sparse[:, 4], sigma_L * Labxy_sparse[:, 0], function='linear', smooth=smooth)
a_rbf = Rbf(Labxy_sparse[:, 0], Labxy_sparse[:, 1], Labxy_sparse[:, 2],
Labxy_sparse[:, 3], Labxy_sparse[:, 4], Labxy_sparse[:, 1], function='linear', smooth=smooth)
b_rbf = Rbf(Labxy_sparse[:, 0], Labxy_sparse[:, 1], Labxy_sparse[:, 2],
Labxy_sparse[:, 3], Labxy_sparse[:, 4], Labxy_sparse[:, 2], function='linear', smooth=smooth)
#Lab_smooth[:, 0] = L_rbf(Labxy[:, 0], Labxy[:, 1], Labxy[:, 2], Labxy[:, 3], Labxy[:, 4])
Lab_smooth[foreground, 0] = L_rbf(*(Labxy.T))
Lab_smooth[foreground, 1] = a_rbf(*(Labxy.T))
Lab_smooth[foreground, 2] = b_rbf(*(Labxy.T))
h, w = image.shape[:2]
Lab_smooth = Lab_smooth.reshape((h, w, 3))
rgb_smooth = Lab2rgb(np.float32(Lab_smooth))
rgb_smooth_8U = to8U(rgb_smooth)
rgb_smooth_8U = setAlpha(rgb_smooth_8U, alpha(image))
return rgb_smooth_8U
def _runImp(self):
image = self._scene.image()
A_8U = alpha(image)
N_32F = self._scene.normal()
if N_32F is None:
return
h_high, w_high = image.shape[:2]
w_low = min(256, w_high)
h_low = w_low * h_high / w_high
N_32F = cv2.resize(N_32F, (w_low, h_low))
if A_8U is not None:
A_8U = cv2.resize(A_8U, (w_low, h_low))
D_32F = depthFromNormal(N_32F, A_8U)
d_scale = w_high / float(w_low)
D_32F = d_scale * D_32F
D_32F = cv2.resize(D_32F, (w_high, h_high))
self._scene.setDepth(D_32F)
self._scene.setDisplayMode(Scene.DisplayDepth)
self._output_info = "(%s, %s)" % (np.min(D_32F), np.max(D_32F))
print self._output_info
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 _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 _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 depthImage(self):
D_min = np.min(self._depth)
D_max = np.max(self._depth)
D_32F = (self._depth - D_min) / (D_max - D_min)
D_8U = to8U(D_32F)
D_8U = gray2rgb(D_8U)
A_8U = alpha(self._image)
D_8U = setAlpha(D_8U, A_8U)
return D_8U
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 bilateralSmoothing(image):
sigma_xy = 0.1
xy = positionFeatures(image) / sigma_xy
Lab = LabFeatures(image)
foreground = foreGroundFeatures(image)
Labxy = np.concatenate((Lab, xy), axis=1)[foreground, :]
sigma_L = 1.0
Labxy[:, 0] = Labxy[:, 0] / sigma_L
Labxy_sparse = shuffle(Labxy, random_state=0)[:1000]
Lab_smooth = np.array(Lab)
smooth = 0.0
L_rbf = Rbf(Labxy_sparse[:, 0],
Labxy_sparse[:, 1],
Labxy_sparse[:, 2],
Labxy_sparse[:, 3],
Labxy_sparse[:, 4],
sigma_L * Labxy_sparse[:, 0],
function='linear',
smooth=smooth)
a_rbf = Rbf(Labxy_sparse[:, 0],
Labxy_sparse[:, 1],
Labxy_sparse[:, 2],
Labxy_sparse[:, 3],
Labxy_sparse[:, 4],
Labxy_sparse[:, 1],
function='linear',
smooth=smooth)
b_rbf = Rbf(Labxy_sparse[:, 0],
Labxy_sparse[:, 1],
Labxy_sparse[:, 2],
Labxy_sparse[:, 3],
Labxy_sparse[:, 4],
Labxy_sparse[:, 2],
function='linear',
smooth=smooth)
#Lab_smooth[:, 0] = L_rbf(Labxy[:, 0], Labxy[:, 1], Labxy[:, 2], Labxy[:, 3], Labxy[:, 4])
Lab_smooth[foreground, 0] = L_rbf(*(Labxy.T))
Lab_smooth[foreground, 1] = a_rbf(*(Labxy.T))
Lab_smooth[foreground, 2] = b_rbf(*(Labxy.T))
h, w = image.shape[:2]
Lab_smooth = Lab_smooth.reshape((h, w, 3))
rgb_smooth = Lab2rgb(np.float32(Lab_smooth))
rgb_smooth_8U = to8U(rgb_smooth)
rgb_smooth_8U = setAlpha(rgb_smooth_8U, alpha(image))
return rgb_smooth_8U
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 _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):
image = self._scene.image()
A_8U = alpha(image)
if A_8U is None:
return
A_8U = cv2.resize(A_8U, None, fx=0.25, fy=0.25)
N0_32F, N_32F = estimateNormal(A_8U)
h, w = image.shape[:2]
N_32F = cv2.resize(N_32F, (w, h))
self._scene.setNormal(N_32F)
self._scene.setDisplayMode(Scene.DisplayNormal)
def keyPressEvent(self, e):
if e.key() == Qt.Key_0:
self._view.render(self._image)
if e.key() == Qt.Key_1:
if self._N_32F is None:
self._interpolateNormal()
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
self._view.render(normalToColor(self._N_32F, A_8U))
if e.key() == Qt.Key_2:
self._interpolateNormal()
if self._N_32F is None:
self._interpolateNormal()
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
self._view.render(normalToColor(self._N_32F, A_8U))
if e.key() == Qt.Key_Delete:
self._normal_constraints.clear()
self._view.update()
def showImage(self, image, title, alpha_clip=True, font_size=15):
plt.subplot(self._num_rows, self._num_cols, self._plot_id)
plt.title(title, fontsize=font_size)
if len(image.shape) == 2:
plt.gray()
show_image = image
if alpha_clip:
if len(image.shape) == 3:
if image.shape[2] == 4:
show_image = trim(image, alpha(image))
plt.imshow(show_image)
plt.axis('off')
self._plot_id += 1
def showImage(self, image, title, alpha_clip=True, font_size=15):
plt.subplot(self._num_rows, self._num_cols, self._plot_id)
plt.title(title, fontsize=font_size)
if len(image.shape) == 2:
plt.gray()
show_image = image
if alpha_clip:
if len(image.shape) == 3:
if image.shape[2] == 4:
show_image = trim(image, alpha(image))
plt.imshow(show_image)
plt.axis('off')
self._plot_id += 1
def _interpolateNormalAMG(self, N0_32F, W_32F, A_8U):
h, w = N0_32F.shape[:2]
A_c, b_c = normalConstraints(W_32F, N0_32F)
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
A_sil, b_sil = silhouetteConstraints(A_8U)
A_L = laplacianMatrix((h, w))
A = 10.0 * A_c + A_L + A_sil
b = 10.0 * b_c + b_sil
N_32F = amg_solver.solve(A, b).reshape(h, w, 3)
N_32F = normalizeImage(N_32F)
return N_32F
def sparseHistogramSampling(image, num_color_bin=24, num_xy_bins=64):
h, w = image.shape[:2]
Lab = LabFeatures(image)
xy = positionFeatures(image)
Labxy = np.concatenate((Lab, xy), axis=1)
num_bins = [
num_color_bin, num_color_bin, num_color_bin, num_xy_bins, num_xy_bins
]
num_color_bins = num_bins[:]
num_color_bins.append(3)
hist_bins = np.zeros(num_bins, dtype=np.float32)
color_bins = np.zeros(num_color_bins, dtype=np.float32)
f_min = np.min(Labxy, axis=0)
f_max = np.max(Labxy, axis=0)
num_bins = np.array(num_bins)
f_ids = (num_bins - 1) * (Labxy - f_min) / (f_max - f_min)
f_ids = np.int32(f_ids)
hist_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3],
f_ids[:, 4]] += 1
color_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3],
f_ids[:, 4]] += Lab[:, :]
hist_positive = hist_bins > 0.0
print np.count_nonzero(hist_positive)
for ci in xrange(3):
color_bins[hist_positive, ci] /= hist_bins[hist_positive]
map_Lab = color_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3],
f_ids[:, 4]].reshape(h, w, -1)
map_rgb = to8U(Lab2rgb(map_Lab))
print image.shape
print map_rgb.shape
map_image = setAlpha(map_rgb, alpha(image))
return map_image
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 _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 sparseHistogramSampling(image, num_color_bin=24, num_xy_bins=64):
h, w = image.shape[:2]
Lab = LabFeatures(image)
xy = positionFeatures(image)
Labxy = np.concatenate((Lab, xy), axis=1)
num_bins = [num_color_bin, num_color_bin, num_color_bin, num_xy_bins, num_xy_bins]
num_color_bins = num_bins[:]
num_color_bins.append(3)
hist_bins = np.zeros(num_bins, dtype=np.float32)
color_bins = np.zeros(num_color_bins, dtype=np.float32)
f_min = np.min(Labxy, axis=0)
f_max = np.max(Labxy, axis=0)
num_bins = np.array(num_bins)
f_ids = (num_bins - 1) * (Labxy - f_min) / (f_max - f_min)
f_ids = np.int32(f_ids)
hist_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3], f_ids[:, 4]] += 1
color_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3], f_ids[:, 4]] += Lab[:, :]
hist_positive = hist_bins > 0.0
print np.count_nonzero(hist_positive)
for ci in xrange(3):
color_bins[hist_positive, ci] /= hist_bins[hist_positive]
map_Lab = color_bins[f_ids[:, 0], f_ids[:, 1], f_ids[:, 2], f_ids[:, 3], f_ids[:, 4]].reshape(h, w, -1)
map_rgb = to8U(Lab2rgb(map_Lab))
print image.shape
print map_rgb.shape
map_image = setAlpha(map_rgb, alpha(image))
return map_image
def _runImp(self):
D_32F = self._scene.depth()
if D_32F is None:
return
image = self._scene.image()
A_8U = alpha(image)
if A_8U is None:
return
self._view = GLView()
self._view.setRGBAD(image, D_32F)
self._view.show()
# A_8U = cv2.resize(A_8U, None, fx=0.25, fy=0.25)
# N0_32F, N_32F = estimateNormal(A_8U)
#
# h, w = image.shape[:2]
# N_32F = cv2.resize(N_32F, (w, h))
# self._scene.setNormal(N_32F)
# self._scene.setDisplayMode(Scene.DisplayNormal)
def _interpolateNormal(self):
if self._normal_constraints.empty():
return
if self._image is None:
return
ps = np.int32(self._normal_constraints.positions())
ns = self._normal_constraints.normals()
h, w = self._image.shape[:2]
N0_32F = np.zeros((h, w, 3))
N0_32F[ps[:, 1], ps[:, 0]] = ns
W_32F = np.zeros((h, w))
W_32F[ps[:, 1], ps[:, 0]] = 1.0
A_8U = None
if self._image.shape[2] == 4:
A_8U = to8U(alpha(self._image))
self._N_32F = self._interpolateNormalImage(N0_32F, W_32F, A_8U)
self._projectConstraints()
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 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 alphaFeatures(image):
h, w = image.shape[:2]
if alpha(image) == None:
return 255 * np.ones((h * w))
features = alpha(image).reshape(h * w)
return features
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
A_32F = to32F(A_8U)
C0_32F = to32F(rgb(C0_8U))
I0_32F = luminance(C0_32F)
initial_normals = ["N_lumo.png", "N0_d.png"]
layer_name = os.path.splitext(os.path.basename(layer_file))[0]
for initial_normal in initial_normals:
N0_32F, AN_8U = loadNormal(
self.characterResultFile(initial_normal,
data_name="BaseDetailSepration"))
N_32F, L, C_32F, M = self._runSFS(C0_32F, A_8U, N0_32F, AN_8U)
L_img = lightSphere(L)
M_img = M.mapImage()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.02,
right=0.98,
top=0.9,
hspace=0.12,
wspace=0.02)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(normalToColor(N0_32F, A_8U),
r'Initial Normal: $N_0$')
plot_grid.showImage(normalToColor(N_32F, A_8U),
r'Estimated Normal: $N$')
plot_grid.showImage(C0_8U, r'Shading: $C_0$')
plot_grid.showImage(setAlpha(C_32F, A_32F),
r'Recovered Shading: $C$')
out_file_path = self.characterResultFile("ToonSFS" +
initial_normal,
layer_name=layer_name)
plt.savefig(out_file_path)
N_trim = trim(N_32F, A_8U)
N0_trim = trim(N0_32F, A_8U)
C0_trim = trim(C0_32F, A_8U)
A_trim = trim(A_8U, A_8U)
out_file_path = self.characterResultFile(initial_normal,
layer_name=layer_name)
saveNormal(out_file_path, N_trim, A_trim)
images = self._relightingImages(N_trim, A_trim, M)
initial_normal_name = os.path.splitext(initial_normal)[0]
video_name = "Relighting" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name,
layer_name=layer_name)
saveVideo(out_file_path, images)
images = self._relightingOffsetImages(L, C0_trim, N0_trim, A_trim,
M)
video_name = "RelightingOffset" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name,
layer_name=layer_name)
saveVideo(out_file_path, images)
def alphaFeatures(image):
h, w = image.shape[:2]
if alpha(image) == None:
return 255 * np.ones((h * w))
features = alpha(image).reshape(h * w)
return features
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
A_32F = to32F(A_8U)
C0_32F = to32F(rgb(C0_8U))
I0_32F = luminance(C0_32F)
initial_normals = ["N_lumo.png", "N0_d.png"]
layer_name = os.path.splitext(os.path.basename(layer_file))[0]
for initial_normal in initial_normals:
N0_32F, AN_8U = loadNormal(self.characterResultFile(initial_normal, data_name="BaseDetailSepration"))
N_32F, L, C_32F, M = self._runSFS(C0_32F, A_8U, N0_32F, AN_8U)
L_img = lightSphere(L)
M_img = M.mapImage()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.02, right=0.98, top=0.9, hspace=0.12, wspace=0.02)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'Initial Normal: $N_0$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'Estimated Normal: $N$')
plot_grid.showImage(C0_8U, r'Shading: $C_0$')
plot_grid.showImage(setAlpha(C_32F, A_32F), r'Recovered Shading: $C$')
out_file_path = self.characterResultFile("ToonSFS" + initial_normal, layer_name=layer_name)
plt.savefig(out_file_path)
N_trim = trim(N_32F, A_8U)
N0_trim = trim(N0_32F, A_8U)
C0_trim = trim(C0_32F, A_8U)
A_trim = trim(A_8U, A_8U)
out_file_path = self.characterResultFile(initial_normal, layer_name=layer_name)
saveNormal(out_file_path, N_trim, A_trim)
images = self._relightingImages(N_trim, A_trim, M)
initial_normal_name = os.path.splitext(initial_normal)[0]
video_name = "Relighting" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name, layer_name=layer_name)
saveVideo(out_file_path, images)
images = self._relightingOffsetImages(L, C0_trim, N0_trim, A_trim, M)
video_name = "RelightingOffset" + initial_normal_name + ".wmv"
out_file_path = self.characterResultFile(video_name, layer_name=layer_name)
saveVideo(out_file_path, images)
def normalColor(self):
A_8U = alpha(self._image)
return normalToColor(self._normal, A_8U)