def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
sfs_method = Wu08SFS(L, C0_32F, A_8U)
sfs_method.run()
N_32F = sfs_method.normal()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 2
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r"Shading: $C$")
plot_grid.showImage(normalToColor(N_32F, A_8U), r"Estimated Normal: $N$")
showMaximize()
def _runColorMap(self, colormap_file, Ng_32F, N0_32F, A_8U):
M_32F = loadColorMap(colormap_file)
L0 = normalizeVector(np.array([-0.2, 0.3, 0.6]))
L0_img = lightSphere(L0)
L0_txt = 0.01 * np.int32(100 * L0)
C0_32F = ColorMapShader(M_32F).diffuseShading(L0, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(setAlpha(C0_32F, A_8U), r'Input image: $\mathbf{c}$', font_size=font_size)
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'Initial normal: $\mathbf{N}_0$')
plot_grid.showImage(L0_img, r'Ground trugh light: $L_g = (%s, %s, %s)$' %(L0_txt[0], L0_txt[1], L0_txt[2]))
plot_grid.showImage(L_img, r'Estimated light: $L = (%s, %s, %s)$' %(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
sfs_method = Wu08SFS(L, C0_32F, A_8U)
sfs_method.run()
N_32F = sfs_method.normal()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 2
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'Shading: $C$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'Estimated Normal: $N$')
showMaximize()
def _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 _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
# if A_8U is None:
# return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
Lab_32F = rgb2Lab(C0_32F)
th_specular = 0.2
th_contour = 0.02
th_material = 0.1
E_32F = DoG(I_32F, sigma=2.0)
contour = th_contour * np.min(E_32F) - E_32F
contour *= 1.0 / np.max(contour)
contour = np.clip(contour, 0.0, 1.0)
specular = E_32F - th_specular * np.max(E_32F)
specular *= 1.0 / np.max(specular)
specular = np.clip(specular, 0.0, 1.0)
material = rgb(C0_8U)
# edge_mask = np.zeros(I_32F.shape, dtype=np.uint8)
# edge_mask[contour > 0.0] = 1.0
# material = cv2.inpaint(material, edge_mask, 3, cv2.INPAINT_TELEA)
for i in xrange(1):
material = cv2.medianBlur(material, ksize=7)
# material = th_material * np.max(np.abs(E_32F)) - np.abs(E_32F)
# material *= 1.0 / np.max(material)
# material = np.clip(material, 0.0, 1.0)
# material[material > 0.0] = 1.0
E_32F[E_32F < 0.0] = 0.0
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
#plot_grid.showImage(setAlpha(C0_32F, material), r'$Material$')
plot_grid.showImage(setAlpha(material, A_8U), r'$Material$')
plot_grid.showImage(setAlpha(C0_32F, contour), r'$Contour$')
plot_grid.showImage(setAlpha(C0_32F, specular), r'$Specular$')
showMaximize()
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
N0_32F, A_8U = loadNormal(
self.characterResultFile("N0_d.png",
data_name="BaseDetailSepration"))
Nd_32F, A_8U = loadNormal(
self.characterResultFile("N_d_smooth.png",
data_name="BaseDetailSepration"))
Nb_32F, A_8U = loadNormal(
self.characterResultFile("N_b_smooth.png",
data_name="BaseDetailSepration"))
W_32F = np.array(Nb_32F[:, :, 2])
W_32F = W_32F
W_32F[W_32F < 0.95] = 0.0
L = lightEstimation(I_32F, N0_32F, A_8U)
# L = lightEstimationByVoting(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
hspace=0.12,
wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'$N$')
plot_grid.showImage(setAlpha(C0_32F, W_32F), r'$Nd_z$')
plot_grid.showImage(
L_img, r'$L: [%s, %s, %s]$' % (L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N_32F, A_8U = normal_data
N_32F = trim(N_32F, A_8U)
A_8U = trim(A_8U, A_8U)
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
I_half = half_lambert.diffuse(N_32F, L)
I_half = setAlpha(gray2rgb(I_half), A_32F)
I_lambert = lambert.diffuse(N_32F, L)
I_lambert = setAlpha(gray2rgb(I_lambert), A_32F)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
hspace=0.05,
wspace=0.05)
fig.suptitle("Depth From Normal", fontsize=font_size)
plt.subplot(1, 4, 1)
plt.title(r'Normal: $N$')
plt.imshow(normalToColor(N_32F, A_8U))
plt.axis('off')
plt.subplot(1, 4, 2)
plt.title(r'Half Lambert: $I_h$')
plt.imshow(I_half)
plt.axis('off')
plt.subplot(1, 4, 3)
plt.title(r'Lambert: $I_l$')
plt.imshow(I_lambert)
plt.axis('off')
showMaximize()
def _runLayer(self, layer_file):
C0_8U = loadRGBA(layer_file)
if C0_8U is None:
return
A_8U = alpha(C0_8U)
if A_8U is None:
return
C0_32F = to32F(rgb(C0_8U))
I_32F = luminance(C0_32F)
N0_32F, A_8U = loadNormal(self.characterResultFile("N0_d.png", data_name="BaseDetailSepration"))
Nd_32F, A_8U = loadNormal(self.characterResultFile("N_d_smooth.png", data_name="BaseDetailSepration"))
Nb_32F, A_8U = loadNormal(self.characterResultFile("N_b_smooth.png", data_name="BaseDetailSepration"))
W_32F = np.array(Nb_32F[:, :, 2])
W_32F = W_32F
W_32F[W_32F < 0.95] = 0.0
L = lightEstimation(I_32F, N0_32F, A_8U)
# L = lightEstimationByVoting(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(C0_8U, r'$C$')
plot_grid.showImage(normalToColor(N0_32F, A_8U), r'$N$')
plot_grid.showImage(setAlpha(C0_32F, W_32F), r'$Nd_z$')
plot_grid.showImage(L_img, r'$L: [%s, %s, %s]$' %(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N_32F, A_8U = normal_data
N_32F = trim(N_32F, A_8U)
A_8U = trim(A_8U, A_8U)
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.3, 0.7]))
I_half = half_lambert.diffuse(N_32F, L)
I_half = setAlpha(gray2rgb(I_half), A_32F)
I_lambert = lambert.diffuse(N_32F, L)
I_lambert = setAlpha(gray2rgb(I_lambert), A_32F)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.05, wspace=0.05)
fig.suptitle("Depth From Normal", fontsize=font_size)
plt.subplot(1, 4, 1)
plt.title(r'Normal: $N$')
plt.imshow(normalToColor(N_32F, A_8U))
plt.axis('off')
plt.subplot(1, 4, 2)
plt.title(r'Half Lambert: $I_h$')
plt.imshow(I_half)
plt.axis('off')
plt.subplot(1, 4, 3)
plt.title(r'Lambert: $I_l$')
plt.imshow(I_lambert)
plt.axis('off')
showMaximize()
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.4, 0.7]))
C0_32F = LambertShader().diffuseShading(L, N0_32F)
I_32F = luminance(C0_32F)
br_field = BrightnessField(I_32F, sigma=5.0)
I_smooth_32F = br_field.smoothBrightness()
dI = br_field.brightnessDifference()
gx, gy = br_field.gradients()
N_32F = br_field.field()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
hspace=0.12,
wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 2
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(I_32F, r'$I$')
plot_grid.showColorMap(dI, r'$dI$')
plot_grid.showColorMap(gx, r'$gx$')
plot_grid.showColorMap(gy, r'$gy$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'$N$')
plot_grid.showImage(N_32F[:, :, 2], r'$N_z$')
showMaximize()
def _runColorMap(self, colormap_file, Ng_32F, N0_32F, A_8U):
M_32F = loadColorMap(colormap_file)
L0 = normalizeVector(np.array([-0.2, 0.3, 0.6]))
L0_img = lightSphere(L0)
L0_txt = 0.01 * np.int32(100 * L0)
C0_32F = ColorMapShader(M_32F).diffuseShading(L0, Ng_32F)
I_32F = luminance(C0_32F)
L = lightEstimation(I_32F, N0_32F, A_8U)
L_txt = 0.01 * np.int32(100 * L)
L_img = lightSphere(L)
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05,
right=0.95,
top=0.9,
hspace=0.12,
wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 1
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(setAlpha(C0_32F, A_8U),
r'Input image: $\mathbf{c}$',
font_size=font_size)
plot_grid.showImage(normalToColor(N0_32F, A_8U),
r'Initial normal: $\mathbf{N}_0$')
plot_grid.showImage(
L0_img, r'Ground trugh light: $L_g = (%s, %s, %s)$' %
(L0_txt[0], L0_txt[1], L0_txt[2]))
plot_grid.showImage(
L_img, r'Estimated light: $L = (%s, %s, %s)$' %
(L_txt[0], L_txt[1], L_txt[2]))
showMaximize()
def _runImp(self):
normal_data = loadNormal(self._data_file)
if normal_data is None:
return
N0_32F, A_8U = normal_data
A_32F = to32F(A_8U)
L = normalizeVector(np.array([-0.2, 0.4, 0.7]))
C0_32F = LambertShader().diffuseShading(L, N0_32F)
I_32F = luminance(C0_32F)
br_field = BrightnessField(I_32F, sigma=5.0)
I_smooth_32F = br_field.smoothBrightness()
dI = br_field.brightnessDifference()
gx, gy = br_field.gradients()
N_32F = br_field.field()
fig, axes = plt.subplots(figsize=(11, 5))
font_size = 15
fig.subplots_adjust(left=0.05, right=0.95, top=0.9, hspace=0.12, wspace=0.05)
fig.suptitle(self.name(), fontsize=font_size)
num_rows = 2
num_cols = 4
plot_grid = SubplotGrid(num_rows, num_cols)
plot_grid.showImage(I_32F, r'$I$')
plot_grid.showColorMap(dI, r'$dI$')
plot_grid.showColorMap(gx, r'$gx$')
plot_grid.showColorMap(gy, r'$gy$')
plot_grid.showImage(normalToColor(N_32F, A_8U), r'$N$')
plot_grid.showImage(N_32F[:, :, 2], r'$N_z$')
showMaximize()
def _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 _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()
