def tangentCoordinates(N_32F):
N = N_32F.reshape(-1, 3)
v_z = np.array([0.0, 0.0, 1.0])
A = np.cross(N, v_z)
sin_theta = normVectors(A)
cos_theta = np.dot(N, v_z)
v_x = np.array([1.0, 0.0, 0.0])
v_y = np.array([0.0, 1.0, 0.0])
T = np.zeros_like(N)
T[:, :] = np.array([1.0, 0.0, 0.0])
B = np.zeros_like(N)
B[:, :] = np.array([0.0, 1.0, 0.0])
sin_valid = sin_theta > 1e-4
T[sin_valid, :] = rotateVectors(A, T[sin_valid, :], cos_theta[sin_valid],
sin_theta[sin_valid])
B[sin_valid, :] = rotateVectors(A, B[sin_valid, :], cos_theta[sin_valid],
sin_theta[sin_valid])
return T, B
def shadingError(self):
C_diff = self._C_32F - self._C0_32F
h, w = C_diff.shape[:2]
C_diff = C_diff.reshape(-1, 3)
C_diff = normVectors(C_diff)
C_diff = C_diff.reshape(h, w)
return C_diff
def _illuminationFromColorDifference(self, Cs):
I0s = np.average(Cs, axis=1)
I0_ids = self._I_ids(I0s)
C_map = np.zeros((self._map_size, Cs.shape[1]))
hist = np.zeros((self._map_size))
C_map[I0_ids, :] += Cs[:, :]
hist[I0_ids] += 1.0
hist_positive = hist > 0
for ci in xrange(3):
C_map[hist_positive, ci] *= 1.0 / hist[hist_positive]
I_map = np.zeros((self._map_size))
I_min, I_max = self._Iminmax
Ims = np.linspace(I_min, I_max, num=self._map_size)
for ci in xrange(3):
C_map[:, ci] = Rbf(Ims[hist_positive],
C_map[hist_positive, ci],
smooth=0.0005)(Ims)
sigma = 0.02
for mi in xrange(self._map_size):
c = C_map[mi]
dc = normVectors(self._map - c)
dc_i = np.argmin(dc)
wc = np.exp(-(dc**2) / (sigma**2))
I = np.dot(wc, Ims) / np.sum(wc)
#I_map[mi] = I_min + (I_max - I_min) * dc_i / float(self._map_size - 1)
I_map[mi] = I
Im_max = 0.0
for mi in xrange(self._map_size):
Im_max = max(I_map[mi], Im_max)
I_map[mi] = Im_max
I_map = Rbf(Ims, I_map, smooth=0.0005)(Ims)
# I_map[np.max(I0_ids):] = I_max
return I_map[I0_ids]
def _selectConstraint(self, p):
if self._normal_constraints.empty():
return False
ps = self._normal_constraints.positions()
dP = normVectors(ps - p)
p_min = np.argmin(dP)
if dP[p_min] < 5:
self._selected_constraint = self._normal_constraints.constraint(p_min)
self._n_old = self._selected_constraint.normal()
return True
return False
def computeErrors(L, C0_32F, C_32F, Ng_32F, N_32F, 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)
C_error = normVectors(C0_32F.reshape(-1, 3) - C_32F.reshape(-1, 3)).reshape(h, w)
C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
C_error = trim(C_error, A_8U)
I_32F = np.clip(LdotN(L, N_32F), 0.0, 1.0)
Ig_32F = np.clip(LdotN(L, Ng_32F), 0.0, 1.0)
I_error = np.abs(I_32F - Ig_32F)
I_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
I_error = trim(I_error, A_8U)
return C_error, N_error, I_error
def _illuminationFromColorDifference(self, Cs):
I0s = np.average(Cs, axis=1)
I0_ids = self._I_ids(I0s)
C_map = np.zeros((self._map_size, Cs.shape[1]))
hist = np.zeros((self._map_size))
C_map[I0_ids, :] += Cs[:, :]
hist[I0_ids] += 1.0
hist_positive = hist > 0
for ci in xrange(3):
C_map[hist_positive, ci] *= 1.0 / hist[hist_positive]
I_map = np.zeros((self._map_size))
I_min, I_max = self._Iminmax
Ims = np.linspace(I_min, I_max, num=self._map_size)
for ci in xrange(3):
C_map[:, ci] = Rbf(Ims[hist_positive], C_map[hist_positive, ci], smooth=0.0005)(Ims)
sigma = 0.02
for mi in xrange(self._map_size):
c = C_map[mi]
dc = normVectors(self._map - c)
dc_i = np.argmin(dc)
wc = np.exp(-(dc ** 2) / (sigma ** 2))
I = np.dot(wc, Ims) / np.sum(wc)
# I_map[mi] = I_min + (I_max - I_min) * dc_i / float(self._map_size - 1)
I_map[mi] = I
Im_max = 0.0
for mi in xrange(self._map_size):
Im_max = max(I_map[mi], Im_max)
I_map[mi] = Im_max
I_map = Rbf(Ims, I_map, smooth=0.0005)(Ims)
# I_map[np.max(I0_ids):] = I_max
return I_map[I0_ids]
def computeErrors(L, C0_32F, C_32F, Ng_32F, N_32F, 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)
C_error = normVectors(C0_32F.reshape(-1, 3) -
C_32F.reshape(-1, 3)).reshape(h, w)
C_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
C_error = trim(C_error, A_8U)
I_32F = np.clip(LdotN(L, N_32F), 0.0, 1.0)
Ig_32F = np.clip(LdotN(L, Ng_32F), 0.0, 1.0)
I_error = np.abs(I_32F - Ig_32F)
I_error[A_8U < 0.5 * np.max(A_8U)] = 0.0
I_error = trim(I_error, A_8U)
return C_error, N_error, I_error
def tangentCoordinates(N_32F):
N = N_32F.reshape(-1, 3)
v_z = np.array([0.0, 0.0, 1.0])
A = np.cross(N, v_z)
sin_theta = normVectors(A)
cos_theta = np.dot(N, v_z)
v_x = np.array([1.0, 0.0, 0.0])
v_y = np.array([0.0, 1.0, 0.0])
T = np.zeros_like(N)
T[:, :] = np.array([1.0, 0.0, 0.0])
B = np.zeros_like(N)
B[:, :] = np.array([0.0, 1.0, 0.0])
sin_valid = sin_theta > 1e-4
T[sin_valid, :] = rotateVectors(A, T[sin_valid, :], cos_theta[sin_valid], sin_theta[sin_valid])
B[sin_valid, :] = rotateVectors(A, B[sin_valid, :], cos_theta[sin_valid], sin_theta[sin_valid])
return T, B
def errorTable():
colormap_files = colorMapFiles()
num_colormap_files = len(colormap_files)
M_errors = np.zeros((num_colormap_files))
for mi, colormap_file in enumerate(colormap_files):
M_32F = loadColorMap(colormap_file)
C_32F = M_32F.reshape(1, len(M_32F), 3)
I_32F = np.linspace(0.0, 1.0, len(M_32F))
I_32F = I_32F.reshape(C_32F.shape[:2])
reflectance = LambertReflectanceEstimation(C_32F, I_32F)
Ml = reflectance.shading(I_32F)[0, :, :]
I0_32F = luminance(C_32F)
IL_32F = luminance(Ml.reshape(1, -1, 3))
I_min, I_max = np.min(I0_32F), np.max(I0_32F)
M_error = normVectors(M_32F - Ml)
#M_errors[mi] = np.mean(M_error) / (I_max - I_min)
# M_errors[mi] = computeGradientDistance(M_32F, Ml) / (I_max - I_min)
#M_errors[mi] = np.linalg.norm(I0_32F - IL_32F) / (I_max - I_min)
M_errors[mi] = np.mean(M_error) / (I_max - I_min)
# M_errors[mi] = np.linalg.norm(M_32F - Ml) / (I_max - I_min)
# M_errors[mi] = compareHist(M_32F, Ml)
file_path = shapeResultFile("ReflectanceEstimation", "ReflectanceError", file_ext=".npy")
np.save(file_path, M_errors)
plt.plot(M_errors)
plt.show()
def 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)
