def computeData(scene):
data_list = []
isophote_mesh = scene.isophoteMesh()
isophote_curves = isophote_mesh.isophoteCurves()
for isophote_curve in isophote_curves:
isophote_segments = isophote_curve.toCurveSegments()
for isophote_segment in isophote_segments:
I = isophote_segment.isoValue()
I = 2.0 * I - 1.0
normals_gt = isophote_segment.normals()
parameters = isophote_segment.arcLengthParameters()
normals_al = NormalConeInterpolation(normals_gt[0],
normals_gt[-1],
isophote_segment.lightDir(),
I).interpolate(parameters)
ps = isophote_segment.points()
normals_error = normVectors(normals_gt - normals_al)
data_list.append({"ps": ps,
"normals_gt": normals_gt,
"normals_al": normals_al,
"normals_error": normals_error})
return data_list
def cleanNormal(N_32F, A_8U):
# N_32F = cv2.bilateralFilter(N_32F, 0, 0.1, 5)
h, w = N_32F.shape[:2]
plt.subplot(1, 2, 1)
plt.gray()
plt.imshow(normVectors(N_32F.reshape(-1, 3)).reshape(h, w))
plt.subplot(1, 2, 2)
plt.gray()
A_32F = to32F(A_8U)
A_32F = cv2.GaussianBlur(A_32F, (0, 0), 3.0)
A_32F = np.clip(10.0 * (A_32F - 0.5) + 0.5, 0.0, 1.0)
A_32F = cv2.GaussianBlur(A_32F, (0, 0), 3.0)
N_fix = A_32F > 0.9
N_bg = A_32F < 0.25
A_32F = np.clip(10.0 * (A_32F - 0.5) + 0.5, 0.0, 1.0)
A_8U = to8U(A_32F)
# plt.imshow(A_8U)
# plt.show()
N_32F_blur = cv2.GaussianBlur(N_32F, (0, 0), 3.0)
for i in xrange(10):
N_32F_blur = cv2.GaussianBlur(N_32F_blur, (0, 0), 3.0)
N_32F_blur[N_fix, :] = N_32F[N_fix, :]
N_32F = N_32F_blur
# N_32F[N_bg, 2] = 0.0
N_32F_normalized = normalizeImage(N_32F)
#A_8U = np.uint8(np.clip(1000.0 * N_32F_normalized[:, :, 2], 0.0, 255.0))
# A_8U = cv2.bilateralFilter(A_8U, 0, 70, 5)
return N_32F_normalized, A_8U
def estimateLbyDistance(cvs_sil, N_sil, I_32F):
I_sampling = PixelSampling(I_32F, num_pixels = 10000)
I_samples = I_sampling.pixels()
I_coords = I_sampling.coordinates()
I_large_ids = I_samples > 0.5 * np.max(I_samples)
# I_max_id = np.argmax(I_samples)
# I_max_coord = I_coords[I_max_id]
#
# print I_max_coord, I_samples[I_max_id], np.max(I_samples)
L = np.array([0.0, 0.0, 0.0])
hist = 0.0
for I_large_id in I_large_ids:
I_coord = I_coords[I_large_id]
dist = normVectors(cvs_sil - I_coord)
dist_max = np.max(dist)
w = np.exp(- (dist ** 2) / (0.2 * dist_max**2))
w *= 1.0 / np.sum(w)
L_i = np.dot(w, N_sil)
L += L_i
hist += 1.0
if hist > 0.0:
L /= hist
print L
Lz = np.sqrt(1.0 - np.dot(L, L))
L[2] = Lz
print L.shape
L = normalizeVector(L)
return L
def computeArcLengthParameters(points):
diff_points = points[1:, :] - points[:-1, :]
dist_points = normVectors(diff_points)
dist_sum = np.sum(dist_points)
parameters = np.zeros(len(points))
arc_length = 0
for pi in range(len(dist_points)):
arc_length += dist_points[pi]
parameters[pi + 1] = arc_length
if dist_sum > 0.00001:
parameters *= (1.0 / dist_sum)
logger.debug("Total arc length: %s" % dist_sum)
return parameters
def computeParameters(self):
cvs = self._points
diff_cvs = cvs[1:, :] - cvs[:-1, :]
dist_cvs = normVectors(diff_cvs)
al_total = np.sum(dist_cvs)
params = np.zeros(len(cvs))
al = 0
for pi in range(len(cvs) - 1):
al += dist_cvs[pi]
params[pi + 1] = al
if al_total > 0.00001:
params *= (1.0 / al_total)
self._params = params
