def _optimize(self):
I_32F = self._I0_32F
h, w = I_32F.shape[:2]
I = I_32F.reshape(h * w)
L = self._L
N0_32F = self._N0_32F
N0 = N0_32F.reshape(-1, 3)
NL = np.clip(np.dot(N0, L), 0.0, 1.0)
I = self._estimateBrightness()
I_32F = I.reshape(h, w)
N = np.array(N0_32F)
A_8U = self._A_8U
constraints = []
w_lap = self._w_lap
constraints.append(image_constraints.laplacianConstraints(w_c=w_lap))
constraints.append(image_constraints.brightnessConstraints(L, I_32F, w_c=1.0))
constraints.append(image_constraints.gradientConstraints(L, I_32F, w_c=0.5))
w_sil = 0.2 * w_lap
constraints.append(image_constraints.silhouetteConstraints(A_8U, w_c=w_sil))
solver_iter = image_solver.solveIterator(constraints,
[image_constraints.postNormalize(th=0.0)])
N = image_solver.solveMG(np.float64(N), solver_iter, iterations=self._iterations)
self._N_32F = N.reshape(h, w, 3)
def _optimize(self):
I_32F = self._I0_32F
h, w = I_32F.shape[:2]
I = I_32F.reshape(h * w)
L = self._L
N0_32F = self._N0_32F
N0 = N0_32F.reshape(-1, 3)
NL = np.clip(np.dot(N0, L), 0.0, 1.0)
I = self._estimateBrightness()
I_32F = I.reshape(h, w)
N = np.array(N0_32F)
A_8U = self._A_8U
constraints = []
w_lap = self._w_lap
constraints.append(image_constraints.laplacianConstraints(w_c=w_lap))
constraints.append(
image_constraints.brightnessConstraints(L, I_32F, w_c=1.0))
constraints.append(
image_constraints.gradientConstraints(L, I_32F, w_c=0.5))
w_sil = 0.2 * w_lap
constraints.append(
image_constraints.silhouetteConstraints(A_8U, w_c=w_sil))
solver_iter = image_solver.solveIterator(
constraints, [image_constraints.postNormalize(th=0.0)])
N = image_solver.solveMG(np.float64(N),
solver_iter,
iterations=self._iterations)
self._N_32F = N.reshape(h, w, 3)
def _optimize(self):
L = self._L
N0_32F = self._N0_32F
C0_32F = self._C0_32F
LdN = LdotN(L, N0_32F)
A_8U = self._A_8U
layer_area = A_8U > 0.5 * np.max(A_8U)
Cs = C0_32F[layer_area].reshape(-1, 3)
Is = LdN[layer_area].flatten()
M = ColorMapEstimation(Cs, Is)
self._Cini_32F = M.shading(LdN.flatten()).reshape(self._C0_32F.shape)
I_recovered = M.illumination(Cs)
I_32F = np.zeros(LdN.shape)
I_32F[:] = np.min(I_recovered)
I_32F[layer_area] = I_recovered
I_dog = DoG(I_32F, sigma=2.0)
sigma = 2.0
I_32F = np.float32(cv2.GaussianBlur(I_32F, (0, 0), sigma))
I_lap = -cv2.Laplacian(np.float32(I_32F), cv2.CV_32F, ksize=1)
I_lap = np.abs(I_lap)
I_lap_median = np.median(I_lap[layer_area])
print "I_lap_mean", np.mean(I_lap[layer_area])
print "I_lap_median", np.median(I_lap[layer_area])
sigma = 0.05
epsilon = 0.0 * I_lap_median
w_min = 0.05
w_max = 1.0
W_32F = w_min + (w_max - w_min) * (1.0 - np.exp(- (I_lap - epsilon) ** 2 / (sigma ** 2)))
W_32F = w_min * np.ones(I_32F.shape[:2])
W_32F[I_lap < epsilon] = w_min
W_32F[I_lap > epsilon] = w_max
W_32F[:, :] = 1.0
constraints = []
#W_32F = np.ones(N0_32F.shape[:2])
#constraints.append(image_constraints.normalConstraints(W_32F, N0_32F, w_c=0.05))
w_lap = self._w_lap
constraints.append(image_constraints.laplacianConstraints(w_c=w_lap))
constraints.append(image_constraints.brightnessConstraints(L, I_32F, w_c=1.0))
constraints.append(image_constraints.gradientConstraints(L, I_32F, w_c=0.2))
w_sil = 0.4 * w_lap
constraints.append(image_constraints.silhouetteConstraints(A_8U, w_c=w_sil))
N_32F = np.array(N0_32F, dtype=np.float64)
solver_iter = image_solver.solveIterator(constraints,
[image_constraints.postNormalize(th=0.0)])
#[image_constraints.postComputeNz()])
N_32F = image_solver.solveMG(N_32F, solver_iter, iterations=self._iterations)
self._N_32F = N_32F
LdN = LdotN(L, N_32F)
I_32F[layer_area] = LdN[layer_area]
self._C_32F = M.shading(I_32F.flatten()).reshape(self._C0_32F.shape)
self._M = M