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 normalToColor(N_32F, A_8U=None):
C_32F = 0.5 * N_32F + 0.5
C_8U = to8U(C_32F)
if A_8U is not None:
C_8U = setAlpha(C_8U, A_8U)
return C_8U
def isophoteCurves(I_32F, iso_values=[0.2, 0.4, 0.6, 0.8], M_8U=None):
I_8U = to8U(I_32F)
isophote_curves = []
for iso_value in iso_values:
isophote_curves.append(isophoteCurve(I_8U, iso_value, M_8U))
return isophote_curves