I_id_max = np.max(I_ids)
Ns_bright = Ns[I_ids == I_id_max, :]
L = np.sum(Ns_bright, axis=0)
L = normalizeVector(L)
print L
return L
def estimateLightDir(input_data):
N_sil = input_data["N_sil"]
I_sil = input_data["I_sil"]
cvs_sil = input_data["cvs_sil"]
I_32F = input_data["I"]
A_8U = input_data["A"]
p_samples = foreGroundSamples(A_8U)
plt.imshow(A_8U)
plt.scatter(p_samples[:, 0], p_samples[:, 1])
plt.show()
N_lumo = lumoNormal(A_8U)
L = estimateLightByCluster(N_lumo, I_32F, p_samples)
output_data = {"L": L}
return output_data
if __name__ == '__main__':
testToon("Lumo", estimateLightDir)
# plt.imshow(I_32F)
# plt.scatter(I_max_coord[0], I_max_coord[1])
# plt.show()
# plt.clf()
def estimateLightDir(input_data):
N_sil = input_data["N_sil"]
I_sil = input_data["I_sil"]
cvs_sil = input_data["cvs_sil"]
I_32F = input_data["I"]
A_8U = input_data["A"]
N_lumo = lumoNormal(A_8U)
plt.imshow(0.5 * N_lumo + 0.5)
plt.show()
L_phi = estimatePhi(N_sil, I_sil)
output_data = {"L": L_phi}
# L = estimateLbyDistance(cvs_sil, N_sil, I_32F)
# output_data = {"L": L}
L = estimateTheta(L_phi, cvs_sil, N_sil, I_32F, A_8U)
# output_data = {"L": L }
return output_data
if __name__ == '__main__':
testToon("Voting", estimateLightDir)