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)