fpdir[X_data_idx[i, 0]][X_data_idx[i, 1]] = prediction[i]
fpdir_porg[X_data_idx[i, 0]][X_data_idx[i, 1]] = prob_list[i]
fpdir_prob[X_data_idx[i, 0]][X_data_idx[i, 1]] = sum(
weightTBL[prediction[i]] * prob_arr[i])
#if i < 3:
# print(i, prediction[i], prob_list[i], sum(weightTBL[prediction[i]]*prob_arr[i]))
# for j in range(180):
# print('{:2d} {:.3f} {:.3f}'.format(j, weightTBL[prediction[i],j], prob_arr[i,j]))
# print(weightTBL[prediction[i],prediction[i]-10:prediction[i]+10], prob_list[i][prediction[i]-10:prediction[i]+10])
#
# 7. write line seg. on fingerprint
#
print("7. write line seg. on fingerprint...")
fmdir = MyL.UT_SetLine2(plt, fpfg, fpdir, fm.shape, False, 'black', 3)
plt.title(sys.argv[2][-11:])
plt.imshow(fmdir, cmap=plt.cm.gray)
ImgPath = 'D:\\Fingerprint\\paper8_NN\\P8NN_Images\\' + sys.argv[
1] + '_' + sys.argv[2][-11:-4] + "_2.png"
plt.savefig(ImgPath, dpi=600, bbox_inches='tight')
if ShowAll: plt.show()
#
# 8. write line seg.
#
print("8. write line seg. ...")
axs = [[None for _ in range(NoWidth)]] * NoWidth
MyL.UT_SetLine(plt, fpfg, fpdir, fm, axs, False, 'white', 3)
plt.title(sys.argv[2][-11:])
