Unverified Commit 43c29389 authored by gsn9's avatar gsn9 Committed by GitHub
Browse files

Merge pull request #622 from gsn9/loggingForTransfomation 

updated transformation module to use logging
parents dc3e93c6 3d59d818

autocnet/transformation/fundamental_matrix.py

+9 −6
Original line number Diff line number Diff line 
import warnings

import logging

import numpy as np

import pandas as pd

from scipy import optimize
@@ -6,6 +6,9 @@ from autocnet.camera import camera 
from autocnet.camera import utils as camera_utils

from autocnet.utils.utils import make_homogeneous, normalize_vector



# set up the logger file

log = logging.getLogger(__name__)



try:

    import cv2

    cv2_avail = True
@@ -207,7 +210,7 @@ def update_fundamental_mask(F, x1, x2, threshold=1.0, index=None, method='reproj 
    elif method == 'fundamental':

        error = compute_fundamental_error(F, x1, x2)

    else:

        warnings.warn('Unknown error method.  Options are "reprojection" or "fundamental".')

        log.warning('Unknown error method.  Options are "reprojection" or "fundamental".')

    mask = pd.DataFrame(np.abs(error) <= threshold, index=index, columns=['fundamental'])

    if index is not None:

        mask.index = index
@@ -295,7 +298,7 @@ def compute_fundamental_matrix(kp1, kp2, method='mle', reproj_threshold=2.0, 
        raise ValueError("Unknown estimation method. Choices are: 'lme', 'ransac', 'lmeds', '8point', or 'normal'.")



    if len(kp1) == 0 or len(kp2) == 0:

        warnings.warn("F-matix computation failed. One of the keypoint args is empty. kp1:{}, kp2:{}.".format(len(kp1), len(kp2)))

        log.warning("F-matix computation failed. One of the keypoint args is empty. kp1:{}, kp2:{}.".format(len(kp1), len(kp2)))

        return None, None



    # OpenCV wants arrays
@@ -312,10 +315,10 @@ def compute_fundamental_matrix(kp1, kp2, method='mle', reproj_threshold=2.0, 
                                         ransacReprojThreshold=reproj_threshold,

                                         confidence=confidence)

    if F is None:

        warnings.warn("F computation failed with no result. Returning None.")

        log.warning("F computation failed with no result. Returning None.")

        return None, None

    if F.shape != (3,3):

        warnings.warn('F computation fell back to 7-point algorithm, not setting F.')

        log.warning('F computation fell back to 7-point algorithm, not setting F.')

        return None, None

    # Ensure that the singularity constraint is met

    F = enforce_singularity_constraint(F)
@@ -337,7 +340,7 @@ def compute_fundamental_matrix(kp1, kp2, method='mle', reproj_threshold=2.0, 
        p1 = camera.camera_from_f(F)

        p = camera.idealized_camera()

        if kp1[mask].shape[0] <=12 or kp2[mask].shape[0] <=12:

            warnings.warn("Unable to apply MLE.  Not enough correspondences.  Returning with a RANSAC computed F matrix.")

            log.warning("Unable to apply MLE.  Not enough correspondences.  Returning with a RANSAC computed F matrix.")

            return F, mask



        # Apply Levenber-Marquardt to perform a non-linear lst. squares fit

autocnet/transformation/tests/test_fundamental_matrix.py

+1 −1
Original line number Diff line number Diff line 
import os

import sys

import unittest

import warnings



sys.path.insert(0, os.path.abspath('..'))



import numpy as np