As I know, OpenCV uses RANSAC in order to solve the problem of findHomography
and it returns some useful parameters like the homograph_mask
.
However, if I want to estimate just 2D transformation which means an Affine Matrix, is there a way to use the same methodology of findHomography
which uses RANSAC and return that mask ?
You can directly use estimateAffinePartial2D : https://docs.opencv.org/4.0.0/d9/d0c/group__calib3d.html#gad767faff73e9cbd8b9d92b955b50062d
cv::Mat cv::estimateAffinePartial2D (
InputArray from,
InputArray to,
OutputArray inliers = noArray(),
int method = RANSAC,
double ransacReprojThreshold = 3,
size_t maxIters = 2000,
double confidence = 0.99,
size_t refineIters = 10
)
for example :
src_pts = np.float32([pic1.key_points[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32([pic2.key_points[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
# Find the transformation between points, standard RANSAC
transformation_matrix, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# Compute a rigid transformation (without depth, only scale + rotation + translation) and RANSAC
transformation_rigid_matrix, rigid_mask = cv2.estimateAffinePartial2D(src_pts, dst_pts)
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