Error with coordinate calculation when angle > 0

[Rupsero]

I am using the Python script to match a list of images with one template. A good match is found for all images. However, sporadically, the coordinate determination fails for some images (nAN).
When I set angle = 0, this error does not occur. Does anyone else have this problem and know the cause, or even have a fix?

[DennisLiu1993]

Paste you error message here.
Maybe refer to this:

Fastest_Image_Pattern_Matching/README.md

Lines 137 to 173 in 00da009

	# Special Note:
	If you encounter an error(exception) on the constructor of opencv class "RotatedRect", modify the content in `types.cpp`:
	this might due to Windows updates
	```cpp
	RotatedRect::RotatedRect(const Point2f& _point1, const Point2f& _point2, const Point2f& _point3)
	{
	Point2f _center = 0.5f * (_point1 + _point3);
	Vec2f vecs[2];
	vecs[0] = Vec2f(_point1 - _point2);
	vecs[1] = Vec2f(_point2 - _point3);
	double x = std::max(norm(_point1), std::max(norm(_point2), norm(_point3)));
	double a = std::min(norm(vecs[0]), norm(vecs[1]));
	// check that given sides are perpendicular
	// this is the line you need to modify
	CV_Assert( std::fabs(vecs[0].ddot(vecs[1])) * a <= FLT_EPSILON * 9 * x * (norm(vecs[0]) * norm(vecs[1])) );
	// wd_i stores which vector (0,1) or (1,2) will make the width
	// One of them will definitely have slope within -1 to 1
	int wd_i = 0;
	if( std::fabs(vecs[1][1]) < std::fabs(vecs[1][0]) ) wd_i = 1;
	int ht_i = (wd_i + 1) % 2;
	float _angle = std::atan(vecs[wd_i][1] / vecs[wd_i][0]) * 180.0f / (float) CV_PI;
	float _width = (float) norm(vecs[wd_i]);
	float _height = (float) norm(vecs[ht_i]);
	center = _center;
	size = Size2f(_width, _height);
	angle = _angle;
	}
	```
	modify threshold value FLT_EPSILON of **CV_Assert line** to a bigger one
	then recompile the source code

[Rupsero]

The problem: The SubPixEsimation function produced NaN coordinates.
Debug:
[SubPixEsimation] NaN/Inf DETECTED! newX=-nan, newY=-nan, newAngle=-nan (detATA=4.86135e-14, detK1=-nan) [Python] NaN in 00004.npy Match[0]: score=0.9180, center=(nan,nan) Missing: 00004

The determinant detATA=4.86135e-14 (≈ 5×10⁻¹⁴) is practically zero.
This means that the matrix matA.t() * matA is singular.
.inv() of a singular matrix returns NaN.
These NaN values propagate throughout the entire calculation.