To specify a location in an image, we need a convention how to do so. Such a convention is set via a coordinate system. There are different coordinate systems used in HALCON. Here, we explain the ones used in 2D.
Pixels are discrete and to address them, we have a coordinate system using only integer values, the pixel coordinate system. For a higher accuracy that goes beyond the pixel grid, we need floating point coordinates, like e.g., . This leads to subpixel accurate coordinate systems. In HALCON, we have three different implementations of subpixel coordinate systems:
Pixel Centered Coordinates, the HALCON Standard Subpixel Coordinate System
Edge Centered Coordinates
Polar Coordinates
Thereof the first two vary only in the coordinate origin,
as visible in the figures below.
Calibration makes it possible to map the image coordinates distances to
real-world distances. For more information about these Calibrated
Coordinates we refer to the “Solution Guide III-C - 3D Vision”.
The pixel coordinate system treats the image as a grid of discrete elements, the pixels. In HALCON, we put the origin in the middle of the upper left pixel. Now, we assign the pixel coordinates specifying its row and column like in a matrix.
Note that this implies for an image of size height width = pixels that the row coordinate values run from to and the column coordinate values from to , as visualized in the figure below.
The origin of this coordinate system is in the center of the upper left image pixel, the axes are in row (r) and column (c) direction, respectively. Therewith this convention embeds the pixel coordinate system. The upper left image corner has the coordinates and for an image of size height width = pixels the bottom right corner has the coordinates (=, remember the coordinate values start at 0). It also implies that a pixel (k,l) covers the area of the rectangle , , , . This convention is called the standard coordinate system, or also Image Coordinate System.
| ( 1) | ( 2) |
If we rotate an image around its origin by (=90 degrees), we want the two images with touching edges but not overlapping with each other. Also, scaling the image is not expected to result in negative image coordinates. For this, the origin has to be set in an image corner. This motivates the following coordinate system.
For this coordinate system we set the origin in the upper left image corner. Thus the center of the upper left pixel has the coordinates and for an image of size height width = pixels the bottom right corner has the coordinates . A pixel covers the area of the rectangle , , , .
For this coordinate system rotations are defined in the mathematically positive direction and thus counterclockwise. A rotation of (=90 degrees) maps the first axis (= x-axis) onto the second axis (= y-axis). Accordingly, the axes have the assignment row: x coordinate, column: y coordinate.
The operator affine_trans_point_2dHomMat2Daffine_trans_point_2d
The operators angle_llangle_lx
In HALCON there is also the case that an operator expects its input in
different coordinate systems.
On the one hand, the object is expected in its usual coordinates, the
standard coordinates.
On the other hand, for the transformation matrix HomMat2DHomMat2D
These operators are
A matrix representing a transformation in pixel centered coordinates can be converted to represent the same transformation (e.g., a rotation around the same point) written in edge centered coordinates, e.g., through
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Note, the operators beginning with projective_ mentioned above
use a projective transformation matrix. These transformation matrices can,
e.g., be obtained from a 3D camera pose. Doing so, the matrix used is
written in a projection of the xy-plane within the 3D coordinate system.
Accordingly, the axes have the assignment row: y coordinate, column:
x coordinate and therewith the coordinates need to be converted.
Results from shape-based matching, like e.g.,
find_generic_shape_modelget_generic_shape_model_result
Results from correlation-based matching, like e.g.,
find_ncc_modelfind_ncc_modelsHomMat2Daffine_trans_contour_xldOperators Expecting Parameters in Different Coordinate Systems.
To display the results found by correlation-based matching, we highly
recommend the usage of the procedure
dev_display_ncc_matching_results.
In the following images we give an example how a displayed
match may look when using the transformation matrix in the correct and
the erroneous coordinate system, respectively.
For the latter one, shown in image (3), the transition matrix is given in
pixel centered coordinates as well and therefore the match shown by
affine_trans_contour_xld
| ( 1) | ( 2) | ( 3) |
In polar coordinates, points are defined through a distance and an angle. The distance is called the radial coordinate and is given in relation to the fix point, the pole. The angular coordinate is given with respect to a defined axis, the polar axis. In HALCON, the pole is specified by and the polar axis is the horizontal axis. The angular coordinate is given in radians.
After a transformation with polar_trans_image_extRadiusStartAngleStartRadiusEndAngleEndAngleStartAngleEndRadiusStartRadiusEndAngleStartAngleEndRadiusStartRadiusEnd
| ( 1) | ( 2) | ( 3) |
RowColumnAngleStartAngleEndRadiusStartRadiusEndpolar_trans_image_extpolar_trans_image_invPolar coordinates are used by the following operators:
In the part before we spoke about coordinates of images. When it comes to the location of the origin of the coordinate system used, images with reduced domains, regions, and models are treated differently than images.
Both images with a reduced domain and regions keep the coordinate system of the image from which they were created. This means, they inherit the origin and the points keep the coordinate values they had in the original image.
Models, on the other side, can have a local coordinate system.
E.g., models obtained over create_generic_shape_model“Solution Guide II-B - Matching”.
While working with pixel units, we can not extract any information about
real-world distances directly.
When a camera is calibrated, it is possible to rectify the images.
In this case one can assign world coordinates to the image.
For further information we refer to the
“Solution Guide III-C - 3D Vision”.
affine_trans_pixelaffine_trans_point_2ddeserialize_hom_mat2dhom_mat2d_composehom_mat2d_determinanthom_mat2d_identityhom_mat2d_inverthom_mat2d_reflecthom_mat2d_reflect_localhom_mat2d_rotatehom_mat2d_rotate_localhom_mat2d_scalehom_mat2d_scale_localhom_mat2d_slanthom_mat2d_slant_localhom_mat2d_to_affine_parhom_mat2d_translatehom_mat2d_translate_localhom_mat2d_transposehom_mat3d_projecthom_vector_to_proj_hom_mat2dpoint_line_to_hom_mat2dprojective_trans_pixelprojective_trans_point_2dserialize_hom_mat2dvector_angle_to_rigidvector_field_to_hom_mat2dvector_to_anisovector_to_hom_mat2dvector_to_proj_hom_mat2dvector_to_proj_hom_mat2d_distortionvector_to_rigidvector_to_similarity