| Operators |
affine_trans_image_size — Apply an arbitrary affine 2D transformation to an image and specify the output image size.
affine_trans_image_size(Image : ImageAffineTrans : HomMat2D, Interpolation, Width, Height : )
affine_trans_image_size applies an arbitrary affine 2D transformation, i.e., scaling, rotation, translation, and slant (skewing), to the images given in Image and returns the transformed images in ImageAffineTrans. The affine transformation is described by the homogeneous transformation matrix given in HomMat2D, which can be created using the operators hom_mat2d_identity, hom_mat2d_scale, hom_mat2d_rotate, hom_mat2d_translate, etc., or be the result of operators like vector_angle_to_rigid.
The components of the homogeneous transformation matrix are interpreted as follows: The row coordinate of the image corresponds to x and the column coordinate corresponds to y of the coordinate system in which the transformation matrix was defined. This is necessary to obtain a right-handed coordinate system for the image. In particular, this assures that rotations are performed in the correct direction. Note that the (x,y) order of the matrices quite naturally corresponds to the usual (row,column) order for coordinates in the image.
The region of the input image is ignored, i.e., assumed to be the full rectangle of the image. The region of the resulting image is set to the transformed rectangle of the input image. If necessary, the resulting image is filled with zero (black) outside of the region of the original image.
Generally, transformed points will lie between pixel coordinates. Therefore, an appropriate interpolation scheme has to be used. The interpolation can also be used to avoid aliasing effects for scaled images. The quality and speed of the interpolation can be set by the parameter Interpolation:
Nearest-neighbor interpolation: The gray value is determined from the nearest pixel's gray value (possibly low quality, very fast).
Bilinear interpolation. The gray value is determined from the four nearest pixels through bilinear interpolation. If the affine transformation contains a scaling with a scale factor < 1, no smoothing is performed, which may cause severe aliasing effects (medium quality and run time).
Bicubic interpolation. The gray value is determined from the nearest pixels through bicubic interpolation. If the affine transformation contains a scaling with a scale factor < 1, no smoothing is performed, which may cause severe aliasing effects (high quality for enlargements, slow).
Bilinear interpolation. The gray value is determined from the four nearest pixels through bilinear interpolation. If the affine transformation contains a scaling with a scale factor < 1, a kind of mean filter is used to prevent aliasing effects (medium quality and run time).
Bilinear interpolation. The gray value is determined from the four nearest pixels through bilinear interpolation. If the affine transformation contains a scaling with a scale factor < 1, a kind of Gaussian filter is used to prevent aliasing effects (high quality, slow).
In addition, the system parameter 'int_zooming' (see set_system) affects the accuracy of the transformation. If 'int_zooming' is set to 'true', the transformation for byte, int2 and uint2 images is carried out internally using fixed point arithmetic, leading to much shorter execution times. However, the accuracy of the transformed gray values is smaller in this case. For byte images, the differences to the more accurate calculation (using 'int_zooming' = 'false') is typically less than two gray levels. Correspondingly, for int2 and uint2 images, the gray value differences are less than 1/128 times the dynamic gray value range of the image, i.e., they can be as large as 512 gray levels if the entire dynamic range of 16 bit is used. Additionally, if a large scale factor is applied and a large output image is obtained, then undefined gray values at the lower and at the right image border may result. The maximum width of this border of undefined gray values can be estimated as , where S is the scale factor in one dimension and I is the size of the output image in the corresponding dimension. For real images, the parameter 'int_zooming' does not affect the accuracy, since the internal calculations are always done using floating point arithmetic.
The size of the target image is specifed by the parameters Width and Height. Note that the image is always clipped at the left and upper edge, i.e., all image parts that have negative coordinates after the transformation are clipped. If the affine transformation (in particular, the translation) is chosen appropriately, a part of the image can be transformed as well as cropped in one call. This is useful, for example, when using the variation model (see compare_variation_model), because with this mechanism only the parts of the image that should be examined, are transformed.
The region of the input image is ignored.
affine_trans_image_size does not use the HALCON standard coordinate system (with the origin in the center of the upper left pixel), but instead uses the same coordinate system as in affine_trans_pixel, i.e., the origin lies in the upper left corner of the upper left pixel. Therefore, applying affine_trans_image_size corresponds to a chain of transformations (see affine_trans_pixel), which is applied to each point of the image (input and output pixels as homogeneous vectors). As an effect, you might get unexpected results when creating affine transformations based on coordinates that are derived from the image, e.g., by operators like area_center_gray. For example, if you use this operator to calculate the center of gravity of a rotationally symmetric image and then rotate the image around this point using hom_mat2d_rotate, the resulting image will not lie on the original one. In such a case, you can compensate this effect by applying the following translations to HomMat2D before using it in affine_trans_image_size:
hom_mat2d_translate(HomMat2D, 0.5, 0.5, HomMat2DTmp) hom_mat2d_translate_local(HomMat2DTmp, -0.5, -0.5, HomMat2DAdapted) affine_trans_image_size(Image, ImageAffineTrans, HomMat2DAdapted, 'constant', Width, Height)
For an explanation of the different 2D coordinate systems used in HALCON, see the introduction of chapter Transformations / 2D Transformations.
Input image.
Transformed image.
Input transformation matrix.
Type of interpolation.
Default value: 'constant'
List of values: 'bicubic', 'bilinear', 'constant', 'nearest_neighbor', 'weighted'
Width of the output image.
Default value: 640
Suggested values: 128, 160, 192, 256, 320, 384, 512, 640, 768
Height of the output image.
Default value: 480
Suggested values: 120, 128, 144, 240, 256, 288, 480, 512, 576
If the matrix HomMat2D represents an affine transformation (i.e., not a projective transformation), affine_trans_image_size returns 2 (H_MSG_TRUE). If the input is empty the behavior can be set via set_system(::'no_object_result',<Result>:). If necessary, an exception is raised.
hom_mat2d_identity, hom_mat2d_translate, hom_mat2d_rotate, hom_mat2d_scale, hom_mat2d_reflect
affine_trans_image, zoom_image_size, zoom_image_factor, mirror_image, rotate_image, affine_trans_region
Foundation
| Operators |