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Leptonica 1.68
C Image Processing Library
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Leptonica 1.68
C Image Processing Library
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3-pt affine transforms on images and coordinates, with sampling and interpolation; gauss-jordan solver More...
#include <stdio.h>#include <stdlib.h>#include <string.h>#include <math.h>#include "allheaders.h"Go to the source code of this file.
3-pt affine transforms on images and coordinates, with sampling and interpolation; gauss-jordan solver
Affine (3 pt) image transformation using a sampled
(to nearest integer) transform on each dest point
PIX *pixAffineSampledPta()
PIX *pixAffineSampled()
Affine (3 pt) image transformation using interpolation
(or area mapping) for anti-aliasing images that are
2, 4, or 8 bpp gray, or colormapped, or 32 bpp RGB
PIX *pixAffinePta()
PIX *pixAffine()
PIX *pixAffinePtaColor()
PIX *pixAffineColor()
PIX *pixAffinePtaGray()
PIX *pixAffineGray()
Affine transform including alpha (blend) component and gamma transform
PIX *pixAffinePtaWithAlpha()
PIX *pixAffinePtaGammaXform()
Affine coordinate transformation
l_int32 getAffineXformCoeffs()
l_int32 affineInvertXform()
l_int32 affineXformSampledPt()
l_int32 affineXformPt()
Interpolation helper functions
l_int32 linearInterpolatePixelGray()
l_int32 linearInterpolatePixelColor()
Gauss-jordan linear equation solver
l_int32 gaussjordan()
Affine image transformation using a sequence of
shear/scale/translation operations
PIX *pixAffineSequential()
One can define a coordinate space by the location of the origin,
the orientation of x and y axes, and the unit scaling along
each axis. An affine transform is a general linear
transformation from one coordinate space to another.
For the general case, we can define the affine transform using
two sets of three (noncollinear) points in a plane. One set
corresponds to the input (src) coordinate space; the other to the
transformed (dest) coordinate space. Each point in the
src corresponds to one of the points in the dest. With two
sets of three points, we get a set of 6 equations in 6 unknowns
that specifies the mapping between the coordinate spaces.
The interface here allows you to specify either the corresponding
sets of 3 points, or the transform itself (as a vector of 6
coefficients).
Given the transform as a vector of 6 coefficients, we can compute
both a a pointwise affine coordinate transformation and an
affine image transformation.
To compute the coordinate transform, we need the coordinate
value (x',y') in the transformed space for any point (x,y)
in the original space. To derive this transform from the
three corresponding points, it is convenient to express the affine
coordinate transformation using an LU decomposition of
a set of six linear equations that express the six coordinates
of the three points in the transformed space as a function of
the six coordinates in the original space. Once we have
this transform matrix , we can transform an image by
finding, for each destination pixel, the pixel (or pixels)
in the source that give rise to it.
This 'pointwise' transformation can be done either by sampling
and picking a single pixel in the src to replicate into the dest,
or by interpolating (or averaging) over four src pixels to
determine the value of the dest pixel. The first method is
implemented by pixAffineSampled() and the second method by
pixAffine(). The interpolated method can only be used for
images with more than 1 bpp, but for these, the image quality
is significantly better than the sampled method, due to
the 'antialiasing' effect of weighting the src pixels.
Interpolation works well when there is relatively little scaling,
or if there is image expansion in general. However, if there
is significant image reduction, one should apply a low-pass
filter before subsampling to avoid aliasing the high frequencies.
A typical application might be to align two images, which
may be scaled, rotated and translated versions of each other.
Through some pre-processing, three corresponding points are
located in each of the two images. One of the images is
then to be (affine) transformed to align with the other.
As mentioned, the standard way to do this is to use three
sets of points, compute the 6 transformation coefficients
from these points that describe the linear transformation,
x' = ax + by + c
y' = dx + ey + f
and use this in a pointwise manner to transform the image.
N.B. Be sure to see the comment in getAffineXformCoeffs(),
regarding using the inverse of the affine transform for points
to transform images.
There is another way to do this transformation; namely,
by doing a sequence of simple affine transforms, without
computing directly the affine coordinate transformation.
We have at our disposal (1) translations (using rasterop),
(2) horizontal and vertical shear about any horizontal and vertical
line, respectively, and (3) non-isotropic scaling by two
arbitrary x and y scaling factors. We also have rotation
about an arbitrary point, but this is equivalent to a set
of three shears so we do not need to use it.
Why might we do this? For binary images, it is usually
more efficient to do such transformations by a sequence
of word parallel operations. Shear and translation can be
done in-place and word parallel; arbitrary scaling is
mostly pixel-wise.
Suppose that we are tranforming image 1 to correspond to image 2.
We have a set of three points, describing the coordinate space
embedded in image 1, and we need to transform image 1 until
those three points exactly correspond to the new coordinate space
defined by the second set of three points. In our image
matching application, the latter set of three points was
found to be the corresponding points in image 2.
The most elegant way I can think of to do such a sequential
implementation is to imagine that we're going to transform
BOTH images until they're aligned. (We don't really want
to transform both, because in fact we may only have one image
that is undergoing a general affine transformation.)
Choose the 3 corresponding points as follows:
- The 1st point is an origin
- The 2nd point gives the orientation and scaling of the
"x" axis with respect to the origin
- The 3rd point does likewise for the "y" axis.
These "axes" must not be collinear; otherwise they are
arbitrary (although some strange things will happen if
the handedness sweeping through the minimum angle between
the axes is opposite).
An important constraint is that we have shear operations
about an arbitrary horizontal or vertical line, but always
parallel to the x or y axis. If we continue to pretend that
we have an unprimed coordinate space embedded in image 1 and
a primed coordinate space embedded in image 2, we imagine
(a) transforming image 1 by horizontal and vertical shears about
point 1 to align points 3 and 2 along the y and x axes,
respectively, and (b) transforming image 2 by horizontal and
vertical shears about point 1' to align points 3' and 2' along
the y and x axes. Then we scale image 1 so that the distances
from 1 to 2 and from 1 to 3 are equal to the distances in
image 2 from 1' to 2' and from 1' to 3'. This scaling operation
leaves the true image origin, at (0,0) invariant, and will in
general translate point 1. The original points 1 and 1' will
typically not coincide in any event, so we must translate
the origin of image 1, at its current point 1, to the origin
of image 2 at 1'. The images should now be aligned. But
because we never really transformed image 2 (and image 2 may
not even exist), we now perform on image 1 the reverse of
the shear transforms that we imagined doing on image 2;
namely, the negative vertical shear followed by the negative
horizontal shear. Image 1 should now have its transformed
unprimed coordinates aligned with the original primed
coordinates. In all this, it is only necessary to keep track
of the shear angles and translations of points during the shears.
What has been accomplished is a general affine transformation
on image 1.
Having described all this, if you are going to use an
affine transformation in an application, this is what you
need to know:
(1) You should NEVER use the sequential method, because
the image quality for 1 bpp text is much poorer
(even though it is about 2x faster than the pointwise sampled
method), and for images with depth greater than 1, it is
nearly 20x slower than the pointwise sampled method
and over 10x slower than the pointwise interpolated method!
The sequential method is given here for purely
pedagogical reasons.
(2) For 1 bpp images, use the pointwise sampled function
pixAffineSampled(). For all other images, the best
quality results result from using the pointwise
interpolated function pixAffinePta() or pixAffine();
the cost is less than a doubling of the computation time
with respect to the sampled function. If you use
interpolation on colormapped images, the colormap will
be removed, resulting in either a grayscale or color
image, depending on the values in the colormap.
If you want to retain the colormap, use pixAffineSampled().
Typical relative timing of pointwise transforms (sampled = 1.0):
8 bpp: sampled 1.0
interpolated 1.6
32 bpp: sampled 1.0
interpolated 1.8
Additionally, the computation time/pixel is nearly the same
for 8 bpp and 32 bpp, for both sampled and interpolated.
Definition in file affine.c.
| #define SWAP | ( | a, | |
| b | |||
| ) | {temp = (a); (a) = (b); (b) = temp;} |
Definition at line 1318 of file affine.c.
Referenced by gaussjordan().
Input: pixs (all depths) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) incolor (L_BRING_IN_WHITE, L_BRING_IN_BLACK) Return: pixd, or null on error
Notes: (1) Brings in either black or white pixels from the boundary. (2) Retains colormap, which you can do for a sampled transform.. (3) The 3 points must not be collinear. (4) The order of the 3 points is arbitrary; however, to compare with the sequential transform they must be in these locations and in this order: origin, x-axis, y-axis. (5) For 1 bpp images, this has much better quality results than pixAffineSequential(), particularly for text. It is about 3x slower, but does not require additional border pixels. The poor quality of pixAffineSequential() is due to repeated quantized transforms. It is strongly recommended that pixAffineSampled() be used for 1 bpp images. (6) For 8 or 32 bpp, much better quality is obtained by the somewhat slower pixAffinePta(). See that function for relative timings between sampled and interpolated. (7) To repeat, use of the sequential transform, pixAffineSequential(), for any images, is discouraged.
Definition at line 268 of file affine.c.
References ERROR_PTR, FREE, getAffineXformCoeffs(), L_BRING_IN_BLACK, L_BRING_IN_WHITE, NULL, pixAffineSampled(), PROCNAME, and ptaGetCount().
Referenced by main(), and pixAffinePta().
Input: pixs (all depths) vc (vector of 6 coefficients for affine transformation) incolor (L_BRING_IN_WHITE, L_BRING_IN_BLACK) Return: pixd, or null on error
Notes: (1) Brings in either black or white pixels from the boundary. (2) Retains colormap, which you can do for a sampled transform.. (3) For 8 or 32 bpp, much better quality is obtained by the somewhat slower pixAffine(). See that function for relative timings between sampled and interpolated.
Definition at line 316 of file affine.c.
References affineXformSampledPt(), ERROR_PTR, GET_DATA_BIT, GET_DATA_BYTE, GET_DATA_DIBIT, GET_DATA_QBIT, L_BRING_IN_BLACK, L_BRING_IN_WHITE, NULL, pixClearAll(), pixcmapAddBlackOrWhite(), pixCreateTemplate(), pixGetColormap(), pixGetData(), pixGetDimensions(), pixGetWpl(), pixSetAll(), pixSetAllArbitrary(), PROCNAME, SET_DATA_BIT_VAL, SET_DATA_BYTE, SET_DATA_DIBIT, SET_DATA_QBIT, HeapElement::x, and HeapElement::y.
Referenced by pixAffine(), and pixAffineSampledPta().
Input: pixs (all depths; colormap ok) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) incolor (L_BRING_IN_WHITE, L_BRING_IN_BLACK) Return: pixd, or null on error
Notes: (1) Brings in either black or white pixels from the boundary (2) Removes any existing colormap, if necessary, before transforming
Definition at line 411 of file affine.c.
References ERROR_PTR, FALSE, L_BRING_IN_BLACK, L_BRING_IN_WHITE, NULL, pixAffinePtaColor(), pixAffinePtaGray(), pixAffineSampledPta(), pixClone(), pixConvertTo8(), pixDestroy(), pixGetDepth(), pixRemoveColormap(), PROCNAME, ptaGetCount(), and REMOVE_CMAP_BASED_ON_SRC.
Referenced by main().
Input: pixs (all depths; colormap ok) vc (vector of 6 coefficients for affine transformation) incolor (L_BRING_IN_WHITE, L_BRING_IN_BLACK) Return: pixd, or null on error
Notes: (1) Brings in either black or white pixels from the boundary (2) Removes any existing colormap, if necessary, before transforming
Definition at line 479 of file affine.c.
References ERROR_PTR, FALSE, L_BRING_IN_WHITE, NULL, pixAffineColor(), pixAffineGray(), pixAffineSampled(), pixClone(), pixConvertTo8(), pixDestroy(), pixGetDepth(), pixRemoveColormap(), PROCNAME, and REMOVE_CMAP_BASED_ON_SRC.
Referenced by main().
Input: pixs (32 bpp) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) colorval (e.g., 0 to bring in BLACK, 0xffffff00 for WHITE) Return: pixd, or null on error
Definition at line 535 of file affine.c.
References ERROR_PTR, FREE, getAffineXformCoeffs(), NULL, pixAffineColor(), pixGetDepth(), PROCNAME, and ptaGetCount().
Referenced by pixAffinePta(), and pixAffinePtaWithAlpha().
Input: pixs (32 bpp) vc (vector of 6 coefficients for affine transformation) colorval (e.g., 0 to bring in BLACK, 0xffffff00 for WHITE) Return: pixd, or null on error
Definition at line 576 of file affine.c.
References affineXformPt(), ERROR_PTR, linearInterpolatePixelColor(), NULL, pixCreateTemplate(), pixGetData(), pixGetDimensions(), pixGetWpl(), pixSetAllArbitrary(), PROCNAME, HeapElement::x, and HeapElement::y.
Referenced by pixAffine(), and pixAffinePtaColor().
Input: pixs (8 bpp) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) grayval (0 to bring in BLACK, 255 for WHITE) Return: pixd, or null on error
Definition at line 629 of file affine.c.
References ERROR_PTR, FREE, getAffineXformCoeffs(), NULL, pixAffineGray(), pixGetDepth(), PROCNAME, and ptaGetCount().
Referenced by pixAffinePta(), and pixAffinePtaWithAlpha().
Input: pixs (8 bpp) vc (vector of 6 coefficients for affine transformation) grayval (0 to bring in BLACK, 255 for WHITE) Return: pixd, or null on error
Definition at line 671 of file affine.c.
References affineXformPt(), ERROR_PTR, linearInterpolatePixelGray(), NULL, pixCreateTemplate(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), pixSetAllArbitrary(), PROCNAME, SET_DATA_BYTE, HeapElement::x, and HeapElement::y.
Referenced by pixAffine(), and pixAffinePtaGray().
| PIX* pixAffinePtaWithAlpha | ( | PIX * | pixs, |
| PTA * | ptad, | ||
| PTA * | ptas, | ||
| PIX * | pixg, | ||
| l_float32 | fract, | ||
| l_int32 | border | ||
| ) |
Input: pixs (32 bpp rgb) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) pixg (<optional> 8 bpp, can be null) fract (between 0.0 and 1.0, with 0.0 fully transparent and 1.0 fully opaque) border (of pixels added to capture transformed source pixels) Return: pixd, or null on error
Notes: (1) The alpha channel is transformed separately from pixs, and aligns with it, being fully transparent outside the boundary of the transformed pixs. For pixels that are fully transparent, a blending function like pixBlendWithGrayMask() will give zero weight to corresponding pixels in pixs. (2) If pixg is NULL, it is generated as an alpha layer that is partially opaque, using . Otherwise, it is cropped to pixs if required and is ignored. The alpha channel in pixs is never used. (3) Colormaps are removed. (4) When pixs is transformed, it doesn't matter what color is brought in because the alpha channel will be transparent (0) there. (5) To avoid losing source pixels in the destination, it may be necessary to add a border to the source pix before doing the affine transformation. This can be any non-negative number. (6) The input and are in a coordinate space before the border is added. Internally, we compensate for this before doing the affine transform on the image after the border is added. (7) The default setting for the border values in the alpha channel is 0 (transparent) for the outermost ring of pixels and (0.5 * fract * 255) for the second ring. When blended over a second image, this (a) shrinks the visible image to make a clean overlap edge with an image below, and (b) softens the edges by weakening the aliasing there. Use l_setAlphaMaskBorder() to change these values.
Definition at line 757 of file affine.c.
References AlphaMaskBorderVals, ERROR_PTR, L_ALPHA_CHANNEL, L_WARNING, NULL, pixAddBorder(), pixAffinePtaColor(), pixAffinePtaGray(), pixCreate(), pixDestroy(), pixGetColormap(), pixGetDepth(), pixGetDimensions(), pixResizeToMatch(), pixSetAll(), pixSetAllArbitrary(), pixSetBorderRingVal(), pixSetRGBComponent(), PROCNAME, ptaDestroy(), and ptaTransform().
Referenced by main(), and pixAffinePtaGammaXform().
| PIX* pixAffinePtaGammaXform | ( | PIX * | pixs, |
| l_float32 | gamma, | ||
| PTA * | ptad, | ||
| PTA * | ptas, | ||
| l_float32 | fract, | ||
| l_int32 | border | ||
| ) |
Input: pixs (32 bpp rgb) gamma (gamma correction; must be > 0.0) ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) fract (between 0.0 and 1.0, with 1.0 fully transparent) border (of pixels to capture transformed source pixels) Return: pixd, or null on error
Notes: (1) This wraps a gamma/inverse-gamma photometric transform around pixAffinePtaWithAlpha(). (2) For usage, see notes in pixAffinePtaWithAlpha() and pixGammaTRCWithAlpha(). (3) The basic idea of a gamma/inverse-gamma transform is to remove any gamma correction before the affine transform, and restore it afterward. The effects can be subtle, but important for some applications. For example, using gamma > 1.0 will cause the dark areas to become somewhat lighter and slightly reduce aliasing effects when blending using the alpha channel.
Definition at line 853 of file affine.c.
References ERROR_PTR, L_WARNING, NULL, pixAffinePtaWithAlpha(), pixDestroy(), pixGammaTRCWithAlpha(), pixGetDepth(), and PROCNAME.
Referenced by main().
Input: ptas (source 3 points; unprimed) ptad (transformed 3 points; primed) &vc (<return> vector of coefficients of transform) Return: 0 if OK; 1 on error
We have a set of six equations, describing the affine transformation that takes 3 points (ptas) into 3 other points (ptad). These equations are:
x1' = c[0]*x1 + c[1]*y1 + c[2] y1' = c[3]*x1 + c[4]*y1 + c[5] x2' = c[0]*x2 + c[1]*y2 + c[2] y2' = c[3]*x2 + c[4]*y2 + c[5] x3' = c[0]*x3 + c[1]*y3 + c[2] y3' = c[3]*x3 + c[4]*y3 + c[5]
This can be represented as
AC = B
where B and C are column vectors
B = [ x1' y1' x2' y2' x3' y3' ] C = [ c[0] c[1] c[2] c[3] c[4] c[5] c[6] ]
and A is the 6x6 matrix
x1 y1 1 0 0 0 0 0 0 x1 y1 1 x2 y2 1 0 0 0 0 0 0 x2 y2 1 x3 y3 1 0 0 0 0 0 0 x3 y3 1
These six equations are solved here for the coefficients C.
These six coefficients can then be used to find the dest point (x',y') corresponding to any src point (x,y), according to the equations
x' = c[0]x + c[1]y + c[2] y' = c[3]x + c[4]y + c[5]
that are implemented in affineXformPt().
!!!!!!!!!!!!!!!!!! Very important !!!!!!!!!!!!!!!!!!!!!!
When the affine transform is composed from a set of simple operations such as translation, scaling and rotation, it is built in a form to convert from the un-transformed src point to the transformed dest point. However, when an affine transform is used on images, it is used in an inverted way: it converts from the transformed dest point to the un-transformed src point. So, for example, if you transform a boxa using transform A, to transform an image in the same way you must use the inverse of A.
For example, if you transform a boxa with a 3x3 affine matrix 'mat', the analogous image transformation must use 'matinv':
boxad = boxaAffineTransform(boxas, mat); affineInvertXform(mat, &matinv); pixd = pixAffine(pixs, matinv, L_BRING_IN_WHITE);
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Definition at line 954 of file affine.c.
References CALLOC, ERROR_INT, FREE, gaussjordan(), NULL, PROCNAME, ptaGetPt(), x1, x2, x3, y1, y2, and y3.
Referenced by pixAffinePtaColor(), pixAffinePtaGray(), and pixAffineSampledPta().
Input: vc (vector of 6 coefficients) *vci (<return> inverted transform) Return: 0 if OK; 1 on error
Notes: (1) The 6 affine transform coefficients are the first two rows of a 3x3 matrix where the last row has only a 1 in the third column. We invert this using gaussjordan(), and select the first 2 rows as the coefficients of the inverse affine transform. (2) Alternatively, we can find the inverse transform coefficients by inverting the 2x2 submatrix, and treating the top 2 coefficients in the 3rd column as a RHS vector for that 2x2 submatrix. Then the 6 inverted transform coefficients are composed of the inverted 2x2 submatrix and the negative of the transformed RHS vector. Why is this so? We have Y = AX + R (2 equations in 6 unknowns) Then X = A'Y - A'R Gauss-jordan solves AF = R and puts the solution for F, which is A'R, into the input R vector.
Definition at line 1045 of file affine.c.
References CALLOC, ERROR_INT, gaussjordan(), NULL, and PROCNAME.
Referenced by main().
Input: vc (vector of 6 coefficients) (x, y) (initial point) (&xp, &yp) (<return> transformed point) Return: 0 if OK; 1 on error
Notes: (1) This finds the nearest pixel coordinates of the transformed point. (2) It does not check ptrs for returned data!
Definition at line 1118 of file affine.c.
References ERROR_INT, and PROCNAME.
Referenced by pixAffineSampled().
Input: vc (vector of 6 coefficients) (x, y) (initial point) (&xp, &yp) (<return> transformed point) Return: 0 if OK; 1 on error
Notes: (1) This computes the floating point location of the transformed point. (2) It does not check ptrs for returned data!
Definition at line 1148 of file affine.c.
References ERROR_INT, and PROCNAME.
Referenced by pixAffineColor(), and pixAffineGray().
| l_int32 linearInterpolatePixelColor | ( | l_uint32 * | datas, |
| l_int32 | wpls, | ||
| l_int32 | w, | ||
| l_int32 | h, | ||
| l_float32 | x, | ||
| l_float32 | y, | ||
| l_uint32 | colorval, | ||
| l_uint32 * | pval | ||
| ) |
Input: datas (ptr to beginning of image data) wpls (32-bit word/line for this data array) w, h (of image) x, y (floating pt location for evaluation) colorval (color brought in from the outside when the input x,y location is outside the image; in 0xrrggbb00 format)) &val (<return> interpolated color value) Return: 0 if OK, 1 on error
Notes: (1) This is a standard linear interpolation function. It is equivalent to area weighting on each component, and avoids "jaggies" when rendering sharp edges.
Definition at line 1187 of file affine.c.
References ERROR_INT, L_BLUE_SHIFT, L_GREEN_SHIFT, L_RED_SHIFT, and PROCNAME.
Referenced by pixAffineColor(), pixBilinearColor(), and pixProjectiveColor().
| l_int32 linearInterpolatePixelGray | ( | l_uint32 * | datas, |
| l_int32 | wpls, | ||
| l_int32 | w, | ||
| l_int32 | h, | ||
| l_float32 | x, | ||
| l_float32 | y, | ||
| l_int32 | grayval, | ||
| l_int32 * | pval | ||
| ) |
Input: datas (ptr to beginning of image data) wpls (32-bit word/line for this data array) w, h (of image) x, y (floating pt location for evaluation) grayval (color brought in from the outside when the input x,y location is outside the image) &val (<return> interpolated gray value) Return: 0 if OK, 1 on error
Notes: (1) This is a standard linear interpolation function. It is equivalent to area weighting on each component, and avoids "jaggies" when rendering sharp edges.
Definition at line 1267 of file affine.c.
References ERROR_INT, GET_DATA_BYTE, and PROCNAME.
Referenced by pixAffineGray(), pixBilinearGray(), pixProjectiveGray(), and pixRandomHarmonicWarp().
Input: a (n x n matrix) b (rhs column vector) n (dimension) Return: 0 if ok, 1 on error
Note side effects: (1) the matrix a is transformed to its inverse (2) the vector b is transformed to the solution X to the linear equation AX = B
Adapted from "Numerical Recipes in C, Second Edition", 1992 pp. 36-41 (gauss-jordan elimination)
Definition at line 1337 of file affine.c.
References CALLOC, ERROR_INT, FREE, NULL, PROCNAME, and SWAP.
Referenced by affineInvertXform(), getAffineXformCoeffs(), getBilinearXformCoeffs(), getProjectiveXformCoeffs(), main(), ptaGetCubicLSF(), ptaGetQuadraticLSF(), and ptaGetQuarticLSF().
Input: pixs ptad (3 pts of final coordinate space) ptas (3 pts of initial coordinate space) bw (pixels of additional border width during computation) bh (pixels of additional border height during computation) Return: pixd, or null on error
Notes: (1) The 3 pts must not be collinear. (2) The 3 pts must be given in this order:
Definition at line 1446 of file affine.c.
References ERROR_PTR, L_BRING_IN_WHITE, NULL, pixAddBorderGeneral(), pixClone(), pixCopy(), pixDestroy(), pixHShearIP(), pixRasteropIP(), pixRemoveBorderGeneral(), pixScale(), pixVShearIP(), PROCNAME, ptaGetCount(), ptaGetIPt(), x1, x2, x3, y1, y2, and y3.
Referenced by main().
Definition at line 117 of file pix2.c.
Referenced by l_setAlphaMaskBorder(), pixAffinePtaWithAlpha(), pixBilinearPtaWithAlpha(), pixProjectivePtaWithAlpha(), pixRotateWithAlpha(), and pixScaleWithAlpha().
1.7.3