Leptonica
1.54
|
#define M_PI 3.14159265358979323846 |
l_int32 applyCubicFit | ( | l_float32 | a, |
l_float32 | b, | ||
l_float32 | c, | ||
l_float32 | d, | ||
l_float32 | x, | ||
l_float32 * | py | ||
) |
Input: a, b, c, d (cubic fit coefficients) x &y (<return> y = a * x^3 + b * x^2 + c * x + d) Return: 0 if OK, 1 on error
Input: a, b (linear fit coefficients) x &y (<return> y = a * x + b) Return: 0 if OK, 1 on error
Input: a, b, c (quadratic fit coefficients) x &y (<return> y = a * x^2 + b * x + c) Return: 0 if OK, 1 on error
l_int32 applyQuarticFit | ( | l_float32 | a, |
l_float32 | b, | ||
l_float32 | c, | ||
l_float32 | d, | ||
l_float32 | e, | ||
l_float32 | x, | ||
l_float32 * | py | ||
) |
Input: a, b, c, d, e (quartic fit coefficients) x &y (<return> y = a * x^4 + b * x^3 + c * x^2 + d * x + e) Return: 0 if OK, 1 on error
Input: x1, y1 (end point of first vector) x2, y2 (end point of second vector) Return: angle (radians), or 0.0 on error
Notes: (1) This gives the angle between two vectors, going between vector1 (x1,y1) and vector2 (x2,y2). The angle is swept out from 1 --> 2. If this is clockwise, the angle is positive, but the result is folded into the interval [-pi, pi].
L_ASET* l_asetCreateFromPta | ( | PTA * | pta | ) |
Input: pta Return: set (using a 64-bit hash of (x,y) as the key)
static l_int32 l_dnaCopyUniquePts | ( | L_DNA * | da, |
PTA * | ptas, | ||
PTA * | ptad | ||
) | [static] |
L_DNAHASH* l_dnaHashCreateFromPta | ( | PTA * | pta | ) |
Input: pta Return: dahash, or null on error
PIX* pixDisplayPta | ( | PIX * | pixd, |
PIX * | pixs, | ||
PTA * | pta | ||
) |
Input: pixd (can be same as pixs or null; 32 bpp if in-place) pixs (1, 2, 4, 8, 16 or 32 bpp) pta (of path to be plotted) Return: pixd (32 bpp RGB version of pixs, with path in green).
Notes: (1) To write on an existing pixs, pixs must be 32 bpp and call with pixd == pixs: pixDisplayPta(pixs, pixs, pta); To write to a new pix, use pixd == NULL and call: pixd = pixDisplayPta(NULL, pixs, pta); (2) On error, returns pixd to avoid losing pixs if called as pixs = pixDisplayPta(pixs, pixs, pta);
PIX* pixDisplayPtaa | ( | PIX * | pixs, |
PTAA * | ptaa | ||
) |
Input: pixs (1, 2, 4, 8, 16 or 32 bpp) ptaa (array of paths to be plotted) Return: pixd (32 bpp RGB version of pixs, with paths plotted in different colors), or null on error
PIX* pixDisplayPtaaPattern | ( | PIX * | pixd, |
PIX * | pixs, | ||
PTAA * | ptaa, | ||
PIX * | pixp, | ||
l_int32 | cx, | ||
l_int32 | cy | ||
) |
Input: pixd (32 bpp) pixs (1, 2, 4, 8, 16 or 32 bpp; 32 bpp if in place) ptaa (giving locations at which the pattern is displayed) pixp (1 bpp pattern to be placed such that its reference point co-locates with each point in pta) cx, cy (reference point in pattern) Return: pixd (32 bpp RGB version of pixs).
Notes: (1) To write on an existing pixs, pixs must be 32 bpp and call with pixd == pixs: pixDisplayPtaPattern(pixs, pixs, pta, ...); To write to a new pix, use pixd == NULL and call: pixd = pixDisplayPtaPattern(NULL, pixs, pta, ...); (2) Puts a random color on each pattern associated with a pta. (3) On error, returns pixd to avoid losing pixs if called as pixs = pixDisplayPtaPattern(pixs, pixs, pta, ...); (4) A typical pattern to be used is a circle, generated with generatePtaFilledCircle()
PIX* pixDisplayPtaPattern | ( | PIX * | pixd, |
PIX * | pixs, | ||
PTA * | pta, | ||
PIX * | pixp, | ||
l_int32 | cx, | ||
l_int32 | cy, | ||
l_uint32 | color | ||
) |
Input: pixd (can be same as pixs or null; 32 bpp if in-place) pixs (1, 2, 4, 8, 16 or 32 bpp) pta (giving locations at which the pattern is displayed) pixp (1 bpp pattern to be placed such that its reference point co-locates with each point in pta) cx, cy (reference point in pattern) color (in 0xrrggbb00 format) Return: pixd (32 bpp RGB version of pixs).
Notes: (1) To write on an existing pixs, pixs must be 32 bpp and call with pixd == pixs: pixDisplayPtaPattern(pixs, pixs, pta, ...); To write to a new pix, use pixd == NULL and call: pixd = pixDisplayPtaPattern(NULL, pixs, pta, ...); (2) On error, returns pixd to avoid losing pixs if called as pixs = pixDisplayPtaPattern(pixs, pixs, pta, ...); (3) A typical pattern to be used is a circle, generated with generatePtaFilledCircle()
PTA* pixFindCornerPixels | ( | PIX * | pixs | ) |
Input: pixs (1 bpp) Return: pta, or null on error
Notes: (1) Finds the 4 corner-most pixels, as defined by a search inward from each corner, using a 45 degree line.
PIX* pixGenerateFromPta | ( | PTA * | pta, |
l_int32 | w, | ||
l_int32 | h | ||
) |
Input: pta w, h (of pix) Return: pix (1 bpp), or null on error
Notes: (1) Points are rounded to nearest ints. (2) Any points outside (w,h) are silently discarded. (3) Output 1 bpp pix has values 1 for each point in the pta.
l_int32 pixPlotAlongPta | ( | PIX * | pixs, |
PTA * | pta, | ||
l_int32 | outformat, | ||
const char * | title | ||
) |
Input: pixs (any depth) pta (set of points on which to plot) outformat (GPLOT_PNG, GPLOT_PS, GPLOT_EPS, GPLOT_X11, GPLOT_LATEX) title (<optional> for plot; can be null) Return: 0 if OK, 1 on error
Notes: (1) We remove any existing colormap and clip the pta to the input pixs. (2) This is a debugging function, and does not remove temporary plotting files that it generates. (3) If the image is RGB, three separate plots are generated.
PTAA* ptaaGetBoundaryPixels | ( | PIX * | pixs, |
l_int32 | type, | ||
l_int32 | connectivity, | ||
BOXA ** | pboxa, | ||
PIXA ** | ppixa | ||
) |
Input: pixs (1 bpp) type (L_BOUNDARY_FG, L_BOUNDARY_BG) connectivity (4 or 8) &boxa (<optional return>=""> bounding boxes of the c.c.) &pixa (<optional return>=""> pixa of the c.c.) Return: ptaa, or null on error
Notes: (1) This generates a ptaa of either fg or bg boundary pixels, where each pta has the boundary pixels for a connected component. (2) We can't simply find all the boundary pixels and then select those within the bounding box of each component, because bounding boxes can overlap. It is necessary to extract and dilate or erode each component separately. Note also that special handling is required for bg pixels when the component touches the pix boundary.
PTAA* ptaaIndexLabelledPixels | ( | PIX * | pixs, |
l_int32 * | pncc | ||
) |
Input: pixs (32 bpp, of indices of c.c.) &ncc (<optional return>=""> number of connected components) Return: ptaa, or null on error
Notes: (1) The pixel values in are the index of the connected component to which the pixel belongs; is typically generated from a 1 bpp pix by pixConnCompTransform(). Background pixels in the generating 1 bpp pix are represented in by 0. We do not check that the pixel values are correctly labelled. (2) Each pta in the returned ptaa gives the pixel locations correspnding to a connected component, with the label of each given by the index of the pta into the ptaa. (3) Initialize with the first pta in ptaa being empty and representing the background value (index 0) in the pix.
Input: ptaad (dest ptaa; add to this one) ptaas (source ptaa; add from this one) istart (starting index in ptaas) iend (ending index in ptaas; use -1 to cat all) Return: 0 if OK, 1 on error
Notes: (1) istart < 0 is taken to mean 'read from the start' (istart = 0) (2) iend < 0 means 'read to the end' (3) if ptas == NULL, this is a no-op
PTAA* ptaaSortByIndex | ( | PTAA * | ptaas, |
NUMA * | naindex | ||
) |
Input: ptaas naindex (na that maps from the new ptaa to the input ptaa) Return: ptaad (sorted), or null on error
l_int32 ptaContainsPt | ( | PTA * | pta, |
l_int32 | x, | ||
l_int32 | y | ||
) |
Input: pta x, y (point) Return: 1 if contained, 0 otherwise or on error
PTA* ptaCyclicPerm | ( | PTA * | ptas, |
l_int32 | xs, | ||
l_int32 | ys | ||
) |
Input: ptas xs, ys (start point; must be in ptas) Return: ptad (cyclic permutation, starting and ending at (xs, ys), or null on error
Notes: (1) Check to insure that (a) ptas is a closed path where the first and last points are identical, and (b) the resulting pta also starts and ends on the same point (which in this case is (xs, ys).
Input: pta dahash (built from pta) x, y (arbitrary points) &index (<return> index into pta if (x,y) is in pta; -1 otherwise) Return: 0 if OK, 1 on error
Notes: (1) Fast lookup in dnaHash associated with a pta, to see if a random point (x,y) is already stored in the hash table.
PTA* ptaGetBoundaryPixels | ( | PIX * | pixs, |
l_int32 | type | ||
) |
Input: pixs (1 bpp) type (L_BOUNDARY_FG, L_BOUNDARY_BG) Return: pta, or null on error
Notes: (1) This generates a pta of either fg or bg boundary pixels. (2) See also pixGeneratePtaBoundary() for rendering of fg boundary pixels.
BOX* ptaGetBoundingRegion | ( | PTA * | pta | ) |
Input: pta Return: box, or null on error
Notes: (1) This is used when the pta represents a set of points in a two-dimensional image. It returns the box of minimum size containing the pts in the pta.
l_int32 ptaGetCubicLSF | ( | PTA * | pta, |
l_float32 * | pa, | ||
l_float32 * | pb, | ||
l_float32 * | pc, | ||
l_float32 * | pd, | ||
NUMA ** | pnafit | ||
) |
Input: pta &a (<optional return>=""> coeff a of LSF: y = ax^3 + bx^2 + cx + d) &b (<optional return>=""> coeff b of LSF) &c (<optional return>=""> coeff c of LSF) &d (<optional return>=""> coeff d of LSF) &nafit (<optional return>=""> numa of least square fit) Return: 0 if OK, 1 on error
Notes: (1) This does a cubic least square fit to the set of points in . That is, it finds coefficients a, b, c and d that minimize:
sum (yi - a*xi*xi*xi -b*xi*xi -c*xi - d)^2 i
Differentiate this expression w/rt a, b, c and d, and solve the resulting four equations for these coefficients in terms of various sums over the input data (xi, yi). The four equations are in the form: f[0][0]a + f[0][1]b + f[0][2]c + f[0][3] = g[0] f[1][0]a + f[1][1]b + f[1][2]c + f[1][3] = g[1] f[2][0]a + f[2][1]b + f[2][2]c + f[2][3] = g[2] f[3][0]a + f[3][1]b + f[3][2]c + f[3][3] = g[3] (2) If is defined, this returns an array of fitted values, corresponding to the two implicit Numa arrays (nax and nay) in pta. Thus, just as you can plot the data in pta as nay vs. nax, you can plot the linear least square fit as nafit vs. nax.
PTA* ptaGetInsideBox | ( | PTA * | ptas, |
BOX * | box | ||
) |
Input: ptas (input pts) box Return: ptad (of pts in ptas that are inside the box), or null on error
Input: pta &a (<optional return>=""> slope a of least square fit: y = ax + b) &b (<optional return>=""> intercept b of least square fit) &nafit (<optional return>=""> numa of least square fit) Return: 0 if OK, 1 on error
Notes: (1) Either or both &a and &b must be input. They determine the type of line that is fit. (2) If both &a and &b are defined, this returns a and b that minimize:
sum (yi - axi -b)^2 i
The method is simple: differentiate this expression w/rt a and b, and solve the resulting two equations for a and b in terms of various sums over the input data (xi, yi). (3) We also allow two special cases, where either a = 0 or b = 0: (a) If &a is given and &b = null, find the linear LSF that goes through the origin (b = 0). (b) If &b is given and &a = null, find the linear LSF with zero slope (a = 0). (4) If is defined, this returns an array of fitted values, corresponding to the two implicit Numa arrays (nax and nay) in pta. Thus, just as you can plot the data in pta as nay vs. nax, you can plot the linear least square fit as nafit vs. nax.
Input: pixs (any depth) x, y (pixel from which we search for nearest neighbors conn (4 or 8 connectivity) Return: pta, or null on error
Notes: (1) Generates a pta of all valid neighbor pixel locations, or null on error.
PTA* ptaGetPixelsFromPix | ( | PIX * | pixs, |
BOX * | box | ||
) |
Input: pixs (1 bpp) box (<optional> can be null) Return: pta, or null on error
Notes: (1) Generates a pta of fg pixels in the pix, within the box. If box == NULL, it uses the entire pix.
l_int32 ptaGetQuadraticLSF | ( | PTA * | pta, |
l_float32 * | pa, | ||
l_float32 * | pb, | ||
l_float32 * | pc, | ||
NUMA ** | pnafit | ||
) |
Input: pta &a (<optional return>=""> coeff a of LSF: y = ax^2 + bx + c) &b (<optional return>=""> coeff b of LSF: y = ax^2 + bx + c) &c (<optional return>=""> coeff c of LSF: y = ax^2 + bx + c) &nafit (<optional return>=""> numa of least square fit) Return: 0 if OK, 1 on error
Notes: (1) This does a quadratic least square fit to the set of points in . That is, it finds coefficients a, b and c that minimize:
sum (yi - a*xi*xi -b*xi -c)^2 i
The method is simple: differentiate this expression w/rt a, b and c, and solve the resulting three equations for these coefficients in terms of various sums over the input data (xi, yi). The three equations are in the form: f[0][0]a + f[0][1]b + f[0][2]c = g[0] f[1][0]a + f[1][1]b + f[1][2]c = g[1] f[2][0]a + f[2][1]b + f[2][2]c = g[2] (2) If is defined, this returns an array of fitted values, corresponding to the two implicit Numa arrays (nax and nay) in pta. Thus, just as you can plot the data in pta as nay vs. nax, you can plot the linear least square fit as nafit vs. nax.
l_int32 ptaGetQuarticLSF | ( | PTA * | pta, |
l_float32 * | pa, | ||
l_float32 * | pb, | ||
l_float32 * | pc, | ||
l_float32 * | pd, | ||
l_float32 * | pe, | ||
NUMA ** | pnafit | ||
) |
Input: pta &a (<optional return>=""> coeff a of LSF: y = ax^4 + bx^3 + cx^2 + dx + e) &b (<optional return>=""> coeff b of LSF) &c (<optional return>=""> coeff c of LSF) &d (<optional return>=""> coeff d of LSF) &e (<optional return>=""> coeff e of LSF) &nafit (<optional return>=""> numa of least square fit) Return: 0 if OK, 1 on error
Notes: (1) This does a quartic least square fit to the set of points in . That is, it finds coefficients a, b, c, d and 3 that minimize:
sum (yi - a*xi*xi*xi*xi -b*xi*xi*xi -c*xi*xi - d*xi - e)^2 i
Differentiate this expression w/rt a, b, c, d and e, and solve the resulting five equations for these coefficients in terms of various sums over the input data (xi, yi). The five equations are in the form: f[0][0]a + f[0][1]b + f[0][2]c + f[0][3] + f[0][4] = g[0] f[1][0]a + f[1][1]b + f[1][2]c + f[1][3] + f[1][4] = g[1] f[2][0]a + f[2][1]b + f[2][2]c + f[2][3] + f[2][4] = g[2] f[3][0]a + f[3][1]b + f[3][2]c + f[3][3] + f[3][4] = g[3] f[4][0]a + f[4][1]b + f[4][2]c + f[4][3] + f[4][4] = g[4] (2) If is defined, this returns an array of fitted values, corresponding to the two implicit Numa arrays (nax and nay) in pta. Thus, just as you can plot the data in pta as nay vs. nax, you can plot the linear least square fit as nafit vs. nax.
l_int32 ptaGetRange | ( | PTA * | pta, |
l_float32 * | pminx, | ||
l_float32 * | pmaxx, | ||
l_float32 * | pminy, | ||
l_float32 * | pmaxy | ||
) |
Input: pta &minx (<optional return>=""> min value of x) &maxx (<optional return>=""> max value of x) &miny (<optional return>=""> min value of y) &maxy (<optional return>=""> max value of y) Return: 0 if OK, 1 on error
Notes: (1) We can use pts to represent pairs of floating values, that are not necessarily tied to a two-dimension region. For example, the pts can represent a general function y(x).
Input: ptas sorttype (L_SORT_BY_X, L_SORT_BY_Y) sortorder (L_SORT_INCREASING, L_SORT_DECREASING) &naindex (<return> index of sorted order into original array) Return: 0 if OK, 1 on error
PTA* ptaIntersectionByAset | ( | PTA * | pta1, |
PTA * | pta2 | ||
) |
Input: pta1, pta2 Return: ptad (intersection of the point sets), or null on error
Notes: (1) See sarrayIntersectionByAset() for the approach. (2) The key is a 64-bit hash from the (x,y) pair. (3) This is slower than ptaIntersectionByHash(), mostly because of the nlogn sort to build up the rbtree. Do not use for large numbers of points (say, > 1M).
PTA* ptaIntersectionByHash | ( | PTA * | pta1, |
PTA * | pta2 | ||
) |
Input: pta1, pta2 Return: ptad (intersection of the point sets), or null on error
Notes: (1) This is faster than ptaIntersectionByAset(), because the bucket lookup is O(n). It should be used if the pts are integers (e.g., representing pixel positions).
Input: ptad (dest pta; add to this one) ptas (source pta; add from this one) istart (starting index in ptas) iend (ending index in ptas; use -1 to cat all) Return: 0 if OK, 1 on error
Notes: (1) istart < 0 is taken to mean 'read from the start' (istart = 0) (2) iend < 0 means 'read to the end' (3) if ptas == NULL, this is a no-op
l_int32 ptaNoisyLinearLSF | ( | PTA * | pta, |
l_float32 | factor, | ||
PTA ** | pptad, | ||
l_float32 * | pa, | ||
l_float32 * | pb, | ||
l_float32 * | pmederr, | ||
NUMA ** | pnafit | ||
) |
Input: pta factor (reject outliers with error greater than this number of medians; typically ~ 3) &ptad (<optional return>=""> with outliers removed) &a (<optional return>=""> slope a of least square fit: y = ax + b) &b (<optional return>=""> intercept b of least square fit) &mederr (<optional return>=""> median error) &nafit (<optional return>=""> numa of least square fit to ptad) Return: 0 if OK, 1 on error
Notes: (1) This does a linear least square fit to the set of points in . It then evaluates the errors and removes points whose error is >= factor * median_error. It then re-runs the linear LSF on the resulting points. (2) Either or both &a and &b must be input. They determine the type of line that is fit. (3) The median error can give an indication of how good the fit is likely to be.
l_int32 ptaNoisyQuadraticLSF | ( | PTA * | pta, |
l_float32 | factor, | ||
PTA ** | pptad, | ||
l_float32 * | pa, | ||
l_float32 * | pb, | ||
l_float32 * | pc, | ||
l_float32 * | pmederr, | ||
NUMA ** | pnafit | ||
) |
Input: pta factor (reject outliers with error greater than this number of medians; typically ~ 3) &ptad (<optional return>=""> with outliers removed) &a (<optional return>=""> coeff a of LSF: y = ax^2 + bx + c) &b (<optional return>=""> coeff b of LSF: y = ax^2 + bx + c) &c (<optional return>=""> coeff c of LSF: y = ax^2 + bx + c) &mederr (<optional return>=""> median error) &nafit (<optional return>=""> numa of least square fit to ptad) Return: 0 if OK, 1 on error
Notes: (1) This does a quadratic least square fit to the set of points in . It then evaluates the errors and removes points whose error is >= factor * median_error. It then re-runs a quadratic LSF on the resulting points.
Input: pta (vertices of a polygon) x, y (point to be tested) &inside (<return> 1 if inside; 0 if outside or on boundary) Return: 1 if OK, 0 on error
The abs value of the sum of the angles subtended from a point by the sides of a polygon, when taken in order traversing the polygon, is 0 if the point is outside the polygon and 2*pi if inside. The sign will be positive if traversed cw and negative if ccw.
PTA* ptaRemoveDupsByAset | ( | PTA * | ptas | ) |
Input: ptas (assumed to be integer values) Return: ptad (with duplicates removed), or null on error
Notes: (1) This is slower than ptaRemoveDupsByHash(), mostly because of the nlogn sort to build up the rbtree. Do not use for large numbers of points (say, > 1M).
l_int32 ptaRemoveDupsByHash | ( | PTA * | ptas, |
PTA ** | pptad, | ||
L_DNAHASH ** | pdahash | ||
) |
Input: ptas (assumed to be integer values) &ptad (<return> unique set of pts; duplicates removed) &dahash (<optional return>=""> dnahash used for lookup) Return: 0 if OK, 1 on error
Notes: (1) Generates a pta with unique values. (2) The dnahash is built up with ptad to assure uniqueness. It can be used to find if a point is in the set: ptaFindPtByHash(ptad, dahash, x, y, &index) (3) The hash of the (x,y) location is simple and fast. It scales up with the number of buckets to insure a fairly random bucket selection for adjacent points. (4) A Dna is used rather than a Numa because we need accurate representation of 32-bit integers that are indices into ptas. Integer --> float --> integer conversion makes errors for integers larger than 10M. (5) This is faster than ptaRemoveDupsByAset(), because the bucket lookup is O(n), although there is a double-loop lookup within the dna in each bucket.
PTA* ptaReplicatePattern | ( | PTA * | ptas, |
PIX * | pixp, | ||
PTA * | ptap, | ||
l_int32 | cx, | ||
l_int32 | cy, | ||
l_int32 | w, | ||
l_int32 | h | ||
) |
Input: ptas ("sparse" input pta) pixp (<optional> 1 bpp pattern, to be replicated in output pta) ptap (<optional> set of pts, to be replicated in output pta) cx, cy (reference point in pattern) w, h (clipping sizes for output pta) Return: ptad (with all points of replicated pattern), or null on error
Notes: (1) You can use either the image or the set of pts . (2) The pattern is placed with its reference point at each point in ptas, and all the fg pixels are colleced into ptad. For , this is equivalent to blitting pixp at each point in ptas, and then converting the resulting pix to a pta.
PTA* ptaReverse | ( | PTA * | ptas, |
l_int32 | type | ||
) |
Input: ptas type (0 for float values; 1 for integer values) Return: ptad (reversed pta), or null on error
Input: ptas sorttype (L_SORT_BY_X, L_SORT_BY_Y) sortorder (L_SORT_INCREASING, L_SORT_DECREASING) &naindex (<optional return>=""> index of sorted order into original array) Return: ptad (sorted version of ptas), or null on error
PTA* ptaSortByIndex | ( | PTA * | ptas, |
NUMA * | naindex | ||
) |
Input: ptas naindex (na that maps from the new pta to the input pta) Return: ptad (sorted), or null on error
PTA* ptaSubsample | ( | PTA * | ptas, |
l_int32 | subfactor | ||
) |
Input: ptas subfactor (subsample factor, >= 1) Return: ptad (evenly sampled pt values from ptas, or null on error
l_int32 ptaTestIntersection | ( | PTA * | pta1, |
PTA * | pta2 | ||
) |
Input: pta1, pta2 Return: bval which is 1 if they have any elements in common; 0 otherwise or on error.
PTA* ptaTransform | ( | PTA * | ptas, |
l_int32 | shiftx, | ||
l_int32 | shifty, | ||
l_float32 | scalex, | ||
l_float32 | scaley | ||
) |
Input: pta shiftx, shifty scalex, scaley Return: pta, or null on error
Notes: (1) Shift first, then scale.
PTA* ptaTranspose | ( | PTA * | ptas | ) |
Input: ptas Return: ptad (with x and y values swapped), or null on error
PTA* ptaUnionByAset | ( | PTA * | pta1, |
PTA * | pta2 | ||
) |
Input: pta1, pta2 Return: ptad (with the union of the set of points), or null on error
Notes: (1) See sarrayRemoveDupsByAset() for the approach. (2) The key is a 64-bit hash from the (x,y) pair. (3) This is slower than ptaUnionByHash(), mostly because of the nlogn sort to build up the rbtree. Do not use for large numbers of points (say, > 1M). (4) The *Aset() functions use the sorted l_Aset, which is just an rbtree in disguise.
PTA* ptaUnionByHash | ( | PTA * | pta1, |
PTA * | pta2 | ||
) |
Input: pta1, pta2 Return: ptad (with the union of the set of points), or null on error
Notes: (1) This is faster than ptaUnionByAset(), because the bucket lookup is O(n). It should be used if the pts are integers (e.g., representing pixel positions).