Leptonica 1.54
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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: 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), or null on error
Notes: (1) To write on an existing pixs, pixs must be 32 bpp and call with pixd == pixs: pixDisplayPta(pixs, pixs, pta); To write on a new pix, set pixd == NULL and call: pixd = pixDisplayPta(NULL, pixs, pta);
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
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.
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.
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.
Input: ptaas naindex (na that maps from the new ptaa to the input ptaa) Return: ptaad (sorted), or null on error
Input: pta x, y (point) Return: 1 if contained, 0 otherwise or on error
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: 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.
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.
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) At least one of: &a and &b must not be null. (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 (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: 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 0 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'
Input: ptas (assumed to be integer values) factor (should be larger than the largest point value; use 0 for default) Return: ptad (with duplicates removed), or null on error
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
Input: ptas subfactor (subsample factor, >= 1) Return: ptad (evenly sampled pt values from ptas, or null on error
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.
const l_int32 DEFAULT_SPREADING_FACTOR = 7500 [static] |