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Leptonica
1.82.0
Image processing and image analysis suite
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#include <math.h>
#include "allheaders.h"
Go to the source code of this file.
Functions | |
static void | blockconvLow (l_uint32 *data, l_int32 w, l_int32 h, l_int32 wpl, l_uint32 *dataa, l_int32 wpla, l_int32 wc, l_int32 hc) |
static void | blockconvAccumLow (l_uint32 *datad, l_int32 w, l_int32 h, l_int32 wpld, l_uint32 *datas, l_int32 d, l_int32 wpls) |
static void | blocksumLow (l_uint32 *datad, l_int32 w, l_int32 h, l_int32 wpl, l_uint32 *dataa, l_int32 wpla, l_int32 wc, l_int32 hc) |
PIX * | pixBlockconv (PIX *pix, l_int32 wc, l_int32 hc) |
PIX * | pixBlockconvGray (PIX *pixs, PIX *pixacc, l_int32 wc, l_int32 hc) |
PIX * | pixBlockconvAccum (PIX *pixs) |
PIX * | pixBlockconvGrayUnnormalized (PIX *pixs, l_int32 wc, l_int32 hc) |
PIX * | pixBlockconvTiled (PIX *pix, l_int32 wc, l_int32 hc, l_int32 nx, l_int32 ny) |
PIX * | pixBlockconvGrayTile (PIX *pixs, PIX *pixacc, l_int32 wc, l_int32 hc) |
l_ok | pixWindowedStats (PIX *pixs, l_int32 wc, l_int32 hc, l_int32 hasborder, PIX **ppixm, PIX **ppixms, FPIX **pfpixv, FPIX **pfpixrv) |
PIX * | pixWindowedMean (PIX *pixs, l_int32 wc, l_int32 hc, l_int32 hasborder, l_int32 normflag) |
PIX * | pixWindowedMeanSquare (PIX *pixs, l_int32 wc, l_int32 hc, l_int32 hasborder) |
l_ok | pixWindowedVariance (PIX *pixm, PIX *pixms, FPIX **pfpixv, FPIX **pfpixrv) |
DPIX * | pixMeanSquareAccum (PIX *pixs) |
PIX * | pixBlockrank (PIX *pixs, PIX *pixacc, l_int32 wc, l_int32 hc, l_float32 rank) |
PIX * | pixBlocksum (PIX *pixs, PIX *pixacc, l_int32 wc, l_int32 hc) |
PIX * | pixCensusTransform (PIX *pixs, l_int32 halfsize, PIX *pixacc) |
PIX * | pixConvolve (PIX *pixs, L_KERNEL *kel, l_int32 outdepth, l_int32 normflag) |
PIX * | pixConvolveSep (PIX *pixs, L_KERNEL *kelx, L_KERNEL *kely, l_int32 outdepth, l_int32 normflag) |
PIX * | pixConvolveRGB (PIX *pixs, L_KERNEL *kel) |
PIX * | pixConvolveRGBSep (PIX *pixs, L_KERNEL *kelx, L_KERNEL *kely) |
FPIX * | fpixConvolve (FPIX *fpixs, L_KERNEL *kel, l_int32 normflag) |
FPIX * | fpixConvolveSep (FPIX *fpixs, L_KERNEL *kelx, L_KERNEL *kely, l_int32 normflag) |
PIX * | pixConvolveWithBias (PIX *pixs, L_KERNEL *kel1, L_KERNEL *kel2, l_int32 force8, l_int32 *pbias) |
void | l_setConvolveSampling (l_int32 xfact, l_int32 yfact) |
PIX * | pixAddGaussianNoise (PIX *pixs, l_float32 stdev) |
l_float32 | gaussDistribSampling (void) |
Variables | |
LEPT_DLL l_int32 | ConvolveSamplingFactX = 1 |
LEPT_DLL l_int32 | ConvolveSamplingFactY = 1 |
Top level grayscale or color block convolution PIX *pixBlockconv()
Grayscale block convolution PIX *pixBlockconvGray() static void blockconvLow()
Accumulator for 1, 8 and 32 bpp convolution PIX *pixBlockconvAccum() static void blockconvAccumLow()
Un-normalized grayscale block convolution PIX *pixBlockconvGrayUnnormalized()
Tiled grayscale or color block convolution PIX *pixBlockconvTiled() PIX *pixBlockconvGrayTile()
Convolution for mean, mean square, variance and rms deviation in specified window l_int32 pixWindowedStats() PIX *pixWindowedMean() PIX *pixWindowedMeanSquare() l_int32 pixWindowedVariance() DPIX *pixMeanSquareAccum()
Binary block sum and rank filter PIX *pixBlockrank() PIX *pixBlocksum() static void blocksumLow()
Census transform PIX *pixCensusTransform()
Generic convolution (with Pix) PIX *pixConvolve() PIX *pixConvolveSep() PIX *pixConvolveRGB() PIX *pixConvolveRGBSep()
Generic convolution (with float arrays) FPIX *fpixConvolve() FPIX *fpixConvolveSep()
Convolution with bias (for non-negative output) PIX *pixConvolveWithBias()
Set parameter for convolution subsampling void l_setConvolveSampling()
Additive gaussian noise PIX *pixAddGaussNoise() l_float32 gaussDistribSampling()
Definition in file convolve.c.
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static |
[in] | data | data of input image, to be convolved |
[in] | w,h,wpl | |
[in] | dataa | data of 32 bpp accumulator |
[in] | wpla | accumulator |
[in] | wc | convolution "half-width" |
[in] | hc | convolution "half-height" |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1). (2) The lack of symmetry between the handling of the first (hc + 1) lines and the last (hc) lines, and similarly with the columns, is due to fact that for the pixel at (x,y), the accumulator values are taken at (x + wc, y + hc), (x - wc - 1, y + hc), (x + wc, y - hc - 1) and (x - wc - 1, y - hc - 1). (3) We compute sums, normalized as if there were no reduced area at the boundary. This under-estimates the value of the boundary pixels, so we multiply them by another normalization factor that is greater than 1. (4) This second normalization is done first for the first hc + 1 lines; then for the last hc lines; and finally for the first wc + 1 and last wc columns in the intermediate lines. (5) The caller should verify that wc < w and hc < h. Under those conditions, illegal reads and writes can occur. (6) Implementation note: to get the same results in the interior between this function and pixConvolve(), it is necessary to add 0.5 for roundoff in the main loop that runs over all pixels. However, if we do that and have white (255) pixels near the image boundary, some overflow occurs for pixels very close to the boundary. We can't fix this by subtracting from the normalized values for the boundary pixels, because this results in underflow if the boundary pixels are black (0). Empirically, adding 0.25 (instead of 0.5) before truncating in the main loop will not cause overflow, but this gives some off-by-1-level errors in interior pixel values. So we add 0.5 for roundoff in the main loop, and for pixels within a half filter width of the boundary, use a L_MIN of the computed value and 255 to avoid overflow during normalization.
Definition at line 321 of file convolve.c.
References GET_DATA_BYTE, and SET_DATA_BYTE.
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static |
[in] | datad | of 8 bpp dest |
[in] | w,h,wpl | of 8 bpp dest |
[in] | dataa | of 32 bpp accum |
[in] | wpla | of 32 bpp accum |
[in] | wc,hc | convolution "half-width" and "half-height" |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1). (2) The lack of symmetry between the handling of the first (hc + 1) lines and the last (hc) lines, and similarly with the columns, is due to fact that for the pixel at (x,y), the accumulator values are taken at (x + wc, y + hc), (x - wc - 1, y + hc), (x + wc, y - hc - 1) and (x - wc - 1, y - hc - 1). (3) Compute sums of ON pixels within the block filter size, normalized between 0 and 255, as if there were no reduced area at the boundary. This under-estimates the value of the boundary pixels, so we multiply them by another normalization factor that is greater than 1. (4) This second normalization is done first for the first hc + 1 lines; then for the last hc lines; and finally for the first wc + 1 and last wc columns in the intermediate lines. (5) Required constraints are: wc < w and hc < h.
Definition at line 1629 of file convolve.c.
References GET_DATA_BYTE, and SET_DATA_BYTE.
[in] | fpixs | 32 bit float array |
[in] | kel | kernel |
[in] | normflag | 1 to normalize kernel to unit sum; 0 otherwise |
Notes: (1) This gives a float convolution with an arbitrary kernel. (2) If normflag == 1, the result is normalized by scaling all kernel values for a unit sum. If the sum of kernel values is very close to zero, the kernel can not be normalized and the convolution will not be performed. A warning is issued. (3) With the FPix, there are no issues about negative array or kernel values. The convolution is performed with single precision arithmetic. (4) To get a subsampled output, call l_setConvolveSampling(). The time to make a subsampled output is reduced by the product of the sampling factors. (5) This uses a mirrored border to avoid special casing on the boundaries.
Definition at line 2203 of file convolve.c.
References fpixAddMirroredBorder(), fpixGetDimensions(), kernelCopy(), kernelGetParameters(), kernelInvert(), and kernelNormalize().
[in] | fpixs | 32 bit float array |
[in] | kelx | x-dependent kernel |
[in] | kely | y-dependent kernel |
[in] | normflag | 1 to normalize kernel to unit sum; 0 otherwise |
Notes: (1) This does a convolution with a separable kernel that is is a sequence of convolutions in x and y. The two one-dimensional kernel components must be input separately; the full kernel is the product of these components. The support for the full kernel is thus a rectangular region. (2) The normflag parameter is used as in fpixConvolve(). (3) Warning: if you use l_setConvolveSampling() to get a subsampled output, and the sampling factor is larger than the kernel half-width, it is faster to use the non-separable version pixConvolve(). This is because the first convolution here must be done on every raster line, regardless of the vertical sampling factor. If the sampling factor is smaller than kernel half-width, it's faster to use the separable convolution. (4) This uses mirrored borders to avoid special casing on the boundaries.
Definition at line 2297 of file convolve.c.
l_float32 gaussDistribSampling | ( | void | ) |
Notes: (1) For an explanation of the Box-Muller method for generating a normally distributed random variable with zero mean and unit standard deviation, see Numerical Recipes in C, 2nd edition, p. 288ff. (2) This can be called sequentially to get samples that can be used for adding noise to each pixel of an image, for example.
Definition at line 2557 of file convolve.c.
void l_setConvolveSampling | ( | l_int32 | xfact, |
l_int32 | yfact | ||
) |
[in] | xfact,yfact | integer >= 1 |
Notes: (1) This sets the x and y output subsampling factors for generic pix and fpix convolution. The default values are 1 (no subsampling).
Definition at line 2465 of file convolve.c.
[in] | pixs | 8 bpp gray or 32 bpp rgb; no colormap |
[in] | stdev | of noise |
Notes: (1) This adds noise to each pixel, taken from a normal distribution with zero mean and specified standard deviation.
Definition at line 2492 of file convolve.c.
[in] | pix | 8 or 32 bpp; or 2, 4 or 8 bpp with colormap |
[in] | wc,hc | half width/height of convolution kernel |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) Returns a copy if either wc or hc are 0 (3) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary.
Definition at line 132 of file convolve.c.
References pixCopy(), and pixGetDimensions().
Referenced by pixBlockconvTiled().
[in] | pixs | 1, 8 or 32 bpp |
Notes: (1) The general recursion relation is a(i,j) = v(i,j) + a(i-1, j) + a(i, j-1) - a(i-1, j-1) For the first line, this reduces to the special case a(i,j) = v(i,j) + a(i, j-1) For the first column, the special case is a(i,j) = v(i,j) + a(i-1, j)
Definition at line 460 of file convolve.c.
References pixCreate(), pixGetData(), and pixGetDimensions().
Referenced by pixBlockconvGrayUnnormalized().
[in] | pixs | 8 bpp |
[in] | pixacc | pix 32 bpp; can be null |
[in] | wc,hc | half width/height of convolution kernel |
Notes: (1) If accum pix is null, make one and destroy it before returning; otherwise, just use the input accum pix. (2) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1). (3) Returns a copy if either wc or hc are 0 (4) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary.
Definition at line 216 of file convolve.c.
References pixCopy(), and pixGetDimensions().
[in] | pixs | 8 bpp gray |
[in] | pixacc | 32 bpp accum pix |
[in] | wc,hc | half width/height of convolution kernel |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) Assumes that the input pixs is padded with (wc + 1) pixels on left and right, and with (hc + 1) pixels on top and bottom. The returned pix has these stripped off; they are only used for computation. (3) Returns a copy if either wc or hc are 0. (4) Require that w > 2 * wc + 3 and h > 2 * hc + 3, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary.
Definition at line 861 of file convolve.c.
References pixCopy(), and pixGetDimensions().
pixBlockconvGrayUnnormalized()
[in] | pixs | 8 bpp |
[in] | wc,hc | half width/height of convolution kernel |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1). (2) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary. (3) Returns a copy if either wc or hc are 0. (3) Adds mirrored border to avoid treating the boundary pixels specially. Note that we add wc + 1 pixels to the left and wc to the right. The added width is 2 * wc + 1 pixels, and the particular choice simplifies the indexing in the loop. Likewise, add hc + 1 pixels to the top and hc to the bottom. (4) To get the normalized result, divide by the area of the convolution kernel: (2 * wc + 1) * (2 * hc + 1) Specifically, do this: pixc = pixBlockconvGrayUnnormalized(pixs, wc, hc); fract = 1. / ((2 * wc + 1) * (2 * hc + 1)); pixMultConstantGray(pixc, fract); pixd = pixGetRGBComponent(pixc, L_ALPHA_CHANNEL); (5) Unlike pixBlockconvGray(), this always computes the accumulation pix because its size is tied to wc and hc. (6) Compare this implementation with pixBlockconvGray(), where most of the code in blockconvLow() is special casing for efficiently handling the boundary. Here, the use of mirrored borders and destination indexing makes the implementation very simple.
Definition at line 642 of file convolve.c.
References pixAddMirroredBorder(), pixBlockconvAccum(), pixCopy(), pixCreate(), pixDestroy(), and pixGetDimensions().
[in] | pix | 8 or 32 bpp; or 2, 4 or 8 bpp with colormap |
[in] | wc,hc | half width/height of convolution kernel |
[in] | nx,ny | subdivision into tiles |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) Returns a copy if either wc or hc are 0. (3) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary. (4) For nx == ny == 1, this defaults to pixBlockconv(), which is typically about twice as fast, and gives nearly identical results as pixBlockconvGrayTile(). (5) If the tiles are too small, nx and/or ny are reduced a minimum amount so that the tiles are expanded to the smallest workable size in the problematic direction(s). (6) Why a tiled version? Three reasons: (a) Because the accumulator is a uint32, overflow can occur for an image with more than 16M pixels. (b) The accumulator array for 16M pixels is 64 MB; using tiles reduces the size of this array. (c) Each tile can be processed independently, in parallel, on a multicore processor.
Definition at line 734 of file convolve.c.
References pixBlockconv(), pixCopy(), and pixGetDimensions().
[in] | pixs | 1 bpp |
[in] | pixacc | pix [optional] 32 bpp |
[in] | wc,hc | half width/height of block sum/rank kernel |
[in] | rank | between 0.0 and 1.0; 0.5 is median filter |
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) This returns a pixd where each pixel is a 1 if the neighborhood (2 * wc + 1) x (2 * hc + 1)) pixels contains the rank fraction of 1 pixels. Otherwise, the returned pixel is 0. Note that the special case of rank = 0.0 is always satisfied, so the returned pixd has all pixels with value 1. (3) If accum pix is null, make one, use it, and destroy it before returning; otherwise, just use the input accum pix (4) If both wc and hc are 0, returns a copy unless rank == 0.0, in which case this returns an all-ones image. (5) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary.
Definition at line 1459 of file convolve.c.
References pixBlocksum(), pixCopy(), pixCreateTemplate(), pixDestroy(), pixGetDimensions(), pixInvert(), pixSetAll(), and pixThresholdToBinary().
[in] | pixs | 1 bpp |
[in] | pixacc | pix [optional] 32 bpp |
[in] | wc,hc | half width/height of block sum/rank kernel |
Notes: (1) If accum pix is null, make one and destroy it before returning; otherwise, just use the input accum pix (2) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (3) Use of wc = hc = 1, followed by pixInvert() on the 8 bpp result, gives a nice anti-aliased, and somewhat darkened, result on text. (4) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. Attempt to reduce the kernel size if necessary. (5) Returns in each dest pixel the sum of all src pixels that are within a block of size of the kernel, centered on the dest pixel. This sum is the number of src ON pixels in the block at each location, normalized to 255 for a block containing all ON pixels. For pixels near the boundary, where the block is not entirely contained within the image, we then multiply by a second normalization factor that is greater than one, so that all results are normalized by the number of participating pixels within the block.
Definition at line 1542 of file convolve.c.
References pixCopy(), and pixGetDimensions().
Referenced by pixBlockrank().
[in] | pixs | 8 bpp |
[in] | halfsize | of square over which neighbors are averaged |
[in] | pixacc | [optional] 32 bpp pix |
Notes: (1) The Census transform was invented by Ramin Zabih and John Woodfill ("Non-parametric local transforms for computing visual correspondence", Third European Conference on Computer Vision, Stockholm, Sweden, May 1994); see publications at http://www.cs.cornell.edu/~rdz/index.htm This compares each pixel against the average of its neighbors, in a square of odd dimension centered on the pixel. If the pixel is greater than the average of its neighbors, the output pixel value is 1; otherwise it is 0. (2) This can be used as an encoding for an image that is fairly robust against slow illumination changes, with applications in image comparison and mosaicing. (3) The size of the convolution kernel is (2 * halfsize + 1) on a side. The halfsize parameter must be >= 1. (4) If accum pix is null, make one, use it, and destroy it before returning; otherwise, just use the input accum pix
Definition at line 1780 of file convolve.c.
[in] | pixs | 8, 16, 32 bpp; no colormap |
[in] | kel | kernel |
[in] | outdepth | of pixd: 8, 16 or 32 |
[in] | normflag | 1 to normalize kernel to unit sum; 0 otherwise |
Notes: (1) This gives a convolution with an arbitrary kernel. (2) The input pixs must have only one sample/pixel. To do a convolution on an RGB image, use pixConvolveRGB(). (3) The parameter outdepth determines the depth of the result. If the kernel is normalized to unit sum, the output values can never exceed 255, so an output depth of 8 bpp is sufficient. If the kernel is not normalized, it may be necessary to use 16 or 32 bpp output to avoid overflow. (4) If normflag == 1, the result is normalized by scaling all kernel values for a unit sum. If the sum of kernel values is very close to zero, the kernel can not be normalized and the convolution will not be performed. A warning is issued. (5) The kernel values can be positive or negative, but the result for the convolution can only be stored as a positive number. Consequently, if it goes negative, the choices are to clip to 0 or take the absolute value. We're choosing to take the absolute value. (Another possibility would be to output a second unsigned image for the negative values.) If you want to get a clipped result, or to keep the negative values in the result, use fpixConvolve(), with the converters in fpix2.c between pix and fpix. (6) This uses a mirrored border to avoid special casing on the boundaries. (7) To get a subsampled output, call l_setConvolveSampling(). The time to make a subsampled output is reduced by the product of the sampling factors. (8) The function is slow, running at about 12 machine cycles for each pixel-op in the convolution. For example, with a 3 GHz cpu, a 1 Mpixel grayscale image, and a kernel with (sx * sy) = 25 elements, the convolution takes about 100 msec.
Definition at line 1880 of file convolve.c.
[in] | pixs | 32 bpp rgb |
[in] | kel | kernel |
Notes: (1) This gives a convolution on an RGB image using an arbitrary kernel (which we normalize to keep each component within the range [0 ... 255]. (2) The input pixs must be RGB. (3) The kernel values can be positive or negative, but the result for the convolution can only be stored as a positive number. Consequently, if it goes negative, we clip the result to 0. (4) To get a subsampled output, call l_setConvolveSampling(). The time to make a subsampled output is reduced by the product of the sampling factors. (5) This uses a mirrored border to avoid special casing on the boundaries.
Definition at line 2082 of file convolve.c.
[in] | pixs | 32 bpp rgb |
[in] | kelx | x-dependent kernel |
[in] | kely | y-dependent kernel |
Notes: (1) This does a convolution on an RGB image using a separable kernel that is a sequence of convolutions in x and y. The two one-dimensional kernel components must be input separately; the full kernel is the product of these components. The support for the full kernel is thus a rectangular region. (2) The kernel values can be positive or negative, but the result for the convolution can only be stored as a positive number. Consequently, if it goes negative, we clip the result to 0. (3) To get a subsampled output, call l_setConvolveSampling(). The time to make a subsampled output is reduced by the product of the sampling factors. (4) This uses a mirrored border to avoid special casing on the boundaries.
Definition at line 2141 of file convolve.c.
PIX* pixConvolveSep | ( | PIX * | pixs, |
L_KERNEL * | kelx, | ||
L_KERNEL * | kely, | ||
l_int32 | outdepth, | ||
l_int32 | normflag | ||
) |
[in] | pixs | 8, 16, 32 bpp; no colormap |
[in] | kelx | x-dependent kernel |
[in] | kely | y-dependent kernel |
[in] | outdepth | of pixd: 8, 16 or 32 |
[in] | normflag | 1 to normalize kernel to unit sum; 0 otherwise |
Notes: (1) This does a convolution with a separable kernel that is is a sequence of convolutions in x and y. The two one-dimensional kernel components must be input separately; the full kernel is the product of these components. The support for the full kernel is thus a rectangular region. (2) The input pixs must have only one sample/pixel. To do a convolution on an RGB image, use pixConvolveSepRGB(). (3) The parameter outdepth determines the depth of the result. If the kernel is normalized to unit sum, the output values can never exceed 255, so an output depth of 8 bpp is sufficient. If the kernel is not normalized, it may be necessary to use 16 or 32 bpp output to avoid overflow. (2) The normflag parameter is used as in pixConvolve(). (4) The kernel values can be positive or negative, but the result for the convolution can only be stored as a positive number. Consequently, if it goes negative, the choices are to clip to 0 or take the absolute value. We're choosing the former for now. Another possibility would be to output a second unsigned image for the negative values. (5) Warning: if you use l_setConvolveSampling() to get a subsampled output, and the sampling factor is larger than the kernel half-width, it is faster to use the non-separable version pixConvolve(). This is because the first convolution here must be done on every raster line, regardless of the vertical sampling factor. If the sampling factor is smaller than kernel half-width, it's faster to use the separable convolution. (6) This uses mirrored borders to avoid special casing on the boundaries.
Definition at line 2010 of file convolve.c.
PIX* pixConvolveWithBias | ( | PIX * | pixs, |
L_KERNEL * | kel1, | ||
L_KERNEL * | kel2, | ||
l_int32 | force8, | ||
l_int32 * | pbias | ||
) |
[in] | pixs | 8 bpp; no colormap |
[in] | kel1 | |
[in] | kel2 | can be null; use if separable |
[in] | force8 | if 1, force output to 8 bpp; otherwise, determine output depth by the dynamic range of pixel values |
[out] | pbias | applied bias |
Notes: (1) This does a convolution with either a single kernel or a pair of separable kernels, and automatically applies whatever bias (shift) is required so that the resulting pixel values are non-negative. (2) The kernel is always normalized. If there are no negative values in the kernel, a standard normalized convolution is performed, with 8 bpp output. If the sum of kernel values is very close to zero, the kernel can not be normalized and the convolution will not be performed. An error message results. (3) If there are negative values in the kernel, the pix is converted to an fpix, the convolution is done on the fpix, and a bias (shift) may need to be applied. (4) If force8 == TRUE and the range of values after the convolution is > 255, the output values will be scaled to fit in [0 ... 255]. If force8 == FALSE, the output will be either 8 or 16 bpp, to accommodate the dynamic range of output values without scaling.
Definition at line 2375 of file convolve.c.
[in] | pixs | 8 bpp grayscale |
Notes: (1) Similar to pixBlockconvAccum(), this computes the sum of the squares of the pixel values in such a way that the value at (i,j) is the sum of all squares in the rectangle from the origin to (i,j). (2) The general recursion relation (v are squared pixel values) is a(i,j) = v(i,j) + a(i-1, j) + a(i, j-1) - a(i-1, j-1) For the first line, this reduces to the special case a(i,j) = v(i,j) + a(i, j-1) For the first column, the special case is a(i,j) = v(i,j) + a(i-1, j)
Definition at line 1377 of file convolve.c.
[in] | pixs | 8 or 32 bpp grayscale |
[in] | wc,hc | half width/height of convolution kernel |
[in] | hasborder | use 1 if it already has (wc + 1 border pixels on left and right, and hc + 1 on top and bottom; use 0 to add kernel-dependent border) |
[in] | normflag | 1 for normalization to get average in window; 0 for the sum in the window (un-normalized) |
Notes: (1) The input and output depths are the same. (2) A set of border pixels of width (wc + 1) on left and right, and of height (hc + 1) on top and bottom, must be on the pix before the accumulator is found. The output pixd (after convolution) has this border removed. If hasborder = 0, the required border is added. (3) Typically, normflag == 1. However, if you want the sum within the window, rather than a normalized convolution, use normflag == 0. (4) This builds a block accumulator pix, uses it here, and destroys it. (5) The added border, along with the use of an accumulator array, allows computation without special treatment of pixels near the image boundary, and runs in a time that is independent of the size of the convolution kernel.
Definition at line 1073 of file convolve.c.
[in] | pixs | 8 bpp grayscale |
[in] | wc,hc | half width/height of convolution kernel |
[in] | hasborder | use 1 if it already has (wc + 1 border pixels on left and right, and hc + 1 on top and bottom; use 0 to add kernel-dependent border) |
Notes: (1) A set of border pixels of width (wc + 1) on left and right, and of height (hc + 1) on top and bottom, must be on the pix before the accumulator is found. The output pixd (after convolution) has this border removed. If hasborder = 0, the required border is added. (2) The advantage is that we are unaffected by the boundary, and it is not necessary to treat pixels within wc and hc of the border differently. This is because processing for pixd only takes place for pixels in pixs for which the kernel is entirely contained in pixs. (3) Why do we have an added border of width (wc + 1) and height (hc + 1), when we only need wc and hc pixels to satisfy this condition? Answer: the accumulators are asymmetric, requiring an extra row and column of pixels at top and left to work accurately. (4) The added border, along with the use of an accumulator array, allows computation without special treatment of pixels near the image boundary, and runs in a time that is independent of the size of the convolution kernel.
Definition at line 1190 of file convolve.c.
l_ok pixWindowedStats | ( | PIX * | pixs, |
l_int32 | wc, | ||
l_int32 | hc, | ||
l_int32 | hasborder, | ||
PIX ** | ppixm, | ||
PIX ** | ppixms, | ||
FPIX ** | pfpixv, | ||
FPIX ** | pfpixrv | ||
) |
[in] | pixs | 8 bpp grayscale |
[in] | wc,hc | half width/height of convolution kernel |
[in] | hasborder | use 1 if it already has (wc + 1 border pixels on left and right, and hc + 1 on top and bottom; use 0 to add kernel-dependent border) |
[out] | ppixm | [optional] 8 bpp mean value in window |
[out] | ppixms | [optional] 32 bpp mean square value in window |
[out] | pfpixv | [optional] float variance in window |
[out] | pfpixrv | [optional] float rms deviation from the mean |
Notes: (1) This is a high-level convenience function for calculating any or all of these derived images. (2) If hasborder = 0, a border is added and the result is computed over all pixels in pixs. Otherwise, no border is added and the border pixels are removed from the output images. (3) These statistical measures over the pixels in the rectangular window are: ~ average value:
(pixm) ~ average squared value: <p*p> (pixms) ~ variance: <(p -
)*(p -
)> = <p*p> -
*
(pixv) ~ square-root of variance: (pixrv) where the brackets < .. > indicate that the average value is to be taken over the window. (4) Note that the variance is just the mean square difference from the mean value; and the square root of the variance is the root mean square difference from the mean, sometimes also called the 'standard deviation'. (5) The added border, along with the use of an accumulator array, allows computation without special treatment of pixels near the image boundary, and runs in a time that is independent of the size of the convolution kernel.
Definition at line 988 of file convolve.c.
[in] | pixm | mean over window; 8 or 32 bpp grayscale |
[in] | pixms | mean square over window; 32 bpp |
[out] | pfpixv | [optional] float variance – the ms deviation from the mean |
[out] | pfpixrv | [optional] float rms deviation from the mean |
Notes: (1) The mean and mean square values are precomputed, using pixWindowedMean() and pixWindowedMeanSquare(). (2) Either or both of the variance and square-root of variance are returned as an fpix, where the variance is the average over the window of the mean square difference of the pixel value from the mean: <(p -
)*(p -
)> = <p*p> -
*
(3) To visualize the results: ~ for both, use fpixDisplayMaxDynamicRange(). ~ for rms deviation, simply convert the output fpix to pix,
Definition at line 1288 of file convolve.c.