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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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Grayscale, color, float, double convolution; windowed mean, block sum and rank filters; census transform. More...
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Grayscale, color, float, double convolution; windowed mean, block sum and rank filters; census transform.
Top level grayscale or color block convolution
PIX *pixBlockconv()
Grayscale block convolution
PIX *pixBlockconvGray()
Accumulator for 1, 8 and 32 bpp convolution
PIX *pixBlockconvAccum()
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()
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()
Set parameter for convolution subsampling
void l_setConvolveSampling()
Definition in file convolve.c.
Input: pix (8 or 32 bpp; or 2, 4 or 8 bpp with colormap) wc, hc (half width/height of convolution kernel) Return: pixd, or null on error
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) Returns a copy if both wc and hc are 0 (3) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs.
Definition at line 91 of file convolve.c.
References COLOR_BLUE, COLOR_GREEN, COLOR_RED, ERROR_PTR, L_INFO_INT2, L_MIN, L_WARNING, NULL, pixBlockconvGray(), pixClone(), pixCopy(), pixCreateRGBImage(), pixDestroy(), pixGetColormap(), pixGetDepth(), pixGetDimensions(), pixGetRGBComponent(), pixRemoveColormap(), PROCNAME, and REMOVE_CMAP_BASED_ON_SRC.
Referenced by main(), pixBlockconvTiled(), pixFastTophat(), pixGetInvBackgroundMap(), pixMinMaxTiles(), pixOtsuAdaptiveThreshold(), pixRotateBinaryNice(), and pixThresholdSpreadNorm().
Input: pix (8 bpp) accum pix (32 bpp; can be null) wc, hc (half width/height of convolution kernel) Return: pix (8 bpp), or null on error
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 both wc and hc are 0. (4) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs.
Definition at line 172 of file convolve.c.
References blockconvLow(), ERROR_PTR, L_INFO_INT2, L_MIN, L_WARNING, NULL, pixBlockconvAccum(), pixClone(), pixCopy(), pixCreateTemplate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Referenced by main(), pixBlockconv(), pixCensusTransform(), pixHalfEdgeByBandpass(), pixMorphGradient(), and pixUnsharpMaskingGray().
Input: pixs (1, 8 or 32 bpp) Return: accum pix (32 bpp), or null on error.
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 247 of file convolve.c.
References blockconvAccumLow(), ERROR_PTR, NULL, pixCreate(), pixGetData(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Referenced by main(), pixBlockconvGray(), pixBlockconvGrayTile(), pixBlockconvGrayUnnormalized(), pixBlocksum(), pixHalfEdgeByBandpass(), pixQuadtreeMean(), pixQuadtreeVariance(), and pixWindowedMean().
pixBlockconvGrayUnnormalized()
Input: pixs (8 bpp) wc, hc (half width/height of convolution kernel) Return: pix (32 bpp; containing the convolution without normalizing for the window size), or null on error
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. (3) Returns a copy if both wc and 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 312 of file convolve.c.
References ERROR_PTR, L_INFO_INT2, L_MIN, L_WARNING, NULL, pixAddMirroredBorder(), pixBlockconvAccum(), pixCopy(), pixCreate(), pixDestroy(), pixGetData(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Input: pix (8 or 32 bpp; or 2, 4 or 8 bpp with colormap) wc, hc (half width/height of convolution kernel) nx, ny (subdivision into tiles) Return: pixd, or null on error
Notes: (1) The full width and height of the convolution kernel are (2 * wc + 1) and (2 * hc + 1) (2) Returns a copy if both wc and hc are 0 (3) Require that w >= 2 * wc + 1 and h >= 2 * hc + 1, where (w,h) are the dimensions of pixs. (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 401 of file convolve.c.
References COLOR_BLUE, COLOR_GREEN, COLOR_RED, ERROR_PTR, L_INFO_INT2, L_MAX, L_MIN, L_WARNING, L_WARNING_INT, NULL, nx, ny, pixBlockconv(), pixBlockconvGrayTile(), pixClone(), pixCopy(), pixCreateRGBImage(), pixCreateTemplateNoInit(), pixDestroy(), pixGetColormap(), pixGetDepth(), pixGetDimensions(), pixGetRGBComponent(), pixRemoveColormap(), pixTilingCreate(), pixTilingDestroy(), pixTilingGetTile(), pixTilingPaintTile(), PROCNAME, and REMOVE_CMAP_BASED_ON_SRC.
Referenced by main().
Input: pixs (8 bpp gray) pixacc (32 bpp accum pix) wc, hc (half width/height of convolution kernel) Return: pixd, or null on error
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 both wc and hc are 0 (4) Require that w > 2 * wc + 1 and h > 2 * hc + 1, where (w,h) are the dimensions of pixs.
Definition at line 525 of file convolve.c.
References ERROR_PTR, L_INFO_INT2, L_MAX, L_MIN, L_WARNING, NULL, pixBlockconvAccum(), pixClone(), pixCopy(), pixCreateTemplate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), PROCNAME, and SET_DATA_BYTE.
Referenced by pixBlockconvTiled().
| l_int32 pixWindowedStats | ( | PIX * | pixs, |
| l_int32 | wc, | ||
| l_int32 | hc, | ||
| l_int32 | hasborder, | ||
| PIX ** | ppixm, | ||
| PIX ** | ppixms, | ||
| FPIX ** | pfpixv, | ||
| FPIX ** | pfpixrv | ||
| ) |
Input: pixs (8 bpp grayscale) wc, hc (half width/height of convolution kernel) 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) &pixm (<optional return>=""> 8 bpp mean value in window) &pixms (<optional return>=""> 32 bpp mean square value in window) &fpixv (<optional return>=""> float variance in window) &fpixrv (<optional return>=""> float rms deviation from the mean) Return: 0 if OK, 1 on error
Notes: (1) This is a high-level convenience function for calculating any or all of these derived images. (2) If = 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:
(pixm)
)*(p -
)> = <p*p> -
*
(pixv)
Definition at line 651 of file convolve.c.
References ERROR_INT, NULL, pixAddBorderGeneral(), pixClone(), pixDestroy(), pixGetDepth(), pixWindowedMean(), pixWindowedMeanSquare(), pixWindowedVariance(), and PROCNAME.
Referenced by main().
Input: pixs (8 or 32 bpp grayscale) wc, hc (half width/height of convolution kernel) 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) normflag (1 for normalization to get average in window; 0 for the sum in the window (un-normalized)) Return: pixd (8 or 32 bpp, average over kernel window)
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 = 0, the required border is added. (3) Typically, == 1. However, if you want the sum within the window, rather than a normalized convolution, use == 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 734 of file convolve.c.
References ERROR_PTR, NULL, pixAddBorderGeneral(), pixBlockconvAccum(), pixClone(), pixCreate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), PROCNAME, and SET_DATA_BYTE.
Referenced by main(), pixFindHistoPeaksHSV(), pixSauvolaBinarize(), and pixWindowedStats().
Input: pixs (8 bpp grayscale) wc, hc (half width/height of convolution kernel) 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) Return: pixd (32 bpp, average over rectangular window of width = 2 * wc + 1 and height = 2 * hc + 1)
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 = 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 and 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 ( + 1) and height ( + 1), when we only need and 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 843 of file convolve.c.
References dpixDestroy(), dpixGetData(), dpixGetWpl(), ERROR_PTR, NULL, pixAddBorderGeneral(), pixClone(), pixCreate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), pixMeanSquareAccum(), and PROCNAME.
Referenced by main(), pixSauvolaBinarize(), and pixWindowedStats().
Input: pixm (mean over window; 8 or 32 bpp grayscale) pixms (mean square over window; 32 bpp) &fpixv (<optional return>=""> float variance -- the ms deviation from the mean) &fpixrv (<optional return>=""> float rms deviation from the mean) Return: 0 if OK, 1 on error
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:
Definition at line 933 of file convolve.c.
References ERROR_INT, fpixCreate(), fpixGetData(), fpixGetWpl(), GET_DATA_BYTE, NULL, pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Referenced by main(), and pixWindowedStats().
Input: pixs (8 bpp grayscale) Return: dpix (64 bit array), or null on error
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 1020 of file convolve.c.
References dpixCreate(), dpixGetData(), dpixGetWpl(), ERROR_PTR, GET_DATA_BYTE, NULL, pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Referenced by pixQuadtreeVariance(), and pixWindowedMeanSquare().
Input: pixs (1 bpp) accum pix (<optional> 32 bpp) wc, hc (half width/height of block sum/rank kernel) rank (between 0.0 and 1.0; 0.5 is median filter) Return: pixd (1 bpp)
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.
Definition at line 1098 of file convolve.c.
References ERROR_PTR, L_INFO_INT2, L_MIN, L_WARNING, NULL, pixBlocksum(), pixCopy(), pixCreateTemplate(), pixDestroy(), pixGetDimensions(), pixInvert(), pixSetAll(), pixThresholdToBinary(), and PROCNAME.
Referenced by main().
Input: pixs (1 bpp) accum pix (<optional> 32 bpp) wc, hc (half width/height of block sum/rank kernel) Return: pixd (8 bpp)
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. (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 1178 of file convolve.c.
References blocksumLow(), ERROR_PTR, L_INFO_INT2, L_MIN, L_WARNING, NULL, pixBlockconvAccum(), pixClone(), pixCopy(), pixCopyResolution(), pixCreate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), and PROCNAME.
Referenced by main(), and pixBlockrank().
Input: pixs (8 bpp) halfsize (of square over which neighbors are averaged) accum pix (<optional> 32 bpp) Return: pixd (1 bpp)
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 1263 of file convolve.c.
References ERROR_PTR, GET_DATA_BYTE, NULL, pixBlockconvGray(), pixCreate(), pixDestroy(), pixGetData(), pixGetDepth(), pixGetDimensions(), pixGetWpl(), PROCNAME, and SET_DATA_BIT.
Referenced by main().
Input: pixs (8, 16, 32 bpp; no colormap) kernel outdepth (of pixd: 8, 16 or 32) normflag (1 to normalize kernel to unit sum; 0 otherwise) Return: pixd (8, 16 or 32 bpp)
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 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. Do not normalize if the kernel has null sum, such as a DoG. (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 the former for now. Another possibility would be to output a second unsigned image for the negative values. (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 1357 of file convolve.c.
References ConvolveSamplingFactX, ConvolveSamplingFactY, L_Kernel::data, ERROR_PTR, GET_DATA_BYTE, GET_DATA_TWO_BYTES, kernelCopy(), kernelDestroy(), kernelGetParameters(), kernelInvert(), kernelNormalize(), NULL, pixAddMirroredBorder(), pixCreate(), pixDestroy(), pixGetColormap(), pixGetData(), pixGetDimensions(), pixGetWpl(), PROCNAME, SET_DATA_BYTE, and SET_DATA_TWO_BYTES.
Referenced by main(), pixConvolveRGB(), and pixConvolveSep().
| PIX* pixConvolveSep | ( | PIX * | pixs, |
| L_KERNEL * | kelx, | ||
| L_KERNEL * | kely, | ||
| l_int32 | outdepth, | ||
| l_int32 | normflag | ||
| ) |
Input: pixs (8, 16, 32 bpp; no colormap) kelx (x-dependent kernel) kely (y-dependent kernel) outdepth (of pixd: 8, 16 or 32) normflag (1 to normalize kernel to unit sum; 0 otherwise) Return: pixd (8, 16 or 32 bpp)
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 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 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 1484 of file convolve.c.
References ConvolveSamplingFactX, ConvolveSamplingFactY, ERROR_PTR, kernelDestroy(), kernelNormalize(), l_setConvolveSampling(), NULL, pixConvolve(), pixDestroy(), pixGetDepth(), and PROCNAME.
Referenced by main(), and pixConvolveRGBSep().
Input: pixs (32 bpp rgb) kernel Return: pixd (32 bpp rgb)
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 1555 of file convolve.c.
References COLOR_BLUE, COLOR_GREEN, COLOR_RED, ERROR_PTR, NULL, pixConvolve(), pixCreateRGBImage(), pixDestroy(), pixGetDepth(), pixGetRGBComponent(), and PROCNAME.
Referenced by main().
Input: pixs (32 bpp rgb) kelx (x-dependent kernel) kely (y-dependent kernel) Return: pixd (32 bpp rgb)
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 1612 of file convolve.c.
References COLOR_BLUE, COLOR_GREEN, COLOR_RED, ERROR_PTR, NULL, pixConvolveSep(), pixCreateRGBImage(), pixDestroy(), pixGetDepth(), pixGetRGBComponent(), and PROCNAME.
Referenced by main().
Input: fpixs (32 bit float array) kernel normflag (1 to normalize kernel to unit sum; 0 otherwise) Return: fpixd (32 bit float array)
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. Do not normalize if the kernel has null sum, such as a DoG. (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 1671 of file convolve.c.
References ConvolveSamplingFactX, ConvolveSamplingFactY, L_Kernel::data, ERROR_PTR, fpixAddMirroredBorder(), fpixCreate(), fpixDestroy(), fpixGetData(), fpixGetDimensions(), fpixGetWpl(), kernelCopy(), kernelDestroy(), kernelGetParameters(), kernelInvert(), kernelNormalize(), NULL, and PROCNAME.
Referenced by fpixConvolveSep(), and main().
Input: fpixs (32 bit float array) kelx (x-dependent kernel) kely (y-dependent kernel) normflag (1 to normalize kernel to unit sum; 0 otherwise) Return: fpixd (32 bit float array)
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 1758 of file convolve.c.
References ConvolveSamplingFactX, ConvolveSamplingFactY, ERROR_PTR, fpixConvolve(), fpixDestroy(), kernelDestroy(), kernelNormalize(), l_setConvolveSampling(), NULL, and PROCNAME.
Referenced by main().
Input: xfact, yfact (integer >= 1) Return: void
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 1816 of file convolve.c.
References ConvolveSamplingFactX, and ConvolveSamplingFactY.
Referenced by fpixConvolveSep(), main(), and pixConvolveSep().
| LEPT_DLL l_int32 ConvolveSamplingFactX = 1 |
Definition at line 70 of file convolve.c.
Referenced by fpixConvolve(), fpixConvolveSep(), l_setConvolveSampling(), pixConvolve(), and pixConvolveSep().
| LEPT_DLL l_int32 ConvolveSamplingFactY = 1 |
Definition at line 71 of file convolve.c.
Referenced by fpixConvolve(), fpixConvolveSep(), l_setConvolveSampling(), pixConvolve(), and pixConvolveSep().
1.7.3