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Updated Tesseract to 3.03 and Leptonica to 1.70.
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Kevin Conley
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TesseractOCR/include/leptonica/allheaders.h
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| /*====================================================================* | ||
| - Copyright (C) 2001 Leptonica. All rights reserved. | ||
| - | ||
| - Redistribution and use in source and binary forms, with or without | ||
| - modification, are permitted provided that the following conditions | ||
| - are met: | ||
| - 1. Redistributions of source code must retain the above copyright | ||
| - notice, this list of conditions and the following disclaimer. | ||
| - 2. Redistributions in binary form must reproduce the above | ||
| - copyright notice, this list of conditions and the following | ||
| - disclaimer in the documentation and/or other materials | ||
| - provided with the distribution. | ||
| - | ||
| - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS | ||
| - ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT | ||
| - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR | ||
| - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL ANY | ||
| - CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, | ||
| - EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, | ||
| - PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR | ||
| - PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY | ||
| - OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING | ||
| - NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS | ||
| - SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| *====================================================================*/ | ||
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| #ifndef LEPTONICA_BILATERAL_H | ||
| #define LEPTONICA_BILATERAL_H | ||
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| /* | ||
| * Contains the following struct | ||
| * struct L_Bilateral | ||
| * | ||
| * | ||
| * For a tutorial introduction to bilateral filters, which apply a | ||
| * gaussian blur to smooth parts of the image while preserving edges, see | ||
| * http://people.csail.mit.edu/sparis/bf_course/slides/03_definition_bf.pdf | ||
| * | ||
| * We give an implementation of a bilateral filtering algorithm given in: | ||
| * "Real-Time O(1) Bilateral Filtering," by Yang, Tan and Ahuja, CVPR 2009 | ||
| * which is at: | ||
| * http://vision.ai.uiuc.edu/~qyang6/publications/cvpr-09-qingxiong-yang.pdf | ||
| * This is based on an earlier algorithm by Sylvain Paris and Frédo Durand: | ||
| * http://people.csail.mit.edu/sparis/publi/2006/eccv/ | ||
| * Paris_06_Fast_Approximation.pdf | ||
| * | ||
| * The kernel of the filter is a product of a spatial gaussian and a | ||
| * monotonically decreasing function of the difference in intensity | ||
| * between the source pixel and the neighboring pixel. The intensity | ||
| * part of the filter gives higher influence for pixels with intensities | ||
| * that are near to the source pixel, and the spatial part of the | ||
| * filter gives higher weight to pixels that are near the source pixel. | ||
| * This combination smooths in relatively uniform regions, while | ||
| * maintaining edges. | ||
| * | ||
| * The advantage of the appoach of Yang et al is that it is separable, | ||
| * so the computation time is linear in the gaussian filter size. | ||
| * Furthermore, it is possible to do much of the computation as a reduced | ||
| * scale, which gives a good approximation to the full resolution version | ||
| * but greatly speeds it up. | ||
| * | ||
| * The bilateral filtered value at x is: | ||
| * | ||
| * sum[y in N(x)]: spatial(|y - x|) * range(|I(x) - I(y)|) * I(y) | ||
| * I'(x) = -------------------------------------------------------------- | ||
| * sum[y in N(x)]: spatial(|y - x|) * range(|I(x) - I(y)|) | ||
| * | ||
| * where I() is the input image, I'() is the filtered image, N(x) is the | ||
| * set of pixels around x in the filter support, and spatial() and range() | ||
| * are gaussian functions: | ||
| * spatial(x) = exp(-x^2 / (2 * s_s^2)) | ||
| * range(x) = exp(-x^2 / (2 * s_r^2)) | ||
| * and s_s and s_r and the standard deviations of the two gaussians. | ||
| * | ||
| * Yang et al use a separable approximation to this, by defining a set | ||
| * of related but separable functions J(k,x), that we call Principal | ||
| * Bilateral Components (PBC): | ||
| * | ||
| * sum[y in N(x)]: spatial(|y - x|) * range(|k - I(y)|) * I(y) | ||
| * J(k,x) = ----------------------------------------------------------- | ||
| * sum[y in N(x)]: spatial(|y - x|) * range(|k - I(y)|) | ||
| * | ||
| * which are computed quickly for a set of n values k[p], p = 0 ... n-1. | ||
| * Then each output pixel is found using a linear interpolation: | ||
| * | ||
| * I'(x) = (1 - q) * J(k[p],x) + q * J(k[p+1],x) | ||
| * | ||
| * where J(k[p],x) and J(k[p+1],x) are PBC for which | ||
| * k[p] <= I(x) and k[p+1] >= I(x), and | ||
| * q = (I(x) - k[p]) / (k[p+1] - k[p]). | ||
| * | ||
| * We can also subsample I(x), create subsampled versions of J(k,x), | ||
| * which are then interpolated between for I'(x). | ||
| * | ||
| * We generate 'pixsc', by optionally downscaling the input image | ||
| * (using area mapping by the factor 'reduction'), and then adding | ||
| * a mirrored border to avoid boundary cases. This is then used | ||
| * to compute 'ncomps' PBCs. | ||
| * | ||
| * The 'spatial_stdev' is also downscaled by 'reduction'. The size | ||
| * of the 'spatial' array is 4 * (reduced 'spatial_stdev') + 1. | ||
| * The size of the 'range' array is 256. | ||
| */ | ||
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| /*------------------------------------------------------------------------* | ||
| * Bilateral filter * | ||
| *------------------------------------------------------------------------*/ | ||
| struct L_Bilateral | ||
| { | ||
| struct Pix *pixs; /* clone of source pix */ | ||
| struct Pix *pixsc; /* downscaled pix with mirrored border */ | ||
| l_int32 reduction; /* 1, 2 or 4x for intermediates */ | ||
| l_float32 spatial_stdev; /* stdev of spatial gaussian */ | ||
| l_float32 range_stdev; /* stdev of range gaussian */ | ||
| l_float32 *spatial; /* 1D gaussian spatial kernel */ | ||
| l_float32 *range; /* one-sided gaussian range kernel */ | ||
| l_int32 minval; /* min value in 8 bpp pix */ | ||
| l_int32 maxval; /* max value in 8 bpp pix */ | ||
| l_int32 ncomps; /* number of intermediate results */ | ||
| l_int32 *nc; /* set of k values (size ncomps) */ | ||
| l_int32 *kindex; /* mapping from intensity to lower k */ | ||
| l_float32 *kfract; /* mapping from intensity to fract k */ | ||
| struct Pixa *pixac; /* intermediate result images (PBC) */ | ||
| l_uint32 ***lineset; /* lineptrs for pixac */ | ||
| }; | ||
| typedef struct L_Bilateral L_BILATERAL; | ||
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| #endif /* LEPTONICA_BILATERAL_H */ |
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