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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 PIX * | pixSauvolaGetThreshold (PIX *pixm, PIX *pixms, l_float32 factor, PIX **ppixsd) |
static PIX * | pixApplyLocalThreshold (PIX *pixs, PIX *pixth) |
l_ok | pixOtsuAdaptiveThreshold (PIX *pixs, l_int32 sx, l_int32 sy, l_int32 smoothx, l_int32 smoothy, l_float32 scorefract, PIX **ppixth, PIX **ppixd) |
PIX * | pixOtsuThreshOnBackgroundNorm (PIX *pixs, PIX *pixim, l_int32 sx, l_int32 sy, l_int32 thresh, l_int32 mincount, l_int32 bgval, l_int32 smoothx, l_int32 smoothy, l_float32 scorefract, l_int32 *pthresh) |
PIX * | pixMaskedThreshOnBackgroundNorm (PIX *pixs, PIX *pixim, l_int32 sx, l_int32 sy, l_int32 thresh, l_int32 mincount, l_int32 smoothx, l_int32 smoothy, l_float32 scorefract, l_int32 *pthresh) |
l_ok | pixSauvolaBinarizeTiled (PIX *pixs, l_int32 whsize, l_float32 factor, l_int32 nx, l_int32 ny, PIX **ppixth, PIX **ppixd) |
l_ok | pixSauvolaBinarize (PIX *pixs, l_int32 whsize, l_float32 factor, l_int32 addborder, PIX **ppixm, PIX **ppixsd, PIX **ppixth, PIX **ppixd) |
PIX * | pixSauvolaOnContrastNorm (PIX *pixs, l_int32 mindiff, PIX **ppixn, PIX **ppixth) |
PIX * | pixThreshOnDoubleNorm (PIX *pixs, l_int32 mindiff) |
l_ok | pixThresholdByConnComp (PIX *pixs, PIX *pixm, l_int32 start, l_int32 end, l_int32 incr, l_float32 thresh48, l_float32 threshdiff, l_int32 *pglobthresh, PIX **ppixd, l_int32 debugflag) |
l_ok | pixThresholdByHisto (PIX *pixs, l_int32 factor, l_int32 halfw, l_float32 delta, l_int32 *pthresh, PIX **ppixd, PIX **ppixhisto) |
=================================================================== Image binarization algorithms are found in: grayquant.c: standard, simple, general grayscale quantization adaptmap.c: local adaptive; mostly gray-to-gray in preparation for binarization binarize.c: special binarization methods, locally adaptive and global. ===================================================================
Adaptive Otsu-based thresholding l_int32 pixOtsuAdaptiveThreshold() 8 bpp
Otsu thresholding on adaptive background normalization PIX *pixOtsuThreshOnBackgroundNorm() 8 bpp
Masking and Otsu estimate on adaptive background normalization PIX *pixMaskedThreshOnBackgroundNorm() 8 bpp
Sauvola local thresholding l_int32 pixSauvolaBinarizeTiled() l_int32 pixSauvolaBinarize() static PIX *pixSauvolaGetThreshold() static PIX *pixApplyLocalThreshold();
Sauvola binarization on contrast normalization PIX *pixSauvolaOnContrastNorm() 8 bpp
Contrast normalization followed by bg normalization and thresholding PIX *pixThreshOnDoubleNorm()
Global thresholding using connected components PIX *pixThresholdByConnComp()
Global thresholding by histogram PIX *pixThresholdByHisto()
Notes: (1) pixOtsuAdaptiveThreshold() computes a global threshold over each tile and performs the threshold operation, resulting in a binary image for each tile. These are stitched into the final result. (2) pixOtsuThreshOnBackgroundNorm() and pixMaskedThreshOnBackgroundNorm() are binarization functions that use background normalization with other techniques. (3) Sauvola binarization computes a local threshold based on the local average and square average. It takes two constants: the window size for the measurement at each pixel and a parameter that determines the amount of normalized local standard deviation to subtract from the local average value. (4) pixThresholdByConnComp() uses the numbers of 4 and 8 connected components at different thresholding to determine if a global threshold can be used (for text or line-art) and the value it should have.
Definition in file binarize.c.
[in] | pixs | 8 bpp grayscale; not colormapped |
[in] | pixth | 8 bpp array of local thresholds |
Definition at line 803 of file binarize.c.
References L_Bilateral::pixs.
PIX* pixMaskedThreshOnBackgroundNorm | ( | PIX * | pixs, |
PIX * | pixim, | ||
l_int32 | sx, | ||
l_int32 | sy, | ||
l_int32 | thresh, | ||
l_int32 | mincount, | ||
l_int32 | smoothx, | ||
l_int32 | smoothy, | ||
l_float32 | scorefract, | ||
l_int32 * | pthresh | ||
) |
pixMaskedThreshOnBackgroundNorm()
[in] | pixs | 8 bpp grayscale; not colormapped |
[in] | pixim | [optional] 1 bpp 'image' mask; can be null |
[in] | sx,sy | tile size in pixels |
[in] | thresh | threshold for determining foreground |
[in] | mincount | min threshold on counts in a tile |
[in] | smoothx | half-width of block convolution kernel width |
[in] | smoothy | half-width of block convolution kernel height |
[in] | scorefract | fraction of the max Otsu score; typ. ~ 0.1 |
[out] | pthresh | [optional] threshold value that was used on the normalized image |
Notes: (1) This begins with a standard background normalization. Additionally, there is a flexible background norm, that will adapt to a rapidly varying background, and this puts white pixels in the background near regions with significant foreground. The white pixels are turned into a 1 bpp selection mask by binarization followed by dilation. Otsu thresholding is performed on the input image to get an estimate of the threshold in the non-mask regions. The background normalized image is thresholded with two different values, and the result is combined using the selection mask. (2) Note that the numbers 255 (for bgval target) and 190 (for thresholding on pixn) are tied together, and explicitly defined in this function. (3) See pixBackgroundNorm() for meaning and typical values of input parameters. For a start, you can try: sx, sy = 10, 15 thresh = 100 mincount = 50 smoothx, smoothy = 2
Definition at line 371 of file binarize.c.
References L_Bilateral::pixs.
l_ok pixOtsuAdaptiveThreshold | ( | PIX * | pixs, |
l_int32 | sx, | ||
l_int32 | sy, | ||
l_int32 | smoothx, | ||
l_int32 | smoothy, | ||
l_float32 | scorefract, | ||
PIX ** | ppixth, | ||
PIX ** | ppixd | ||
) |
[in] | pixs | 8 bpp |
[in] | sx,sy | desired tile dimensions; actual size may vary |
[in] | smoothx,smoothy | half-width of convolution kernel applied to threshold array: use 0 for no smoothing |
[in] | scorefract | fraction of the max Otsu score; typ. 0.1; use 0.0 for standard Otsu |
[out] | ppixth | [optional] array of threshold values found for each tile |
[out] | ppixd | [optional] thresholded input pixs, based on the threshold array |
Notes: (1) The Otsu method finds a single global threshold for an image. This function allows a locally adapted threshold to be found for each tile into which the image is broken up. (2) The array of threshold values, one for each tile, constitutes a highly downscaled image. This array is optionally smoothed using a convolution. The full width and height of the convolution kernel are (2 * smoothx + 1) and (2 * smoothy + 1). (3) The minimum tile dimension allowed is 16. If such small tiles are used, it is recommended to use smoothing, because without smoothing, each small tile determines the splitting threshold independently. A tile that is entirely in the image bg will then hallucinate fg, resulting in a very noisy binarization. The smoothing should be large enough that no tile is only influenced by one type (fg or bg) of pixels, because it will force a split of its pixels. (4) To get a single global threshold for the entire image, use input values of sx and sy that are larger than the image. For this situation, the smoothing parameters are ignored. (5) The threshold values partition the image pixels into two classes: one whose values are less than the threshold and another whose values are greater than or equal to the threshold. This is the same use of 'threshold' as in pixThresholdToBinary(). (6) The scorefract is the fraction of the maximum Otsu score, which is used to determine the range over which the histogram minimum is searched. See numaSplitDistribution() for details on the underlying method of choosing a threshold. (7) This uses enables a modified version of the Otsu criterion for splitting the distribution of pixels in each tile into a fg and bg part. The modification consists of searching for a minimum in the histogram over a range of pixel values where the Otsu score is within a defined fraction, scorefract, of the max score. To get the original Otsu algorithm, set scorefract == 0. (8) N.B. This method is NOT recommended for images with weak text and significant background noise, such as bleedthrough, because of the problem noted in (3) above for tiling. Use Sauvola.
Definition at line 157 of file binarize.c.
References L_Bilateral::pixs.
PIX* pixOtsuThreshOnBackgroundNorm | ( | PIX * | pixs, |
PIX * | pixim, | ||
l_int32 | sx, | ||
l_int32 | sy, | ||
l_int32 | thresh, | ||
l_int32 | mincount, | ||
l_int32 | bgval, | ||
l_int32 | smoothx, | ||
l_int32 | smoothy, | ||
l_float32 | scorefract, | ||
l_int32 * | pthresh | ||
) |
pixOtsuThreshOnBackgroundNorm()
[in] | pixs | 8 bpp grayscale; not colormapped |
[in] | pixim | [optional] 1 bpp 'image' mask; can be null |
[in] | sx,sy | tile size in pixels |
[in] | thresh | threshold for determining foreground |
[in] | mincount | min threshold on counts in a tile |
[in] | bgval | target bg val; typ. > 128 |
[in] | smoothx | half-width of block convolution kernel width |
[in] | smoothy | half-width of block convolution kernel height |
[in] | scorefract | fraction of the max Otsu score; typ. 0.1 |
[out] | pthresh | [optional] threshold value that was used on the normalized image |
Notes: (1) This does background normalization followed by Otsu thresholding. Otsu binarization attempts to split the image into two roughly equal sets of pixels, and it does a very poor job when there are large amounts of dark background. By doing a background normalization first, to get the background near 255, we remove this problem. Then we use a modified Otsu to estimate the best global threshold on the normalized image. (2) See pixBackgroundNorm() for meaning and typical values of input parameters. For a start, you can try: sx, sy = 10, 15 thresh = 100 mincount = 50 bgval = 255 smoothx, smoothy = 2
Definition at line 273 of file binarize.c.
References L_Bilateral::pixs.
l_ok pixSauvolaBinarize | ( | PIX * | pixs, |
l_int32 | whsize, | ||
l_float32 | factor, | ||
l_int32 | addborder, | ||
PIX ** | ppixm, | ||
PIX ** | ppixsd, | ||
PIX ** | ppixth, | ||
PIX ** | ppixd | ||
) |
[in] | pixs | 8 bpp grayscale; not colormapped |
[in] | whsize | window half-width for measuring local statistics |
[in] | factor | factor for reducing threshold due to variance; >= 0 |
[in] | addborder | 1 to add border of width (whsize + 1) on all sides |
[out] | ppixm | [optional] local mean values |
[out] | ppixsd | [optional] local standard deviation values |
[out] | ppixth | [optional] threshold values |
[out] | ppixd | [optional] thresholded image |
Notes: (1) The window width and height are 2 * whsize + 1. The minimum value for whsize is 2; typically it is >= 7.. (2) The local statistics, measured over the window, are the average and standard deviation. (3) The measurements of the mean and standard deviation are performed inside a border of (whsize + 1) pixels. If pixs does not have these added border pixels, use addborder = 1 to add it here; otherwise use addborder = 0. (4) The Sauvola threshold is determined from the formula: t = m * (1 - k * (1 - s / 128)) where: t = local threshold m = local mean k = factor (>= 0) [ typ. 0.35 ] s = local standard deviation, which is maximized at 127.5 when half the samples are 0 and half are 255. (5) The basic idea of Niblack and Sauvola binarization is that the local threshold should be less than the median value, and the larger the variance, the closer to the median it should be chosen. Typical values for k are between 0.2 and 0.5.
Definition at line 611 of file binarize.c.
References L_Bilateral::pixs, and L_Bilateral::pixsc.
l_ok pixSauvolaBinarizeTiled | ( | PIX * | pixs, |
l_int32 | whsize, | ||
l_float32 | factor, | ||
l_int32 | nx, | ||
l_int32 | ny, | ||
PIX ** | ppixth, | ||
PIX ** | ppixd | ||
) |
[in] | pixs | 8 bpp grayscale, not colormapped |
[in] | whsize | window half-width for measuring local statistics |
[in] | factor | factor for reducing threshold due to variance; >= 0 |
[in] | nx,ny | subdivision into tiles; >= 1 |
[out] | ppixth | [optional] Sauvola threshold values |
[out] | ppixd | [optional] thresholded image |
Notes: (1) The window width and height are 2 * whsize + 1. The minimum value for whsize is 2; typically it is >= 7. (2) For nx == ny == 1, this defaults to pixSauvolaBinarize(). (3) Why a tiled version? (a) A uint32 is used for the mean value accumulator, so overflow can occur for an image with more than 16M pixels. (b) A dpix is used to accumulate mean square values, and it can only accommodate images with less than 2^28 pixels. Using tiles reduces the size of all the arrays. (c) Each tile can be processed independently, in parallel, on a multicore processor. (4) The Sauvola threshold is determined from the formula: t = m * (1 - k * (1 - s / 128)) See pixSauvolaBinarize() for details.
Definition at line 484 of file binarize.c.
References L_Bilateral::pixs.
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static |
[in] | pixm | 8 bpp grayscale; not colormapped |
[in] | pixms | 32 bpp |
[in] | factor | factor for reducing threshold due to variance; >= 0 |
[out] | ppixsd | [optional] local standard deviation |
Notes: (1) The Sauvola threshold is determined from the formula: t = m * (1 - k * (1 - s / 128)) where: t = local threshold m = local mean k = factor (>= 0) [ typ. 0.35 ] s = local standard deviation, which is maximized at 127.5 when half the samples are 0 and half are 255. (2) See pixSauvolaBinarize() for other details. (3) Important definitions and relations for computing averages: v == pixel value E(p) == expected value of p == average of p over some pixel set S(v) == square of v == v * v mv == E(v) == expected pixel value == mean value ms == E(S(v)) == expected square of pixel values == mean square value var == variance == expected square of deviation from mean == E(S(v - mv)) = E(S(v) - 2 * S(v * mv) + S(mv)) = E(S(v)) - S(mv) = ms - mv * mv s == standard deviation = sqrt(var) So for evaluating the standard deviation in the Sauvola threshold, we take s = sqrt(ms - mv * mv)
Definition at line 721 of file binarize.c.
[in] | pixs | 8 or 32 bpp |
[in] | mindiff | minimum diff to accept as valid in contrast normalization. Use ~130 for noisy images. |
[out] | ppixn | [optional] intermediate output from contrast normalization |
[out] | ppixth | [optional] threshold array for binarization |
Notes: (1) This composite operation is good for adaptively removing dark background.
Definition at line 864 of file binarize.c.
References L_Bilateral::pixs.
l_ok pixThresholdByConnComp | ( | PIX * | pixs, |
PIX * | pixm, | ||
l_int32 | start, | ||
l_int32 | end, | ||
l_int32 | incr, | ||
l_float32 | thresh48, | ||
l_float32 | threshdiff, | ||
l_int32 * | pglobthresh, | ||
PIX ** | ppixd, | ||
l_int32 | debugflag | ||
) |
[in] | pixs | depth > 1, colormap OK |
[in] | pixm | [optional] 1 bpp mask giving region to ignore by setting pixels to white; use NULL if no mask |
[in] | start,end,incr | binarization threshold levels to test |
[in] | thresh48 | threshold on normalized difference between the numbers of 4 and 8 connected components |
[in] | threshdiff | threshold on normalized difference between the number of 4 cc at successive iterations |
[out] | pglobthresh | [optional] best global threshold; 0 if no threshold is found |
[out] | ppixd | [optional] image thresholded to binary, or null if no threshold is found |
[in] | debugflag | 1 for plotted results |
Notes: (1) This finds a global threshold based on connected components. Although slow, it is reasonable to use it in a situation where (a) the background in the image is relatively uniform, and (b) the result will be fed to an OCR program that accepts 1 bpp images and works best with easily segmented characters. The reason for (b) is that this selects a threshold with a minimum number of both broken characters and merged characters. (2) If the pix has color, it is converted to gray using the max component. (3) Input 0 to use default values for any of these inputs: start, end, incr, thresh48, threshdiff. (4) This approach can be understood as follows. When the binarization threshold is varied, the numbers of c.c. identify four regimes: (a) For low thresholds, text is broken into small pieces, and the number of c.c. is large, with the 4 c.c. significantly exceeding the 8 c.c. (b) As the threshold rises toward the optimum value, the text characters coalesce and there is very little difference between the numbers of 4 and 8 c.c, which both go through a minimum. (c) Above this, the image background gets noisy because some pixels are(thresholded to foreground, and the numbers of c.c. quickly increase, with the 4 c.c. significantly larger than the 8 c.c. (d) At even higher thresholds, the image background noise coalesces as it becomes mostly foreground, and the number of c.c. drops quickly. (5) If there is no global threshold that distinguishes foreground text from background (e.g., weak text over a background that has significant variation and/or bleedthrough), this returns 1, which the caller should check.
Definition at line 1013 of file binarize.c.
References L_Bilateral::pixs.
l_ok pixThresholdByHisto | ( | PIX * | pixs, |
l_int32 | factor, | ||
l_int32 | halfw, | ||
l_float32 | delta, | ||
l_int32 * | pthresh, | ||
PIX ** | ppixd, | ||
PIX ** | ppixhisto | ||
) |
[in] | pixs | gray 8 bpp, no colormap |
[in] | factor | subsampling factor >= 1 |
[in] | halfw | half of window width for smoothing; use 0 for default |
[in] | delta | relative amount to resolve peaks and valleys; in (0 ... 1], use 0 for default |
[out] | pthresh | best global threshold; 0 if no threshold is found |
[out] | ppixd | [optional] thresholded 1 bpp pix |
[out] | ppixhisto | [optional] rescaled histogram of gray values |
Notes: (1) This finds a global threshold. It is best for an image that has a fairly well-defined fg and bg. (2) If it finds a good threshold and ppixd is defined, the binarized image is returned in otherwise it return null. (3) Suggest using default values for half and delta. (4) Returns 0 in pthresh if it can't find a good threshold.
Definition at line 1169 of file binarize.c.
References L_Bilateral::maxval, and L_Bilateral::pixs.
pixTheshOnDoubleNorm()
[in] | pixs | 8 or 32 bpp |
[in] | mindiff | minimum diff to accept as valid in contrast normalization. Use ~130 for noisy images. |
Notes: (1) This composite operation is good for adaptively removing dark background. (2) The threshold for the binarization uses an estimate based on Otsu adaptive thresholding.
Definition at line 920 of file binarize.c.
References L_Bilateral::pixs.