Image::Leptonica::Func::compare
version 0.04
compare.c
compare.c Test for pix equality l_int32 pixEqual() l_int32 pixEqualWithAlpha() l_int32 pixEqualWithCmap() l_int32 pixUsesCmapColor() Binary correlation l_int32 pixCorrelationBinary() Difference of two images of same size l_int32 pixDisplayDiffBinary() l_int32 pixCompareBinary() l_int32 pixCompareGrayOrRGB() l_int32 pixCompareGray() l_int32 pixCompareRGB() l_int32 pixCompareTiled() Other measures of the difference of two images of the same size NUMA *pixCompareRankDifference() l_int32 pixTestForSimilarity() l_int32 pixGetDifferenceStats() NUMA *pixGetDifferenceHistogram() l_int32 pixGetPerceptualDiff() l_int32 pixGetPSNR() Translated images at the same resolution l_int32 pixCompareWithTranslation() l_int32 pixBestCorrelation()
l_int32 pixBestCorrelation ( PIX *pix1, PIX *pix2, l_int32 area1, l_int32 area2, l_int32 etransx, l_int32 etransy, l_int32 maxshift, l_int32 *tab8, l_int32 *pdelx, l_int32 *pdely, l_float32 *pscore, l_int32 debugflag )
pixBestCorrelation() Input: pix1 (1 bpp) pix2 (1 bpp) area1 (number of on pixels in pix1) area2 (number of on pixels in pix2) etransx (estimated x translation of pix2 to align with pix1) etransy (estimated y translation of pix2 to align with pix1) maxshift (max x and y shift of pix2, around the estimated alignment location, relative to pix1) tab8 (<optional> sum tab for ON pixels in byte; can be NULL) &delx (<optional return> best x shift of pix2 relative to pix1 &dely (<optional return> best y shift of pix2 relative to pix1 &score (<optional return> maximum score found; can be NULL) debugflag (<= 0 to skip; positive to generate output. The integer is used to label the debug image.) Return: 0 if OK, 1 on error Notes: (1) This maximizes the correlation score between two 1 bpp images, by starting with an estimate of the alignment (@etransx, @etransy) and computing the correlation around this. It optionally returns the shift (@delx, @dely) that maximizes the correlation score when pix2 is shifted by this amount relative to pix1. (2) Get the centroids of pix1 and pix2, using pixCentroid(), to compute (@etransx, @etransy). Get the areas using pixCountPixels(). (3) The centroid of pix2 is shifted with respect to the centroid of pix1 by all values between -maxshiftx and maxshiftx, and likewise for the y shifts. Therefore, the number of correlations computed is: (2 * maxshiftx + 1) * (2 * maxshifty + 1) Consequently, if pix1 and pix2 are large, you should do this in a coarse-to-fine sequence. See the use of this function in pixCompareWithTranslation().
l_int32 pixCompareBinary ( PIX *pix1, PIX *pix2, l_int32 comptype, l_float32 *pfract, PIX **ppixdiff )
pixCompareBinary() Input: pix1 (1 bpp) pix2 (1 bpp) comptype (L_COMPARE_XOR, L_COMPARE_SUBTRACT) &fract (<return> fraction of pixels that are different) &pixdiff (<optional return> pix of difference) Return: 0 if OK; 1 on error Notes: (1) The two images are aligned at the UL corner, and do not need to be the same size. (2) If using L_COMPARE_SUBTRACT, pix2 is subtracted from pix1. (3) The total number of pixels is determined by pix1.
l_int32 pixCompareGray ( PIX *pix1, PIX *pix2, l_int32 comptype, l_int32 plottype, l_int32 *psame, l_float32 *pdiff, l_float32 *prmsdiff, PIX **ppixdiff )
pixCompareGray() Input: pix1 (8 or 16 bpp, not cmapped) pix2 (8 or 16 bpp, not cmapped) comptype (L_COMPARE_SUBTRACT, L_COMPARE_ABS_DIFF) plottype (gplot plot output type, or 0 for no plot) &same (<optional return> 1 if pixel values are identical) &diff (<optional return> average difference) &rmsdiff (<optional return> rms of difference) &pixdiff (<optional return> pix of difference) Return: 0 if OK; 1 on error Notes: (1) See pixCompareGrayOrRGB() for details. (2) Use pixCompareGrayOrRGB() if the input pix are colormapped.
l_int32 pixCompareGrayOrRGB ( PIX *pix1, PIX *pix2, l_int32 comptype, l_int32 plottype, l_int32 *psame, l_float32 *pdiff, l_float32 *prmsdiff, PIX **ppixdiff )
pixCompareGrayOrRGB() Input: pix1 (8 or 16 bpp gray, 32 bpp rgb, or colormapped) pix2 (8 or 16 bpp gray, 32 bpp rgb, or colormapped) comptype (L_COMPARE_SUBTRACT, L_COMPARE_ABS_DIFF) plottype (gplot plot output type, or 0 for no plot) &same (<optional return> 1 if pixel values are identical) &diff (<optional return> average difference) &rmsdiff (<optional return> rms of difference) &pixdiff (<optional return> pix of difference) Return: 0 if OK; 1 on error Notes: (1) The two images are aligned at the UL corner, and do not need to be the same size. If they are not the same size, the comparison will be made over overlapping pixels. (2) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (3) If RGB, each component is compared separately. (4) If type is L_COMPARE_ABS_DIFF, pix2 is subtracted from pix1 and the absolute value is taken. (5) If type is L_COMPARE_SUBTRACT, pix2 is subtracted from pix1 and the result is clipped to 0. (6) The plot output types are specified in gplot.h. Use 0 if no difference plot is to be made. (7) If the images are pixelwise identical, no difference plot is made, even if requested. The result (TRUE or FALSE) is optionally returned in the parameter 'same'. (8) The average difference (either subtracting or absolute value) is optionally returned in the parameter 'diff'. (9) The RMS difference is optionally returned in the parameter 'rmsdiff'. For RGB, we return the average of the RMS differences for each of the components.
l_int32 pixCompareRGB ( PIX *pix1, PIX *pix2, l_int32 comptype, l_int32 plottype, l_int32 *psame, l_float32 *pdiff, l_float32 *prmsdiff, PIX **ppixdiff )
pixCompareRGB() Input: pix1 (32 bpp rgb) pix2 (32 bpp rgb) comptype (L_COMPARE_SUBTRACT, L_COMPARE_ABS_DIFF) plottype (gplot plot output type, or 0 for no plot) &same (<optional return> 1 if pixel values are identical) &diff (<optional return> average difference) &rmsdiff (<optional return> rms of difference) &pixdiff (<optional return> pix of difference) Return: 0 if OK; 1 on error Notes: (1) See pixCompareGrayOrRGB() for details.
NUMA * pixCompareRankDifference ( PIX *pix1, PIX *pix2, l_int32 factor )
pixCompareRankDifference() Input: pix1 (8 bpp gray or 32 bpp rgb, or colormapped) pix2 (8 bpp gray or 32 bpp rgb, or colormapped) factor (subsampling factor; use 0 or 1 for no subsampling) Return: narank (numa of rank difference), or null on error Notes: (1) This answers the question: if the pixel values in each component are compared by absolute difference, for any value of difference, what is the fraction of pixel pairs that have a difference of this magnitude or greater. For a difference of 0, the fraction is 1.0. In this sense, it is a mapping from pixel difference to rank order of difference. (2) The two images are aligned at the UL corner, and do not need to be the same size. If they are not the same size, the comparison will be made over overlapping pixels. (3) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (4) If RGB, pixel differences for each component are aggregated into a single histogram.
l_int32 pixCompareTiled ( PIX *pix1, PIX *pix2, l_int32 sx, l_int32 sy, l_int32 type, PIX **ppixdiff )
pixCompareTiled() Input: pix1 (8 bpp or 32 bpp rgb) pix2 (8 bpp 32 bpp rgb) sx, sy (tile size; must be > 1) type (L_MEAN_ABSVAL or L_ROOT_MEAN_SQUARE) &pixdiff (<return> pix of difference) Return: 0 if OK; 1 on error Notes: (1) With L_MEAN_ABSVAL, we compute for each tile the average abs value of the pixel component difference between the two (aligned) images. With L_ROOT_MEAN_SQUARE, we compute instead the rms difference over all components. (2) The two input pix must be the same depth. Comparison is made using UL corner alignment. (3) For 32 bpp, the distance between corresponding tiles is found by averaging the measured difference over all three components of each pixel in the tile. (4) The result, pixdiff, contains one pixel for each source tile.
l_int32 pixCompareWithTranslation ( PIX *pix1, PIX *pix2, l_int32 thresh, l_int32 *pdelx, l_int32 *pdely, l_float32 *pscore, l_int32 debugflag )
pixCompareWithTranslation() Input: pix1, pix2 (any depth; colormap OK) thresh (threshold for converting to 1 bpp) &delx (<return> x translation on pix2 to align with pix1) &dely (<return> y translation on pix2 to align with pix1) &score (<return> correlation score at best alignment) debugflag (1 for debug output; 0 for no debugging) Return: 0 if OK, 1 on error Notes: (1) This does a coarse-to-fine search for best translational alignment of two images, measured by a scoring function that is the correlation between the fg pixels. (2) The threshold is used if the images aren't 1 bpp. (3) With debug on, you get a pdf that shows, as a grayscale image, the score as a function of shift from the initial estimate, for each of the four levels. The shift is 0 at the center of the image. (4) With debug on, you also get a pdf that shows the difference at the best alignment between the two images, at each of the four levels. The red and green pixels show locations where one image has a fg pixel and the other doesn't. The black pixels are where both images have fg pixels, and white pixels are where neither image has fg pixels.
l_int32 pixCorrelationBinary ( PIX *pix1, PIX *pix2, l_float32 *pval )
pixCorrelationBinary() Input: pix1 (1 bpp) pix2 (1 bpp) &val (<return> correlation) Return: 0 if OK; 1 on error Notes: (1) The correlation is a number between 0.0 and 1.0, based on foreground similarity: (|1 AND 2|)**2 correlation = -------------- |1| * |2| where |x| is the count of foreground pixels in image x. If the images are identical, this is 1.0. If they have no fg pixels in common, this is 0.0. If one or both images have no fg pixels, the correlation is 0.0. (2) Typically the two images are of equal size, but this is not enforced. Instead, the UL corners are aligned.
PIX * pixDisplayDiffBinary ( PIX *pix1, PIX *pix2 )
pixDisplayDiffBinary() Input: pix1 (1 bpp) pix2 (1 bpp) Return: pixd (4 bpp cmapped), or null on error Notes: (1) This gives a color representation of the difference between pix1 and pix2. The color difference depends on the order. The pixels in pixd have 4 colors: * unchanged: black (on), white (off) * on in pix1, off in pix2: red * on in pix2, off in pix1: green (2) This aligns the UL corners of pix1 and pix2, and crops to the overlapping pixels.
l_int32 pixEqual ( PIX *pix1, PIX *pix2, l_int32 *psame )
pixEqual() Input: pix1 pix2 &same (<return> 1 if same; 0 if different) Return: 0 if OK; 1 on error Notes: (1) Equality is defined as having the same pixel values for each respective image pixel. (2) This works on two pix of any depth. If one or both pix have a colormap, the depths can be different and the two pix can still be equal. (3) This ignores the alpha component for 32 bpp images. (4) If both pix have colormaps and the depths are equal, use the pixEqualWithCmap() function, which does a fast comparison if the colormaps are identical and a relatively slow comparison otherwise. (5) In all other cases, any existing colormaps must first be removed before doing pixel comparison. After the colormaps are removed, the resulting two images must have the same depth. The "lowest common denominator" is RGB, but this is only chosen when necessary, or when both have colormaps but different depths. (6) For images without colormaps that are not 32 bpp, all bits in the image part of the data array must be identical.
l_int32 pixEqualWithAlpha ( PIX *pix1, PIX *pix2, l_int32 use_alpha, l_int32 *psame )
pixEqualWithAlpha() Input: pix1 pix2 use_alpha (1 to compare alpha in RGBA; 0 to ignore) &same (<return> 1 if same; 0 if different) Return: 0 if OK; 1 on error Notes: (1) See notes in pixEqual(). (2) This is more general than pixEqual(), in that for 32 bpp RGBA images, where spp = 4, you can optionally include the alpha component in the comparison.
l_int32 pixEqualWithCmap ( PIX *pix1, PIX *pix2, l_int32 *psame )
pixEqualWithCmap() Input: pix1 pix2 &same Return: 0 if OK, 1 on error Notes: (1) This returns same = TRUE if the images have identical content. (2) Both pix must have a colormap, and be of equal size and depth. If these conditions are not satisfied, it is not an error; the returned result is same = FALSE. (3) We then check whether the colormaps are the same; if so, the comparison proceeds 32 bits at a time. (4) If the colormaps are different, the comparison is done by slow brute force.
NUMA * pixGetDifferenceHistogram ( PIX *pix1, PIX *pix2, l_int32 factor )
pixGetDifferenceHistogram() Input: pix1 (8 bpp gray or 32 bpp rgb, or colormapped) pix2 (8 bpp gray or 32 bpp rgb, or colormapped) factor (subsampling factor; use 0 or 1 for no subsampling) Return: na (Numa of histogram of differences), or null on error Notes: (1) The two images are aligned at the UL corner, and do not need to be the same size. If they are not the same size, the comparison will be made over overlapping pixels. (2) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (3) If RGB, the maximum difference between pixel components is saved in the histogram.
l_int32 pixGetDifferenceStats ( PIX *pix1, PIX *pix2, l_int32 factor, l_int32 mindiff, l_float32 *pfractdiff, l_float32 *pavediff, l_int32 printstats )
pixGetDifferenceStats() Input: pix1 (8 bpp gray or 32 bpp rgb, or colormapped) pix2 (8 bpp gray or 32 bpp rgb, or colormapped) factor (subsampling factor; use 0 or 1 for no subsampling) mindiff (minimum pixel difference to be counted; > 0) &fractdiff (<return> fraction of pixels with diff greater than or equal to mindiff) &avediff (<return> average difference of pixels with diff greater than or equal to mindiff, less mindiff) printstats (use 1 to print normalized histogram to stderr) Return: 0 if OK, 1 on error Notes: (1) This takes a threshold @mindiff and describes the difference between two images in terms of two numbers: (a) the fraction of pixels, @fractdiff, whose difference equals or exceeds the threshold @mindiff, and (b) the average value @avediff of the difference in pixel value for the pixels in the set given by (a), after you subtract @mindiff. The reason for subtracting @mindiff is that you then get a useful measure for the rate of falloff of the distribution for larger differences. For example, if @mindiff = 10 and you find that @avediff = 2.5, it says that of the pixels with diff > 10, the average of their diffs is just mindiff + 2.5 = 12.5. This is a fast falloff in the histogram with increasing difference. (2) The two images are aligned at the UL corner, and do not need to be the same size. If they are not the same size, the comparison will be made over overlapping pixels. (3) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (4) If RGB, the maximum difference between pixel components is saved in the histogram.
l_int32 pixGetPSNR ( PIX *pix1, PIX *pix2, l_int32 factor, l_float32 *ppsnr )
pixGetPSNR() Input: pix1, pix2 (8 or 32 bpp; no colormap) factor (sampling factor; >= 1) &psnr (<return> power signal/noise ratio difference) Return: 0 if OK, 1 on error Notes: (1) This computes the power S/N ratio, in dB, for the difference between two images. By convention, the power S/N for a grayscale image is ('log' == log base 10, and 'ln == log base e): PSNR = 10 * log((255/MSE)^2) = 4.3429 * ln((255/MSE)^2) = -4.3429 * ln((MSE/255)^2) where MSE is the mean squared error. Here are some examples: MSE PSNR --- ---- 10 28.1 3 38.6 1 48.1 0.1 68.1 (2) If pix1 and pix2 have the same pixel values, the MSE = 0.0 and the PSNR is infinity. For that case, this returns PSNR = 1000, which corresponds to the very small MSE of about 10^(-48).
l_int32 pixGetPerceptualDiff ( PIX *pixs1, PIX *pixs2, l_int32 sampling, l_int32 dilation, l_int32 mindiff, l_float32 *pfract, PIX **ppixdiff1, PIX **ppixdiff2 )
pixGetPerceptualDiff() Input: pix1 (8 bpp gray or 32 bpp rgb, or colormapped) pix2 (8 bpp gray or 32 bpp rgb, or colormapped) sampling (subsampling factor; use 0 or 1 for no subsampling) dilation (size of grayscale or color Sel; odd) mindiff (minimum pixel difference to be counted; > 0) &fract (<return> fraction of pixels with diff greater than mindiff) &pixdiff1 (<optional return> showing difference (gray or color)) &pixdiff2 (<optional return> showing pixels of sufficient diff) Return: 0 if OK, 1 on error Notes: (1) This takes 2 pix and determines, using 2 input parameters: * @dilation specifies the amount of grayscale or color dilation to apply to the images, to compensate for a small amount of misregistration. A typical number might be 5, which uses a 5x5 Sel. Grayscale dilation expands lighter pixels into darker pixel regions. * @mindiff determines the threshold on the difference in pixel values to be counted -- two pixels are not similar if their difference in value is at least @mindiff. For color pixels, we use the maximum component difference. (2) The pixelwise comparison is always done with the UL corners aligned. The sizes of pix1 and pix2 need not be the same, although in practice it can be useful to scale to the same size. (3) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (4) Two optional diff images can be retrieved (typ. for debugging): pixdiff1: the gray or color difference pixdiff2: thresholded to 1 bpp for pixels exceeding @mindiff (5) The returned value of fract can be compared to some threshold, which is application dependent. (6) This method is in analogy to the two-sided hausdorff transform, except here it is for d > 1. For d == 1 (see pixRankHaustest()), we verify that when one pix1 is dilated, it covers at least a given fraction of the pixels in pix2, and v.v.; in that case, the two pix are sufficiently similar. Here, we do an analogous thing: subtract the dilated pix1 from pix2 to get a 1-sided hausdorff-like transform. Then do it the other way. Take the component-wise max of the two results, and threshold to get the fraction of pixels with a difference below the threshold.
l_int32 pixTestForSimilarity ( PIX *pix1, PIX *pix2, l_int32 factor, l_int32 mindiff, l_float32 maxfract, l_float32 maxave, l_int32 *psimilar, l_int32 printstats )
pixTestForSimilarity() Input: pix1 (8 bpp gray or 32 bpp rgb, or colormapped) pix2 (8 bpp gray or 32 bpp rgb, or colormapped) factor (subsampling factor; use 0 or 1 for no subsampling) mindiff (minimum pixel difference to be counted; > 0) maxfract (maximum fraction of pixels allowed to have diff greater than or equal to mindiff) maxave (maximum average difference of pixels allowed for pixels with diff greater than or equal to mindiff, after subtracting mindiff) &similar (<return> 1 if similar, 0 otherwise) printstats (use 1 to print normalized histogram to stderr) Return: 0 if OK, 1 on error Notes: (1) This takes 2 pix that are the same size and determines using 3 input parameters if they are "similar". The first parameter @mindiff establishes a criterion of pixel-to-pixel similarity: two pixels are not similar if their difference in value is at least mindiff. Then @maxfract and @maxave are thresholds on the number and distribution of dissimilar pixels allowed for the two pix to be similar. If the pix are to be similar, neither threshold can be exceeded. (2) In setting the @maxfract and @maxave thresholds, you have these options: (a) Base the comparison only on @maxfract. Then set @maxave = 0.0 or 256.0. (If 0, we always ignore it.) (b) Base the comparison only on @maxave. Then set @maxfract = 1.0. (c) Base the comparison on both thresholds. (3) Example of values that can be expected at mindiff = 15 when comparing lossless png encoding with jpeg encoding, q=75: (smoothish bg) fractdiff = 0.01, avediff = 2.5 (natural scene) fractdiff = 0.13, avediff = 3.5 To identify these images as 'similar', select maxfract and maxave to be upper bounds of what you expect. (4) See pixGetDifferenceStats() for a discussion of why we subtract mindiff from the computed average diff of the nonsimilar pixels to get the 'avediff' returned by that function. (5) If there is a colormap, it is removed and the result is either gray or RGB depending on the colormap. (6) If RGB, the maximum difference between pixel components is saved in the histogram.
l_int32 pixUsesCmapColor ( PIX *pixs, l_int32 *pcolor )
pixUsesCmapColor() Input: pixs &color (<return>) Return: 0 if OK, 1 on error Notes: (1) This returns color = TRUE if three things are obtained: (a) the pix has a colormap (b) the colormap has at least one color entry (c) a color entry is actually used (2) It is used in pixEqual() for comparing two images, in a situation where it is required to know if the colormap has color entries that are actually used in the image.
Zakariyya Mughal <zmughal@cpan.org>
This software is copyright (c) 2014 by Zakariyya Mughal.
This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.
To install Image::Leptonica, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Image::Leptonica
CPAN shell
perl -MCPAN -e shell install Image::Leptonica
For more information on module installation, please visit the detailed CPAN module installation guide.