NAME
Image::Leptonica::Func::compare
VERSION
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()FUNCTIONS
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().pixCompareBinary
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.pixCompareGray
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.pixCompareGrayOrRGB
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.pixCompareRGB
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.pixCompareRankDifference
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.pixCompareTiled
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.pixCompareWithTranslation
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.pixCorrelationBinary
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.pixDisplayDiffBinary
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.pixEqual
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.pixEqualWithAlpha
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.pixEqualWithCmap
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.pixGetDifferenceHistogram
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.pixGetDifferenceStats
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.pixGetPSNR
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).pixGetPerceptualDiff
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.pixTestForSimilarity
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.pixUsesCmapColor
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.AUTHOR
Zakariyya Mughal <zmughal@cpan.org>
COPYRIGHT AND LICENSE
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.