/*!
* \brief pixColorSegment()
*
* \param[in] pixs 32 bpp; 24-bit color
* \param[in] maxdist max euclidean dist to existing cluster
* \param[in] maxcolors max number of colors allowed in first pass
* \param[in] selsize linear size of sel for closing to remove noise
* \param[in] finalcolors max number of final colors allowed after 4th pass
* \param[in] debugflag 1 for debug output; 0 otherwise
* \return pixd 8 bit with colormap, or NULL on error
*
* <pre>
* Color segmentation proceeds in four phases:
*
* Phase 1: pixColorSegmentCluster()
* The image is traversed in raster order. Each pixel either
* becomes the representative for a new cluster or is assigned to an
* existing cluster. Assignment is greedy. The data is stored in
* a colormapped image. Three auxiliary arrays are used to hold
* the colors of the representative pixels, for fast lookup.
* The average color in each cluster is computed.
*
* Phase 2. pixAssignToNearestColor()
* A second non-greedy clustering pass is performed, where each pixel
* is assigned to the nearest cluster average. We also keep track
* of how many pixels are assigned to each cluster.
*
* Phase 3. pixColorSegmentClean()
* For each cluster, starting with the largest, do a morphological
* closing to eliminate small components within larger ones.
*
* Phase 4. pixColorSegmentRemoveColors()
* Eliminate all colors except the most populated 'finalcolors'.
* Then remove unused colors from the colormap, and reassign those
* pixels to the nearest remaining cluster, using the original pixel values.
*
* Notes:
* (1) The goal is to generate a small number of colors.
* Typically this would be specified by 'finalcolors',
* a number that would be somewhere between 3 and 6.
* The parameter 'maxcolors' specifies the maximum number of
* colors generated in the first phase. This should be
* larger than finalcolors, perhaps twice as large.
* If more than 'maxcolors' are generated in the first phase
* using the input 'maxdist', the distance is repeatedly
* increased by a multiplicative factor until the condition
* is satisfied. The implicit relation between 'maxdist'
* and 'maxcolors' is thus adjusted programmatically.
* (2) As a very rough guideline, given a target value of 'finalcolors',
* here are approximate values of 'maxdist' and 'maxcolors'
* to start with:
*
* finalcolors maxcolors maxdist
* ----------- --------- -------
* 3 6 100
* 4 8 90
* 5 10 75
* 6 12 60
*
* For a given number of finalcolors, if you use too many
* maxcolors, the result will be noisy. If you use too few,
* the result will be a relatively poor assignment of colors.
* </pre>
*/
PIX *
pixColorSegment(PIX *pixs,
l_int32 maxdist,
l_int32 maxcolors,
l_int32 selsize,
l_int32 finalcolors,
l_int32 debugflag)
{
l_int32 *countarray;
PIX *pixd;
PROCNAME("pixColorSegment");
if (!pixs)
return (PIX *)ERROR_PTR("pixs not defined", procName, NULL);
if (pixGetDepth(pixs) != 32)
return (PIX *)ERROR_PTR("must be rgb color", procName, NULL);
/* Phase 1; original segmentation */
pixd = pixColorSegmentCluster(pixs, maxdist, maxcolors, debugflag);
if (!pixd)
return (PIX *)ERROR_PTR("pixd not made", procName, NULL);
if (debugflag) {
lept_mkdir("lept/segment");
pixWriteDebug("/tmp/lept/segment/colorseg1.png", pixd, IFF_PNG);
}
/* Phase 2; refinement in pixel assignment */
if ((countarray = (l_int32 *)LEPT_CALLOC(256, sizeof(l_int32))) == NULL) {
pixDestroy(&pixd);
return (PIX *)ERROR_PTR("countarray not made", procName, NULL);
}
pixAssignToNearestColor(pixd, pixs, NULL, LEVEL_IN_OCTCUBE, countarray);
if (debugflag)
pixWriteDebug("/tmp/lept/segment/colorseg2.png", pixd, IFF_PNG);
/* Phase 3: noise removal by separately closing each color */
pixColorSegmentClean(pixd, selsize, countarray);
LEPT_FREE(countarray);
if (debugflag)
pixWriteDebug("/tmp/lept/segment/colorseg3.png", pixd, IFF_PNG);
/* Phase 4: removal of colors with small population and
* reassignment of pixels to remaining colors */
pixColorSegmentRemoveColors(pixd, pixs, finalcolors);
return pixd;
}
/*
* pixDebugFlipDetect()
*
* Input: filename (for output debug file)
* pixs (input to pix*Detect)
* pixhm (hit-miss result from ascenders or descenders)
* enable (1 to enable this function; 0 to disable)
* Return: void
*/
static void
pixDebugFlipDetect(const char *filename,
PIX *pixs,
PIX *pixhm,
l_int32 enable)
{
PIX *pixt, *pixthm;
if (!enable) return;
/* Display with red dot at counted locations */
pixt = pixConvert1To4Cmap(pixs);
pixthm = pixMorphSequence(pixhm, "d5.5", 0);
pixSetMaskedCmap(pixt, pixthm, 0, 0, 255, 0, 0);
pixWriteDebug(filename, pixt, IFF_PNG);
pixDestroy(&pixthm);
pixDestroy(&pixt);
return;
}
/*!
* \brief pixFindBaselines()
*
* \param[in] pixs 1 bpp, 300 ppi
* \param[out] ppta [optional] pairs of pts corresponding to
* approx. ends of each text line
* \param[in] pixadb for debug output; use NULL to skip
* \return na of baseline y values, or NULL on error
*
* <pre>
* Notes:
* (1) Input binary image must have text lines already aligned
* horizontally. This can be done by either rotating the
* image with pixDeskew(), or, if a projective transform
* is required, by doing pixDeskewLocal() first.
* (2) Input null for &pta if you don't want this returned.
* The pta will come in pairs of points (left and right end
* of each baseline).
* (3) Caution: this will not work properly on text with multiple
* columns, where the lines are not aligned between columns.
* If there are multiple columns, they should be extracted
* separately before finding the baselines.
* (4) This function constructs different types of output
* for baselines; namely, a set of raster line values and
* a set of end points of each baseline.
* (5) This function was designed to handle short and long text lines
* without using dangerous thresholds on the peak heights. It does
* this by combining the differential signal with a morphological
* analysis of the locations of the text lines. One can also
* combine this data to normalize the peak heights, by weighting
* the differential signal in the region of each baseline
* by the inverse of the width of the text line found there.
* </pre>
*/
NUMA *
pixFindBaselines(PIX *pixs,
PTA **ppta,
PIXA *pixadb)
{
l_int32 h, i, j, nbox, val1, val2, ndiff, bx, by, bw, bh;
l_int32 imaxloc, peakthresh, zerothresh, inpeak;
l_int32 mintosearch, max, maxloc, nloc, locval;
l_int32 *array;
l_float32 maxval;
BOXA *boxa1, *boxa2, *boxa3;
GPLOT *gplot;
NUMA *nasum, *nadiff, *naloc, *naval;
PIX *pix1, *pix2;
PTA *pta;
PROCNAME("pixFindBaselines");
if (ppta) *ppta = NULL;
if (!pixs || pixGetDepth(pixs) != 1)
return (NUMA *)ERROR_PTR("pixs undefined or not 1 bpp", procName, NULL);
/* Close up the text characters, removing noise */
pix1 = pixMorphSequence(pixs, "c25.1 + e15.1", 0);
/* Estimate the resolution */
if (pixadb) pixaAddPix(pixadb, pixScale(pix1, 0.25, 0.25), L_INSERT);
/* Save the difference of adjacent row sums.
* The high positive-going peaks are the baselines */
if ((nasum = pixCountPixelsByRow(pix1, NULL)) == NULL) {
pixDestroy(&pix1);
return (NUMA *)ERROR_PTR("nasum not made", procName, NULL);
}
h = pixGetHeight(pixs);
nadiff = numaCreate(h);
numaGetIValue(nasum, 0, &val2);
for (i = 0; i < h - 1; i++) {
val1 = val2;
numaGetIValue(nasum, i + 1, &val2);
numaAddNumber(nadiff, val1 - val2);
}
numaDestroy(&nasum);
if (pixadb) { /* show the difference signal */
lept_mkdir("lept/baseline");
gplotSimple1(nadiff, GPLOT_PNG, "/tmp/lept/baseline/diff", "Diff Sig");
pix2 = pixRead("/tmp/lept/baseline/diff.png");
pixaAddPix(pixadb, pix2, L_INSERT);
}
/* Use the zeroes of the profile to locate each baseline. */
array = numaGetIArray(nadiff);
ndiff = numaGetCount(nadiff);
numaGetMax(nadiff, &maxval, &imaxloc);
numaDestroy(&nadiff);
/* Use this to begin locating a new peak: */
peakthresh = (l_int32)maxval / PEAK_THRESHOLD_RATIO;
/* Use this to begin a region between peaks: */
zerothresh = (l_int32)maxval / ZERO_THRESHOLD_RATIO;
naloc = numaCreate(0);
naval = numaCreate(0);
inpeak = FALSE;
for (i = 0; i < ndiff; i++) {
if (inpeak == FALSE) {
if (array[i] > peakthresh) { /* transition to in-peak */
inpeak = TRUE;
mintosearch = i + MIN_DIST_IN_PEAK; /* accept no zeros
* between i and mintosearch */
max = array[i];
maxloc = i;
}
} else { /* inpeak == TRUE; look for max */
if (array[i] > max) {
max = array[i];
maxloc = i;
mintosearch = i + MIN_DIST_IN_PEAK;
} else if (i > mintosearch && array[i] <= zerothresh) { /* leave */
inpeak = FALSE;
numaAddNumber(naval, max);
numaAddNumber(naloc, maxloc);
}
}
}
LEPT_FREE(array);
/* If array[ndiff-1] is max, eg. no descenders, baseline at bottom */
if (inpeak) {
numaAddNumber(naval, max);
numaAddNumber(naloc, maxloc);
}
if (pixadb) { /* show the raster locations for the peaks */
gplot = gplotCreate("/tmp/lept/baseline/loc", GPLOT_PNG, "Peak locs",
"rasterline", "height");
gplotAddPlot(gplot, naloc, naval, GPLOT_POINTS, "locs");
gplotMakeOutput(gplot);
gplotDestroy(&gplot);
pix2 = pixRead("/tmp/lept/baseline/loc.png");
pixaAddPix(pixadb, pix2, L_INSERT);
}
numaDestroy(&naval);
/* Generate an approximate profile of text line width.
* First, filter the boxes of text, where there may be
* more than one box for a given textline. */
pix2 = pixMorphSequence(pix1, "r11 + c20.1 + o30.1 +c1.3", 0);
if (pixadb) pixaAddPix(pixadb, pix2, L_COPY);
boxa1 = pixConnComp(pix2, NULL, 4);
pixDestroy(&pix1);
pixDestroy(&pix2);
if (boxaGetCount(boxa1) == 0) {
numaDestroy(&naloc);
boxaDestroy(&boxa1);
L_INFO("no compnents after filtering\n", procName);
return NULL;
}
boxa2 = boxaTransform(boxa1, 0, 0, 4., 4.);
boxa3 = boxaSort(boxa2, L_SORT_BY_Y, L_SORT_INCREASING, NULL);
boxaDestroy(&boxa1);
boxaDestroy(&boxa2);
/* Optionally, find the baseline segments */
pta = NULL;
if (ppta) {
pta = ptaCreate(0);
*ppta = pta;
}
if (pta) {
nloc = numaGetCount(naloc);
nbox = boxaGetCount(boxa3);
for (i = 0; i < nbox; i++) {
boxaGetBoxGeometry(boxa3, i, &bx, &by, &bw, &bh);
for (j = 0; j < nloc; j++) {
numaGetIValue(naloc, j, &locval);
if (L_ABS(locval - (by + bh)) > 25)
continue;
ptaAddPt(pta, bx, locval);
ptaAddPt(pta, bx + bw, locval);
break;
}
}
}
boxaDestroy(&boxa3);
if (pixadb && pta) { /* display baselines */
l_int32 npts, x1, y1, x2, y2;
pix1 = pixConvertTo32(pixs);
npts = ptaGetCount(pta);
for (i = 0; i < npts; i += 2) {
ptaGetIPt(pta, i, &x1, &y1);
ptaGetIPt(pta, i + 1, &x2, &y2);
pixRenderLineArb(pix1, x1, y1, x2, y2, 2, 255, 0, 0);
}
pixWriteDebug("/tmp/lept/baseline/baselines.png", pix1, IFF_PNG);
pixaAddPix(pixadb, pixScale(pix1, 0.25, 0.25), L_INSERT);
pixDestroy(&pix1);
}
return naloc;
}
/*!
* \brief pixUpDownDetectGeneralDwa()
*
* \param[in] pixs 1 bpp, deskewed, English text
* \param[out] pconf confidence that text is rightside-up
* \param[in] mincount min number of up + down; use 0 for default
* \param[in] npixels number of pixels removed from each side of word box
* \param[in] debug 1 for debug output; 0 otherwise
* \return 0 if OK, 1 on error
*
* <pre>
* Notes:
* (1) See the notes in pixUpDownDetectGeneral() for usage.
* </pre>
*/
l_int32
pixUpDownDetectGeneralDwa(PIX *pixs,
l_float32 *pconf,
l_int32 mincount,
l_int32 npixels,
l_int32 debug)
{
char flipsel1[] = "flipsel1";
char flipsel2[] = "flipsel2";
char flipsel3[] = "flipsel3";
char flipsel4[] = "flipsel4";
l_int32 countup, countdown, nmax;
l_float32 nup, ndown;
PIX *pixt, *pix0, *pix1, *pix2, *pix3, *pixm;
PROCNAME("pixUpDownDetectGeneralDwa");
if (!pconf)
return ERROR_INT("&conf not defined", procName, 1);
*pconf = 0.0;
if (!pixs || pixGetDepth(pixs) != 1)
return ERROR_INT("pixs not defined or not 1 bpp", procName, 1);
if (mincount == 0)
mincount = DEFAULT_MIN_UP_DOWN_COUNT;
if (npixels < 0)
npixels = 0;
lept_mkdir("lept/orient");
/* One of many reasonable pre-filtering sequences: (1, 8) and (30, 1).
* This closes holes in x-height characters and joins them at
* the x-height. There is more noise in the descender detection
* from this, but it works fairly well. */
pixt = pixMorphSequenceDwa(pixs, "c1.8 + c30.1", 0);
/* Be sure to add the border before the flip DWA operations! */
pix0 = pixAddBorderGeneral(pixt, ADDED_BORDER, ADDED_BORDER,
ADDED_BORDER, ADDED_BORDER, 0);
pixDestroy(&pixt);
/* Optionally, make a mask of the word bounding boxes, shortening
* each of them by a fixed amount at each end. */
pixm = NULL;
if (npixels > 0) {
l_int32 i, nbox, x, y, w, h;
BOX *box;
BOXA *boxa;
pix1 = pixMorphSequenceDwa(pix0, "o10.1", 0);
boxa = pixConnComp(pix1, NULL, 8);
pixm = pixCreateTemplate(pix1);
pixDestroy(&pix1);
nbox = boxaGetCount(boxa);
for (i = 0; i < nbox; i++) {
box = boxaGetBox(boxa, i, L_CLONE);
boxGetGeometry(box, &x, &y, &w, &h);
if (w > 2 * npixels)
pixRasterop(pixm, x + npixels, y - 6, w - 2 * npixels, h + 13,
PIX_SET, NULL, 0, 0);
boxDestroy(&box);
}
boxaDestroy(&boxa);
}
/* Find the ascenders and optionally filter with pixm.
* For an explanation of the procedure used for counting the result
* of the HMT, see comments in pixUpDownDetectGeneral(). */
pix1 = pixFlipFHMTGen(NULL, pix0, flipsel1);
pix2 = pixFlipFHMTGen(NULL, pix0, flipsel2);
pixOr(pix1, pix1, pix2);
if (pixm)
pixAnd(pix1, pix1, pixm);
pix3 = pixReduceRankBinaryCascade(pix1, 1, 1, 0, 0);
pixCountPixels(pix3, &countup, NULL);
pixDestroy(&pix1);
pixDestroy(&pix2);
pixDestroy(&pix3);
/* Find the ascenders and optionally filter with pixm. */
pix1 = pixFlipFHMTGen(NULL, pix0, flipsel3);
pix2 = pixFlipFHMTGen(NULL, pix0, flipsel4);
pixOr(pix1, pix1, pix2);
if (pixm)
pixAnd(pix1, pix1, pixm);
pix3 = pixReduceRankBinaryCascade(pix1, 1, 1, 0, 0);
pixCountPixels(pix3, &countdown, NULL);
pixDestroy(&pix1);
pixDestroy(&pix2);
pixDestroy(&pix3);
/* Evaluate statistically, generating a confidence that is
* related to the probability with a gaussian distribution. */
nup = (l_float32)(countup);
ndown = (l_float32)(countdown);
nmax = L_MAX(countup, countdown);
if (nmax > mincount)
*pconf = 2. * ((nup - ndown) / sqrt(nup + ndown));
if (debug) {
if (pixm) pixWriteDebug("/tmp/lept/orient/pixm2.png", pixm, IFF_PNG);
fprintf(stderr, "nup = %7.3f, ndown = %7.3f, conf = %7.3f\n",
nup, ndown, *pconf);
if (*pconf > DEFAULT_MIN_UP_DOWN_CONF)
fprintf(stderr, "Text is rightside-up\n");
if (*pconf < -DEFAULT_MIN_UP_DOWN_CONF)
fprintf(stderr, "Text is upside-down\n");
}
pixDestroy(&pix0);
pixDestroy(&pixm);
return 0;
}
