Rewrote column test, again (todo: actually analyse results)
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@ -539,60 +539,300 @@ class EdgeDetect{
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// helper functions
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// helper functions
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_columnTest2(image, top, bottom, colsIn, colsOut, reverseSearchDirection) {
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// Column tests
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let tmpI;
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// Here's a fun thing. I reckon this bit of code could potentially run often enough that L1/L2 cache misses
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let edgeDetectCount = 0;
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// could really start to add up (especially if I figure the RAM usage problem which causes insane RAM usage
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if(reverseSearchDirection){
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// if you run this 30-60 times a second)
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for(var i = bottom - this.conf.canvasImageDataRowLength; i >= top; i-= this.conf.canvasImageDataRowLength){
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//
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for(let c = 0; c < colsIn.length; c++){
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// so here's two functions. _columnTest3_cross has some optimization that tries to minimize cache misses,
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if (colsIn[c].blackFound && colsIn[c].imageFound) {
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// but the problem is that I don't actually know 100% what I'm doing so it might be pointless. It scans the
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// če smo našli obe točki, potem ne pregledujemo več.
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// image array line-by-line, rather than column-by-column. This has some advantages (e.g. we can end the
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// if we found both points, we don't continue anymore
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// search for letterbox early), and some disadvantages (the code is a mess)
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//
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// some time later down the line, I might actually implement _columnTest3_singleCol, which does shit in the
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// opposite direction (column-by-column rather than row-by-row)
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_columnTest3_cross(image, top, bottom, colsIn, colsOut, reverseSearchDirection) {
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// this function is such a /r/badcode bait.
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//
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// this is the shit we do to avoid function calls and one extra if sentence/code repetition
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// pretend I was drunk when I wrote this
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let tmpi, lastTmpI = 0, edgeDetectCount = 0, edgeDetectColsLeft = colsIn.length;
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let tmpVal = 0;
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let increment, arrayStart, arrayEnd;
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let loopCond, loopComparator, loopIndex;
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if (reverseSearchDirection) {
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increment = -this.conf.canvasImageDataRowLength;
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arrayStart = bottom - this.conf.canvasImageDataRowLength;
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arrayEnd = top;
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// this is a hack so we get pointer-like things rather than values
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loopCond = {compare: {i: arrayEnd}, index: {i: 0}}
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loopComparator = loopCond.index;
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loopIndex = loopCond.compare;
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} else {
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increment = this.conf.canvasImageDataRowLength;
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arrayStart = top;
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arrayEnd = bottom;
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// this is a hack so we get pointer-like things rather than values
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loopCond = {compare: {i: arrayEnd}, index: {i: 0}}
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loopComparator = loopCond.compare;
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loopIndex = loopCond.index;
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}
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// keep temporary column data in a separate column array:
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const colsTmp = new Array(colsIn.length);
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for (let i = 0; i < colsTmp.length; i++) {
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colsTmp[i] = {
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blackFound: false,
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imageFound: false, // misleading name — also true if we ran over gradientSampleSize pixels from image
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// whether that actually count as an image depends on how aggressive gradientDetection is
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lastValue: -1,
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diffIndex: 0,
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diffs: new Array(this.settings.active.arDetect.blackbar.gradientSampleSize).fill(0)
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}
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}
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// Things to keep in mind: loopCond.index.i is always index.
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// loopIndex.i could actually be loopCond.compare.i (comparator) and
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// loopComparator.i could actually be loopCond.index.i (real index)
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for (loopCond.index.i = arrayStart; loopIndex.i < loopComparator.i; loopCond.index.i += increment) {
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// če smo našli toliko mejnih točk, da s preostalimi stolpci ne moremo doseči naše meje, potem prenehamo
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// if we found enough edge points so that we couldn't top that limit with remaining columns, then we stop
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// searching forward
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edgeDetectColsLeft -= edgeDetectCount;
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if (edgeDetectColsLeft < this.colsThreshold || edgeDetectCount >= this.colsThreshold) {
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break;
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}
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edgeDetectCount = 0;
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// če v eni vrstici dobimo dovolj točk, ki grejo čez našo mejo zaznavanja, potem bomo nehali
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// the first line that goes over our detection treshold wins
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for (let c = 0; c < colsIn.length; c++) {
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// there's really no point in checking this column if we already found image point
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if (colsTmp[c].imageFound) {
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continue;
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}
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tmpI = loopCond.index.i + (colsIn[c].value << 2);
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// najprej preverimo, če je piksel presegel mejo črnega robu
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// first we check whether blackbarThreshold was exceeded
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if (! colsTmp[c].blackFound) {
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if( image[tmpI] > this.blackbarThreshold ||
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image[tmpI + 1] > this.blackbarThreshold ||
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image[tmpI + 2] > this.blackbarThreshold ){
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colsOut[c].black = ~~(i / this.conf.canvasImageDataRowLength); // note — this value is off by one
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colsOut[c].col = colsIn[c].value;
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colsTmp[c].blackFound = true;
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// prisili, da se zanka izvede še enkrat ter preveri,
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// ali trenuten piksel preseže tudi imageThreshold
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//
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// force the loop to repeat this step and check whether
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// current pixel exceeds imageThreshold as well
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c--;
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continue;
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continue;
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}
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}
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tmpI = i + (colsIn[c].value << 2);
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} else {
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// če smo dobili piksel, ki presega blackbar, preverimo do gradientSampleSize dodatnih pikslov.
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// ko dobimo piksel čez imageTreshold oz. gradientSampleSize, nastavimo imageFound. Ali je to veljavno
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// bomo preverili v koraku analize, ki sledi kasneje
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//
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// if we found a pixel that exceeds blackbar, we check up to gradientSampleSize additional pixels.
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// when we get a pixel over imageTreshold or gradientSampleSize, we flip the imageFound. We'll bother
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// with whether that's legit in analysis step, which will follow soon (tm)
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// najprej preverimo, če je piksel presegel mejo črnega robu
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if (image[tmpI] > this.imageThreshold ||
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// first we check whether blackbarThreshold was exceeded
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image[tmpI + 1] > this.imageThreshold ||
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if(! colsIn[c].blackFound) {
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image[tmpI + 2] > this.imageThreshold ){
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if( image[tmpI] > this.blackbarThreshold ||
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image[tmpI + 1] > this.blackbarThreshold ||
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colsOut[c].image = ~~(i / this.conf.canvasImageDataRowLength)
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image[tmpI + 2] > this.blackbarThreshold ){
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colsOut[c].black = (i / this.conf.canvasImageDataRowLength) - 1;
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colsOut[c].col = colsIn[c].value;
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colsIn[c].blackFound = 1;
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// prisili, da se zanka izvede še enkrat ter preveri,
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// ali trenuten piksel preseže tudi imageThreshold
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//
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// force the loop to repeat this step and check whether
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// current pixel exceeds imageThreshold as well
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c--;
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continue;
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}
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} else {
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if (colsIn[c].blackFound++ > this.settings.active.arDetect.blackbar.gradientSampleSize) {
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colsIn[c].imageFound = true;
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continue;
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}
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// zatem preverimo, če je piksel presegel mejo, po kateri sklepamo, da
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// predstavlja sliko. Preverimo samo, če smo v stolpcu že presegli
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// blackThreshold
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//
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// then we check whether pixel exceeded imageThreshold
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if (image[tmpI] > this.imageThreshold ||
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image[tmpI + 1] > this.imageThreshold ||
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image[tmpI + 2] > this.imageThreshold ){
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colsOut[c].image = (i / this.conf.canvasImageDataRowLength)
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colsTmp[c].imageFound = true;
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colsIn[c].imageFound = true;
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edgeDetectCount++;
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edgeDetectCount++;
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}
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// v vsakem primeru shranimo razliko med prejšnjim in trenutnim pikslom za kasnejšo analizo
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// in any case, save the difference between the current and the previous pixel for later analysis
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colsTmp[c].lastValue = image[tmpI] + image[tmpI+1] + image[tmpI+2];
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if (colsTmp[c].diffIndex !== 0) {
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colsTmp[c].diffs[colsTmp.diffIndex] = colsTmp[c].lastValue - colsTmp[c].diffs[diffIndex - 1];
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}
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cols[c].diffIndex++;
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if (colsTmp[c].diffIndex > this.settings.active.arDetect.blackbar.gradientSampleSize) {
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colsTmp[c].imageFound = true;
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continue;
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}
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}
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}
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}
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}
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_columnTest3_singleCol(image, top, bottom, colsIn, colsOut, reverseSearchDirection) {
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}
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_columnTest2(image, top, bottom, colsIn, colsOut, reverseSearchDirection) {
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let tmpI;
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let lastTmpI = 0;
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let edgeDetectCount = 0;
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for(const c in colsOut) {
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c.diffs = [];
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}
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if (reverseSearchDirection) {
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if (this.settings.active.arDetect.blackbar.antiGradientMode === AntiGradientMode.Disabled) {
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// todo: remove gradient detection code from this branch
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for(var i = bottom - this.conf.canvasImageDataRowLength; i >= top; i-= this.conf.canvasImageDataRowLength){
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for(let c = 0; c < colsIn.length; c++){
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if (colsIn[c].blackFound && colsIn[c].imageFound) {
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// če smo našli obe točki, potem ne pregledujemo več.
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// if we found both points, we don't continue anymore
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continue;
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}
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tmpI = i + (colsIn[c].value << 2);
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// najprej preverimo, če je piksel presegel mejo črnega robu
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// first we check whether blackbarThreshold was exceeded
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if(! colsIn[c].blackFound) {
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if( image[tmpI] > this.blackbarThreshold ||
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image[tmpI + 1] > this.blackbarThreshold ||
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image[tmpI + 2] > this.blackbarThreshold ){
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colsOut[c].black = (i / this.conf.canvasImageDataRowLength) - 1;
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colsOut[c].col = colsIn[c].value;
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colsIn[c].blackFound = 1;
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// prisili, da se zanka izvede še enkrat ter preveri,
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// ali trenuten piksel preseže tudi imageThreshold
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//
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// force the loop to repeat this step and check whether
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// current pixel exceeds imageThreshold as well
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c--;
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continue;
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}
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} else {
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if (colsIn[c].blackFound++ > this.settings.active.arDetect.blackbar.gradientSampleSize) {
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colsIn[c].imageFound = true;
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continue;
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}
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// zatem preverimo, če je piksel presegel mejo, po kateri sklepamo, da
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// predstavlja sliko. Preverimo samo, če smo v stolpcu že presegli
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// blackThreshold
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//
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// then we check whether pixel exceeded imageThreshold
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if (image[tmpI] > this.imageThreshold ||
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image[tmpI + 1] > this.imageThreshold ||
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image[tmpI + 2] > this.imageThreshold ){
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colsOut[c].image = (i / this.conf.canvasImageDataRowLength)
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colsIn[c].imageFound = true;
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edgeDetectCount++;
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}
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}
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}
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}
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}
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if(edgeDetectCount >= this.colsThreshold) {
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break;
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}
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}
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}
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if(edgeDetectCount >= this.colsThreshold) {
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} else {
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break;
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// anti-gradient detection
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for(var i = bottom - this.conf.canvasImageDataRowLength; i >= top; i-= this.conf.canvasImageDataRowLength){
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for(let c = 0; c < colsIn.length; c++){
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if (colsIn[c].blackFound && colsIn[c].imageFound) {
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// če smo našli obe točki, potem ne pregledujemo več.
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// if we found both points, we don't continue anymore.
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if (colsIn[c].analysisDone) {
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continue;
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}
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if (colsOut[c].diffs.length < 5) {
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colsIn[c].analysisDone = true;
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}
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// average analysis — if steps between pixels are roughly equal, we're looking at a gradient
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let sum_avg = 0;
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for (let i = 2; i <= colsOut[c].diffs; i++) {
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sum_avg += colsOut[c].diffs[i-1] - colsOut[c].diffs[i];
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}
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sum_avg /= colsOut[c].diffs.length - 2;
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for (let i = 2; i <= colsOut[c].diffs; i++) {
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sum_avg += colsOut[c].diffs[i-1] - colsOut[c].diffs[i];
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}
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continue;
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}
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tmpI = i + (colsIn[c].value << 2);
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// najprej preverimo, če je piksel presegel mejo črnega robu
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// first we check whether blackbarThreshold was exceeded
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if(! colsIn[c].blackFound) {
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if( image[tmpI] > this.blackbarThreshold ||
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image[tmpI + 1] > this.blackbarThreshold ||
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image[tmpI + 2] > this.blackbarThreshold ){
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colsOut[c].black = (i / this.conf.canvasImageDataRowLength) - 1;
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colsOut[c].col = colsIn[c].value;
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colsIn[c].blackFound = 1;
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// prisili, da se zanka izvede še enkrat ter preveri,
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// ali trenuten piksel preseže tudi imageThreshold
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//
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// force the loop to repeat this step and check whether
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// current pixel exceeds imageThreshold as well
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c--;
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colsOut[c].lastImageValue = image[tmpI] + image[tmpI+1] + image[tmpI+2];
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continue;
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}
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} else {
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// če smo dobili piksel, ki presega blackbar, preverimo do gradientSampleSize dodatnih pikslov.
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// ko dobimo piksel čez imageTreshold oz. gradientSampleSize, izračunamo ali gre za gradient.
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if (colsIn[c].blackFound++ > this.settings.active.arDetect.blackbar.gradientSampleSize) {
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colsIn[c].imageFound = true;
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continue;
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}
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// zatem preverimo, če je piksel presegel mejo, po kateri sklepamo, da
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// predstavlja sliko. Preverimo samo, če smo v stolpcu že presegli
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// blackThreshold
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//
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// then we check whether pixel exceeded imageThreshold
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if (image[tmpI] > this.imageThreshold ||
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image[tmpI + 1] > this.imageThreshold ||
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image[tmpI + 2] > this.imageThreshold ){
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colsOut[c].image = (i / this.conf.canvasImageDataRowLength)
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colsIn[c].imageFound = true;
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edgeDetectCount++;
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}
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// shranimo razliko med prejšnjim in trenutnim pikslom za kasnejšo analizo
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// save difference between current and previous pixel for later analysis
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const imageValue = image[tmpI] + image[tmpI+1] + image[tmpI+2];
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colsOut[c].diffs.push(imageValue - colsOut[c].lastImage);
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colsOut[c].lastImageValue = imageValue;
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}
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}
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if(edgeDetectCount >= this.colsThreshold) {
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break;
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}
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}
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}
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}
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}
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} else {
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} else {
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