Replaced most of the usages of parallel_for with that of parallel_for_.
This should allow many algorithms to take advantage of more parallelization technologies.
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@@ -1165,15 +1165,10 @@ void CascadeClassifier::detectMultiScale( const Mat& image, vector<Rect>& object
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int stripCount, stripSize;
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#ifdef HAVE_TBB
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const int PTS_PER_THREAD = 1000;
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stripCount = ((processingRectSize.width/yStep)*(processingRectSize.height + yStep-1)/yStep + PTS_PER_THREAD/2)/PTS_PER_THREAD;
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stripCount = std::min(std::max(stripCount, 1), 100);
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stripSize = (((processingRectSize.height + stripCount - 1)/stripCount + yStep-1)/yStep)*yStep;
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#else
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stripCount = 1;
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stripSize = processingRectSize.height;
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#endif
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if( !detectSingleScale( scaledImage, stripCount, processingRectSize, stripSize, yStep, factor, candidates,
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rejectLevels, levelWeights, outputRejectLevels ) )
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@@ -582,7 +582,6 @@ int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramid *H,
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// For each component perform searching
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for (i = 0; i < kComponents; i++)
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{
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#ifdef HAVE_TBB
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int error = searchObjectThreshold(H, &(filters[componentIndex]), kPartFilters[i],
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b[i], maxXBorder, maxYBorder, scoreThreshold,
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&(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]),
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@@ -598,13 +597,6 @@ int searchObjectThresholdSomeComponents(const CvLSVMFeaturePyramid *H,
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free(partsDisplacementArr);
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return LATENT_SVM_SEARCH_OBJECT_FAILED;
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}
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#else
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(void)numThreads;
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searchObjectThreshold(H, &(filters[componentIndex]), kPartFilters[i],
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b[i], maxXBorder, maxYBorder, scoreThreshold,
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&(pointsArr[i]), &(levelsArr[i]), &(kPointsArr[i]),
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&(scoreArr[i]), &(partsDisplacementArr[i]));
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#endif
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estimateBoxes(pointsArr[i], levelsArr[i], kPointsArr[i],
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filters[componentIndex]->sizeX, filters[componentIndex]->sizeY, &(oppPointsArr[i]));
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componentIndex += (kPartFilters[i] + 1);
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