C++ GpuMat::release方法代码示例

void cv::gpu::BruteForceMatcher_GPU_base::makeGpuCollection(GpuMat& trainCollection, GpuMat& maskCollection,
    const vector<GpuMat>& masks)
{
    if (empty())
        return;

    if (masks.empty())
    {
        Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2Db)));

        DevMem2Db* trainCollectionCPU_ptr = trainCollectionCPU.ptr<DevMem2Db>();

        for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr)
            *trainCollectionCPU_ptr = trainDescCollection[i];

        trainCollection.upload(trainCollectionCPU);
        maskCollection.release();
    }
    else
    {
        CV_Assert(masks.size() == trainDescCollection.size());

        Mat trainCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(DevMem2Db)));
        Mat maskCollectionCPU(1, static_cast<int>(trainDescCollection.size()), CV_8UC(sizeof(PtrStepb)));

        DevMem2Db* trainCollectionCPU_ptr = trainCollectionCPU.ptr<DevMem2Db>();
        PtrStepb* maskCollectionCPU_ptr = maskCollectionCPU.ptr<PtrStepb>();

        for (size_t i = 0, size = trainDescCollection.size(); i < size; ++i, ++trainCollectionCPU_ptr, ++maskCollectionCPU_ptr)
        {
            const GpuMat& train = trainDescCollection[i];
            const GpuMat& mask = masks[i];

            CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.cols == train.rows));

            *trainCollectionCPU_ptr = train;
            *maskCollectionCPU_ptr = mask;
        }

        trainCollection.upload(trainCollectionCPU);
        maskCollection.upload(maskCollectionCPU);
    }
}

void cv::gpu::ORB_GPU::mergeKeyPoints(GpuMat& keypoints)
{
    using namespace cv::gpu::device::orb;

    int nAllkeypoints = 0;

    for (int level = 0; level < nLevels_; ++level)
        nAllkeypoints += keyPointsCount_[level];

    if (nAllkeypoints == 0)
    {
        keypoints.release();
        return;
    }

    ensureSizeIsEnough(ROWS_COUNT, nAllkeypoints, CV_32FC1, keypoints);

    int offset = 0;

    for (int level = 0; level < nLevels_; ++level)
    {
        if (keyPointsCount_[level] == 0)
            continue;

        float sf = getScale(scaleFactor_, firstLevel_, level);

        GpuMat keyPointsRange = keypoints.colRange(offset, offset + keyPointsCount_[level]);

        float locScale = level != firstLevel_ ? sf : 1.0f;

        mergeLocation_gpu(keyPointsPyr_[level].ptr<short2>(0), keyPointsRange.ptr<float>(0), keyPointsRange.ptr<float>(1), keyPointsCount_[level], locScale, 0);

        GpuMat range = keyPointsRange.rowRange(2, 4);
        keyPointsPyr_[level](Range(1, 3), Range(0, keyPointsCount_[level])).copyTo(range);

        keyPointsRange.row(4).setTo(Scalar::all(level));
        keyPointsRange.row(5).setTo(Scalar::all(patchSize_ * sf));

        offset += keyPointsCount_[level];
    }
}

//cuda version
void LKTracker::normCrossCorrelation(const GpuMat& img1, const GpuMat& img2, const GpuMat& gPoints1, const GpuMat& gPoints2, const vector<Point2f>& points1, const vector<Point2f> points2) {
	GpuMat res;
	GpuMat rec0;
	GpuMat rec1;

	similarity.clear();
	for (int i = 0; i < points1.size(); i++) {
		if (status[i] == 1) {
			Rect loc0(points1[i].x, points1[i].y, 10, 10);
			Rect loc1(points2[i].x, points2[i].y, 10, 10);
			rec0 = GpuMat(img1, loc0);
			rec1 = GpuMat(img2, loc1);
			gpu::matchTemplate(rec0, rec1, res, CV_TM_CCOEFF_NORMED);

			similarity.push_back(((float *)(res.data))[0]);
		}
		else {
			similarity.push_back(0.0);
		}
	}
	rec0.release();
	rec1.release();
	res.release();
}

void cv::gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask)
{
    using namespace cv::gpu::device::gfft;

    CV_Assert(qualityLevel > 0 && minDistance >= 0 && maxCorners >= 0);
    CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == image.size()));

    ensureSizeIsEnough(image.size(), CV_32F, eig_);

    if (useHarrisDetector)
        cornerHarris(image, eig_, Dx_, Dy_, buf_, blockSize, 3, harrisK);
    else
        cornerMinEigenVal(image, eig_, Dx_, Dy_, buf_, blockSize, 3);

    double maxVal = 0;
    minMax(eig_, 0, &maxVal, GpuMat(), minMaxbuf_);

    ensureSizeIsEnough(1, std::max(1000, static_cast<int>(image.size().area() * 0.05)), CV_32FC2, tmpCorners_);

    int total = findCorners_gpu(eig_, static_cast<float>(maxVal * qualityLevel), mask, tmpCorners_.ptr<float2>(), tmpCorners_.cols);

    if (total == 0)
    {
        corners.release();
        return;
    }

    sortCorners_gpu(eig_, tmpCorners_.ptr<float2>(), total);

    if (minDistance < 1)
        tmpCorners_.colRange(0, maxCorners > 0 ? std::min(maxCorners, total) : total).copyTo(corners);
    else
    {
        vector<Point2f> tmp(total);
        Mat tmpMat(1, total, CV_32FC2, (void*)&tmp[0]);
        tmpCorners_.colRange(0, total).download(tmpMat);

        vector<Point2f> tmp2;
        tmp2.reserve(total);

        const int cell_size = cvRound(minDistance);
        const int grid_width = (image.cols + cell_size - 1) / cell_size;
        const int grid_height = (image.rows + cell_size - 1) / cell_size;

        std::vector< std::vector<Point2f> > grid(grid_width * grid_height);

        for (int i = 0; i < total; ++i)
        {
            Point2f p = tmp[i];

            bool good = true;

            int x_cell = static_cast<int>(p.x / cell_size);
            int y_cell = static_cast<int>(p.y / cell_size);

            int x1 = x_cell - 1;
            int y1 = y_cell - 1;
            int x2 = x_cell + 1;
            int y2 = y_cell + 1;

            // boundary check
            x1 = std::max(0, x1);
            y1 = std::max(0, y1);
            x2 = std::min(grid_width - 1, x2);
            y2 = std::min(grid_height - 1, y2);

            for (int yy = y1; yy <= y2; yy++)
            {
                for (int xx = x1; xx <= x2; xx++)
                {
                    vector<Point2f>& m = grid[yy * grid_width + xx];

                    if (!m.empty())
                    {
                        for(size_t j = 0; j < m.size(); j++)
                        {
                            float dx = p.x - m[j].x;
                            float dy = p.y - m[j].y;

                            if (dx * dx + dy * dy < minDistance * minDistance)
                            {
                                good = false;
                                goto break_out;
                            }
                        }
                    }
                }
            }

break_out:

            if(good)
            {
                grid[y_cell * grid_width + x_cell].push_back(p);

                tmp2.push_back(p);

                if (maxCorners > 0 && tmp2.size() == static_cast<size_t>(maxCorners))
                    break;
            }
//.........这里部分代码省略.........

void cv::gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err)
{
    using namespace cv::gpu::device::pyrlk;

    if (prevPts.empty())
    {
        nextPts.release();
        status.release();
        if (err) err->release();
        return;
    }

    dim3 block, patch;
    calcPatchSize(winSize, block, patch, isDeviceArch11_);

    CV_Assert(prevImg.type() == CV_8UC1 || prevImg.type() == CV_8UC3 || prevImg.type() == CV_8UC4);
    CV_Assert(prevImg.size() == nextImg.size() && prevImg.type() == nextImg.type());
    CV_Assert(maxLevel >= 0);
    CV_Assert(winSize.width > 2 && winSize.height > 2);
    CV_Assert(patch.x > 0 && patch.x < 6 && patch.y > 0 && patch.y < 6);
    CV_Assert(prevPts.rows == 1 && prevPts.type() == CV_32FC2);

    if (useInitialFlow)
        CV_Assert(nextPts.size() == prevPts.size() && nextPts.type() == CV_32FC2);
    else
        ensureSizeIsEnough(1, prevPts.cols, prevPts.type(), nextPts);

    GpuMat temp1 = (useInitialFlow ? nextPts : prevPts).reshape(1);
    GpuMat temp2 = nextPts.reshape(1);
    multiply(temp1, Scalar::all(1.0 / (1 << maxLevel) / 2.0), temp2);

    ensureSizeIsEnough(1, prevPts.cols, CV_8UC1, status);
    status.setTo(Scalar::all(1));

    if (err)
        ensureSizeIsEnough(1, prevPts.cols, CV_32FC1, *err);

    // build the image pyramids.

    prevPyr_.resize(maxLevel + 1);
    nextPyr_.resize(maxLevel + 1);

    int cn = prevImg.channels();

    if (cn == 1 || cn == 4)
    {
        prevImg.convertTo(prevPyr_[0], CV_32F);
        nextImg.convertTo(nextPyr_[0], CV_32F);
    }
    else
    {
        cvtColor(prevImg, dx_calcBuf_, COLOR_BGR2BGRA);
        dx_calcBuf_.convertTo(prevPyr_[0], CV_32F);

        cvtColor(nextImg, dx_calcBuf_, COLOR_BGR2BGRA);
        dx_calcBuf_.convertTo(nextPyr_[0], CV_32F);
    }

    for (int level = 1; level <= maxLevel; ++level)
    {
        pyrDown(prevPyr_[level - 1], prevPyr_[level]);
        pyrDown(nextPyr_[level - 1], nextPyr_[level]);
    }

    loadConstants(make_int2(winSize.width, winSize.height), iters);

    for (int level = maxLevel; level >= 0; level--)
    {
        if (cn == 1)
        {
            lkSparse1_gpu(prevPyr_[level], nextPyr_[level],
                prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
                level, block, patch);
        }
        else
        {
            lkSparse4_gpu(prevPyr_[level], nextPyr_[level],
                prevPts.ptr<float2>(), nextPts.ptr<float2>(), status.ptr(), level == 0 && err ? err->ptr<float>() : 0, prevPts.cols,
                level, block, patch);
        }
    }
}