翻译代码 if (_m_mInputMat.empty()) { return; } if (_m_mInputMat.channels() > 1) { cvtColor(_m_mInputMat, _m_mInputMat, COLOR_BGR2GRAY); } Mat RotateMat = getRotationMatrix2D(_m_pdCenter, -_m_dAngle, 1); warpAffine(_m_mInputMat, _m_mInputMat, RotateMat, _m_mInputMat.size(), WARP_INVERSE_MAP | INTER_CUBIC); Mat newCenter = RotateMat * (Mat_<double>(3, 1) << _m_pdCenter.x, _m_pdCenter.y, 1); double x = newCenter.at<double>(0, 0); double y = newCenter.at<double>(1, 0); Mat M = (Mat_<double>(2, 3) << 1, 0, x - _m_dLength * 0.5, 0, 1, y - _m_dHeight * 0.5); warpAffine(_m_mInputMat, _m_mInputMat, M, Size2d(_m_dLength, _m_dHeight), WARP_INVERSE_MAP | INTER_CUBIC);

这是一段OpenCV的C++代码。该代码首先判断输入的图像是否为空，如果是空的，则直接返回。接着，如果输入的图像通道数大于1，则将其转换为灰度图像。然后，通过getRotationMatrix2D函数得到一个旋转矩阵，将其作用于输入图像，得到一个旋转后的图像。接下来，计算出旋转后的图像的中心点，并根据中心点、长度和宽度得到一个仿射变换矩阵。最后，将该仿射变换矩阵作用于旋转后的图像，得到一个大小为指定长度和宽度的图像。

相关问题

void Extract1DEdge::GetProfieMat() { if (m_mInputMat.empty()) { return; } if (m_mInputMat.channels() > 1) { cvtColor(m_mInputMat, m_mInputMat, COLOR_BGR2GRAY); } Mat RotateMat = getRotationMatrix2D(m_pdCenter, -m_dAngle, 1); warpAffine(m_mInputMat, m_mInputMat, RotateMat, m_mInputMat.size(), WARP_INVERSE_MAP); Mat newCenter = RotateMat * (Mat_<double>(3, 1) << m_pdCenter.x, m_pdCenter.y, 1); double x = newCenter.at<double>(0, 0); double y = newCenter.at<double>(1, 0); Mat M = (Mat_<double>(2, 3) << 1, 0, x - m_dLength * 0.5, 0, 1, y - m_dHeight * 0.5); warpAffine(m_mInputMat, m_mInputMat, M, Size2d(m_dLength, m_dHeight), WARP_INVERSE_MAP); }如何保持效果不变的情况下加速这段代码给个例程

可以尝试使用多线程来加速这段代码。具体可以将图像切分成多个小块，每个小块分配一个线程来处理，最后将小块合并成完整的图像。这样可以利用多核CPU的优势，提高代码的运行效率。以下是伪代码示例：

void Extract1DEdge::GetProfieMat() {
    if (m_mInputMat.empty()) {
        return;
    }
    if (m_mInputMat.channels() > 1) {
        cvtColor(m_mInputMat, m_mInputMat, COLOR_BGR2GRAY);
    }
    Mat RotateMat = getRotationMatrix2D(m_pdCenter, -m_dAngle, 1);
    warpAffine(m_mInputMat, m_mInputMat, RotateMat, m_mInputMat.size(), WARP_INVERSE_MAP);
    Mat newCenter = RotateMat * (Mat_<double>(3, 1) << m_pdCenter.x, m_pdCenter.y, 1);
    double x = newCenter.at<double>(0, 0);
    double y = newCenter.at<double>(1, 0);
    Mat M = (Mat_<double>(2, 3) << 1, 0, x - m_dLength * 0.5, 0, 1, y - m_dHeight * 0.5);
    
    // 切分图像
    vector<Mat> subMats;
    const int numThreads = 4; // 可以根据实际情况调整线程数量
    const int rowsPerThread = m_mInputMat.rows / numThreads;
    for (int i = 0; i < numThreads; i++) {
        int startRow = i * rowsPerThread;
        int endRow = (i == numThreads - 1) ? m_mInputMat.rows : (startRow + rowsPerThread);
        Mat subMat = m_mInputMat.rowRange(startRow, endRow);
        subMats.push_back(subMat);
    }
    
    // 多线程处理
    vector<thread> threads;
    for (int i = 0; i < numThreads; i++) {
        threads.push_back(thread([&, i]() {
            Mat subMat = subMats[i];
            warpAffine(subMat, subMat, M, Size2d(m_dLength, m_dHeight), WARP_INVERSE_MAP);
        }));
    }
    for (auto& t : threads) {
        t.join();
    }
    
    // 合并图像
    for (int i = 1; i < numThreads; i++) {
        Mat subMat = subMats[i];
        subMat.copyTo(m_mInputMat.rowRange(i * rowsPerThread, (i == numThreads - 1) ? m_mInputMat.rows : ((i + 1) * rowsPerThread)));
    }
}

需要注意的是，多线程处理图像时需要注意线程安全，避免多个线程同时访问同一块内存区域。在上述示例中，使用了引用捕获方式来访问外部变量。此外，也可以使用互斥锁或其他线程同步机制来保证线程安全。

void Extract1DEdgeCircle::GetProfieMat() { if (m_mInputMat.empty()) { return; } if (m_mInputMat.channels() > 1) { cvtColor(m_mInputMat, m_mInputMat, COLOR_BGR2GRAY); } //Get ROI mat. RotatedRect rMaskRegion(m_pdCenter, Size2f(GetPPDistance(m_pdStart, m_pdEnd) + 10, m_dLength + 10), m_dAngle); Point2f rRegionPoints[4]; rMaskRegion.points(rRegionPoints); Mat mask = Mat::zeros(m_mInputMat.size(), CV_8UC1); Point ppt[] = { rRegionPoints[0], rRegionPoints[1], rRegionPoints[2], rRegionPoints[3] }; const Point* pts[] = { ppt }; int npt[] = { 4 }; fillPoly(mask, pts, npt, 1, Scalar::all(255), 8); Mat RoiMat = Mat::zeros(m_mInputMat.size(), m_mInputMat.type()); bitwise_and(m_mInputMat, m_mInputMat, RoiMat, mask); Mat RotateMat = getRotationMatrix2D(m_pdCenter, -m_dAngle, 1); warpAffine(RoiMat, RoiMat, RotateMat, m_mInputMat.size(), WARP_INVERSE_MAP); Mat newCenter = RotateMat * (Mat_<double>(3, 1) << m_pdCenter.x, m_pdCenter.y, 1); double x = newCenter.at<double>(0, 0); double y = newCenter.at<double>(1, 0); Mat M = (Mat_<double>(2, 3) << 1, 0, x - m_dLength * 0.5, 0, 1, y - m_dHeight * 0.5); warpAffine(RoiMat, m_mInputMat, M, Size2d(m_dLength, m_dHeight), WARP_INVERSE_MAP); }这段代码如何使用AVX2指令集加速

To use AVX2 instructions to accelerate this code, we need to identify the parts of the code that can be parallelized and vectorized. One potential candidate is the image warping operations (i.e., `warpAffine` function calls).

To use AVX2 instructions, we need to use the `cv::parallel_for_` function to parallelize the loop that applies the warping operations to each pixel in the image.

Next, we need to vectorize the code inside the loop using AVX2 instructions. We can use the `cv::v_load` function to load 8 consecutive pixels (assuming a 8-byte data type) into an AVX2 register, and the `cv::v_gather` function to gather non-consecutive pixels into an AVX2 register. We can then perform the necessary arithmetic operations using AVX2 instructions and store the results back to memory using the `cv::v_store` function.

Here is an example of how the code inside the loop can be vectorized using AVX2 instructions:

__m256i vindex = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
for (int i = 0; i < src.rows; i++)
{
    uchar* src_ptr = src.ptr<uchar>(i);
    uchar* dst_ptr = dst.ptr<uchar>(i);
    for (int j = 0; j < src.cols; j += 8)
    {
        __m256i vsrc = cv::v_load(src_ptr + j);
        __m256i vx = _mm256_add_epi32(_mm256_mul_epu32(_mm256_cvtepu8_epi32(vindex), vx_step), vx_offset);
        __m256i vy = _mm256_add_epi32(_mm256_mul_epu32(_mm256_cvtepu8_epi32(vindex), vy_step), vy_offset);
        __m256i vx_lo = _mm256_cvtepi32_epi64(_mm256_extracti128_si256(vx, 0));
        __m256i vx_hi = _mm256_cvtepi32_epi64(_mm256_extracti128_si256(vx, 1));
        __m256i vy_lo = _mm256_cvtepi32_epi64(_mm256_extracti128_si256(vy, 0));
        __m256i vy_hi = _mm256_cvtepi32_epi64(_mm256_extracti128_si256(vy, 1));
        __m256i vx_lo_32 = _mm256_cvtepi64_epi32(vx_lo);
        __m256i vx_hi_32 = _mm256_cvtepi64_epi32(vx_hi);
        __m256i vy_lo_32 = _mm256_cvtepi64_epi32(vy_lo);
        __m256i vy_hi_32 = _mm256_cvtepi64_epi32(vy_hi);
        __m256i vsrc00 = cv::v_gather(src_ptr, src_step, vx_lo_32, vy_lo_32, _mm256_setzero_si256(), 1);
        __m256i vsrc01 = cv::v_gather(src_ptr, src_step, vx_hi_32, vy_lo_32, _mm256_setzero_si256(), 1);
        __m256i vsrc10 = cv::v_gather(src_ptr, src_step, vx_lo_32, vy_hi_32, _mm256_setzero_si256(), 1);
        __m256i vsrc11 = cv::v_gather(src_ptr, src_step, vx_hi_32, vy_hi_32, _mm256_setzero_si256(), 1);
        __m256i vsrc0 = _mm256_packs_epi32(vsrc00, vsrc01);
        __m256i vsrc1 = _mm256_packs_epi32(vsrc10, vsrc11);
        __m256i vsrc = _mm256_packus_epi16(vsrc0, vsrc1);
        cv::v_store(dst_ptr + j, vsrc);
    }
}

Note that this is just an example, and the actual implementation may depend on the specifics of the code and the hardware platform.