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我需要将使用CImg 库加载的图像转换为可以在 OpenCV 中使用的图像格式。

问题是 CImg 创建了 uchar 数组，其中数据以下列方式存储（在 3 通道图像的情况下）：

  1. 首先是红色通道的像素，
  2. 然后绿色通道的所有像素都跟随，
  3. 然后 - 蓝色通道。

它看起来像这样：RRRRRR .... GGGGGG ... BBBBB B ...

OpenCV 以不同的方式存储数据：BGRBGRBGRBG R...

这是我从 CImg 转换为 IplImage 的代码：

CImg<uint8_t> src;
src.load_jpeg_buffer(srcData, size);
size_t width = src._width;
size_t height = src._height;
size_t nChannels = src._spectrum;
size_t depth = 8;

IplImage* m_image = cvCreateImage(cvSize(width, height), depth, nChannels); 
for(size_t i = 0; i < height; i++)
{
    for(size_t j = 0; j < width;j++)
    {
        for(size_t k = 0; k < nChannels; k++)
        { 
            ((m_image->imageData + i * m_image->widthStep))[j * nChannels + nChannels - 1 - k] =
                    src._data[k * src.size() / 3 + k + (i * m_image->widthStep + j * nChannels) / 3];   
        }
    }
}

这段代码运行良好。OpenCV 格式的转换图像是原始图像的完整副本。

我用 valgrind 测试了这段代码。它说它会导致很多内存问题。我找不到这个内存问题的原因。

I will be grateful, if you have any ideas on this matter! Or may be you know another method, which can load image from buffer in OpenCV (not cvDecodeImage).

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1 回答 1

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The problems weren't in my code. As I found out OpenCV library functions cause memory problems. Examples of messages of valgrind are:

Use of uninitialised value of size 8
==16460==    at 0x6500539: void cv::CvtColorLoop<cv::RGB2Gray<unsigned char> >(cv::Mat const&, cv::Mat&, cv::RGB2Gray<unsigned char> const&) (in /usr/local/lib/libopencv_imgproc.so.2.4.2) 

==16488== 151,812 bytes in 1 blocks are possibly lost in loss record 3,419 of 3,425
==16488==    at 0x4C2B6CD: malloc (in /usr/lib/valgrind/vgpreload_memcheck-amd64-linux.so)
==16488==    by 0x56A2470: cv::fastMalloc(unsigned long) (in /usr/local/lib/libopencv_core.so.2.4.2)

==16488== LEAK SUMMARY:
==16488==    definitely lost: 19,988 bytes in 171 blocks
==16488==    indirectly lost: 15,201,012 bytes in 311 blocks
==16488==      possibly lost: 1,202,769 bytes in 3,618 blocks
==16488==    still reachable: 693,651 bytes in 3,733 blocks
==16488==         suppressed: 0 bytes in 0 blocks
于 2013-01-23T13:50:15.927 回答