OpenCV一些常用工具
文章目录
- 前言
- 准备工作
- 封装为cv::Mat格式
- 格式转换
- ROI裁剪
- JPG编码
- 缩放
- 常见错误集锦
前言
在使用音视频处理芯片时，比较多的芯片支持硬件编解码以及格式转换等，减少了系统资源的耗损(CPU,GPU等)。在部分芯片中硬件编解码或转换需要自行按照demo封装，测试等可使用OpenCV前期先做测试处理，在使用过程中，遇到一些问题，在此做下记录以备后续之需。
准备工作
1）OpenCV中大量使用了二维数组，因此需要弄清楚宽高， 分辨率常使用宽x高表示， OpenCV中经常使用高x宽表示，对应的是rows和cols，即：
1920（width）x1080(height) -> 1920(cols)*1080(rows)
OpenCV封装基本都是列(rows)在前， 行（cols）在后。
2）因为yuv涉及到1.5小数，因此必须保证宽高是偶数，部分芯片还要求16位对齐，因此需要考虑这点，如无需考虑对齐问题， 可将w和h都减少1个像素即可（保证不会越界）。针对对齐，可以参考海思的上对齐和下对齐(后续补充，如有需要参考的同学留言， 我及时附上)。
3）常用的原始数据打开工具，支持各种YUV和BGR原始数据查看链接: 原始数据查看工具
封装为cv::Mat格式
1）使用OpenCV，首先需要将数据封装成cv::Mat格式，YUV420SP或YUV420P封装如下：
typedef struct rawInfo{
unsigned int width;
unsigned int height;
unsigned char *pData;
}
假定该pData已分配了堆栈
cv::Mat yuvMat(height*3/2, width, CV8UC1, pData);
这里要非常注意height在前，width在后，
格式转换
OpenCV中非常多的接口工具输入输出都是基于cv::Mat的，比较推BGR24，
比如使用编码等。
1）将YUV转为BGR24，转换按照如下：
cv::cvtColor(yuvMat, bgrMat, cv::COLOR_YUV420sp2BGR);
其中yuvMat为转换前数据， bgrMat为转化后的数据，本例是将yuv420sp转为BGR24，如果有其他需求，请使用如下宏对应选择。
enum ColorConversionCodes {
COLOR_BGR2BGRA = 0, //!< add alpha channel to RGB or BGR image
COLOR_RGB2RGBA = COLOR_BGR2BGRA,
COLOR_BGRA2BGR = 1, //!< remove alpha channel from RGB or BGR image
COLOR_RGBA2RGB = COLOR_BGRA2BGR,
COLOR_BGR2RGBA = 2, //!< convert between RGB and BGR color spaces (with or without alpha channel)
COLOR_RGB2BGRA = COLOR_BGR2RGBA,
COLOR_RGBA2BGR = 3,
COLOR_BGRA2RGB = COLOR_RGBA2BGR,
COLOR_BGR2RGB = 4,
COLOR_RGB2BGR = COLOR_BGR2RGB,
COLOR_BGRA2RGBA = 5,
COLOR_RGBA2BGRA = COLOR_BGRA2RGBA,
COLOR_BGR2GRAY = 6, //!< convert between RGB/BGR and grayscale, @ref color_convert_rgb_gray "color conversions"
COLOR_RGB2GRAY = 7,
COLOR_GRAY2BGR = 8,
COLOR_GRAY2RGB = COLOR_GRAY2BGR,
COLOR_GRAY2BGRA = 9,
COLOR_GRAY2RGBA = COLOR_GRAY2BGRA,
COLOR_BGRA2GRAY = 10,
COLOR_RGBA2GRAY = 11,
COLOR_BGR2BGR565 = 12, //!< convert between RGB/BGR and BGR565 (16-bit images)
COLOR_RGB2BGR565 = 13,
COLOR_BGR5652BGR = 14,
COLOR_BGR5652RGB = 15,
COLOR_BGRA2BGR565 = 16,
COLOR_RGBA2BGR565 = 17,
COLOR_BGR5652BGRA = 18,
COLOR_BGR5652RGBA = 19,
COLOR_GRAY2BGR565 = 20, //!< convert between grayscale to BGR565 (16-bit images)
COLOR_BGR5652GRAY = 21,
COLOR_BGR2BGR555 = 22, //!< convert between RGB/BGR and BGR555 (16-bit images)
COLOR_RGB2BGR555 = 23,
COLOR_BGR5552BGR = 24,
COLOR_BGR5552RGB = 25,
COLOR_BGRA2BGR555 = 26,
COLOR_RGBA2BGR555 = 27,
COLOR_BGR5552BGRA = 28,
COLOR_BGR5552RGBA = 29,
COLOR_GRAY2BGR555 = 30, //!< convert between grayscale and BGR555 (16-bit images)
COLOR_BGR5552GRAY = 31,
COLOR_BGR2XYZ = 32, //!< convert RGB/BGR to CIE XYZ, @ref color_convert_rgb_xyz "color conversions"
COLOR_RGB2XYZ = 33,
COLOR_XYZ2BGR = 34,
COLOR_XYZ2RGB = 35,
COLOR_BGR2YCrCb = 36, //!< convert RGB/BGR to luma-chroma (aka YCC), @ref color_convert_rgb_ycrcb "color conversions"
COLOR_RGB2YCrCb = 37,
COLOR_YCrCb2BGR = 38,
COLOR_YCrCb2RGB = 39,
COLOR_BGR2HSV = 40, //!< convert RGB/BGR to HSV (hue saturation value), @ref color_convert_rgb_hsv "color conversions"
COLOR_RGB2HSV = 41,
COLOR_BGR2Lab = 44, //!< convert RGB/BGR to CIE Lab, @ref color_convert_rgb_lab "color conversions"
COLOR_RGB2Lab = 45,
COLOR_BGR2Luv = 50, //!< convert RGB/BGR to CIE Luv, @ref color_convert_rgb_luv "color conversions"
COLOR_RGB2Luv = 51,
COLOR_BGR2HLS = 52, //!< convert RGB/BGR to HLS (hue lightness saturation), @ref color_convert_rgb_hls "color conversions"
COLOR_RGB2HLS = 53,
COLOR_HSV2BGR = 54, //!< backward conversions to RGB/BGR
COLOR_HSV2RGB = 55,
COLOR_Lab2BGR = 56,
COLOR_Lab2RGB = 57,
COLOR_Luv2BGR = 58,
COLOR_Luv2RGB = 59,
COLOR_HLS2BGR = 60,
COLOR_HLS2RGB = 61,
COLOR_BGR2HSV_FULL = 66,
COLOR_RGB2HSV_FULL = 67,
COLOR_BGR2HLS_FULL = 68,
COLOR_RGB2HLS_FULL = 69,
COLOR_HSV2BGR_FULL = 70,
COLOR_HSV2RGB_FULL = 71,
COLOR_HLS2BGR_FULL = 72,
COLOR_HLS2RGB_FULL = 73,
COLOR_LBGR2Lab = 74,
COLOR_LRGB2Lab = 75,
COLOR_LBGR2Luv = 76,
COLOR_LRGB2Luv = 77,
COLOR_Lab2LBGR = 78,
COLOR_Lab2LRGB = 79,
COLOR_Luv2LBGR = 80,
COLOR_Luv2LRGB = 81,
COLOR_BGR2YUV = 82, //!< convert between RGB/BGR and YUV
COLOR_RGB2YUV = 83,
COLOR_YUV2BGR = 84,
COLOR_YUV2RGB = 85,
//! YUV 4:2:0 family to RGB
COLOR_YUV2RGB_NV12 = 90,
COLOR_YUV2BGR_NV12 = 91,
COLOR_YUV2RGB_NV21 = 92,
COLOR_YUV2BGR_NV21 = 93,
COLOR_YUV420sp2RGB = COLOR_YUV2RGB_NV21,
COLOR_YUV420sp2BGR = COLOR_YUV2BGR_NV21,
COLOR_YUV2RGBA_NV12 = 94,
COLOR_YUV2BGRA_NV12 = 95,
COLOR_YUV2RGBA_NV21 = 96,
COLOR_YUV2BGRA_NV21 = 97,
COLOR_YUV420sp2RGBA = COLOR_YUV2RGBA_NV21,
COLOR_YUV420sp2BGRA = COLOR_YUV2BGRA_NV21,
COLOR_YUV2RGB_YV12 = 98,
COLOR_YUV2BGR_YV12 = 99,
COLOR_YUV2RGB_IYUV = 100,
COLOR_YUV2BGR_IYUV = 101,
COLOR_YUV2RGB_I420 = COLOR_YUV2RGB_IYUV,
COLOR_YUV2BGR_I420 = COLOR_YUV2BGR_IYUV,
COLOR_YUV420p2RGB = COLOR_YUV2RGB_YV12,
COLOR_YUV420p2BGR = COLOR_YUV2BGR_YV12,
COLOR_YUV2RGBA_YV12 = 102,
COLOR_YUV2BGRA_YV12 = 103,
COLOR_YUV2RGBA_IYUV = 104,
COLOR_YUV2BGRA_IYUV = 105,
COLOR_YUV2RGBA_I420 = COLOR_YUV2RGBA_IYUV,
COLOR_YUV2BGRA_I420 = COLOR_YUV2BGRA_IYUV,
COLOR_YUV420p2RGBA = COLOR_YUV2RGBA_YV12,
COLOR_YUV420p2BGRA = COLOR_YUV2BGRA_YV12,
COLOR_YUV2GRAY_420 = 106,
COLOR_YUV2GRAY_NV21 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_NV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_YV12 = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_IYUV = COLOR_YUV2GRAY_420,
COLOR_YUV2GRAY_I420 = COLOR_YUV2GRAY_420,
COLOR_YUV420sp2GRAY = COLOR_YUV2GRAY_420,
COLOR_YUV420p2GRAY = COLOR_YUV2GRAY_420,
//! YUV 4:2:2 family to RGB
COLOR_YUV2RGB_UYVY = 107,
COLOR_YUV2BGR_UYVY = 108,
//COLOR_YUV2RGB_VYUY = 109,
//COLOR_YUV2BGR_VYUY = 110,
COLOR_YUV2RGB_Y422 = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_Y422 = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGB_UYNV = COLOR_YUV2RGB_UYVY,
COLOR_YUV2BGR_UYNV = COLOR_YUV2BGR_UYVY,
COLOR_YUV2RGBA_UYVY = 111,
COLOR_YUV2BGRA_UYVY = 112,
//COLOR_YUV2RGBA_VYUY = 113,
//COLOR_YUV2BGRA_VYUY = 114,
COLOR_YUV2RGBA_Y422 = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_Y422 = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGBA_UYNV = COLOR_YUV2RGBA_UYVY,
COLOR_YUV2BGRA_UYNV = COLOR_YUV2BGRA_UYVY,
COLOR_YUV2RGB_YUY2 = 115,
COLOR_YUV2BGR_YUY2 = 116,
COLOR_YUV2RGB_YVYU = 117,
COLOR_YUV2BGR_YVYU = 118,
COLOR_YUV2RGB_YUYV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUYV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGB_YUNV = COLOR_YUV2RGB_YUY2,
COLOR_YUV2BGR_YUNV = COLOR_YUV2BGR_YUY2,
COLOR_YUV2RGBA_YUY2 = 119,
COLOR_YUV2BGRA_YUY2 = 120,
COLOR_YUV2RGBA_YVYU = 121,
COLOR_YUV2BGRA_YVYU = 122,
COLOR_YUV2RGBA_YUYV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUYV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2RGBA_YUNV = COLOR_YUV2RGBA_YUY2,
COLOR_YUV2BGRA_YUNV = COLOR_YUV2BGRA_YUY2,
COLOR_YUV2GRAY_UYVY = 123,
COLOR_YUV2GRAY_YUY2 = 124,
//CV_YUV2GRAY_VYUY = CV_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_Y422 = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_UYNV = COLOR_YUV2GRAY_UYVY,
COLOR_YUV2GRAY_YVYU = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUYV = COLOR_YUV2GRAY_YUY2,
COLOR_YUV2GRAY_YUNV = COLOR_YUV2GRAY_YUY2,
//! alpha premultiplication
COLOR_RGBA2mRGBA = 125,
COLOR_mRGBA2RGBA = 126,
//! RGB to YUV 4:2:0 family
COLOR_RGB2YUV_I420 = 127,
COLOR_BGR2YUV_I420 = 128,
COLOR_RGB2YUV_IYUV = COLOR_RGB2YUV_I420,
COLOR_BGR2YUV_IYUV = COLOR_BGR2YUV_I420,
COLOR_RGBA2YUV_I420 = 129,
COLOR_BGRA2YUV_I420 = 130,
COLOR_RGBA2YUV_IYUV = COLOR_RGBA2YUV_I420,
COLOR_BGRA2YUV_IYUV = COLOR_BGRA2YUV_I420,
COLOR_RGB2YUV_YV12 = 131,
COLOR_BGR2YUV_YV12 = 132,
COLOR_RGBA2YUV_YV12 = 133,
COLOR_BGRA2YUV_YV12 = 134,
//! Demosaicing
COLOR_BayerBG2BGR = 46,
COLOR_BayerGB2BGR = 47,
COLOR_BayerRG2BGR = 48,
COLOR_BayerGR2BGR = 49,
COLOR_BayerBG2RGB = COLOR_BayerRG2BGR,
COLOR_BayerGB2RGB = COLOR_BayerGR2BGR,
COLOR_BayerRG2RGB = COLOR_BayerBG2BGR,
COLOR_BayerGR2RGB = COLOR_BayerGB2BGR,
COLOR_BayerBG2GRAY = 86,
COLOR_BayerGB2GRAY = 87,
COLOR_BayerRG2GRAY = 88,
COLOR_BayerGR2GRAY = 89,
//! Demosaicing using Variable Number of Gradients
COLOR_BayerBG2BGR_VNG = 62,
COLOR_BayerGB2BGR_VNG = 63,
COLOR_BayerRG2BGR_VNG = 64,
COLOR_BayerGR2BGR_VNG = 65,
COLOR_BayerBG2RGB_VNG = COLOR_BayerRG2BGR_VNG,
COLOR_BayerGB2RGB_VNG = COLOR_BayerGR2BGR_VNG,
COLOR_BayerRG2RGB_VNG = COLOR_BayerBG2BGR_VNG,
COLOR_BayerGR2RGB_VNG = COLOR_BayerGB2BGR_VNG,
//! Edge-Aware Demosaicing
COLOR_BayerBG2BGR_EA = 135,
COLOR_BayerGB2BGR_EA = 136,
COLOR_BayerRG2BGR_EA = 137,
COLOR_BayerGR2BGR_EA = 138,
COLOR_BayerBG2RGB_EA = COLOR_BayerRG2BGR_EA,
COLOR_BayerGB2RGB_EA = COLOR_BayerGR2BGR_EA,
COLOR_BayerRG2RGB_EA = COLOR_BayerBG2BGR_EA,
COLOR_BayerGR2RGB_EA = COLOR_BayerGB2BGR_EA,
//! Demosaicing with alpha channel
COLOR_BayerBG2BGRA = 139,
COLOR_BayerGB2BGRA = 140,
COLOR_BayerRG2BGRA = 141,
COLOR_BayerGR2BGRA = 142,
COLOR_BayerBG2RGBA = COLOR_BayerRG2BGRA,
COLOR_BayerGB2RGBA = COLOR_BayerGR2BGRA,
COLOR_BayerRG2RGBA = COLOR_BayerBG2BGRA,
COLOR_BayerGR2RGBA = COLOR_BayerGB2BGRA,
COLOR_COLORCVT_MAX = 143
};
2）将bgr转为yuv420sp，因为OpenCV不支持，可参考如下代码：
void BGR2YUV_NV21(const cv::Mat &src, cv::Mat &dst, int w, int h)
{
// 先转BGR到YUV_I420
cv::cvtColor(src, dst, CV_BGR2YUV_I420);
#if 0 //测试代码
char filename[128] = {0};
snprintf(filename, 128, "%dx%d.yuv420", w, h);
FILE *fp = fopen(filename, "wb+");
if(fp)
{
fwrite(dst.data, 1, w*h*3/2, fp);fclose(fp); fp = 0;
}
#endif
// 再从YUV_I420手动转为NV12/NV21
int offset = w* h; // Y的长度
int len = w * h / 2;
int halflen = len / 2; //U和V的长度
unsigned char tmp[len]; //tmp保存原始UV的数据
unsigned char *data = dst.data;
memcpy(tmp, data + offset, len);
for (int i = 0; i < halflen; ++i) {
data[offset + i * 2] = tmp[halflen + i]; // V
data[offset + i * 2 + 1] = tmp[i]; // U
}
}
ROI裁剪
在人工智能中，经常使用到车牌识别，人脸识别等特征提取，会使用到抠图，使用过程中经常会出现如下错误实例：
cv::Mat roi = bgrMat(cv::Rect(x, y, width, height)); 或
cv::Mat roi(bgrMat, cv::Rect(top,left, width, height));
根据上面的拿到的数据会发现异常，异常原因是使用了浅拷贝，此时使用深拷贝的操作即可：
1）使用clone方式：roi.clone().data拿到数据。
2）使用copyTO方式：
cv::Mat roi = bgrMat(cv::Rect(top, left, width, height)); //在原始bgr中裁剪
cv::Mat Droi(stBoxRect.height, stBoxRect.width, CV_8UC3);//构造裁剪后的Mat对象
roi.copyTo(Droi); //将ROI数据深复制到Drop对象中
JPG编码
参考如下：
std::vector img_encode;
cv::Mat bgrimg(height, width, CV_8UC3);
memcpy(bgrimg.data, bgrdata, width * height* 3); //bgrdata为unsigned char*数据
std::vector param= std::vector(2);
param[0] = CV_IMWRITE_JPEG_QUALITY;
param[1] = 75;
cv::imencode(".jpg", bgrimg, img_encode, param);
int nSize = img_encode.size();
for(int i = 0; i < nSize ; i++){
jpgbuffer[i] = img_encode[i];
}
缩放
cv::resize(src, dis, Size(w,h));
注意缩放使用INTER_AREA能保证bgr数据缩放不丢失
常见错误集锦
1)原始数据unsign char 数据保存文件正常，封装cv::Mat后图像有差异，原因使用了错误的方式cv::Mat yuvMat(height, width*3/2, CV8UC1, pData)，正确方式为cv::Mat yuvMat(height3/2, width, CV8UC1, pData);
2）抠图问题
2-1）抠图完全看不到抠图的数据，原因是使用了浅复制；
2-2）抠图可以看见图像(深复制)，但图像和原始数据有差异，原因参考常见错误集锦中的1）,异常图片如下附件。
