目錄
4、顏色空間轉換代碼ColorConversionCodes
前言
上一篇文章,我們講到了掩膜操作,我們自己寫掩膜操作的過程中,將圖像轉化爲灰度圖像。我們使用了轉換色彩空間。今天我們就來講下在opencv中的轉換色彩空間和轉換圖像類型。
一、轉換顏色空間
1、什麼是顏色空間
我們的世界是五彩繽紛的,通過不同的顏色,帶給我們不同的視覺盛宴。其實這些不同的顏色,都是可以由基本的顏色組成的,比如我們經常說的光學三原色、顏料三原色,我們可以通過三種顏色的配比,得到不同種各式各樣的顏色。
比如上面這幅圖的右面的圖,我們可以構建一個三維的空間,當每個方向取不同的值,我們就能得到不同的顏色,這個就是顏色空間。
2、顏色空間有哪些
我們知道什麼是顏色空間,我們就來說一下常見的顏色空間有哪些吧!
1.BGR系列
最常見的就是BGR系列了,其中:
B表示blue,藍色;
G表示green,綠色;
R表示red,紅色;
我們可以通過不同的組合得到不同的顏色;每個取值範圍都是0-255,如果用16進製表示就是 0-FF。
BGR系列表示有一定的問題:
1.RGB 顏色空間利用三個顏色分量的線性組合來表示顏色,任何顏色都與這三個分量有關。
2.自然界中,由於光照等問題的影響,顏色發生變化,而是哪個顏色分量和光照都有關,所以圖像亮度改變,三個通道的顏色都會改變。
3.人眼睛對不同顏色的敏感程度不同,有時候難以對一個顏色進行區分。
4.適用於圖像顯示,不適用於圖像處理。
我們可以打開電腦的畫圖工具,輸入不同的值,來獲取不同的圖像:
2.灰度空間
灰度空間算是簡化的BGR空間,BGR有三個通道,分別表示三個像素分量,灰度空間只有一個通道,取值範圍也是0-255,值越大,顏色越趨向於白色,值越小,顏色越趨向於黑色。
對於下圖,就是一個從0一直取值到255之後的圖像:
3.HSV系列
除了BGR系列,我們最常見的是HSV系列了,其中:
H表示Hue,色調;用角度度量,取值範圍爲0°~360°,從紅色開始按逆時針方向計算,紅色爲0°,綠色爲120°,藍色爲240°。它們的補色是:黃色爲60°,青色爲180°,紫色爲300°;
S表示Saturation,飽和度;一種顏色,可以看成是某種光譜色與白色混合的結果。其中光譜色所佔的比例愈大,顏色接近光譜色的程度就愈高,顏色的飽和度也就愈高。飽和度高,顏色則深而豔。光譜色的白光成分爲0,飽和度達到最高。通常取值範圍爲0%~100%,值越大,顏色越飽和。
V表示Value,明度;明度表示顏色明亮的程度,對於光源色,明度值與發光體的光亮度有關;對於物體色,此值和物體的透射比或反射比有關。通常取值範圍爲0%(黑)到100%(白)。
所以我們表示HSV空間,通常使用一個柱面座標系:
4.其他
除了上面的,還有很多顏色空間,比如:
1.CMY是工業印刷採用的顏色空間。它與RGB對應。簡單的類比RGB來源於是物體發光,而CMY是依據反射光得到的。具體應用如打印機:一般採用四色墨盒,即CMY加黑色墨盒。
2.Lab:Lab顏色空間是由CIE(國際照明委員會)制定的一種色彩模式。自然界中任何一點色都可以在Lab空間 中表達出來,色彩空間比RGB空間大。Lab用數字化的方法來描述人的視覺感應。彌補了RGB和CMYK模式必須依賴於設備色彩特性的不足。
3.HSL:與HSV類似,主要差別在於L和V,L表示的是亮度,強調白色的亮度如何;V表示的是明度,表示光的亮度,可以是任何顏色光的亮度;
3、API——cvtColor
在opencv中提供了專門的API來調整色彩空間:
void cvtColor(
InputArray src,
OutputArray dst,
int code,
int dstCn = 0
);
函數參數含義如下:
(1)InputArray類型的points,輸入圖像。
(2)OutputArray類型的dst,輸出圖像。
(3)int類型的code,顏色空間轉換代碼(具體請看“ColorConversionCodes”)。
(4)bool類型的returnPoints,目標圖像中的通道數;如果參數爲0,則通道數自動從src和code派生。
在使用過程中,我們需要指定轉換代碼,第四個參數一般都是默認。舉個例子:
cvtColor(src, src1, COLOR_BGR2GRAY);
重點在於,第三個參數,都有哪些取值呢?接下來,讓我們詳細來看一下:
4、顏色空間轉換代碼ColorConversionCodes
我們上面接觸到了一個轉換代碼:
上面確實是我們最常用的,我們經常需要將一個彩色圖像,轉化爲一個灰度圖像,然後做後續的一些操作,當然,我們還有其他的很多轉換代碼:
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
};
我們使用不同的類型來看一下結果:
原圖如下:
代碼如下:
cvtColor(src, src1, COLOR_BGR2GRAY);
imshow("src gray", src1);
cvtColor(src, src1, COLOR_BGR2HSV);
imshow("src hsv", src1);
cvtColor(src, src1, COLOR_BGR2XYZ);
imshow("src xyz", src1);
結果如下:
其他的大家也可以嘗試一下,看一下效果怎麼樣。
二、轉換圖像類型
1、圖像類型引入
圖像也分爲很多種類型,我們之前也接觸過,就是創建一個Mat類,其構造函數有的需要指定圖像類型,我想大家應該還記得我們在講Mat類型的時候,基本類型中有的需要指定圖像類型:
Mat(Size size, int type);
Mat(int rows, int cols, int type);
包括後面基於基本類型的構造函數,也需要類型。
圖像類型的概念,我們之前也有接觸過一些,比如三通道,單通道,灰度圖像獲取像素指針時候的,需要指定類型。
所以,圖像生成之後,類型也就隨之產生。
讓我們走進常見圖像類型,來深入瞭解一下吧!
2、常見圖像類型
我們在interface.h文件中可以看到所有的類型:
#define CV_8U 0
#define CV_8S 1
#define CV_16U 2
#define CV_16S 3
#define CV_32S 4
#define CV_32F 5
#define CV_64F 6
#define CV_16F 7
#define CV_MAT_DEPTH_MASK (CV_DEPTH_MAX - 1)
#define CV_MAT_DEPTH(flags) ((flags) & CV_MAT_DEPTH_MASK)
#define CV_MAKETYPE(depth,cn) (CV_MAT_DEPTH(depth) + (((cn)-1) << CV_CN_SHIFT))
#define CV_MAKE_TYPE CV_MAKETYPE
#define CV_8UC1 CV_MAKETYPE(CV_8U,1)
#define CV_8UC2 CV_MAKETYPE(CV_8U,2)
#define CV_8UC3 CV_MAKETYPE(CV_8U,3)
#define CV_8UC4 CV_MAKETYPE(CV_8U,4)
#define CV_8UC(n) CV_MAKETYPE(CV_8U,(n))
#define CV_8SC1 CV_MAKETYPE(CV_8S,1)
#define CV_8SC2 CV_MAKETYPE(CV_8S,2)
#define CV_8SC3 CV_MAKETYPE(CV_8S,3)
#define CV_8SC4 CV_MAKETYPE(CV_8S,4)
#define CV_8SC(n) CV_MAKETYPE(CV_8S,(n))
#define CV_16UC1 CV_MAKETYPE(CV_16U,1)
#define CV_16UC2 CV_MAKETYPE(CV_16U,2)
#define CV_16UC3 CV_MAKETYPE(CV_16U,3)
#define CV_16UC4 CV_MAKETYPE(CV_16U,4)
#define CV_16UC(n) CV_MAKETYPE(CV_16U,(n))
#define CV_16SC1 CV_MAKETYPE(CV_16S,1)
#define CV_16SC2 CV_MAKETYPE(CV_16S,2)
#define CV_16SC3 CV_MAKETYPE(CV_16S,3)
#define CV_16SC4 CV_MAKETYPE(CV_16S,4)
#define CV_16SC(n) CV_MAKETYPE(CV_16S,(n))
#define CV_32SC1 CV_MAKETYPE(CV_32S,1)
#define CV_32SC2 CV_MAKETYPE(CV_32S,2)
#define CV_32SC3 CV_MAKETYPE(CV_32S,3)
#define CV_32SC4 CV_MAKETYPE(CV_32S,4)
#define CV_32SC(n) CV_MAKETYPE(CV_32S,(n))
#define CV_32FC1 CV_MAKETYPE(CV_32F,1)
#define CV_32FC2 CV_MAKETYPE(CV_32F,2)
#define CV_32FC3 CV_MAKETYPE(CV_32F,3)
#define CV_32FC4 CV_MAKETYPE(CV_32F,4)
#define CV_32FC(n) CV_MAKETYPE(CV_32F,(n))
#define CV_64FC1 CV_MAKETYPE(CV_64F,1)
#define CV_64FC2 CV_MAKETYPE(CV_64F,2)
#define CV_64FC3 CV_MAKETYPE(CV_64F,3)
#define CV_64FC4 CV_MAKETYPE(CV_64F,4)
#define CV_64FC(n) CV_MAKETYPE(CV_64F,(n))
#define CV_16FC1 CV_MAKETYPE(CV_16F,1)
#define CV_16FC2 CV_MAKETYPE(CV_16F,2)
#define CV_16FC3 CV_MAKETYPE(CV_16F,3)
#define CV_16FC4 CV_MAKETYPE(CV_16F,4)
#define CV_16FC(n) CV_MAKETYPE(CV_16F,(n))
//! @}
對於下面的這些,我們發現它的構成是固定的:
CV_ + 數字 + U\S\F + C + 數字:
(1)第一個數字取值爲8,16,32
(2)第二個數字取值爲1,2,3,4
上面是我們的直觀印象,其實它的定義如下:
CV_ + <bit_depth> + U\S\F + C + <number_of_channels>
其中:
CV_ :就是一個前綴,沒有實際含義
<bit_depth> :位數,指的是圖像像素的位數,如果一個像素佔8位內存空間,這個位置上就是8
U\S\F :像素值類型,其中:U是指unsigned int,無符號整型;S是指signed int,有符號整型;F是指float,單精度浮點型。
C + <number_of_channels> :指定圖像通道數,如果爲1,是指單通道,又叫灰度圖;如果是3,是指三通道,是我們常見的彩色圖像;如果爲4,指的是帶Alpha通道的RGB彩色圖像。
3、API——convertTo
在opencv中提供了API來轉換圖像類型:
inline
void GpuMat::convertTo(OutputArray dst, int rtype) const
{
convertTo(dst, rtype, Stream::Null());
}
inline
void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, double beta) const
{
convertTo(dst, rtype, alpha, beta, Stream::Null());
}
inline
void GpuMat::convertTo(OutputArray dst, int rtype, double alpha, Stream& stream) const
{
convertTo(dst, rtype, alpha, 0.0, stream);
}
這裏面都調用了下面的函數:
//! converts GpuMat to another datatype with scaling (Non-Blocking call)
CV_WRAP void convertTo(OutputArray dst, int rtype, double alpha, double beta, Stream& stream) const;
一般來說,我們只考慮前兩個參數,含義如下:
(1)OutputArray類型的dst,輸出圖像。
(2)int類型的rtype,圖像轉換類型。
舉個例子:
src.convertTo(src2, CV_32FC1);
這個其實並不常用,重點還是第一個,所以我們要熟練掌握圖像的顏色空間轉換。