Color Reduce

還是使用經(jīng)典的Reduce Color的例子，即對(duì)圖像中的像素表達(dá)進(jìn)行量化。如常見(jiàn)的RGB24圖像有256×256×256中顏色，通過(guò)Reduce Color將每個(gè)通道的像素減少8倍至256/8=32種，則圖像只有32×32×32種顏色。假設(shè)量化減少的倍數(shù)是N，則代碼實(shí)現(xiàn)時(shí)就是簡(jiǎn)單的value/N*N，通常我們會(huì)再加上N/2以得到相鄰的N的倍數(shù)的中間值，最后圖像被量化為(256/N)×(256/N)×(256/N)種顏色。

方法零：.ptr和[]操作符

Mat最直接的訪問(wèn)方法是通過(guò).ptr<>函數(shù)得到一行的指針，并用[]操作符訪問(wèn)某一列的像素值。

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//?using?.ptr?and?[]??

void?colorReduce0(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

??????????????????data[i]=?data[i]/div*div?+?div/2;??

????????????}????????????????????

??????}??

}??

方法一：.ptr和指針操作

除了[]操作符，我們可以移動(dòng)指針*++的組合方法訪問(wèn)某一行中所有像素的值。

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//?using?.ptr?and?*?++???

void?colorReduce1(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

?????????????????*data++=?*data/div*div?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法二：.ptr、指針操作和取模運(yùn)算

方法二和方法一的訪問(wèn)方式相同，不同的是color reduce用模運(yùn)算代替整數(shù)除法

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//?using?.ptr?and?*?++?and?modulo??

void?colorReduce2(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

??????????????????int?v=?*data;??

??????????????????*data++=?v?-?v%div?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法三：.ptr、指針運(yùn)算和位運(yùn)算

由于進(jìn)行量化的單元div通常是2的整次方，因此所有的乘法和除法都可以用位運(yùn)算表示。

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//?using?.ptr?and?*?++?and?bitwise??

void?colorReduce3(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

????????????*data++=?*data&mask?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法四：指針運(yùn)算

方法四和方法三量化處理的方法相同，不同的是用指針運(yùn)算代替*++操作。

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//?direct?pointer?arithmetic??

void?colorReduce4(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????int?step=?image.step;?//?effective?width??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????//?get?the?pointer?to?the?image?buffer??

??????uchar?*data=?image.data;??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????for?(int?i=0;?i<nc;?i++)?{??

????????????*(data+i)=?*data&mask?+?div/2;??

????????????}?//?end?of?row???????????????????

????????????data+=?step;??//?next?line??

??????}??

}??

方法五：.ptr、*++、位運(yùn)算以及image.cols * image.channels()

這種方法就是沒(méi)有計(jì)算nc，基本是個(gè)充數(shù)的方法。

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//?using?.ptr?and?*?++?and?bitwise?with?image.cols?*?image.channels()??

void?colorReduce5(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<image.cols?*?image.channels();?i++)?{??

????????????*data++=?*data&mask?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

?

方法六：連續(xù)圖像

Mat提供了isContinuous()函數(shù)用來(lái)查看Mat在內(nèi)存中是不是連續(xù)存儲(chǔ)，如果是則圖片被存儲(chǔ)在一行中。

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//?using?.ptr?and?*?++?and?bitwise?(continuous)??

void?colorReduce6(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?*?image.channels();?//?total?number?of?elements?per?line??

??????if?(image.isContinuous())??{??

??????????//?then?no?padded?pixels??

??????????nc=?nc*nr;???

??????????nr=?1;??//?it?is?now?a?1D?array??

???????}??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

????????????*data++=?*data&mask?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法七：continuous+channels

與方法六基本相同，也是充數(shù)的。

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//?using?.ptr?and?*?++?and?bitwise?(continuous+channels)??

void?colorReduce7(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?;?//?number?of?columns??

??????if?(image.isContinuous())??{??

??????????//?then?no?padded?pixels??

??????????nc=?nc*nr;???

??????????nr=?1;??//?it?is?now?a?1D?array??

???????}??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

????????????*data++=?*data&mask?+?div/2;??

????????????*data++=?*data&mask?+?div/2;??

????????????*data++=?*data&mask?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法八：Mat _iterator

真正有區(qū)別的方法來(lái)啦，用Mat提供的迭代器代替前面的[]操作符或指針，血統(tǒng)純正的官方方法~

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//?using?Mat_?iterator???

void?colorReduce8(cv::Mat?&image,?int?div=64)?{??

??????//?get?iterators??

??????cv::Mat_<cv::Vec3b>::iterator?it=?image.begin<cv::Vec3b>();??

??????cv::Mat_<cv::Vec3b>::iterator?itend=?image.end<cv::Vec3b>();??

??????for?(?;?it!=?itend;?++it)?{??

????????(*it)[0]=?(*it)[0]/div*div?+?div/2;??

????????(*it)[1]=?(*it)[1]/div*div?+?div/2;??

????????(*it)[2]=?(*it)[2]/div*div?+?div/2;??

??????}??

}??

?

方法九：Mat_ iterator 和位運(yùn)算

把方法八中的乘除法換成位運(yùn)算。

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//?using?Mat_?iterator?and?bitwise??

void?colorReduce9(cv::Mat?&image,?int?div=64)?{??

??????//?div?must?be?a?power?of?2??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????//?get?iterators??

??????cv::Mat_<cv::Vec3b>::iterator?it=?image.begin<cv::Vec3b>();??

??????cv::Mat_<cv::Vec3b>::iterator?itend=?image.end<cv::Vec3b>();??

??????for?(?;?it!=?itend;?++it)?{??

????????(*it)[0]=?(*it)[0]&mask?+?div/2;??

????????(*it)[1]=?(*it)[1]&mask?+?div/2;??

????????(*it)[2]=?(*it)[2]&mask?+?div/2;??

??????}??

}??

方法十：MatIterator_

和方法八基本相同。

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//?using?MatIterator_???

void?colorReduce10(cv::Mat?&image,?int?div=64)?{??

??????cv::Mat_<cv::Vec3b>?cimage=?image;??

??????cv::Mat_<cv::Vec3b>::iterator?it=cimage.begin();??

??????cv::Mat_<cv::Vec3b>::iterator?itend=cimage.end();??

??????for?(?;?it!=?itend;?it++)?{???

????????(*it)[0]=?(*it)[0]/div*div?+?div/2;??

????????(*it)[1]=?(*it)[1]/div*div?+?div/2;??

????????(*it)[2]=?(*it)[2]/div*div?+?div/2;??

??????}??

}??

?

方法十一：圖像坐標(biāo)

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//?using?(j,i)??

void?colorReduce11(cv::Mat?&image,?int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols;?//?number?of?columns??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????for?(int?i=0;?i<nc;?i++)?{??

??????????????????image.at<cv::Vec3b>(j,i)[0]=?????image.at<cv::Vec3b>(j,i)[0]/div*div?+?div/2;??

??????????????????image.at<cv::Vec3b>(j,i)[1]=?????image.at<cv::Vec3b>(j,i)[1]/div*div?+?div/2;??

??????????????????image.at<cv::Vec3b>(j,i)[2]=?????image.at<cv::Vec3b>(j,i)[2]/div*div?+?div/2;??

????????????}?//?end?of?row???????????????????

??????}??

}??

方法十二：創(chuàng)建輸出圖像

之前的方法都是直接修改原圖，方法十二新建了輸出圖像，主要用于后面的時(shí)間對(duì)比。

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//?with?input/ouput?images??

void?colorReduce12(const?cv::Mat?&image,?//?input?image???

?????????????????cv::Mat?&result,??????//?output?image??

?????????????????int?div=64)?{??

??????int?nr=?image.rows;?//?number?of?rows??

??????int?nc=?image.cols?;?//?number?of?columns??

??????//?allocate?output?image?if?necessary??

??????result.create(image.rows,image.cols,image.type());??

??????//?created?images?have?no?padded?pixels??

??????nc=?nc*nr;???

??????nr=?1;??//?it?is?now?a?1D?array??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????for?(int?j=0;?j<nr;?j++)?{??

??????????uchar*?data=?result.ptr<uchar>(j);??

??????????const?uchar*?idata=?image.ptr<uchar>(j);??

??????????for?(int?i=0;?i<nc;?i++)?{??

????????????*data++=?(*idata++)&mask?+?div/2;??

????????????*data++=?(*idata++)&mask?+?div/2;??

????????????*data++=?(*idata++)&mask?+?div/2;??

??????????}?//?end?of?row???????????????????

??????}??

}??

方法十三：重載操作符

Mat重載了+&等操作符，可以直接將兩個(gè)Scalar(B,G,R)數(shù)據(jù)進(jìn)行位運(yùn)算和數(shù)學(xué)運(yùn)算。

[cpp]?view plaincopy

//?using?overloaded?operators??

void?colorReduce13(cv::Mat?&image,?int?div=64)?{??

??????int?n=?static_cast<int>(log(static_cast<double>(div))/log(2.0));??

??????//?mask?used?to?round?the?pixel?value??

??????uchar?mask=?0xFF<<n;?//?e.g.?for?div=16,?mask=?0xF0??

??????//?perform?color?reduction??

??????image=(image&cv::Scalar(mask,mask,mask))+cv::Scalar(div/2,div/2,div/2);??

}??

時(shí)間對(duì)比

通過(guò)迭代二十次取平均時(shí)間，得到每種方法是運(yùn)算時(shí)間如下。

可以看到，指針*++訪問(wèn)和位運(yùn)算是最快的方法；而不斷的計(jì)算image.cols*image.channles()花費(fèi)了大量重復(fù)的時(shí)間；另外迭代器訪問(wèn)雖然安全，但性能遠(yuǎn)低于指針運(yùn)算；通過(guò)圖像坐標(biāo)(j,i)訪問(wèn)時(shí)最慢的，使用重載操作符直接運(yùn)算效率最高。

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