关于汽车颜色识别

#1，首先说说我自己做的一个小程序

有参考CSDN中的一篇博客，具体文章链接找不到了。

主要思想：将RGB颜色空间中的图像转换到HSV空间，根据常见九种颜色的H、S、V范围来判断给定图像中的汽车最大概率属于哪种色系的车。

常见颜色的HSV范围：（也是参考的网上的为主）

程序接口：【输入】Mat类型，汽车图片的patch；【输出】string类型，上面九种颜色中的一种；

算法流程图：

#2，存在的问题

可能原因：我个人觉得，HSV空间中的值与实际肉眼中的颜色并不是线性的映射关系，所以单纯用一个线性范围来给出一中颜色的HSV值是有问题的，实验结果也验证了我这个猜想。很多车子在进去检测时都会有误，如下给出几种：

尽管代码有问题，但还是在此贴出，以备之后改进：

/***********************************
-----------------------------------

Name:  sophia
Date:  20161206
Email: hxinwen1218@sina.com

-----------------------------------
Function: Reco
Identification the color of the cars.

************************************/

#include<iostream>
#include<vector>
#include<string>
#include<conio.h>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>

using namespace std;
using namespace cv;


string ImgClass(Mat image);

/*
number	color
0		blue;
1		orange;
2		yellow;
3		green;
4		violet
5		red;
6		white;
7		grey;
8		black;
9		red;
*/

int main(){
	while (1)
	{ 
		string img_name;
		cout << "Please enter the name of the image: " << endl;
		cin >> img_name;
		string imgN = img_name + ".jpg";
		Mat img = imread(imgN);
		string className;

		className=ImgClass(img);

		cout << "The class of the car is: " << className << endl;


		char ch;
		ch = _getch();
		if (ch == 27)
			break;
		else
			continue;
	}

	system("Pause");
	return 0;
}

string ImgClass(Mat image){
	int classNum = 10;
	int lowH, highH, lowS, highS, lowV, highV;
	vector<double> ptRate(classNum, 0.0);
	string outClass;

	for (int ic = 0; ic < classNum; ic++)
	{
		switch (ic)
		{
		case 0://blue
			lowH = 100;
			highH = 124;
			lowS = 63;
			highS = 255;
			lowV = 76;
			highV = 220;
			break;
		case 1://orange
			lowH = 11;
			highH = 25;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		case 2://yellow
			lowH = 22;
			highH = 37;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		case 3:
			lowH = 35;
			highH = 77;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		case 4:
			lowH = 125;
			highH = 155;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		case 5:
			lowH = 0;
			highH = 10;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		case 6://white
			lowH = 0;
			highH = 180;
			lowS = 0;
			highS = 25;
			lowV = 225;
			highV = 255;
			break;
		case 7://grey
			lowH = 0;
			highH = 180;
			lowS = 28;
			highS = 40;
			lowV = 30;
			highV = 221;
			break;
		case 8://black
			lowH = 0;
			highH = 180;
			lowS = 0;
			highS = 255;
			lowV = 0;
			highV = 30;
			break;
		case 9://red
			lowH = 156;
			highH = 180;
			lowS = 43;
			highS = 255;
			lowV = 46;
			highV = 255;
			break;
		}

		Mat imgHSV;
		vector<Mat> hsvSplit;
		cvtColor(image, imgHSV, COLOR_BGR2HSV);

		split(imgHSV, hsvSplit);
		equalizeHist(hsvSplit[2], hsvSplit[2]);
		merge(hsvSplit, imgHSV);

		Mat imgThresholded;
		inRange(imgHSV, Scalar(lowH, lowS, lowV), Scalar(highH, highS, highV), imgThresholded);

		int nonZeroNum = 0;
		vector<Mat> channelsImg;
		split(imgThresholded, channelsImg);
		Mat_<uchar> imgResult = channelsImg[0];
		for (int ia = 0; ia < imgResult.rows; ia++)
			for (int ib = 0; ib < imgResult.cols; ib++)
				if (imgResult(ia, ib) != 0)
					nonZeroNum++;

		double rateCac = (double)nonZeroNum / (double)(imgResult.rows*imgResult.cols);
		ptRate[ic] = rateCac;
	}

	double curRate = 0.0;
	int classN;
	for (int id = 0; id < ptRate.size(); id++)
	{
		if (ptRate[id] > curRate)
		{
			curRate = ptRate[id];
			classN = id;
		}
	}
	
	switch (classN){
	case 0:
		outClass = "blue";
		break;
	case 1:
		outClass = "orange";
		break;
	case 2:
		outClass = "yellow";
		break;
	case 3:
		outClass = "green";
		break;
	case 4:
		outClass = "violet";
		break;
	case 5:
		outClass = "red";
		break;
	case 6:
		outClass = "white";
		break;
	case 7:
		outClass = "grey";
		break;
	case 8:
		outClass = "black";
		break;
	case 9:
		outClass = "red";
	}

	return outClass;
}

如果您有更好的想法，请多多指教！