The introduction of HSI color model and RGB turn HSI?

1. Why should I introduce the HSI color model?

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         saturation and the larger the more vivid);

Brightness : is a subjective factor, which is actually not measurable (brightness and image grayscale is the luminance of the color).

In general, the RGB model is more suitable for image color generation, and the HSI model is suitable for image description.

2. Conversion from RGB to HSI

The component of the RGB mode is defined as a cell on a cube, and the HSI model is defined in a color triangle as follows.

For a primary RGB color image, each RGB pixel can be used as the following formula to find the HSI component:

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HSI Color space Model:

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Code 1-1-1:HSV Channel Separation

//converted to HSI 
 Cv::mat img_h, img_s, Img_v, IMGHSV ; 
 Std::vector<cv::mat> Hsv_vec; 
 Cv::cvtcolor (SRCIMAGE,IMGHSV, CV_BGR2HSV); 
 Cv::imshow ("HSV", IMGHSV); 
 Cv::waitkey (0); 
 
//split HSV Channel 
 Cv::split (Imghsv,hsv_vec); 
 IMG_H=HSV_VEC[0]; 
 Img_s=hsv_vec[1]; 
 Img_v=hsv_vec[2]; 
 
//Conversion channel data type 
 Img_h.convertto (img_h,cv_32f); 
 Img_s.convertto (img_s,cv_32f); 
 Img_v.convertto (img_v,cv_32f); 
 
//Calculate the maximum value for each channel 
 double max_h,max_s,max_v; 
 Cv::minmaxidx (Img_h,0,&max_h); 
 Cv::minmaxidx (img_s,0,&max_s); 
 Cv::minmaxidx (IMG_V,0,&MAX_V); 
 
//Each channel normalized 
 Img_h/=max_h; 
 img_s/=max_s; 
 Img_v/=max_v;    

Conclusion: The H, S and I components are calculated by the formula to obtain the image. The above image (car license plate recognition) can be analyzed, compared to the original RGB image, HSI Image Color Information utilization is higher, more suitable for target analysis and target segmentation and other scenarios.

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The introduction of HSI color model and RGB turn HSI?