Opencv combat [1] Face detection and partial processing of ROI area (transformation of Joe Bilo!!)

Steps:

1. Use Opencv’s own classifier to detect human faces

Preliminary knowledge: Haar feature classifier

The Haar feature classifier is an XML file that describes the Haar feature values of various parts of the human body. This includes faces, eyes, lips, and so on. Haar feature classifier storage address: D:\ opencV \ Opencv4.0 \ Opencv4.0.0 \sources\data\ Haarcascades D:\opencv\opencv4.0\opencv4.0.0\build\etc\haarcascades

haarcascade_eye.xml ; Only open eyes can be detected haarcascade_eye_tree_eyeglasses.xml; Haarcascade_frontalcatface.xml haarCascade_frontalcatface_extende.xml haarCascade_frontalface_alt.xml haarcascade_frontalface_alt_tree.xml haarcascade_frontalface_alt2.xml; XML haarCascade_fullbody. XML haarCascade_lefteye_2Montes.xml haarcascade_licence_plate_rus_16stages.xml haarcascade_lowerbody.xml haarcascade_profileface.xml haarcascade_righteye_2splits.xml haarcascade_russian_plate_number.xml haarcascade_smile.xml haarcascade_upperbody.xmlCopy the code

DetectMultiScale in detail

void detectMultiScale(
	const Mat& image,
	CV_OUT vector<Rect>& objects,
	double scaleFactor = 1.1.int minNeighbors = 3.int flags = 0,
	Size minSize = Size(),
	Size maxSize = Size()
);
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#include <opencv2/opencv.hpp>
#include <iostream>
#include "windows.h"
#include <stdio.h>
#include "My_ImageProssing_base.h"

using namespace cv;
using namespace std;


// Training file path
string xmlPath = "D: \ \ opencv \ \ opencv4.0 \ \ opencv4.0.0 \ \ build \ \ etc \ \ haarcascades \ \ haarcascade_frontalface_alt2 XML";

int main(int argc, char** atgv) {
	Mat img = imread("D:\ opencv_picture_test\\beauty\ Pei Jiaxin.png".0);
	//Mat img = imread("D:\ opencv_picture_test\\ test2.jpg");
	imshow("input image", img);
	// [1] load classifiers
	CascadeClassifier detector;
	detector.load(xmlPath);
	if(! detector.load(xmlPath))   // Load the training file
	{
		cout << "Cannot load the specified XML file" << endl;
		return - 1;
	}
	[2] Detect faces and store information in rectangular faces
	vector<Rect> faces;
	//OpenCV Rect class usage
	// If you create a Rect object, Rect has the following functions:
	//rect.area(); // Return the area of rect
	//rect.size(); // Return the size of rect
	//rect.tl(); // Return the coordinates of the upper left vertex of rect
	//rect.br(); // Return the coordinates of the lower right vertex of rect
	//rect.width(); // Return the width of rect
	//rect.height(); // Return the height of rect
	//rect.contains(Point(x, y)); // Return a Boolean variable to determine whether rect contains a Point(x, y)
	detector.detectMultiScale(img,faces, 1.1.3.0);// Call the classifier object
	// [3] frame the face
	for (size_t t = 0; t < faces.size(a); t++) {rectangle(img, faces[t], Scalar(0.0.255), 2.8);
		// Faces [t] represents the rectangle class of the TTH face
		//2 indicates the width of the line. 8 is linear. The default value is 8
	}
	namedWindow("Result", WINDOW_NORMAL);     // Define window display properties
	imshow("Result", img);
	waitKey(0);
	return 0;
}
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2, some processing in the rectangular box of the face (only grayscale image processing here)

1. Mosaic (here we use mean filtering)

// create a Mosaic function
void mosaic(Mat& srcImage, Mat& dstImage, int times)
{
	blur(srcImage,dstImage, Size(times*2+1, times * 2 + 1));
}
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2, Change to another image (use resize, resize according to the area of the face) // Transform into Joe Biro

void paste(Mat& srcImage, Mat& dstImage)
{
	resize(srcImage,dstImage, dstImage.size());
}
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3. Skin smoothing (bilateral filtering)

void clearFreckle(Mat& srcImage, Mat& dstImage, int times)
{
	bilateralFilter(srcImage, dstImage,times, times*2, times/2);
}
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4. Video processing (template)

VideoCapture capture(0);
/ / VideoCapture capture (" D: \ \ opencv_picture_test \ \ videos \ \ face. Avi ");
while (1)
	{
		Mat img;
		capture >> img;	// Reads the current frame
		//imshow(" original video ", img); // Displays the current frame
		cvtColor(img,img, COLOR_BGR2GRAY);		// Convert to grayscale
		Mat dstImage = img.clone(a);[2] Detect faces and store information in rectangular faces
		vector<Rect> faces;
		detector.detectMultiScale(img, faces, 1.1.3.0);// Call the classifier object
		// [3] Modify region information.// Complete
		imshow("Processed video.", dstImage);	// Displays the current frame
		if(waitKey(10) > =0 ) break;	/ / delay 10 ms
	}
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Complete code (video + texture) :

#include <opencv2/opencv.hpp>
#include <iostream>
#include "windows.h"
#include <stdio.h>
#include "My_ImageProssing_base.h"

using namespace cv;
using namespace std;


// Training file path
string xmlPath = "D: \ \ opencv \ \ opencv4.0 \ \ opencv4.0.0 \ \ build \ \ etc \ \ haarcascades \ \ haarcascade_frontalface_alt2 XML";
// create a Mosaic function
void mosaic(Mat& srcImage, Mat& dstImage, int times)
{
	blur(srcImage,dstImage, Size(times*2+1, times * 2 + 1));
}
void clearFreckle(Mat& srcImage, Mat& dstImage, int times)
{
	bilateralFilter(srcImage, dstImage,times, times*2, times/2);
}
void paste(Mat& srcImage, Mat& dstImage)
{
	resize(srcImage,dstImage, dstImage.size());
}
int main(a) 
{
	//Mat img = imread("D:\ opencv_picture_test\\beauty\\ pei-gia.png ",0);
	Mat img2 = imread("D:\ opencv_picture_test\\ interesting picture \\ pig.jpg".0);
	//imshow("input image", img);
	// [1] load classifiers
	CascadeClassifier detector;
	detector.load(xmlPath);
	if(! detector.load(xmlPath))// Load the training file
	{
		cout << "Cannot load the specified XML file" << endl;
		return - 1;
	}
	// Call the camera
	VideoCapture capture(0);		Int a=1;
	/ / VideoCapture capture (" D: \ \ opencv_picture_test \ \ videos \ \ face. Avi "); Int a=1;
	while (1)
	{
		Mat img;
		capture >> img;	// Reads the current frame
		//imshow(" original video ", img); // Displays the current frame
		cvtColor(img,img, COLOR_BGR2GRAY);		// Convert to grayscale
		Mat dstImage = img.clone();
		[2] Detect faces and store information in rectangular faces
		vector<Rect> faces;
		detector.detectMultiScale(img, faces, 1.1.3.0);// Call the classifier object
		// [3] Modify region information
		for (size_t t = 0; t < faces.size(); t++)
		{
			int rows = faces[t].height;
			int cols = faces[t].width;
			int start_y = faces[t].y;
			int start_x = faces[t].x;
			Mat ROI(rows, cols, CV_8UC1, Scalar(0));
			Mat dstROI(rows, cols, CV_8UC1, Scalar(0));
			for (int j = 0; j < rows; j++)/ / line cycle
			{
				for (int i = 0; i < cols; i++)/ / cycle
				{
					//------- [start processing each pixel] ---------------
					ROI.at<uchar>(j, i) = img.at<uchar>(j + start_y, i + start_x);
					//------- [End] ---------------}}// create a Mosaic
			//mosaic(ROI,dstROI,10);
			/ / map
			paste(img2,dstROI);
			// Remove freckles (skin)
			//clearFreckle(ROI, dstROI, 10);
			for (int j = 0; j < rows; j++)/ / line cycle
			{
				for (int i = 0; i < cols; i++)/ / cycle
				{
					//------- [start processing each pixel] ---------------
					dstImage.at<uchar>(j + start_y, i + start_x) = dstROI.at<uchar>(j, i);
					//------- [End] ---------------
				}
			}
		}
		imshow("Processed video.", dstImage);	// Displays the current frame
		if(waitKey(10) > =0 ) break;	/ / delay 10 ms
	}
	return 0;
}
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Results show

Refer to the link

XML, Eye_tree_eyeglasses. XML, and HaarCascade_Lefteye_2Montes.xml are distinguished by OpenCV face recognition — detectMultiScale function