Opencv数据及收集python脚本(生成CSV文件)

#!/usr/bin/env python
import sys
import os.path

# This is a tiny script to help you creating a CSV file from a face
# database with a similar hierarchie:
#
#  philipp@mango:~/facerec/data/at$ tree
#  .
#  |-- README
#  |-- s1
#  |   |-- 1.pgm
#  |   |-- ...
#  |   |-- 10.pgm
#  |-- s2
#  |   |-- 1.pgm
#  |   |-- ...
#  |   |-- 10.pgm
#  ...
#  |-- s40
#  |   |-- 1.pgm
#  |   |-- ...
#  |   |-- 10.pgm
#

if __name__ == "__main__":

    # if len(sys.argv) != 2:
    #   print("usage: create_csv <base_path>")
    #   sys.exit(1)
    BASE_PATH = "E:/DeskTop/FaceOpencv/FaceResource/att_faces_pgm"
    SEPARATOR = ";"
    fh = open("E:/DeskTop/FaceOpencv/FaceResource/att_faces_pgm/at.txt", 'w')
    label = 0
    for dirname, dirnames, filenames in os.walk(BASE_PATH):
        for subdirname in dirnames:
            subject_path = os.path.join(dirname, subdirname)
            for filename in os.listdir(subject_path):
                abs_path = "%s/%s" % (subject_path, filename)
                print("%s%s%d" % (abs_path, SEPARATOR, label))
                fh.write(abs_path)
                fh.write(SEPARATOR)
                fh.write(str(label))
                fh.write("\n")
            label = label + 1
    fh.close()

1、获取人脸图像时，直接获取的大小与官方给出的模型大小一直进行保存
https://blog.csdn.net/qq_42449351/article/details/99052241?utm_medium=distribute.pc_relevant.none-task-blog-2

#include <iostream>
#include <opencv2\opencv.hpp>
#include <vector>
#include<stdio.h>
using namespace std;
using namespace cv;
int main()
{
    CascadeClassifier cascada;
    //将opencv官方训练好的人脸识别分类器拷贝到自己的工程目录中
    cascada.load("F:\\video\\pic\\haarcascade_frontalface_alt2.xml");
    VideoCapture cap(0);  //0表示电脑自带的，如果用一个外接摄像头，将0变成1
    Mat frame, myFace;
    int pic_num = 1;
    while (1) {
        //摄像头读图像
        cap >> frame;
        vector<Rect> faces;//vector容器存检测到的faces
        Mat frame_gray;
        
        cvtColor(frame, frame_gray, COLOR_BGR2GRAY);//转灰度化，减少运算
        
        cascada.detectMultiScale(frame_gray, faces, 1.1, 4, CV_HAAR_DO_ROUGH_SEARCH, Size(70, 70), Size(1000, 1000));
        printf("检测到人脸个数：%d\n", faces.size());
       
        //识别到的脸用矩形圈出
        for (int i = 0; i < faces.size(); i++)
        {
            rectangle(frame, faces[i], Scalar(255, 0, 0), 2, 8, 0);
        }
        //当只有一个人脸时，开始拍照
        if (faces.size() == 1)
        {
            Mat faceROI = frame_gray(faces[0]);//在灰度图中将圈出的脸所在区域裁剪出
            //cout << faces[0].x << endl;//测试下face[0].x
            resize(faceROI, myFace, Size(92, 112));//将兴趣域size为92*112
            putText(frame, to_string(pic_num), faces[0].tl(), 3, 1.2, (0, 0, 225), 2, 0);//在 faces[0].tl()的左上角上面写序号
            string filename = format("F:\\video\\%d.jpg", pic_num); //图片的存放位置，frmat的用法跟QString差不对
            imwrite(filename, myFace);//存在当前目录下
            imshow(filename, myFace);//显示下size后的脸
            waitKey(500);//等待500us
            destroyWindow(filename);//:销毁指定的窗口
            pic_num++;//序号加1
            if (pic_num == 11)
            {
                return 0;//当序号为11时退出循环,一共拍10张照片
            }
        }
        int c = waitKey(10);
        if ((char)c == 27) { break; } //10us内输入esc则退出循环
        imshow("frame", frame);//显示视频流
        waitKey(100);//等待100us
    }
    return 0;
 
}

2、获取到一个大小的人脸图像，人后利用代码自己进行分割处理
https://blog.csdn.net/shangpapa3/article/details/60763634?utm_medium=distribute.pc_relevant.none-task-blog-2_defaultbaidujs_baidulandingword~default-0.no_search_link&spm=1001.2101.3001.4242

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/objdetect/objdetect.hpp>

using namespace cv;

int main()
{
    char file_img[160];
    Mat t_img[160];
    vector<Rect> faces;
    CascadeClassifier face_cascade;
    face_cascade.load("haarcascade_frontalface_alt.xml");

    //Size dst_size;
    //读取训练样本
    for (int i = 0; i < 160; i++)
    {
        Mat img_gray;
        int d = i + 1;
        sprintf(file_img, "C:\\Users\\Apple\\Desktop\\头2\\%d.jpg", d);
        Mat img = imread(file_img, 1);
        cvtColor(img, img_gray, COLOR_BGR2GRAY);
        equalizeHist(img_gray, img_gray);

        //-- Detect faces;
        //检测人脸
        face_cascade.detectMultiScale(img_gray, faces, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING, Size(60, 60),Size(240,240));
        if (faces.size() == 0)
        {

            continue;

        }
        Mat faceROI = img(faces[0]);
        Mat MyFace;
        resize(faceROI, MyFace, Size(90, 112));
        cvtColor(MyFace, MyFace, COLOR_BGR2GRAY);
        string  str = format("C:\\Users\\Apple\\Desktop\\头3\\%d.jpg", d);
        imwrite(str, MyFace);
        imshow("ii", MyFace);
        waitKey(10);
    }
}

https://blog.csdn.net/qq_43131852/article/details/102485061