近几个月的笑脸识别研究过程中踩了很多坑，担心记录在本地容易不小心给删了，记录一份放在网上

回顾学习之路，程序上从C++开始，到执着于Python,从实现简单的图像剪切到自己构建卷积神经网络，筚路蓝缕，我心依旧。

以下为关于笑脸识别的个人自学记录，不具备科学的严谨性，仅作参考。

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【程序：批量尺寸修改】

#用于修改尺寸
from skimage import data_dir,io,transform,color
import numpy as np

def convert_gray(f,**args):
 rgb=io.imread(f)    #依次读取rgb图片
 dst=transform.resize(rgb,(256,256))  #将图片大小转换为256*256
 return dst

ImagePath='/users/liuzuoli/facedata/asix/spider2/sp'
# 保存路径
str='/users/liuzuoli/facedata/asix/spider2/*.jpg'
coll = io.ImageCollection(str,load_func=convert_gray)
for i in range(len(coll)):
    io.imsave(ImagePath+'/'+np.str(i)+'.jpg',coll[i])  #循环保存图片

【程序：截取人脸的函数】

https://blog.csdn.net/u012162613/article/details/43523507

**def** saveFaces(image_name):
 faces = detectFaces(image_name)
  **if** faces:
 #将人脸保存在save_dir目录下。
 #Image模块：Image.open获取图像句柄，crop剪切图像(剪切的区域就是detectFaces返回的坐标)，save保存。
 save_dir = image_name.split('.')[0]+"_faces"
 os.mkdir(save_dir)
 count = 0
 **for** (x1,y1,x2,y2) **in** faces:
 file_name = os.path.join(save_dir,str(count)+".jpg")
 Image.open(image_name).crop((x1,y1,x2,y2)).save(file_name)
 count+=1

【程序-人脸画出68个点】

import dlib                     #人脸识别的库dlib
import numpy as np              #数据处理的库numpy
import cv2                      #图像处理的库OpenCv

# dlib预测器
detector = dlib.get_frontal_face_detector()
#PREDICTOR_PATH =             "/Users/liuzuoli/PycharmProjects/68dlib/shape_predictor_68_face_landmarks.dat"
  predictor =dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
#这里出现了一个报错
path="/users/liuzuoli/test/"

# cv2读取图像
img=cv2.imread(path+"pic3.JPG")

# 取灰度
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# 人脸数rects
rects = detector(img_gray, 0)

for i in range(len(rects)):
    landmarks = np.matrix([[p.x, p.y] for p in predictor(img, rects[i]).parts()])

for idx, point in enumerate(landmarks):
    # 68点的坐标
    pos = (point[0, 0], point[0, 1])

    # 利用cv2.circle给每个特征点画一个圈，共68个
    cv2.circle(img, pos, 6, color=(0, 255, 0))

    # 利用cv2.putText输出1-68,font后面的参数可以调整
    font = cv2.FONT_HERSHEY_SIMPLEX
    cv2.putText(img, str(idx+1), pos, font, 0.6, (0, 0, 255), 1, cv2.LINE_AA)

cv2.namedWindow("img", 2)
cv2.imshow("img", img)
cv2.waitKey(0)

【人脸检测-未完全实现2】

 #include <opencv/cv.hpp>
#include <stdio.h>  
#include <stdlib.h>
 #include <string.h>
 #include <assert.h>
 #include <math.h>
 #include <float.h>
 #include <limits.h>
 #include <time.h>
 #include <ctype.h>

 static CvMemStorage* storage = 0;   //创建一个内存存储器，来统一管理各种动态对象的内存
 static CvHaarClassifierCascade* cascade = 0;    //分类器

void detect_and_draw( IplImage* image );    //检测人脸并标记

const char* cascade_name =
 "/usr/local/Cellar/opencv/3.4.1_2/share/OpenCV/haarcascades/haarcascade_frontalface_alt_tree.xml";      //分类器名称

  int main( int argc, char** argv )
  {
CvCapture* capture = 0;     //视频的结构体
IplImage *frame, *frame_copy = 0;   //读取每一帧的结构体
cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );    //载入分类器   
if( !cascade )
{
    fprintf( stderr, "ERROR: Could not load classifier cascade\n" );
    fprintf( stderr,
            "Usage: facedetect --cascade=\"<cascade_path>\" 
   [filename|camera_index]\n" );
    return -1;
}
storage = cvCreateMemStorage(0);
cvNamedWindow( "result", 1 );   //1表示autosize
  
//检测视频
capture = cvCaptureFromCAM(-1); //调用摄像头

if( capture )
{
    for(;;)
    {
        if( !cvGrabFrame( capture ))    //从摄像头或者视频文件中抓取帧
            break;
        frame = cvRetrieveFrame( capture );     //取回由函数cvGrabFrame抓取的图像
        if( !frame )
            break;
        if( !frame_copy )   //复制图像
            frame_copy = cvCreateImage( cvSize(frame->width,frame->height),
                                       IPL_DEPTH_8U, frame->nChannels );
        
        if( frame->origin == IPL_ORIGIN_TL )    //图像顶点是否在顶-左
            cvCopy( frame, frame_copy, 0 );
        else
            cvFlip( frame, frame_copy, 0 );     //翻转
        
        IplImage *equ = cvCreateImage(cvGetSize(frame_copy),8, 1);
        IplImage *gray = cvCreateImage(cvGetSize(frame_copy), 8, 1);
        cvCvtColor(frame_copy, gray, CV_BGR2GRAY);      //转灰度图
        cvEqualizeHist(gray, equ);      //直方图均衡化
        
        //cvNamedWindow("yuantu");
        //cvNamedWindow("equ");
        //cvShowImage("yuantu",gray);
        //cvShowImage("equ",equ);
        
        detect_and_draw( frame_copy );      //人脸检测并标记
        
        if( cvWaitKey(1) >= 0 )
            break;
        //cvReleaseImage(&gray);
        //cvReleaseImage(&equ);
    }
    
    
    
    cvReleaseImage( &frame_copy );
    cvReleaseCapture( &capture );
}

cvWaitKey(-1); //检测图片的时候，等待显示
cvDestroyWindow("result");

return 0;
}

void detect_and_draw( IplImage* img )
{
static CvScalar colors[] =      //用8种颜色标记人脸
{
    {0,0,255},
    {0,128,255},
    {0,255,255},
    {0,255,0},
    {255,128,0},
    {255,255,0},
    {255,0,0},
    {255,0,255}
};

double scale = 1.2;     //缩放因子
IplImage* gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 );
IplImage* small_img = cvCreateImage( cvSize( cvRound (img->width/scale),        //四舍五入
                                            cvRound (img->height/scale)),
                                    8, 1 );
int i;

cvCvtColor( img, gray, CV_BGR2GRAY );       //转灰度图
cvResize( gray, small_img, CV_INTER_LINEAR );   //调整大小
cvEqualizeHist( small_img, small_img );     //使灰度图象直方图均衡化
cvClearMemStorage( storage );       //重置

if( cascade )
{
    double t = (double)cvGetTickCount();    //返回从操作系统启动所经过的毫秒数
    CvSeq* faces = cvHaarDetectObjects( small_img, cascade, storage,
                                       1.1, 2, 0/*CV_HAAR_DO_CANNY_PRUNING*/,
                                       cvSize(30, 30) );
    
    printf("face's total is %d\n",faces->total);
    
    t = (double)cvGetTickCount() - t;
    printf( "detection time = %gms\n", t/((double)cvGetTickFrequency()*1000.) );    //cvGetTickFrequency()返回每秒的计时周期数
    for( i = 0; i < (faces ? faces->total : 0); i++ )
    {
        CvRect* r = (CvRect*)cvGetSeqElem( faces, i );
        CvRect tr(r->x,r->y,r->width,r->height);
        //用矩形框框出
        cvRectangle(img, cvPoint(r->x * scale, r->y * scale), cvPoint( (r->x + r->width) * scale, (r->y + r->height)  * scale), colors[i%8], 3, 8, 0);
        
        //用原型框出
        CvPoint center;
        int radius;
        center.x = cvRound((r->x + r->width*0.5)*scale);
        center.y = cvRound((r->y + r->height*0.5)*scale);
        radius = cvRound((r->width + r->height)*0.25*scale);
        cvCircle( img, center, radius, colors[i%8], 3, 8, 0 );
        
        //用ROI截取人脸区域
        cvSetImageROI(small_img, tr);   //用缩放后的图，设置源图像ROI
        CvSize size1 = cvSize(r->width, r->height);
        IplImage* roi_img = cvCreateImage(size1,small_img->depth,small_img->nChannels);
        cvCopy(small_img,roi_img);      //复制图像
        cvResetImageROI(small_img);     //源图像用完后，清空ROI
        cvNamedWindow("picture", CV_WINDOW_AUTOSIZE);
        cvShowImage("picture", roi_img);
        //cvReleaseImage( &roi_img );
        //cvDestroyWindow("picture");
    }
}

cvShowImage( "result", img );
cvReleaseImage( &gray );
cvReleaseImage( &small_img );
}