[OpenCV实战]2 人脸识别算法对比

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在本教程中，我们将讨论各种人脸检测方法，并对各种方法进行比较。下面是主要的人脸检测方法：

1 OpenCV中的Haar Cascade人脸分类器；

2 OpenCV中的深度学习人脸分类器；

3 Dlib中的hog人脸分类器；

4 Dlib中的深度学习人脸分类器。

Dlib是一个C++工具包（也有python版本），代码地址：

本文不涉及任何原理，只讲具体的应用。所有代码模型见：

如果没有积分（系统自动设定资源分数）看看参考链接。我搬运过来的，大修改没有。pch是预编译文件。Opencv版本3.4.3以上。

1 OpenCV中的Haar Cascade人脸分类器

基于Haar Cascade的人脸检测器自2001年提出以来，一直是人脸检测领域的研究热点。这种模型和其变种在这里找到：

这种方法优点在CPU上几乎是实时工作的，方法简单可以在不同的尺度上检测人脸。实际就是一个级联分类器，参数可以调整，网上有相关资料。但是不管怎么调整误报率很高，而且人脸框选结果不是那么准确。

代码

C++:

#include "pch.h"
#include "face_detection.h"

/**
 * @brief 人脸检测haar级联
 *
 * @param frame 原图
 * @param faceCascadePath 模型文件
 * @return Mat
 */
Mat detectFaceHaar(Mat frame, string faceCascadePath)

    //图像缩放
    auto inHeight = 300;
    auto inWidth = 0;
    if (!inWidth)
    
        inWidth = (int)(((float)frame.cols / (float)frame.rows) * inHeight);
    
    resize(frame, frame, Size(inWidth, inHeight));

    //转换为灰度图
    Mat frameGray = frame.clone();
    //cvtColor(frame, frameGray, CV_BGR2GRAY);

    //级联分类器
    CascadeClassifier faceCascade;
    faceCascade.load(faceCascadePath);
    std::vector<Rect> faces;
    faceCascade.detectMultiScale(frameGray, faces);

    for (size_t i = 0; i < faces.size(); i++)
    
        int x1 = faces[i].x;
        int y1 = faces[i].y;
        int x2 = faces[i].x + faces[i].width;
        int y2 = faces[i].y + faces[i].height;
        Rect face_rect(Point2i(x1, y1), Point2i(x2, y2));
        rectangle(frameGray, face_rect, cv::Scalar(0, 255, 0), 2, 4);
    
    return frameGray;

python:

from __future__ import division
import cv2
import time
import sys

def detectFaceOpenCVHaar(faceCascade, frame, inHeight=300, inWidth=0):
    frameOpenCVHaar = frame.copy()
    frameHeight = frameOpenCVHaar.shape[0]
    frameWidth = frameOpenCVHaar.shape[1]
    if not inWidth:
        inWidth = int((frameWidth / frameHeight) * inHeight)

    scaleHeight = frameHeight / inHeight
    scaleWidth = frameWidth / inWidth

    frameOpenCVHaarSmall = cv2.resize(frameOpenCVHaar, (inWidth, inHeight))
    frameGray = cv2.cvtColor(frameOpenCVHaarSmall, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(frameGray)
    bboxes = []
    for (x, y, w, h) in faces:
        x1 = x
        y1 = y
        x2 = x + w
        y2 = y + h
        cvRect = [int(x1 * scaleWidth), int(y1 * scaleHeight),
                  int(x2 * scaleWidth), int(y2 * scaleHeight)]
        bboxes.append(cvRect)
        cv2.rectangle(frameOpenCVHaar, (cvRect[0], cvRect[1]), (cvRect[2], cvRect[3]), (0, 255, 0),
                      int(round(frameHeight / 150)), 4)
    return frameOpenCVHaar, bboxes

if __name__ == "__main__" :
    source = 0
    if len(sys.argv) > 1:
        source = sys.argv[1]

    faceCascade = cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)

    cap = cv2.VideoCapture(source)
    hasFrame, frame = cap.read()

    vid_writer = cv2.VideoWriter(output-haar-.avi.format(str(source).split(".")[0]),cv2.VideoWriter_fourcc(M,J,P,G), 15, (frame.shape[1],frame.shape[0]))

    frame_count = 0
    tt_opencvHaar = 0
    while(1):
        hasFrame, frame = cap.read()
        if not hasFrame:
            break
        frame_count += 1

        t = time.time()
        outOpencvHaar, bboxes = detectFaceOpenCVHaar(faceCascade, frame)
        tt_opencvHaar += time.time() - t
        fpsOpencvHaar = frame_count / tt_opencvHaar

        label = "OpenCV Haar ; FPS : :.2f".format(fpsOpencvHaar)
        cv2.putText(outOpencvHaar, label, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 0, 255), 3, cv2.LINE_AA)

        cv2.imshow("Face Detection Comparison", outOpencvHaar)

        vid_writer.write(outOpencvHaar)
        if frame_count == 1:
            tt_opencvHaar = 0

        k = cv2.waitKey(10)
        if k == 27:
            break
    cv2.destroyAllWindows()
    vid_writer.release()

2 OpenCV中的深度学习人脸分类器

OpenCV3.3以上版本就有该分类器的模型。模型来自论文：

但是提供了两种不同的模型。一种是16位浮点数的caffe人脸模型(5.4MB)，另外一种是8bit量化后的tensorflow人脸模型(2.7MB)。量化是指比如可以用0~255表示原来32个bit所表示的精度，通过牺牲精度来降低每一个权值所需要占用的空间。通常情况深度学习模型会有冗余计算量，冗余性决定了参数个数。因此合理的量化网络也可保证精度的情况下减小模型的存储体积，不会对网络的精度造成影响。具体可以看看深度学习fine-tuning的论文。通常这种操作可以稍微降低精度，提高速度，大大减少模型体积。

这种方法速度慢了点，但是精度不错。对于调用模型代码写的很清楚。但是tensorflow模型有点小问题，可能只能在opencv3.4.3以上版本通过readNet函数调用。

代码

C++:

#include "pch.h"
#include "face_detection.h"

//检测图像宽高
const size_t inWidth = 300;
const size_t inHeight = 300;
//缩放比例
const double inScaleFactor = 1.0;
//阈值
const double confidenceThreshold = 0.7;
//均值
const cv::Scalar meanVal(104.0, 177.0, 123.0);

/**
 * @brief 人脸检测Opencv ssd
 *
 * @param frame 原图
 * @param configFile 模型结构定义文件
 * @param weightFile 模型文件
 * @return Mat
 */
Mat detectFaceOpenCVDNN(Mat frame, string configFile, string weightFile)

    Mat frameOpenCVDNN = frame.clone();
    Net net;
    Mat inputBlob;
    int frameHeight = frameOpenCVDNN.rows;
    int frameWidth = frameOpenCVDNN.cols;
    //获取文件后缀
    string suffixStr = configFile.substr(configFile.find_last_of(.) + 1);
    //判断是caffe模型还是tensorflow模型
    if (suffixStr == "prototxt")
    
        net = dnn::readNetFromCaffe(configFile, weightFile);
        inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, false, false);
    
    else
    
        //bug
        //net = dnn::readNetFromTensorflow(configFile, weightFile);
        net = dnn::readNet(configFile, weightFile);
        inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, cv::Size(inWidth, inHeight), meanVal, true, false);
    

    //读图检测
    net.setInput(inputBlob, "data");
    cv::Mat detection = net.forward("detection_out");
    cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());

    for (int i = 0; i < detectionMat.rows; i++)
    
        //分类精度
        float confidence = detectionMat.at<float>(i, 2);
        if (confidence > confidenceThreshold)
        
            //左上角坐标
            int x1 = static_cast<int>(detectionMat.at<float>(i, 3) * frameWidth);
            int y1 = static_cast<int>(detectionMat.at<float>(i, 4) * frameHeight);
            //右下角坐标
            int x2 = static_cast<int>(detectionMat.at<float>(i, 5) * frameWidth);
            int y2 = static_cast<int>(detectionMat.at<float>(i, 6) * frameHeight);
            //画框
            cv::rectangle(frameOpenCVDNN, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 255, 0), 2, 4);
        
    
    return frameOpenCVDNN;

python

from __future__ import division
import cv2
import time
import sys

def detectFaceOpenCVDnn(net, frame):
    frameOpencvDnn = frame.copy()
    frameHeight = frameOpencvDnn.shape[0]
    frameWidth = frameOpencvDnn.shape[1]
    blob = cv2.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], False, False)

    net.setInput(blob)
    detections = net.forward()
    bboxes = []
    for i in range(detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        if confidence > conf_threshold:
            x1 = int(detections[0, 0, i, 3] * frameWidth)
            y1 = int(detections[0, 0, i, 4] * frameHeight)
            x2 = int(detections[0, 0, i, 5] * frameWidth)
            y2 = int(detections[0, 0, i, 6] * frameHeight)
            bboxes.append([x1, y1, x2, y2])
            cv2.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight/150)), 8)
    return frameOpencvDnn, bboxes

if __name__ == "__main__" :

    # OpenCV DNN supports 2 networks.
    # 1. FP16 version of the original caffe implementation ( 5.4 MB )
    # 2. 8 bit Quantized version using Tensorflow ( 2.7 MB )
    DNN = "TF"
    if DNN == "CAFFE":
        modelFile = "models/res10_300x300_ssd_iter_140000_fp16.caffemodel"
        configFile = "models/deploy.prototxt"
        net = cv2.dnn.readNetFromCaffe(configFile, modelFile)
    else:
        modelFile = "models/opencv_face_detector_uint8.pb"
        configFile = "models/opencv_face_detector.pbtxt"
        net = cv2.dnn.readNetFromTensorflow(modelFile, configFile)

    conf_threshold = 0.7

    source = 0
    if len(sys.argv) > 1:
        source = sys.argv[1]

    cap = cv2.VideoCapture(source)
    hasFrame, frame = cap.read()

    vid_writer = cv2.VideoWriter(output-dnn-.avi.format(str(source).split(".")[0]),cv2.VideoWriter_fourcc(M,J,P,G), 15, (frame.shape[1],frame.shape[0]))

    frame_count = 0
    tt_opencvDnn = 0
    while(1):
        hasFrame, frame = cap.read()
        if not hasFrame:
            break
        frame_count += 1

        t = time.time()
        outOpencvDnn, bboxes = detectFaceOpenCVDnn(net,frame)
        tt_opencvDnn += time.time() - t
        fpsOpencvDnn = frame_count / tt_opencvDnn
        label = "OpenCV DNN ; FPS : :.2f".format(fpsOpencvDnn)
        cv2.putText(outOpencvDnn, label, (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1.4, (0, 0, 255), 3, cv2.LINE_AA)

        cv2.imshow("Face Detection Comparison", outOpencvDnn)

        vid_writer.write(outOpencvDnn)
        if frame_count == 1:
            tt_opencvDnn = 0

        k = cv2.waitKey(10)
        if k == 27:
            break
    cv2.destroyAllWindows()
    vid_writer.release()

3 Dlib中的hog人脸分类器和Dlib中的hog人脸分类器

Dlib就没有运行了，因为要编译嫌麻烦。而且opencv自带的已经足够了。Dlib里面人脸分类器调用和opencv一样。

Dlib所用的人脸数据见：

Hog(2825张图像):

dnn(7220张图像)：

4 方法比较

OpencvDNN综合来说是最好的方法。不过要opencv3.43以上，对尺寸要求不高，速度精度都不错。如果追求高精度用caffe模型就行了，opencv3.4.1以上就可以了。OpenCV DNN低版本对tensorflow模型支持不好。

Dlib Hog在CPU下，检测速度最快但是小图像(人脸像素70以下)是无效的。因此第二个推荐是Hog。

Dlib DNN在GPU下，应该是最好的选择，精度都是最高的，但是有点慢。

Haar Cascade不推荐太古老了，而且错误率很高。

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