[OpenCV实战]2 人脸识别算法对比

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人脸识别算法对比

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： http://dlib.net/

本文不涉及任何原理，只讲具体的应用。所有代码模型见：

https://download.csdn.net/download/luohenyj/

https://github.com/luohenyueji/OpenCV-Practical-Exercise 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：

https://github.com/opencv/opencv/tree/master/data/haarcascades

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： https://arxiv.org/abs/1512.02325

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__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):

http://dlib.net/files/data/dlib_face_detector_training_data.tar.gz

dnn(7220张图像)：

http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz

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。

添加图片注释，不超过 140 字（可选）

添加图片注释，不超过 140 字（可选）

参考

https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/

引用链接

[1] http://dlib.net/ : http://dlib.net/

[2] https://download.csdn.net/download/luohenyj/ : https://download.csdn.net/download/luohenyj/

[3] https://github.com/luohenyueji/OpenCV-Practical-Exercise : https://github.com/luohenyueji/OpenCV-Practical-Exercise

[4] https://github.com/opencv/opencv/tree/master/data/haarcascades : https://github.com/opencv/opencv/tree/master/data/haarcascades

[5] https://arxiv.org/abs/1512.02325 : https://arxiv.org/abs/1512.02325

[6] http://dlib.net/files/data/dlib_face_detector_training_data.tar.gz : http://dlib.net/files/data/dlib_face_detector_training_data.tar.gz

[7] http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz : http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz

[8] https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/ : https://www.learnopencv.com/face-detection-opencv-dlib-and-deep-learning-c-python/