In the last two posts in this series, we introduced the concept and architecture behind the Facecam, and looked at the desktop-based component to build our face recognition database (facedata.xml).
In this post we'll take a look at the Raspberry Pi-resident face recognition engine. This component is implemented as a daemon (which is basically the Unix/Linux equivalent of a Windows service, for those unfamiliar with the term) that looks in an "input" folder for images to process and populates an "output" folder with the results of the face recognition process.
In the next post in the series, we'll look at the implementation of a simple daemon to process the text files in this "output" folder, sending the text to an LED message-board.
But back to today's topic… the first thing we need to do is to install OpenCV on the Raspberry Pi, for which this post proved very helpful.
# Install OpenCV and its samples
sudo pacman -S python2-numpy opencv opencv-samples
# Add the pi user to the video group
# (good for testing webcam-related samples)
sudo usermod -a -G video pi
At this stage it should be possible for you to browse to and test the OpenCV samples, a step I'm going to skip here.
Next up, we can get the source for the daemon onto the local system and build it. Now at some point I'll get around to creating a makefile for this, but as it's only one .cpp file for now I haven't bothered, although I have put it in a ZIP along with a couple of XML files it depends on.
sudo pacman -S unzip gcc pkg-config
cd
mkdir facerecog
cd facerecog
wget "/wp-content/uploads/files/FacRecSrc.zip"
unzip FacRecSrc.zip
g++ `pkg-config opencv --cflags --libs` FaceRecDaemon.cpp -o facerecd
sudo cp facerecd /etc/rc.d
It should now be possible to run the daemon by launching the facerecd command, but first we need to create our input and output directories:
cd
mkdir –p faces/in
mkdir faces/out
mkdir faces/debug
facerecd
To avoid having to launch this manually, we can edit the rc.conf file to have it launch on boot:
sudo nano /etc/rc.conf
Then if you scroll down to the last line in the file, you can make sure both motion and facerecd are there:
DAEMONS=(!hwclock syslog-ng network openntpd @netfs @crond @sshd @motion facerecd)
You'll also need to edit the motion.conf file, to hook it up to the face detection component:
sudo nano /etc/motion/motion.conf
You need to make sure motion is saving images, and that they get copied to the "input" folder. These are the settings I changed:
output normal
jpeg_filename %C-%v-%q
on_picture_save cp %f /home/pi/faces/in
This breaks the uploader Python script we introduced way back when, but for now I'm just fine with that (it'll be a trivial change in the code to strip of the frame number from the JPG filename).
Let's now take a look at he C++ source for our daemon. To get started with writing the daemon, I took the boilerplate code from this article. I then took a big chunk of Shervin Emami's original face recognition implementation, but stripped out a lot that wasn't needed (as we're only reading from the database and don't need to train it, for instance).
Most of this was using an earlier version of the OpenCV API (which seems to be standard C functions), but I also ended up including some more recent C++ code – OpenCV thankfully provides helpful functions to interoperate between its C and C++ APIs – but that also means the code is less consistent than I'd have liked. At some point I'd like to come back and rework the C-based implementation to use C++ – I'm currently using a horrible mix of strings and char*, just for starters – but I have to find the time (and right now this is working well enough for my purposes). Part of the issue stems from my decision – right or wrong – to code inside Visual Studio, transfer the .cpp across to the Pi via SFTP and then build locally using gcc. If this loop was streamlined – by cross compiling directly on Windows or OSX, or even by editing the C++ code directly on the Pi – then I might be more inclined to take the time to rework the implementation.
Here's the C++ code from FaceRecDaemon.cpp:
#include <sys/types.h>
#include <sys/stat.h>
#include <stdio.h>
#include <stdlib.h>
#include <fcntl.h>
#include <errno.h>
#include <unistd.h>
#include <dirent.h>
#include <string.h>
#include <string>
#include <vector>
#include <iostream>
#include <fstream>
#include <algorithm>
#include <syslog.h>
#include "cv.h"
#include "cvaux.h"
#include "highgui.h"
#include "opencv2/opencv.hpp"
#ifndef BOOL
#define BOOL bool
#endif
using namespace std;
using namespace cv;
// Input and output folder locations
const char * inDir = "/home/pi/faces/in";
const char * outDir = "/home/pi/faces/out";
const char * debugDir = "/home/pi/faces/debug";
// Haar Cascade file, used for Face Detection
const char *faceCascadeFilename =
"/home/pi/facerecog/haarcascade_frontalface_alt.xml";
const char *lblCascadeFilename =
"/home/pi/facerecog/lbpcascade_frontalface.xml";
const char *faceDataXml = "/home/pi/facerecog/facedata.xml";
// Set to 0 if you dont want images of the Eigenvectors
// saved to files (for debugging)
int SAVE_EIGENFACE_IMAGES = 1;
// You might get better recognition accuracy if you enable this
//#define USE_MAHALANOBIS_DISTANCE
// Global variables
IplImage ** face
ImgArr = 0; // array of face images
CvMat * personNumTruthMat = 0; // array of person numbers
// Array of person names (indexed by the person number)
vector<string> personNames;
// Default dimensions for faces in the face recognition database
int faceWidth = 120;
int faceHeight = 90;
// The number of people in the training set
int nPersons = 0;
// The number of training images
int nTrainFaces = 0;
// The number of eigenvalues
int nEigens = 0;
// The average image
IplImage * pAvgTrainImg = 0;
// Eigenvectors
IplImage ** eigenVectArr = 0;
// Eigenvalues
CvMat * eigenValMat = 0;
// Projected training faces
CvMat * projectedTrainFaceMat = 0;
CvMemStorage* storage = 0;
static IplImage * small_img = 0;
double scale = 3;
CascadeClassifier faceCascade;
#define PAD_FACE 40
#define PAD_FACE_2 80
// Function prototypes
int getdir(string dir, vector<string> &files);
int loadTrainingData(CvMat ** pTrainPersonNumMat);
int findNearestNeighbor(float * projectedTestFace);
int findNearestNeighbor(
float * projectedTestFace, float *pConfidence
);
bool recognizeFromFile(
const char *inputFile,
const char *outputFile,
CvHaarClassifierCascade *cascade,
float * projectedTestFace,
CvMat *trainPersonNumMat
);
IplImage* resizeImage(
const IplImage *origImg, int newWidth, int newHeight
);
CvRect cropRect(const CvRect rect, int w, int h);
IplImage* cropImage(
CvRect detectFaceInImage(
IplImage* img, CascadeClassifier &cascade
);
void execute();
// Get the list of files in a directory
int getdir(string dir, vector<string> &files)
{
DIR *dp;
struct dirent *dirp;
if((dp = opendir(dir.c_str())) == NULL)
{
char msg[200];
snprintf(
msg,
sizeof(msg)-1,
"Error(%d) opening %s",
errno,
dir.c_str()
);
syslog(LOG_INFO, msg);
return errno;
}
while ((dirp = readdir(dp)) != NULL)
{
files.push_back(string(dirp->d_name));
}
closedir(dp);
sort(files.begin(), files.end());
return 0;
}
// Open the training data from the file 'facedata.xml'
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
// Create a file-storage interface
CvFileStorage* fileStorage =
cvOpenFileStorage(
faceDataXml, 0, CV_STORAGE_READ
);
if (!fileStorage)
{
syslog(LOG_INFO, "Unable to open training database.");
return 0;
}
// Load the names
personNames.clear(); // Make sure it starts as empty
nPersons = cvReadIntByName(fileStorage, 0, "nPersons", 0);
if (nPersons == 0)
{
syslog(LOG_INFO, "No people found in training database.");
return 0;
}
// Load each person's name
for (int i=0; i < nPersons; i++)
{
string sPersonName;
char varname[200];
snprintf(varname, sizeof(varname)-1, "personName_%d", (i+1));
sPersonName = cvReadStringByName(fileStorage, 0, varname);
personNames.push_back(sPersonName);
}
// Load the data
nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0);
nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0);
*pTrainPersonNumMat =
(CvMat*)cvReadByName(fileStorage, 0, "trainPersonNumMat", 0);
eigenValMat =
(CvMat*)cvReadByName(fileStorage, 0, "eigenValMat", 0);
projectedTrainFaceMat =
(CvMat*)cvReadByName(fileStorage, 0, "projectedTrainFaceMat", 0);
pAvgTrainImg =
(IplImage*)cvReadByName(fileStorage, 0, "avgTrainImg", 0);
eigenVectArr =
(IplImage**)cvAlloc(nTrainFaces*sizeof(IplImage *));
for (int i=0; i < nEigens; i++)
{
char varname[200];
snprintf(varname, sizeof(varname)-1, "eigenVect_%d", i);
eigenVectArr[i] =
(IplImage *)cvReadByName(fileStorage, 0, varname, 0);
}
// release the file-storage interface
cvReleaseFileStorage(&fileStorage);
syslog(LOG_INFO, "Face recognition database loaded.");
return 1;
}
// Find the most likely person based on a detection.
// Returns the index, and stores the confidence value
// into pConfidence.
int findNearestNeighbor(
float * projectedTestFace, float *pConfidence
)
{
double leastDistSq = DBL_MAX;
int iNearest = 0;
for (int iTrain=0; iTrain < nTrainFaces; iTrain++)
{
double distSq = 0;
for (int i=0; i < nEigens; i++)
{
float d_i =
projectedTestFace[i] -
projectedTrainFaceMat->data.fl[iTrain * nEigens + i];
#ifdef USE_MAHALANOBIS_DISTANCE
// Mahalanobis distance (might give better results
// than Eucalidean distance)
distSq += d_i*d_i / eigenValMat->data.fl[i];
#else
// Euclidean distance
distSq += d_i*d_i;
#endif
}
if (distSq < leastDistSq)
{
leastDistSq = distSq;
iNearest = iTrain;
}
}
// Return the confidence level based on the Euclidean distance,
// so that similar images should give a confidence between
// 0.5 to 1.0, and very different images should give a
// confidence between 0.0 to 0.5
*pConfidence =
1.0f -
sqrt(
leastDistSq / (float)(nTrainFaces * nEigens)
) / 255.0f;
// Return the found index
return iNearest;
}
// Make sure the given rectangle is completely within the given
// image dimensions
// Creates a new image copy that is of a desired size
IplImage* resizeImage(
const IplImage *origImg, int newWidth, int newHeight
)
{
int origWidth = 0;
int origHeight = 0;
if (origImg != NULL)
{
origWidth = origImg->width;
origHeight = origImg->height;
}
if (
origImg == NULL ||
newWidth <= 0 || newHeight <= 0 ||
origWidth <= 0 || origHeight <= 0
)
{
syslog(
LOG_INFO, "ERROR in resizeImage: Bad desired image size."
);
closelog();
exit(1);
}
// Scale the image to the new dimensions, even if the aspect< /p>
// ratio will be changed
IplImage *outImg =
cvCreateImage(
cvSize(newWidth, newHeight),
origImg->depth,
origImg->nChannels
);
if (newWidth > origImg->width && newHeight > origImg->height)
{
// Make the image larger
cvResetImageROI((IplImage*)origImg);
// CV_INTER_CUBIC or CV_INTER_LINEAR is good for enlarging
cvResize(origImg, outImg, CV_INTER_LINEAR);
}
else
{
// Make the image smaller
cvResetImageROI((IplImage*)origImg);
// CV_INTER_AREA is good for shrinking/decimation,
// but bad at enlarging
cvResize(origImg, outImg, CV_INTER_AREA);
}
return outImg;
}
CvRect cropRect(const CvRect rectIn, int w, int h)
{
CvRect roi = CvRect(rectIn);
// Make sure the displayed image is within the viewing dimensions
// Limit the bottom-right from past the image
if (roi.x + roi.width > w)
roi.width = w - roi.x;
if (roi.y + roi.height > h)
roi.height = h - roi.y;
// Limit the top-left from before the image
if (roi.x < 0)
roi.x = 0;
if (roi.y < 0)
roi.y = 0;
// Limit the top-left from after the image
if (roi.x > w-1)
roi.x = w-1;
if (roi.y > h-1)
roi.y = h-1;
// Limit the negative sizes
if (roi.width < 0)
roi.width = 0;
if (roi.height < 0)
roi.height = 0;
// Limit the large sizes
if (roi.width > w)
roi.width = w - roi.x;
if (roi.height > h)
roi.height = h - roi.y;
return roi;
}
// Returns a new image, a cropped version of the original
IplImage* cropImage(const IplImage *img, const CvRect region)
{
CvSize size;
size.height = img->height;
size.width = img->width;
if (img->depth != IPL_DEPTH_8U)
{
syslog(
LOG_INFO,
"ERROR in cropImage: unknown image depth given in cropImage()."
);
closelog();
exit(1);
}
// First create a new (color or greyscale) IPL Image and copy
// contents of img into it
IplImage *imageTmp =
cvCreateImage(size, IPL_DEPTH_8U, img->nChannels);
cvCopy(img, imageTmp, NULL);
// Create a new image of the detected region
// Set region of interest to that surrounding the face
CvRect checked = cropRect(region, img->width, img->height);
cvSetImageROI(imageTmp, checked);
// Copy region of interest (i.e. face) into a new iplImage
// (imageRGB) and return it
size.width = checked.width;
size.height = checked.height;
IplImage *imageRGB =
cvCreateImage(size, IPL_DEPTH_8U, img->nChannels);
cvCopy(imageTmp, imageRGB, NULL); // Copy just the region.
cvReleaseImage(&imageTmp);
return imageRGB;
}
// Perform face detection on the input image, using the given Haar
// cascade classifier. Assumes greyscale for input images.
// Returns a rectangle for the detected region in the given image.
CvRect detectFaceInImage(
IplImage* img, CascadeClassifier &cascade
)
{
CvRect found_face;
if (!small_img)
{
small_img =
cvCreateImage(
cvSize(
cvRound(img->width / scale),
cvRound(img->height / scale)
),
8, 1
);
}
cvResize(img, small_img, CV_INTER_LINEAR);
cvEqualizeHist(small_img, small_img);
Mat imgMat(small_img);
// Detect objects in the small grayscale image.
vector<Rect> objects;
cascade.detectMultiScale(
imgMat,
objects,
1.1f,
2,
CASCADE_FIND_BIGGEST_OBJECT,
Size(20, 20)
);
if (objects.size() > 0)
{
// Found at least one face
Rect r = (Rect)objects.at(0);
found_face.x = (int)((double)r.x * scale);
found_face.y = (int)((double)r.y * scale);
found_face.width = (int)((double)r.width * scale);
found_face.height = (int)((double)r.height * scale);
}
else
{
// Couldn't find the face
found_face = cvRect(-1,-1,-1,-1);
}
return found_face;
}
string convert(const string& line)
{
int len = line.length();
string nLine = "";
if (len)
{
nLine += line[0];
for (int i = 1; i < len; ++i)
{
if (isupper(line[i]))
nLine += ' ';
nLine += line[i];
}
}
return nLine;
}
// Recognize the person in the supplied image
bool recognizeFromFile(
const char *inputFile,
const char *outputFile,
const char *debugFile,
CvHaarClassifierCascade *cascade,
float * projectedTestFace,
CvMat *trainPersonNumMat
)
{
//syslog(LOG_INFO, "recognizeFromFile begins");
// Load the image directly as greyscale
IplImage *checkImg =
cvLoadImage(inputFile, CV_LOAD_IMAGE_GRAYSCALE);
if (!checkImg)
{
char msg[200];
snprintf(
msg,
sizeof(msg)-1,
"ERROR in recognizeFromFile(): Bad input image file: %s",
inputFile
);
syslog(LOG_INFO, msg);
return false;
}
// We'll try to detect the face using LBP
CvRect faceRect = detectFaceInImage(checkImg, faceCascade);
// Make sure a valid face was detected
if (faceRect.width > 0)
{
// Get the detected face image
IplImage *faceImg = cropImage(checkImg, faceRect);
// Make sure the image is the same dimensions as the
// training images
IplImage *sizedImg =
resizeImage(faceImg, faceWidth, faceHeight);
// Give the image a standard brightness and contrast,
// in case it was too dark or low contrast
// Create an empty greyscale image
IplImage *equalizedImg =
cvCreateImage(cvGetSize(sizedImg), 8, 1);
cvEqualizeHist(sizedImg, equalizedImg);
if (!equalizedImg)
{
syslog(
LOG_INFO,
"ERROR in recognizeFromFile(): no input image."
);
closelog();
exit(1);
}
char imageFile[200];
strcpy(imageFile, debugFile);
char *dot = strrchr(imageFile, '.');
*dot = 0;
cvSaveImage(imageFile, equalizedImg);
// If the face rec database has been loaded, then try to
< p style="margin: 0px"> // recognize the person currently detected
if (nEigens > 0)
{
// Project the test image onto the PCA subspace
cvEigenDecomposite(
equalizedImg,
nEigens,
eigenVectArr,
0, 0,
pAvgTrainImg,
projectedTestFace
);
// Check which person it is most likely to be
float confidence;
int iNearest =
findNearestNeighbor(projectedTestFace, &confidence);
int nearest = trainPersonNumMat->data.i[iNearest];
// Write results to the output file
char msg[200];
snprintf(
msg,
sizeof(msg)-1,
"Recognised %s with a confidence of %f",
(nearest > 0 ? personNames[nearest-1].c_str() : "nobody"),
confidence
);
syslog(LOG_INFO, msg);
string message =
(nearest > 0 ?
convert(personNames[nearest-1]) :
"Unrecognized"
);
std::ofstream o1(outputFile);
o1 << message.c_str() << std::endl;
std::ofstream o2(debugFile);
o2 << message.c_str() << std::endl;
}
// Free the resources used for this frame
cvReleaseImage(&faceImg);
cvReleaseImage(&sizedImg);
cvReleaseImage(&equalizedImg);
}
cvReleaseImage(&checkImg);
return true;
}
int main(void)
{
// Our process ID and Session ID
pid_t pid, sid;
// Fork off the parent process
pid = fork();
if (pid < 0)
{
exit(EXIT_FAILURE);
}
// If we got a good PID, then we can exit the parent process
if (pid > 0)
{
exit(EXIT_SUCCESS);
}
// Change the file mode mask
umask(0);
// Open any logs here
openlog("facerecd", LOG_PID|LOG_CONS, LOG_USER);
// Create a new SID for the child process
sid = setsid();
if (sid < 0)
{
// Log the failure
syslog(LOG_INFO, "Unable to get SID.");
closelog();
exit(EXIT_FAILURE);
}
// Change the current working directory
if (chdir("/") < 0)
{
// Log the failure
syslog(LOG_INFO, "Unable to change working directory.");
closelog();
exit(EXIT_FAILURE);
}
// Close out the standard file descriptors
close(STDIN_FILENO);
close(STDOUT_FILENO);
close(STDERR_FILENO);
// Daemon-specific initialization goes here
struct stat st = {0};
if (stat(inDir, &st) == -1)
{
mkdir(inDir, 0700);
}
if (stat(outDir, &st) == -1)
{
mkdir(outDir, 0700);
}
if (stat(debugDir, &st) == -1)
{
mkdir(debugDir, 0700);
}
execute();
exit(EXIT_SUCCESS);
}
void execute()
{
// Load the previously saved training data
CvMat *trainPersonNumMat = 0;
if (loadTrainingData(&trainPersonNumMat))
{
faceWidth = pAvgTrainImg->width;
faceHeight = pAvgTrainImg->height;
}
else
{
closelog();
return;
}
// Project the test images onto the PCA subspace
float *projectedTestFace =
(float *)cvAlloc(nEigens*sizeof(float));
// Load the HaarCascade classifier for face detection
faceCascade.load(lblCascadeFilename);
if (faceCascade.empty())
{
syslog(
LOG_INFO,
"ERROR in main loop: Could not load face detection classifier."
);
closelog();
exit(1);
}
CvHaarClassifierCascade* cascade =
(CvHaarClassifierCascade*)cvLoad(faceCascadeFilename, 0, 0, 0);
if (!cascade)
{
syslog(
LOG_INFO,
"ERROR in main loop: Could not load face detection classifier."
);
closelog();
exit(1);
}
vector<string> files = vector<string>();
const char *contents = NULL;
/* The Big Loop */
while (1)
{
// Get the files in our "in" directory
files.clear();
getdir(inDir, files);
if ((int)files.size() >= 3)
contents = files[2].c_str();
else
contents = NULL;
if (contents != NULL)
{
char input[256];
input[0] = 0;
strcat(input, inDir);
strcat(input, "/");
strcat(input, contents);
char *last = strrchr(input, '/');
char output[256];
output[0] = 0;
strcat(output, outDir);
strcat(output, "/");
strcat(output, last + 1);
strcat(output, ".txt");
char debug[256];
debug[0] = 0;
strcat(debug, debugDir);
strcat(debug, "/");
strcat(debug, last + 1);
strcat(debug, ".txt");
if (
recognizeFromFile(
input,
output,
debug,
cascade,
projectedTestFace,
trainPersonNumMat
)
)
{
remove(input);
}
}
sleep(0.5); /* wait half a second */
}
cvReleaseHaarClassifierCascade(&cascade);
if (storage)
{
cvReleaseMemStorage(&storage);
}
if (cascade)
{
cvReleaseHaarClassifierCascade(&cascade);
}
cvReleaseImage(&small_img);
closelog();
}
The daemon's job is to scan the incoming images – saved by the motion detection component and copied into the "input" folder – and find the largest contained face, should one exist. Any detected faces get extracted (and a copy gets saved to our "debug" folder, just so we can see how it's working) and checked against the face recognition database (once again using the Eigenface algorithm). The results get stored to a text file in the "output" folder, to be displayed on the LED message-board.
The most time-consuming part of this process – by at least an order of magnitude – is the initial face detection. I originally made use of Haar-like face detection, but even with some optimisations I borrowed from an iOS-based OpenCV implementation, I still found it far too slow on the Raspberry Pi (I as looking for <1s results and was finding anything from 5s to 90s, depending on the settings). There's a lot that can be tweaked to control this – such as increasing the minimum size of the object to be detected – but despite various attempts, it still remained too slow.
At Shervin's suggestion – he even kindly shared some implementation details from his upcoming book, OpenCV 2 Hotshot: RAW – I ended up making use of LBP face detection, instead. This is apparently only available in the C++ API – hence the mixing of the C and C++ in today's implementation – and the good news is that it certainly brought the cost of the detection down within acceptable limits.
The bad news is that it generates lots of false positives, which means portions of the image often end up getting detected as faces and the Eigenface algorithm often seems to recognise these faces in the database with a depressingly high confidence level.
I'm sure there's lots that can be done to improve the quality of the face detection, such as using a mask image. Here's a thought… if Motion was able to save the information from its "locate" feature to be picked up by the facerec
d, that would be even better: we'd only have to search the area of motion in the image.
Philipp Wagner kindly suggested adopting the approach shown in this tutorial, which may well help with performance and/or accuracy. I haven't yet taken a very close look, as integrating this approach would probably lead to quite fundamental changes in the implementation (and I've just got too much else going on, at the moment).
Even though the accuracy leaves a lot to be desired, the results are often very entertaining, but we'll see more about that, next time. And every so often lightning strikes and the correct person is flagged.
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