얼굴인식 활용하기 (Raspberry Pi + Python + OpenCV)
Installing OpenCV For Python
To install OpenCV for Python, all you have to do is use apt-get like below:
sudo apt-get install python-opencv
To test the installation of OpenCV, run this Python script, it will switch on your camera for video streaming if it is working.
import cv
cv.NamedWindow("w1", cv.CV_WINDOW_AUTOSIZE)
camera_index = 0
capture = cv.CaptureFromCAM(camera_index)
def repeat():
global capture #declare as globals since we are assigning to them now
global camera_index
frame = cv.QueryFrame(capture)
cv.ShowImage("w1", frame)
c = cv.WaitKey(10)
if(c=="n"): #in "n" key is pressed while the popup window is in focus
camera_index += 1 #try the next camera index
capture = cv.CaptureFromCAM(camera_index)
if not capture: #if the next camera index didn't work, reset to 0.
camera_index = 0
capture = cv.CaptureFromCAM(camera_index)
while True:
repeat()
Simple Example of Raspberry Pi Face Recognition
This example is a demonstration for Raspberry Pi face recognition using haar-like features. it finds faces in the camera and puts a red square around it. I am surprised how fast the detection is given the limited capacity of the Raspberry Pi (about 3 to 4 fps). Although it’s still much slower than a laptop, but it would still be useful in some robotics applications.
You will need to download this trained face file:
http://stevenhickson-code.googlecode.com/svn/trunk/AUI/Imaging/face.xml
#!/usr/bin/python
The program finds faces in a camera image or video stream and displays a red box around them.
import sys
import cv2.cv as cv
from optparse import OptionParser
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "time taken for detection = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("video", img)
if __name__ == '__main__':
parser = OptionParser(usage = "usage: %prog [options] [filename|camera_index]")
parser.add_option("-c", "--cascade", action="store", dest="cascade", type="str", help="Haar cascade file, default %default", default = "../data/haarcascades/haarcascade_frontalface_alt.xml")
(options, args) = parser.parse_args()
cascade = cv.Load(options.cascade)
if len(args) != 1:
parser.print_help()
sys.exit(1)
input_name = args[0]
if input_name.isdigit():
capture = cv.CreateCameraCapture(int(input_name))
else:
capture = None
cv.NamedWindow("video", 1)
#size of the video
width = 160
height = 120
if width is None:
width = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH))
else:
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_WIDTH,width)
if height is None:
height = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT))
else:
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_HEIGHT,height)
if capture:
frame_copy = None
while True:
frame = cv.QueryFrame(capture)
if not frame:
cv.WaitKey(0)
break
if not frame_copy:
frame_copy = cv.CreateImage((frame.width,frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, frame_copy)
else:
cv.Flip(frame, frame_copy, 0)
detect_and_draw(frame_copy, cascade)
if cv.WaitKey(10) >= 0:
break
else:
image = cv.LoadImage(input_name, 1)
detect_and_draw(image, cascade)
cv.WaitKey(0)
cv.DestroyWindow("video")
To run this program, type in this command in your VNC Viewer’s terminal:
python facedetect.py --cascade=face.xml 0
The number at the end represents the number of your video device.
Face Tracking in Raspberry Pi with pan-tilt Servos
In this example I will be using the Wall-E Robot‘s camera and pan-tilt servo head.
The idea is simple. Raspberry Pi detects the position of the face, sends a command to the Arduino. Arduino will convert the command into servo position and turn the camera. I am using i2c to connect Raspberry Pi and Arduino.
Note: I am still trying to optimize the code for this example, so the result is still not great, but it gives you the idea how it works. I will come back and update this post as soon as I am happy with the result.
Arduino Source Code
Here is the Arduino code. Note that this code uses a very dummy and basic open loop control method, I only use this because of its simplicity. For a more optimal control method, please see Color Tracking Using PID.
In this example it basically waits for commands from the Raspberry Pi and turn the head around. The commands are expected to be integer 1, 2, 3 or 4, each represents a direction that it should turn the camera to. While it’s turning, the variable ‘state’ will be set to zero, so Raspberry Pi will stop detecting or sending any more command to avoid turning the camera too far, because of the delays.
Like I mentioned at the beginning, this is an open loop control system, and still has a lot of room to improve. I made it so simple just for some people to pick up more easily.
#include <Wire.h>
#define SLAVE_ADDRESS 0x04
byte command;
byte state;
// Servo code
#include <Servo.h>
Servo servoNeckX;
Servo servoNeckY;
const byte servoNeckX_pin = 3;
const byte servoNeckY_pin = 4;
const int lrServoMax = 2300; // looking right
const int lrServoMin = 700;
const int udServoMax = 2100; // looking down
const int udServoMin = 750; // looking up
int posX = 1500;
int posY = 1300;
// End of Servo code
void setup() {
servoNeckX.attach(servoNeckX_pin);
servoNeckY.attach(servoNeckY_pin);
servoNeckX.writeMicroseconds(posX);
delay(100);
servoNeckY.writeMicroseconds(posY);
delay(100);
// initialize i2c as slave
Wire.begin(SLAVE_ADDRESS);
// define callbacks for i2c communication
Wire.onReceive(receiveData);
Wire.onRequest(sendData);
Serial.begin(9600); // start serial for output
Serial.println("Ready!");
state = 1;
}
void loop() {
delay(20);
}
// callback for received data
void receiveData(int byteCount){
while(Wire.available()) {
state = 0; // moving servos
command = Wire.read();
Serial.print("command received: ");
Serial.println(command);
switch (command){
case 1:
// lift head (-Y)
posY = constrain(posY-20, udServoMin, udServoMax);
servoNeckY.writeMicroseconds(posY);
break;
case 2:
// lower head (+Y)
posY = constrain(posY+20, udServoMin, udServoMax);
servoNeckY.writeMicroseconds(posY);
break;
case 3:
// turn head left (+X)
posX = constrain(posX+20, lrServoMin, lrServoMax);
servoNeckX.writeMicroseconds(posX);
break;
case 4:
// turn head right (-X)
posX = constrain(posX-20, lrServoMin, lrServoMax);
servoNeckX.writeMicroseconds(posX);
break;
}
state = 1; // finished moving servos
}
}
// callback for sending data
void sendData(){
Wire.write(state);
}
The Python Code is similar to the first example. I added some code necessary for i2c communication and a few lines after a face is detected, so it sends commands to the Arduino.
#!/usr/bin/python
import sys
import cv2.cv as cv
from optparse import OptionParser
min_size = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale), cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "time taken for detection = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("video", img)
if __name__ == '__main__':
parser = OptionParser(usage = "usage: %prog [options] [filename|camera_index]")
parser.add_option("-c", "--cascade", action="store", dest="cascade", type="str", help="Haar cascade file, default %default", default = "../data/haarcascades/haarcascade_frontalface_alt.xml")
(options, args) = parser.parse_args()
cascade = cv.Load(options.cascade)
if len(args) != 1:
parser.print_help()
sys.exit(1)
input_name = args[0]
if input_name.isdigit():
capture = cv.CreateCameraCapture(int(input_name))
else:
capture = None
cv.NamedWindow("video", 1)
#size of the video
width = 160
height = 120
if width is None:
width = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_WIDTH))
else:
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_WIDTH,width)
if height is None:
height = int(cv.GetCaptureProperty(capture, cv.CV_CAP_PROP_FRAME_HEIGHT))
else:
cv.SetCaptureProperty(capture,cv.CV_CAP_PROP_FRAME_HEIGHT,height)
if capture:
frame_copy = None
while True:
frame = cv.QueryFrame(capture)
if not frame:
cv.WaitKey(0)
break
if not frame_copy:
frame_copy = cv.CreateImage((frame.width,frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, frame_copy)
else:
cv.Flip(frame, frame_copy, 0)
detect_and_draw(frame_copy, cascade)
if cv.WaitKey(10) >= 0:
break
else:
image = cv.LoadImage(input_name, 1)
detect_and_draw(image, cascade)
cv.WaitKey(0)
cv.DestroyWindow("video")
Possible Raspberry Pi Face Recognition Improvement
For face recognition on an embedded system, I think LBP is a better choice, because it does all the calculations in integers. Haar uses floats, whick is a killer for embedded/mobile. LBP is a few times faster, but about 10-20% less accurate than Haar.
참고자료 :