OpenCV Face Recognition In Python Part 1

OpenCV Face Recognition | How to write a face recognition in python (tutorial) Part-1¶

In my last post we learnt how to setup opencv and python and wrote this code to detect faces in the
frame

Now lets take it to the next level¶

lets create a program, which not only detect face but also recognize the person and tag that person in the frame



To make a face recognition program, first we need to train the recognizer with dataset of previously captured faces along with its ID, for example we have two person then first person will have ID 1 and 2nd person will have ID 2, So all the images of person one in the dataset will have ID 1 and all the images of the 2nd person in the dataset will have ID 2, then we will use those dataset images to train the recognizer to predict the 1 of an newly presented face from the live video frame

So lets break the program into 3 major part:

1. Dataset Creator
2. Trainer
3. Detector

In this post we are going to see how to create a program to ganerate dataset for our face recognition program

Dataset Generator¶

Lets create the dataset generator script, open your IDLE and create a new file and save it in your project folder and make sure you also have the haarcascade_frontalface_default.xml file in the same folder

Just like in the previous post we will need to do the following first:

• cv2 library
• create a video capture object
• cascadeClassifier object

In [ ]:

import cv2
cam = cv2.VideoCapture(0)
detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Our dataset generator is going to capture few sample faces of one person from the live video frame
and assign a ID to it and it will save those samples in a folder which we are going to create now and we will name it dataSet

So create a folder named dataSet in the same location where you have saved your .py script

We are going to follow this naming convention for the sample images to make sure they dont mixed up with other person's image samples

User.[ID].[SampleNumber].jpg

for example if the user id is 2 and its 10th sample from the sample list then the file name will be

User.2.10.jpg

Why this format?? well we can easily get which user's face it is from its file name while loading the image for the training the recognizer

OK, now lets get the user id from the shell as input, and initialize a counter variable to store the sample number

In [ ]:

Id=raw_input('enter your id: ')
sampleNum=0

Now let start the main loop, we will take 20 samples from the video feed and will save it in the dataSet folder that we created previously

In [ ]:

while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

    cv2.imshow('frame',img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

We wrote the above code to detect face in my earlier post, So we are going to modify this code to make the dataset generator for our face recognizer program

In [ ]:

while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        
        #incrementing sample number 
        sampleNum=sampleNum+1
        #saving the captured face in the dataset folder
        cv2.imwrite("dataSet/user."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w]) #

        cv2.imshow('frame',img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

So we added this two lines there to get the sample number and save the face in jpg format with our naming convention

and for those who dont know what we captured the face, its this "gray[y:y+h,x:x+w]" part where x,y is the top left coordinate of the face rectangle and h,w is the height and the weight of the face in terms of pixels

but this code will take samples vary rapidly like 20 samples in a second.. but we dont want that, we want to capture faces from different angles and for that it needs to be slow.

for that we need to increase the delay between the frames
and we need to break the loop after it took 20 samples so we need to change few more things in the above code

In [ ]:

while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        
        #incrementing sample number 
        sampleNum=sampleNum+1
        #saving the captured face in the dataset folder
        cv2.imwrite("dataSet/User."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w]) #

        cv2.imshow('frame',img)
    #wait for 100 miliseconds 
    if cv2.waitKey(100) & 0xFF == ord('q'):
        break
    # break if the sample number is morethan 20
    elif sampleNum>20:
        break

There we go, now it will wait for 100 between frames which will give you time to move your face to get a different angle and it will close after taking 20 samples

So our main loop is done now we just have to release the camera and close the windows

In [ ]:

cam.release()
cv2.destroyAllWindows()

Lets test it¶

If we run this code now then we will see that it will capture faces from the live video and will save it in the dataSet folder

looks good... Now we have our dataset we can now train the recognizer to learn the faces from this dataset

In the next post we will create the trainer portion of the code

NOW THE COMPLETE CODE IN ONE PIECE¶

In [ ]:

import cv2
cam = cv2.VideoCapture(0)
detector=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Id=raw_input('enter your id')
sampleNum=0
while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        
        #incrementing sample number 
        sampleNum=sampleNum+1
        #saving the captured face in the dataset folder
        cv2.imwrite("dataSet/User."+Id +'.'+ str(sampleNum) + ".jpg", gray[y:y+h,x:x+w]) #

        cv2.imshow('frame',img)
    #wait for 100 miliseconds 
    if cv2.waitKey(100) & 0xFF == ord('q'):
        break
    # break if the sample number is morethan 20
    elif sampleNum>20:
        break
cam.release()
cv2.destroyAllWindows()

THE COMPLETE VIDEO TUTORIAL¶