How To Setup OpenCV-Python | Write A Face Detection Code

How To Setup OpenCV And Python, Get Started With Your First OpenCV Code¶

Hi there, this is me Anirban, writing my first blog here, I am a crazy person, and if you are like me you might like the stuff that are in my blog.
In my posts I will give you detailed description of the project that we are going to work on as well as a video tutorial of the same at the end of the post.
So without wasting anymore time lets move to the main stuff.

To setup opencv in python environment you will need these things ready

• Python 2.x (NOT 3.x)
• OpenCV 2.x (Again NOT 3.x)
• Numpy library (later will download it using pip)

First thing first download python and install it in its default location (i.e c:/python27)
after you have installed it download the opencv and extract it, go to "opencv/Build/python/2.7/x86" folder and copy "cv2.pyd" file to "c:/python27/Lib/site-packages/" folder.

And now we are ready to use opencv in python. just one single problem is there, Opencv uses numpy library for its images so we have to install numpy library too, Lets do that

Go to start and type "cmd" you will see the command prompt icon right click on it and select "run as administrator" this will bring us to the cmd window,
Now type
"cd c:/python27/scripts/"
hit enter then type
"pip install numpy"
This will install the numpy library in your python packages

Now We Are Ready To Do Some Coding¶

Go to Start and search "IDLE" and open it
To use opencv we need to import the opencv library first,

In [2]:

import cv2

After that we need to import the numpy library

In [3]:

import numpy as np

so now we can use opencv and numpy in our code

Lets Do Face detection¶

Now that everything is setup and running lets write a code to detect faces from the webcam.
This is kind of hello world program for opencv
The method that we are going to use in this program is a cascade classifier, which can be loaded with a pretrained xml file, these xml files are hard to train but luckily we dont have to worry opencv already has many of these pretrained classifier ready for us.

To use the face detector classifier we need to copy the classifier xml file from the "[opencv extracted folder]/sources/data/haarcascades/", and then copy the file haarcascade_frontalface_default.xml to your project folder (same location where you will save the python program)

Now thats done we can proceed further
we can load the classifier now

In [4]:

detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

let add the video capture object now

In [5]:

cap=cv2.VideoCapture(0)

In the above line VideoCapture has one argument which is device id, for inbuilt webcam its usually '0', and if you have some other webcam you can change that number so see that is your webcam's Id

so lets test the camera now

In [21]:

ret,img=cap.read()
cv2.imshow('windowname',img)
cv2.waitKey(0)

Out[21]:

looks like its working fine

in the above code we read the image from the video capture object using cap.read() method, it returns one status variable which is just True/False and the captured frame then we used imshow() method to display the image, here first argument is the window name and second argument is the image that we want to display, the third line we used waitKey(10) is used for a delay of 10 milisecond it is important for the imshow() method to work properly

Before using the face detector we need comvert the captured image to Gray scale

In [22]:

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Now lets apply the face detector to detect faces in our captured image

In [23]:

faces = detector.detectMultiScale(gray, 1.3, 5)

the above line will get the x,y and height,width of all the faces present in the captured image in a list, So now we have to loop through all the faces and draw rectangle there

In [24]:

for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

I think its clear what this code is doing, let me explain the rectangle() the first argument is the input image in which we are going to draw the rectangles, second is the x,y coordinate of the face, then the height and weight, after that we are specifying the color of the line which is in the form of (blue,green,red) and you can adjust the value of each color, the range is 0-255, in this case its a green line, and the last argument is the line thickness
Now that we have marked the faces with green rectangles we can display them

In [25]:

cv2.imshow('frame',img)
cv2.waitKey(0)

Out[25]:

now to detect face from the webcam live, we need to create a loop which will get the images form the webcam frame by frame and detect the faces and show them in a window. so if we arrange the above code in a loop it will look like this

In [ ]:

while(True):
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.3, 5)
    if(len(faces)!=0):
        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
    

You can see that I changed the "waitKey" , because it aslo returns the value of the key pressed in the keyboard so we are comparing it with the key 'q' if its true the we are breaking the loop

after the program ends we need to release the video capture object and distroy all the windows

In [26]:

cap.release()
cv2.destroyAllWindows()

Now The Complete Code In One Piece¶

In [ ]:

import numpy as np
import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)

while(True):
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    if(len(faces)!=0):
        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
    
cap.release()
cv2.destroyAllWindows()

The Complete Video Tutorial¶

Summary¶

In this post, you discovered how to setup opencv in python, and wrote your own face detection program
Specifically, you learned the most basic steps in opencv including:

• How to use a classifier.
• How to read images from camera
• How to Draw on images
• How to display images

Do you have any questions about opencv or about this tutorial?
Ask your question in the comments and I will do my best to answer.