Face Recognition Code In Python Using OpenCV Free Download

The Face Recognition Code In Python Using OpenCV was developed using Python Programming, this Face Detection In Python using Haar cascades is a machine learning based approach where a cascade function is trained with a set of input data.

OpenCV already contains many pre-trained classifiers for face, eyes, smiles, etc.. Today we will be using the face classifier. You can experiment with other classifiers as well.

About The Project

A Face Recognition Code in Python can detect faces of people in an images, faces contains a list of coordinates for the rectangular regions where faces were found.

We use these coordinates to draw the rectangles in our image. And also the Face Detection Using Python Project can detect also real time faces in a camera on your laptop or pc.

Similarly, the Face Detection In Python Using Opencv can detect faces in videos. As you know videos are basically made up of frames, which are still images. So we perform the face detection for each frame in a video.

The only difference here is that we use an infinite loop to loop through each frame in the video. We use cap.read() to read each frame.

The first value returned is a flag that indicates if the frame was read correctly or not. We don’t need it. The second value returned is the still frame on which we will be performing the detection.

Project Information’s

In this Code For Face Detection In Python also includes a Face Recognition Project In Python With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start creating a Face Recognition Code In Python Using OpenCV, make sure that you have installed Python in your computer.

Prerequisites

Before starting with this Python project with source code, you should be familiar with the computer vision library of Python that is OpenCV and Numpy.

OpenCV and Numpy are the Python packages that are necessary for this project in Python. To install them, simply run this pip command in your terminal:

pip install opencv-python numpy

Steps on How To Build Face Recognition Code In Python Using OpenCV

Face Recognition In An Image

I have here the step by step program which can detect faces in an Image.

1. Import OpenCV

Code:

import cv2

Explanation:

The code given above is the required libraries to be import.

2. Load Face Cascade Dataset

Code:

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Explanation:

The code given above is the face_cascade dataset which can be extracted when we run the project to detect faces in an image.

3. Read The Input Image

Code:

# Read the input image
img = cv2.imread('sample.jpg')

Explanation:

The code given above is the function to read the input image.

4. Convert Image Into Grayscale

Code:

# Convert into grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

Explanation:

The code given above is the function to convert the image into grayscale.

5. Detect Faces

Code:

# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

Explanation:

The code given above is the function to detect faces from the given image.

6. Draw Rectangle Around The Faces

Code:

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

Explanation:

The code given above is the function to draw rectangle around the faces in an image.

7. Display The Output

Code:

# Display the output
cv2.imshow('img', img)
cv2.waitKey()

Explanation:

The code given above is the function to display the output.

Output

Complete Source Code of Face Recognition In An Image

import cv2

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# Read the input image
img = cv2.imread('sample.jpg')

# Convert into grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)

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

# Display the output
cv2.imshow('img', img)
cv2.waitKey()

Real-Time Face Recognition Using Web Cam

Here’s the step by step program on How To Detect Faces In Real-Time using Web Cam.

1. Import OpenCV

Code:

import cv2

Explanation:

The code given above is the required libraries to be import.

2. Load Cascade Dataset

Code:

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Explanation:

The code given above is the face_cascade dataset which can be extracted when we run the project to detect faces in an image.

3. Capture Video From Web Cam

Code:

# To capture video from webcam. 
cap = cv2.VideoCapture(0)

Explanation:

The code given above is the function to capture video from webcam.

4. Convert To Grayscale

Code:

# Read the frame
    _, img = cap.read()
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

The code given above is the function to convert video into grayscale.

5. Detect Faces

Code:

# Detect the faces
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

Explanation:

The code given above is the function to detect faces in the web cam.

6. Draw Rectangle Around The Faces

Code:

# Draw the rectangle around each face
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)

Explanation:

The code given above is the function to draw rectangle around the faces from the web cam.

Real-Time Face Recognition Output

Complete Source Code of Face Recognition in Real-Time

import cv2

# Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# To capture video from webcam. 
cap = cv2.VideoCapture(0)
# To use a video file as input 
# cap = cv2.VideoCapture('filename.mp4')
while True:
    # Read the frame
    _, img = cap.read()
    # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect the faces
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
    # Draw the rectangle around each face
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
    # Display
    cv2.imshow('img', img)
    # Stop if escape key is pressed
    k = cv2.waitKey(30) & 0xff
    if k==27:
        break
# Release the VideoCapture object
cap.release()

Download Source Code below

Summary

Facial detection is a powerful and common use-case of Machine Learning. It can be used to automatize manual tasks such as school attendance and law enforcement. In the other hand, it can be used for biometric authorization.

OpenCV uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not.

Instead, there are thousands of small patterns and features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve.

Inquiries

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