Real-Time Profile Face Detection Using OpenCV Python With Source Code

The Real-Time Profile Face Detection OpenCV Python was developed using Python OpenCV, in computer vision, one essential problem we are trying to figure out is to automatically detect objects in an image without human intervention.

Face detection can be thought of as a problem where we detect human faces in an image.

A Profile Face Detection OpenCV Python is usually the first step towards many face-related technologies, such as face recognition or verification.

However, face detection can have very useful applications. The most successful application of face detection would probably be photo taking.

When you take a photo of your friends, the face detection algorithm built into your digital camera detects where the faces are and adjusts the focus accordingly.

The very first task we perform is detecting faces in the image or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.

In this Face Detection OpenCV Python also includes a downloadable Python Project 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 don’t know what Python IDE to use, I have here a list of the Best Python IDE for Windows, Linux, and Mac OS that will suit you.

I also have here How to Download and Install the Latest Version of Python on Windows.

To start executing Real-Time Profile Face Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.

Real-Time Profile Face Detection Using OpenCV in Python Tutorial With Source Code

Time needed: 5 minutes

These are the steps on how to run Real-Time Profile Face Detection OpenCV Python With Source Code

Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve downloaded to your PyCharm IDE.
Step 3: Run the project.
Lastly, run the project with the command “py main.py”

Installed Libraries

import cv2

Complete Source Code

import cv2

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

Output:

Download the Source Code below

DOWNLOAD

Summary

In this article, we will learn what is face recognition and how is different from face detection. We will go briefly over the theory of face recognition and then jump on to the coding section.

At the end of this article, you will be able to make a face recognition program for recognizing faces in images as well as on live webcam feeds.

Inquiries

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Real-Time Profile Face Detection Using OpenCV in Python