Real-Time Smile Detection OpenCV Python With Source Code

The Real-Time Smile Detection OpenCV Python was developed using Python OpenCV, In this article, we are going to build a smile detector using OpenCV which takes in a live feed from a webcam.

The smile/happiness detector that we are going to implement would be a raw one, there exist many better ways to implement it.

A Smile Detection OpenCV Python face data is stored as tuples of coordinates. Here, x and y define the coordinate of the upper-left corner of the face frame, and w and h define the width and height of the frame.

The cv2.rectangle the function takes in the arguments frame, upper-left coordinates of the face, lower-right coordinates, the RGB code for the rectangle (that would contain within it the detected face), and the thickness of the rectangle.

The roi_gray defines the region of interest of the face and roi_color does the same for the original frame.

In this Real-Time Smile Detection OpenCV also includes a downloadable Project With Source Code for free, just find the downloadable source code below and click to start downloading.

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

Real-Time Smile Detection OpenCV Python With Source Code: Steps on how to run the project

Time needed: 5 minutes

These are the steps on how to run Real-Time Smile 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_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('haarcascade_smile.xml')

#faces  = face_cascade.detectMultiScale(gray, 1.3, 5)


def detect(gray, frame):
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), ((x + w), (y + h)), (255, 0, 0), 2)
        roi_gray = gray[y:y + h, x:x + w]
        roi_color = frame[y:y + h, x:x + w]
        smiles = smile_cascade.detectMultiScale(roi_gray, 1.8, 20)

        for (sx, sy, sw, sh) in smiles:
            cv2.rectangle(roi_color, (sx, sy), ((sx + sw), (sy + sh)), (0, 0, 255), 2)
    return frame


video_capture = cv2.VideoCapture(0)
while True:
    # Captures video_capture frame by frame
    _, frame = video_capture.read()

    # To capture image in monochrome
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # calls the detect() function
    canvas = detect(gray, frame)

    # Displays the result on camera feed
    cv2.imshow('Video', canvas)

    # The control breaks once q key is pressed
    if cv2.waitKey(1) & 0xff == ord('q'):
        break

# Release the capture once all the processing is done.
video_capture.release()
cv2.destroyAllWindows()

Output:

Download the Source Code below

DOWNLOAD

Summary

Emotion detectors are used in many industries, one being the media industry where it is important for the companies to determine the public reaction to their products.

In this article, we are going to build a smile detector using OpenCV which takes in a live feed from a webcam.

The smile/happiness detector that we are going to implement would be a raw one, there exist many better ways to implement it.

Inquiries

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Real-Time Smile Detection using OpenCV Python