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 live feed from 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, w and h define the width and height of the frame.
cv2.rectangle 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.
Watch the video here to see the full running source code of Real-Time Smile Detection OpenCV Python with Source Code
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 in 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 download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
Complete Source Code
# 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)
video_capture = cv2.VideoCapture(0)
# 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
# The control breaks once q key is pressed
if cv2.waitKey(1) & 0xff == ord('q'):
# Release the capture once all the processing is done.
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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 live feed from webcam. The smile/happiness detector that we are going to implement would be a raw one, there exist many better ways to implement it.
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