Smile Detection In Python OpenCV With HaarCascade

In this Python OpenCV article i want to show you Smile Detection With HaarCascade Classifier. so in this article iam using an image, you need to have an image in your working directory.

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So now we are going to have a simple introduction to HaarCascade Classifier

What Is HaarCascade Classifier ?

So Haar Cascade is basically a classifier which is used to detect the object for which it has been trained for, from the source. also The Haar Cascade is trained by superimposing the positive image over a set of negative images. The training is generally done on a server and on various stages. Better results are obtained by using high quality images and increasing the amount of stages for which the classifier is trained.

you can also used predefined Haar Cascades which are available on github

So now this is the complete code for Smile Detection In Python OpenCV With HaarCascade

import cv2




image = cv2.imread("smile.png")
smile_cascade = cv2.CascadeClassifier('haarcascade_smile.xml')
smiles  = smile_cascade.detectMultiScale(image, scaleFactor = 1.8, minNeighbors = 20)


for (sx, sy, sw, sh) in smiles:
            cv2.rectangle(image, (sx, sy), ((sx + sw), (sy + sh)), (0, 255,0), 5)



cv2.imshow("Smile Detected", image)



cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

image = cv2.imread("smile.png")

smile_cascade = cv2.CascadeClassifier('haarcascade_smile.xml')

smiles = smile_cascade.detectMultiScale(image, scaleFactor = 1.8, minNeighbors = 20)

for (sx, sy, sw, sh) in smiles:

cv2.rectangle(image, (sx, sy), ((sx + sw), (sy + sh)), (0, 255,0), 5)

cv2.imshow("Smile Detected", image)

cv2.waitKey(0)

cv2.destroyAllWindows()

So in this line of code we have loaded the image, you need to have an image in your working directory.

image = cv2.imread("smile.png")

1	image = cv2.imread("smile.png")

This is for loading our Haar Cascade Classifier that we have already copied in our directory

smile_cascade = cv2.CascadeClassifier('haarcascade_smile.xml')

1	smile_cascade = cv2.CascadeClassifier('haarcascade_smile.xml')

detecMultiScale() function is for detecting objects if it finds a face in the image it will return in the form of x,y,w,h. and it needs some parameters.

ScaleFactor: This is parameter is for specifying how much the image size is reduced at each image scale.

minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it, this parameter will affect the quality of the detected faces.

smiles  = smile_cascade.detectMultiScale(image, scaleFactor = 1.8, minNeighbors = 20)

1	smiles = smile_cascade.detectMultiScale(image, scaleFactor = 1.8, minNeighbors = 20)

In this code we want to draw rectangle to the smiles in the image.

for (sx, sy, sw, sh) in smiles:
            cv2.rectangle(image, (sx, sy), ((sx + sw), (sy + sh)), (0, 255,0), 5)

1 2	for (sx, sy, sw, sh) in smiles: cv2.rectangle(image, (sx, sy), ((sx + sw), (sy + sh)), (0, 255,0), 5)

In this line of code we want to show our image

cv2.imshow("Smile Detected", image)

1	cv2.imshow("Smile Detected", image)

Run the complete code and this will be the result

Smile Detection In Python OpenCV With HaarCascade

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