Detect Cat Faces in Real-Time using Python-OpenCV

Face Detection is a technology used to identify faces in images. We can implement it using Python's OpenCV which provides pre-trained haar classifiers for detecting faces in animals such as the haarcascade_frontalcatface.xml and haarcascade_frontalcatface_extended.xml files in the haar cascades directory. In this article we will try detecting cats in images.

1. Importing Libraries

We import the OpenCV and Matplotlib libraries to handle image processing and visualization.

import cv2
import numpy as np
import matplotlib.pyplot as plt

2. Loading Haar Cascade Classifiers

We load pre-trained Haar Cascade XML files for detecting faces and eyes. You can download it from here.

cat_cascade = cv2.CascadeClassifier('/content/haarcascade_frontalcatface.xml')

3. Defining a Function for Face Detection

We will define a function to detect the cat's face in an image. Faces in the image are detected and marked with with rectangles. We can also adjust the min.Neighbour parameter which stands for minimum neighbors.

def detect_cats(img):
    cat_img = img.copy()
    cat_rect = cat_cascade.detectMultiScale(cat_img, scaleFactor=1.2, minNeighbors=2)

    for (x, y, w, h) in car_rect:
        cv2.rectangle(cat_img, (x, y), (x + w, y + h), (255, 255, 255), 10)

    return cat_img

4. Converting to BGR to RGB

The BGR image is converted to RGB for detection.

img = cv2.imread('/content/cat.webp')
plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

Output:

5. Displaying the Image with Detected Face

We visualize the image with rectangles drawn around detected faces.

img = cv2.imread('/content/cat.webp')
img_copy1 = img.copy()

cats = detect_cats(img_copy1)
plt.imshow(cv2.cvtColor(cats, cv2.COLOR_BGR2RGB))
plt.show()

Output:

We acn see that our model is able to detect cat in image.