Core Concept

This code captures video from the webcam, detects faces in real-time using the Haar Cascade Classifier, and draws rectangles around the detected faces. The video feed is displayed in a window, and the program can be terminated by pressing the 'Esc' key.

Importing Necessary Library

python

import cv2

This imports the OpenCV library, which is widely used for computer vision tasks.

Loading the Haar Cascade Classifier

python

alg = "haarcascade_frontalface_default.xml" haar_cascade = cv2.CascadeClassifier(alg)

• alg: Specifies the XML file that contains the Haar Cascade classifier data for detecting frontal faces.
• cv2.CascadeClassifier(alg): Loads the Haar Cascade classifier from the XML file into the haar_cascade object.

Accessing the Webcam

python

cam = cv2.VideoCapture(0)

• cv2.VideoCapture(0): Initializes the webcam for video capture. The argument 0 typically refers to the default webcam on the computer.

Real-Time Face Detection Loop

python

while True: _, img = cam.read()

• cam.read(): Captures a frame from the webcam. The underscore _ is used to ignore the first return value (a boolean indicating if the frame was read correctly), and img stores the captured frame.

Converting the Image to Grayscale

python

grayImg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

• cv2.cvtColor(img, cv2.COLOR_BGR2GRAY): Converts the captured color image to grayscale. This is done because face detection using Haar Cascades works better on grayscale images.

Detecting Faces

python

face = haar_cascade.detectMultiScale(grayImg, 1.3, 4)

• haar_cascade.detectMultiScale(grayImg, 1.3, 4): Detects faces in the grayscale image. The method returns a list of rectangles where faces are detected.
  • grayImg: The input grayscale image.
  • 1.3: The scale factor for the image pyramid (how much the image size is reduced at each image scale).
  • 4: The minimum number of neighbors each candidate rectangle should have to retain it.

Drawing Rectangles Around Detected Faces

python

 `for (x, y, w, h) in face:
    x1,y1=x+w, y+h

    cv2.line(img, (x,y), (x+30, y),(0, 255, 0), 6)
    cv2.line(img, (x,y), (x, y+30),(0, 255, 0), 6)

    cv2.line(img, (x1,y), (x1-30, y),(0, 255, 0), 6)
    cv2.line(img, (x1,y), (x1, y+30),(0, 255, 0), 6)

    cv2.line(img, (x,y1), (x+30, y1),(0, 255, 0), 6)
    cv2.line(img, (x,y1), (x, y1-30),(0, 255, 0), 6)

    cv2.line(img, (x1,y1), (x1-30, y1),(0, 255, 0), 6)
    cv2.line(img, (x1,y1), (x1, y1-30),(0, 255, 0), 6)` 

• Iterates over the list of detected faces. For each face, a rectangle is drawn on the original image (img):
  • (x, y): The top-left corner of the rectangle.
  • (x, y1): The bottom-left corner of the rectangle.
  • (x1, y): The bottom-right corner of the rectangle.
  • (x1, y1): The bottom-leftcorner of the rectangle.
  • (0, 0, 255): The color of the rectangle (red in BGR format).
  • 2: The thickness of the rectangle's border.

Displaying the Image with Detected Faces

python

cv2.imshow("Face Detection", img)

• cv2.imshow("Face Detection", img): Displays the image with detected faces in a window titled "Face Detection".

Breaking the Loop

python

key = cv2.waitKey(10) if key == 27: break

• cv2.waitKey(10): Waits for 10 milliseconds for a key press.
• if key == 27: Checks if the 'Esc' key (ASCII value 27) is pressed. If so, it breaks the loop and stops the program.

Releasing Resources

python

cam.release() cv2.destroyAllWindows()

• cam.release(): Releases the webcam resource.
• cv2.destroyAllWindows(): Closes all OpenCV windows.

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