[Solved] Help needed for face detection -deep learning

  • I have a code for detecting faces but now i need to count no:of faces .link text

    # OpenCV Python program to detect cars in video frame
    # import libraries of python OpenCV 
    import cv2
    #import numpy as np
    # capture frames from a video
    cap = cv2.VideoCapture('video.avi')
    # Trained XML classifiers describes some features of some object we want to detect
    car_cascade = cv2.CascadeClassifier('cars.xml')
    # loop runs if capturing has been initialized.
    while True:
        # reads frames from a video
        ret, frames = cap.read()
        # convert to gray scale of each frames
        gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
        #color fill white
        # or img[:] = 255
        # Detects cars of different sizes in the input image
        cars = car_cascade.detectMultiScale(gray, 1.1, 1)
        # To draw a rectangle in each cars
        for (x,y,w,h) in cars:
       # Display frames in a window 
        cv2.imshow('video2', frames)
        # Wait for Esc key to stop
        if cv2.waitKey(33) == 27:
    # De-allocate any associated memory usage

  • Hi @Nandu, You can increment a variable each time when detect faces, is that help?

  • @salmanfaris yeah that's what i want.But i am not able to understand where i should place my variable in the above mentioned code.

  • Follow These steps

    1. Create a virtual enviroment and activate virtial environment
    python -m venv venv

    Activate venv for windows using following command:


    For Ubuntu:

    source venv/bin/activate
    1. Install necessary packages on venv
    pip install opencv-python
    pip install imutils
    1. Create Folder structure as shown below in your workspace
    ├─ cascades
    │  └─ haarcascade_frontalface_default.xml
    ├─ detect_faces.py
    ├─ images
    │  └─ obama.jpg
    ├─ utilities
    │  └─ facedetector.py
    1. Program for utililities/facedetector.py given below:
    import cv2
    class FaceDetector:
        def __init__(self, face_cascade_path):
            # Load the face detector
            self.face_cascade = cv2.CascadeClassifier(face_cascade_path)
        def detect(self, image, scale_factor=1.2, min_neighbors=3):
            # Detect faces in the image
            boxes = self.face_cascade.detectMultiScale(image, scale_factor, min_neighbors, flags=cv2.CASCADE_SCALE_IMAGE, minSize=(30,30))
            # Return the bounding boxes
            return boxes
    1. program on detect_faces.py
    from utilities.facedetector import FaceDetector
    import imutils
    import cv2
    # Define paths
    image_path = 'images/obama.jpg'
    cascade_path = 'cascades/haarcascade_frontalface_default.xml'
    # Load the image and convert it to greyscale
    image = cv2.imread(image_path)
    image = imutils.resize(image, 600, 600)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # Find faces in the image
    detector = FaceDetector(cascade_path)
    face_boxes = detector.detect(gray, 1.2, 5)
    print("{} face(s) found".format(len(face_boxes)))
    # Loop over the faces and draw a rectangle around each
    for (x, y, w, h) in face_boxes:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
    # Show the detected faces
    cv2.imshow("Faces", image)
    1. Links to necessary files:
      Haar cascade frontal face
      Obama Family Image

  • @arunksoman thankyou 🥳

  • @Nandu But I have to mention that it is not a deep learning method. It is based on Integral images(Viola-Jones algorithm), which is basically something about ML. From opencv 3.4.3 there is a DNN module. This module help us to load caffemodels, torch models as well as tensorflow models. You can find out caffemodels on the Internet in order to detect faces. Using those we can make face detection quite efficiently. If you have any doubt feel free to ask here.

  • @arunksoman how this code helps me to count faces if deeplearning isn't used.

  • @Nandu Please read the comment given above carefully and search how the viola-jones algorithm works. Sorry for misunderstanding what you say. That is why edited comment.

  • @Nandu Did you complete? excited to see.

  • @salmanfaris in the below terminal count shows.some steps i have followed in a different manner.Thank you for helping me!🙂


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