[Solved] Help needed for face detection -deep learning

[Nandu]

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
    frames.fill(255)
    # 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:
        cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,0),-1)
 
   # Display frames in a window 
    
    cv2.imshow('video2', frames)
     
    # Wait for Esc key to stop
    if cv2.waitKey(33) == 27:
        break
 
# De-allocate any associated memory usage
cv2.destroyAllWindows()

[salmanfaris]

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

[Nandu]

@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.

[arunksoman]

Follow These steps

1. Create a virtual enviroment and activate virtial environment

python -m venv venv

Activate venv for windows using following command:

.\venv\Scripts\activate

For Ubuntu:

source venv/bin/activate

2. Install necessary packages on venv

pip install opencv-python

pip install imutils

3. Create Folder structure as shown below in your workspace

TestPrograms  
|
├─ cascades
│  └─ haarcascade_frontalface_default.xml
├─ detect_faces.py
├─ images
│  └─ obama.jpg
├─ utilities
│  └─ facedetector.py

4. 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

5. 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)
if(cv2.waitKey(0)):
 cv2.destroyAllWindows()

6. Links to necessary files:
   Haar cascade frontal face
   Obama Family Image

[Nandu]

@arunksoman thankyou 🥳

[arunksoman]

@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.

[Nandu]

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

[arunksoman]

@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.

[salmanfaris]

@Nandu Did you complete? excited to see.

[Nandu]

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