Count number of Faces using Python - OpenCV

Last Updated : 03 Jan, 2023

Prerequisites: Face detection using dlib and openCV

In this article, we will use image processing to detect and count the number of faces. We are not supposed to get all the features of the face. Instead, the objective is to obtain the bounding box through some methods i.e. coordinates of the face in the image, depending on different areas covered by the number of the coordinates, number faces that will be computed.

Required libraries:

OpenCV library in python is a computer vision library, mostly used for image processing, video processing, and analysis, facial recognition and detection, etc.
Dlib library in python contains the pre-trained facial landmark detector, that is used to detect the (x, y) coordinates that map to facial structures on the face.
Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays.

Below is the step-wise approach to Count the Number of faces:

Step 1: Import required libraries.

Python3

# Import libraries import cv2 import numpy as np import dlib

Step 2: Open the default camera to capture faces and use the dlib library to get coordinates.

Python3

# (0) in VideoCapture is used to # connect to your computer's default camera cap = cv2.VideoCapture(0) # Get the coordinates detector = dlib.get_frontal_face_detector()

Step 3: Count the number of faces.

Capture the frames continuously.
Convert the frames to grayscale(not necessary).
Take an iterator i and initialize it to zero.
Each time you get the coordinates to the face structure in the frame, increment the iterator by 1.
Plot the box around each detected face along with its face count.

Python3

while True: # Capture frame-by-frame ret, frame = cap.read() frame = cv2.flip(frame, 1) # Our operations on the frame come here gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = detector(gray) # Counter to count number of faces i = 0 for face in faces: x, y = face.left(), face.top() x1, y1 = face.right(), face.bottom() cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2) # Increment the iterartor each time you get the coordinates i = i+1 # Adding face number to the box detecting faces cv2.putText(frame, 'face num'+str(i), (x-10, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) print(face, i) # Display the resulting frame cv2.imshow('frame', frame)

Step 4: Terminate the loop.

Python3

# Enter key 'q' to break the loop if cv2.waitKey(1) & 0xFF == ord('q'): break

Step 5: Clear windows.

Python3

# When everything done, release # the capture and destroy the windows cap.release() cv2.destroyAllWindows()

Below is the complete program of the above approach:

Python3

# Import required libraries import cv2 import numpy as np import dlib # Connects to your computer's default camera cap = cv2.VideoCapture(0) # Detect the coordinates detector = dlib.get_frontal_face_detector() # Capture frames continuously while True: # Capture frame-by-frame ret, frame = cap.read() frame = cv2.flip(frame, 1) # RGB to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = detector(gray) # Iterator to count faces i = 0 for face in faces: # Get the coordinates of faces x, y = face.left(), face.top() x1, y1 = face.right(), face.bottom() cv2.rectangle(frame, (x, y), (x1, y1), (0, 255, 0), 2) # Increment iterator for each face in faces i = i+1 # Display the box and faces cv2.putText(frame, 'face num'+str(i), (x-10, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) print(face, i) # Display the resulting frame cv2.imshow('frame', frame) # This command let's us quit with the "q" button on a keyboard. if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the capture and destroy the windows cap.release() cv2.destroyAllWindows()