OpenCV provides a nice face detection framework. We just need to load the cascade file and use it to detect the faces in an image. Let's see how to do it:
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('./cascade_files/haarcascade_frontalface_alt.xml')
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_rects = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in face_rects:
cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3)
cv2.imshow('Face Detector', frame)
c = cv2.waitKey(1)
if c == 27:
break
cap.release()
cv2.destroyAllWindows()If you run the above code, it will look something like the following image:
We need a classifier model that can be used to detect the faces in an image. OpenCV provides an xml file that can be used for this purpose. We use the function CascadeClassifier to load the xml file. Once we start capturing the input frames from the webcam, we convert it to grayscale and use the function detectMultiScale to get the bounding boxes for all the faces in the current image. The second argument in this function specifies the jump in the scaling factor. As in, if we don't find an image in the current scale, the next size to check will be, in our case, 1.3 times bigger than the current size. The last parameter is a threshold that specifies the number of adjacent rectangles needed to keep the current rectangle. It can be used to increase the robustness of the face detector.