Face Detection Script in Python

Method # 1

import cv2 #pip install opencv-python

import pathlib

# take the cascade file for frontal face detection
cascade_path = pathlib.Path(cv2.__file__).parent.absolute()/"data/haarcascade_frontalface_default.xml"
# print(cascade_path)

# training classifier on the cascade file
clf = cv2.CascadeClassifier(str(cascade_path))

# face detection in camera
camera = cv2.VideoCapture(0) # 0 is for default camera

# face detection in video
# camera = cv2.VideoCapture("cud.mp4")

while True:
      _, frame = camera.read() # _ ignores the first return value
      # some Python functions return multiple values, often we don’t need all of them.

      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

      faces = clf.detectMultiScale(
            gray,
            scaleFactor = 1.1,
            minNeighbors=5, # higher this number, less faces are found
            minSize = (30, 30), # size of window
            flags = cv2.CASCADE_SCALE_IMAGE)

      # plotting rectangle around faces
      for (x,y,width,height) in faces:
            cv2.rectangle(frame, (x, y), (x+width, y+height), (0,255,255), 2) # 2 is thickness

      # show the frame with title "Faces"
      cv2.imshow("Faces", frame)

      # to close the frame
      if cv2.waitKey(1) == ord("q"):
            break

camera.release()
cv2.destroyAllWindows()

Python

Method # 2

import cv2 #pip install opencv-python

face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
# "haarcascade_frontalface_default.xml" files resides in same folder as .py file

img = cv2.imread("me.jpg")

# now convert the image to gray-scale so it is readable
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.1, 4,)

for (x,y,w,h) in faces:
      cv2.rectangle(img, (x, y), (x+w, y+h), (255, 225, 0), 2)

cv2.imshow("My Image", img)
cv2.waitKey() # upon any key pressed, exits

cv2.imwrite("abdur_face_detected.jpg", img) # saves image with face detected

Python