Haar_cascade.py

# load in a haar cascade classifier for detecting frontal faces
face_cascade = cv2.CascadeClassifier('detector_architectures/haarcascade_frontalface_default.xml')

# run the detector
# the output here is an array of detections; the corners of each detection box
# if necessary, modify these parameters until you successfully identify every face in a given image
faces = face_cascade.detectMultiScale(image, 1.2, 2)

# make a copy of the original image to plot detections on
image_with_detections = image.copy()

# loop over the detected faces, mark the image where each face is found
for (x,y,w,h) in faces:
    # draw a rectangle around each detected face
    # you may also need to change the width of the rectangle drawn depending on image resolution
    cv2.rectangle(image_with_detections,(x,y),(x+w,y+h),(255,0,0),3) 

fig = plt.figure(figsize=(9,9))

plt.imshow(image_with_detections)