recognize.py

import os
import sys
import pickle
import cv2
from imutils import paths
import face_recognition

# find path of xml file containing haarcascade file
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# load the known faces and embeddings saved in last file
data = pickle.loads(open('face_encodings', "rb").read())
# find path to the image we want to detect face
image_path = 'samples/Ainyava.jpg'
image = cv2.imread(image_path)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# convert image to Greyscale for haarcascade
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(60, 60),
                                      flags=cv2.CASCADE_SCALE_IMAGE)
# the facial embeddings for face in input
encodings = face_recognition.face_encodings(rgb)
names = []

# loop over the facial embeddings incase
# we have multiple embeddings for multiple fcaes
for encoding in encodings:
    
    # compare encodings with encodings in data["encodings"]
    # matches contain array with boolean values and True for the embeddings it matches closely
    # and False for rest
    matches = face_recognition.compare_faces(data['encodings'], encoding)

    #set name Unknown if no encoding matches
    name = 'Unknown'
    # check to see if we have found a match
    if True in matches:
        #Find positions at which we get True and store them
        matched_indexes = [i for (i, b) in enumerate(matches) if b]
        counts = {}
        # loop over the matched indexes and maintain a count for
        # each recognized face face
        for i in matched_indexes:
            # check the names at respective indexes we stored in matchedIdxs
            name = data['names'][i]
            # increase count for the name we got
            counts[name] = counts.get(name, 0) + 1
            # set name which has highest count (to increase accuracy)
            name = max(counts, key=counts.get)

    # update the list of names
    names.append(name)

    # loop over the recognized faces
    for ( (x, y, w, h), name) in zip(faces, names):
        # draw reactangle around face and the predicted name on the image
        cv2.rectangle(image, (x,y), (x+w, y+h), (0, 0, 255), 2)
        cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)

# open a window to display the results
cv2.imshow(f'Detected face for {image_path}', image)
cv2.waitKey(0)
cv2.destroyAllWindows()