face_recognizer.py

from tkinter import CASCADE
from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import argparse
import imutils
import pickle
import time
import cv2

CASCADE_FILE = "haarcascade_frontalface_default.xml"
ENCODINGS_FILE = "encodings.pickle"

# load the known faces and embeddings along with OpenCV's Haar
# cascade for face detection
print("[INFO] loading encodings + face detector...")
data = pickle.loads(open(ENCODINGS_FILE, "rb").read())
detector = cv2.CascadeClassifier(CASCADE_FILE)

class FaceRecognizer:
    def __init__(self) -> None:
        pass

    def recognize(self, frame):
        frame = imutils.resize(frame, width=500)
    
        # convert the input frame from (1) BGR to grayscale (for face
        # detection) and (2) from BGR to RGB (for face recognition)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # detect faces in the grayscale frame
        rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
            minNeighbors=3, minSize=(30, 30))
        # OpenCV returns bounding box coordinates in (x, y, w, h) order
        # but we need them in (top, right, bottom, left) order, so we
        # need to do a bit of reordering
        boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]
        # compute the facial embeddings for each face bounding box
        encodings = face_recognition.face_encodings(rgb, boxes)
        names = []

            # loop over the facial embeddings
        for encoding in encodings:
            # attempt to match each face in the input image to our known
            # encodings
            matches = face_recognition.compare_faces(data["encodings"],
                encoding)
            name = "Unknown"
            # check to see if we have found a match
            if True in matches:
                # find the indexes of all matched faces then initialize a
                # dictionary to count the total number of times each face
                # was matched
                matchedIdxs = [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 matchedIdxs:
                    name = data["names"][i]
                    counts[name] = counts.get(name, 0) + 1
                # determine the recognized face with the largest number
                # of votes (note: in the event of an unlikely tie Python
                # will select first entry in the dictionary)
                name = max(counts, key=counts.get)
            
            # update the list of names
            names.append(name)

            # loop over the recognized faces
        for ((top, right, bottom, left), name) in zip(boxes, names):
            # draw the predicted face name on the image
            cv2.rectangle(frame, (left, top), (right, bottom),
                (0, 255, 0), 2)
            y = top - 15 if top - 15 > 15 else top + 15
            cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
                0.75, (0, 255, 0), 2)
        return frame