facerecog.py

import cv2

recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "Cascades/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX

# iniciate id counter
id = 0
# names related to ids: example ==> Robert: id=1,
names = ['none','Robert Downey','Scarlett Johanson','musk','SRK','Benedict','ashish']
cam = cv2.VideoCapture(0)
cam.set(3, 640)  # set video width
cam.set(4, 480)  # set video height
# Define min window size to be recognized as a face
minW = 0.1 * cam.get(3)
minH = 0.1 * cam.get(4)
while True:
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(int(minW), int(minH)),
    )
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        id, confidence = recognizer.predict(gray[y:y + h, x:x + w])

        if (confidence < 100):
            id = names[id]
            confidence = "  {0}%".format(round(100 - confidence))
        else:
            id = "unknown"
            confidence = "  {0}%".format(round(100 - confidence))

        cv2.putText(
            img,
            str(id),
            (x + 5, y - 5),
            font,
            1,
            (255, 255, 255),
            2
        )
        cv2.putText(
            img,
            str(confidence),
            (x + 5, y + h - 5),
            font,
            1,
            (255, 255, 0),
            1
        )

    cv2.imshow('camera', img)
    k = cv2.waitKey(20) & 0xff  # Press 'ESC' for exiting video
    if k == 27:
        break

print("\n [INFO] Exiting Program")
cam.release()
cv2.destroyAllWindows()