faces.py

import cv2
import pickle


face_cascade = cv2.CascadeClassifier('data//haarcascades//haarcascade_frontalface_alt2.xml')
eye_cascade = cv2.CascadeClassifier('data//haarcascades//haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('data//haarcascades//haarcascade_smile.xml')


recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("trainer.yml")

labels = {"persone_name": 1}
with open("labels.pickle", "rb") as f:
    labels = pickle.load(f)
    labels = {v: k for k, v in labels.items()}

capture = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray,
                                          scaleFactor=1.5,
                                          minNeighbors=5)
    for (x, y, w, h) in faces:
        # print(x, y, w, h)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = frame[y:y+h, x:x+w]

        # Recognize: Deep Learned Model- Predict; Keras Tenserflow Pytorch Scikit-Learn
        id_, conf = recognizer.predict(roi_gray)
        if conf >= 45:
            # print(id_)
            # print(labels[id_])
            font = cv2.FONT_HERSHEY_SIMPLEX
            name = labels[id_]
            color = (255, 255, 255)
            stroke = 2
            cv2.putText(frame, name, (x, y), font, 1, color, stroke, cv2.LINE_AA)

        img_item = "my-image.png"
        cv2.imwrite(img_item, roi_color)

        color = (255, 0, 0)
        stroke = 2
        end_cord_x = x + w
        end_cord_y = y + h
        cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)

        eyes = eye_cascade.detectMultiScale(roi_gray)
        for ex, ey, ew, eh in eyes:
            cv2.rectangle(frame, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), stroke)

        smile = smile_cascade.detectMultiScale(roi_gray)
        for ex, ey, ew, eh in eyes:
            cv2.rectangle(frame, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), stroke)

    cv2.imshow('frame', frame)  # image show
    if cv2.waitKey(20) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
capture.release()
cv2.destroyAllWindows()