face_recognition/verify_face.py

import face_recognition
import cv2
import numpy as np
import joblib
import time

# This is a super simple (but slow) example of running face recognition on live video from your webcam.
# There's a second example that's a little more complicatedq but runs faster.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

clf = joblib.load('saved_model_2.pkl')
classes = clf.classes_

threshold = 0.9

dummy_data = [
    {
        "name": "Bayu",
        "address": "299 St Louis Road Oak Forest, IL 60452",
        "nik": "1000076456784631"
    },
    {
        "name": "Dio",
        "address": "22 Whitemarsh St. Mansfield, MA 02048",
        "nik": "1000024792887549"
    },
    {
        "name": "Hadi",
        "address": "643 Honey Creek Dr. Milledgeville, GA 31061",
        "nik": "1000038502830420"
    },
    {
        "name": "Kevin",
        "address": "881 Cooper Ave. Hummelstown, PA 17036",
        "nik": "1000045356476664"
    },
    {
        "name": "Matrix",
        "address": "580 Glenwood Dr. Garner, NC 27529",
        "nik": "1000023452134598"
    },
    {
        "name": "Surya",
        "address": "909 South St Paul Street Hopewell, VA 23860",
        "nik": "1000075656784734"
    },
]

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]

    # Find all the faces and face enqcodings in the frame of video
    face_locations = face_recognition.face_locations(rgb_frame)
    print(face_locations)
    no = len(face_locations)
    print("Number of faces detected: ", no)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

    # Loop through each face in this frame of video
    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
        start_time = time.perf_counter_ns()
        proba_list = clf.predict_proba([face_encoding])
        end_time = time.perf_counter_ns()
        process_time = end_time - start_time
        i = np.argmax(proba_list)
        proba = list(*proba_list)[i]
        name = dummy_data[i]["name"]
        print(name, "{:.2f}".format(proba), proba_list, process_time)

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        if proba > threshold:
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame, "{} {:.2f}".format(name, proba), (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)


    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()