facematcher/facematcher.py

#!/usr/bin/env python3
import cv2
import numpy as np
import face_recognition
import os
import time
import argparse

def match(paths, cap, show):
    faces = []
    faceencs = []
    for path in paths:
        face = face_recognition.load_image_file(path)
        faces.append(face)
        encs = face_recognition.face_encodings(face)
        if len(encs) == 0:
            print("Warning: "+path+" has no face!")
            continue
        faceencs.append(encs[0])

    if len(faceencs) == 0:
        print("Warning: No valid faces!")

    matching = False

    while not matching:
        ret, frame = cap.read()
        if cv2.mean(frame)[0] < 30:
            continue

        scale = 0.25
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
        small_rgb_frame = cv2.resize(rgb_frame, (0, 0), fx=scale, fy=scale)

        framelocs = face_recognition.face_locations(small_rgb_frame)
        frameencs = face_recognition.face_encodings(small_rgb_frame, framelocs)

        # Loop through each face in this frame of video
        for (top, right, bottom, left), frameenc in zip(framelocs, frameencs):
            # See if the face is a match for the known face(s)
            dists = face_recognition.face_distance(faceencs, frameenc)
            dist = dists[0]
            for d in dists:
                if d < dist:
                    dist = d
            print("Distance: "+str(dist))

            if show:
                cv2.rectangle(
                    rgb_frame,
                    (int(left / scale), int(top / scale)),
                    (int(right / scale), int(bottom / scale)),
                    (0, 0, 255), 2)

            # If a match was found in known_face_encodings, just use the first one.
            if dist <= 0.4:
                matching = True

        if show:
            cv2.imshow("frame", rgb_frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

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

    if matching:
        print("Matches")
        exit(0)
    else:
        exit(1)

def record(path, cap):
    def draw_face_rec(name, frame):
        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
        framelocs = face_recognition.face_locations(rgb_frame)
        frameencs = face_recognition.face_encodings(rgb_frame, framelocs)
        for (top, right, bottom, left), frameenc in zip(framelocs, frameencs):
            cv2.rectangle(rgb_frame, (left, top), (right, bottom), (0, 0, 255), 2)

        cv2.imshow(name, rgb_frame)

    while True:
        ret, frame = cap.read()
        if cv2.mean(frame)[0] < 30:
            continue

        cv2.imshow("frame", frame)

        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break
        elif key == ord('\r'):
            cv2.imshow("frame", frame)
            cv2.waitKey(1)
            draw_face_rec("frame", frame)
            while True:
                key = cv2.waitKey(0) & 0xFF
                if key == ord('\r'):
                    cv2.imwrite(path, frame)
                    return
                elif key == 27: # esc
                    break

parser = argparse.ArgumentParser()
subs = parser.add_subparsers(dest="command", required=True)

sub_match = subs.add_parser("match")
sub_match.add_argument(
    "-d", "--device", type=int, default=0,
    help="the index of the video device")
sub_match.add_argument(
    "-s", "--show", default=False, action="store_true",
    help="show what the camera sees")
sub_match.add_argument(
    "faces", type=str, nargs="+",
    help="the source image file(s)")

sub_record = subs.add_parser("record")
sub_record.add_argument(
    "-d", "--device", type=int, default=0,
    help="the index of the video device")
sub_record.add_argument(
    "face", type=str,
    help="the destination image file")

args = parser.parse_args()

if args.command == "match":
    match(args.faces, cv2.VideoCapture(args.device), args.show)
elif args.command == "record":
    record(args.face, cv2.VideoCapture(args.device))