facerecognition.py

#!/usr/bin/env python
# coding: utf8
"""MMM-Facial-Recognition - MagicMirror Module
Face Recognition Testing Script
The MIT License (MIT)

Copyright (c) 2016 Paul-Vincent Roll (MIT License)
Based on work by Tony DiCola (Copyright 2013) (MIT License)

Run this script to test your training data.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""
import cv2  # OpenCV Library
import lib.face as face
import lib.config as config
import time
import os
import signal
import sys

model = config.model(config.RECOGNITION_ALGORITHM, config.POSITIVE_THRESHOLD)

# set preview to False to disable picamera preview
preview = True
camera = config.get_camera(preview)

print('Loading training data...')
model.load("training.xml")
print('Training data loaded!')


def clean_shutdown(signum, frame):
    """Release camera and close windows
    """
    camera.stop()
    cv2.destroyAllWindows()
    sys.exit(0)


signal.signal(signal.SIGINT, clean_shutdown)
signal.signal(signal.SIGTERM, clean_shutdown)
signal.signal(signal.SIGSEGV, clean_shutdown)

# Loadking the training data should give enough time to
# warm up the picamera. If not, uncomment the following
# line
# time.sleep(1)

while True:
    # camera video feed
    frame = camera.read()

    image = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)

    faces = face.detect_faces(image)

    if faces is not None:
        for i in range(0, len(faces)):
            if faces[i] is None:
                print("Bad face object None")
                continue
            if len(faces[i]) != 4:
                print("Bad face object {0}".format(faces[i]))
                continue
            x, y, w, h = faces[i]
            # x and y coordinates of the face
            x_face = x
            y_face = y
            if config.RECOGNITION_ALGORITHM == 1:
                crop = face.crop(image, x, y, w, h)
            else:
                crop = face.resize(face.crop(image, x, y, w, h))

            # confidence the lower the stronger the match
            label, confidence = model.predict(crop)

            match = "None"
            label_str = "None"
            if (label != -1 and label != 0):
                label_str = config.user_label(label)

            # the closer confidence is to zer the stronger the match
            if confidence < 0.6 * config.POSITIVE_THRESHOLD:
                label_str = 'Strong:' + label_str
            elif confidence < config.POSITIVE_THRESHOLD:
                label_str = 'Weak:' + label_str
            elif confidence < 1.5 * config.POSITIVE_THRESHOLD:
                label_str = "Guess: " + label_str
            else:
                lavel_str = "Unknown"

            print(label_str)

            # face rectable
            cv2.rectangle(frame, (x, y), (x + w, y + h), 255)
            # height
            cv2.putText(frame,
                        str(int(h)),
                        (x + w, y + h + 15),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        0.5,
                        (255, 255, 255),
                        1)
            # label user
            cv2.putText(frame,
                        label_str,
                        (x - 3, y - 8),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        1.5,
                        (255, 255, 255),
                        1)
            # confidence
            cv2.putText(frame,
                        str(int(confidence)),
                        (x - 2, y + h + 15),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        0.5,
                        (255, 255, 255),
                        1)
            if h > 250:
                # If person is close enough, mark the eyes
                eyes = face.detect_eyes(face.crop(image, x, y, w, h))
                for i in range(0, len(eyes)):
                    x, y, w, h = eyes[i]
                    cv2.rectangle(frame,
                                  (x + x_face, y + y_face - 30),
                                  (x + x_face + w + 10, y + y_face + h - 40),
                                  (94, 255, 0))
                    cv2.putText(frame, "Eye " + str(i),
                                (x + x_face, y + y_face - 5),
                                cv2.FONT_HERSHEY_SIMPLEX,
                                0.5,
                                (255, 255, 255),
                                1)

    if ('DISPLAY' in os.environ):
        # Display Image
        cv2.imshow('Facial recognition', frame)
        if cv2.waitKey(1) == ord('q'):
            break
    else:
        print('No windowing system, writing face.jpg image')
        cv2.imwrite('face.jpg', frame)
        camera.stop()
        break


clean_shutdown()