#!/usr/bin/python3
"""
RoboVision
______________
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Project Author/Architect: Navjot Singh <weavebytes@gmail.com>
"""
#
# a trainer for faces, inorder to identify faces with names/labels
#
# reference
# https://www.youtube.com/watch?v=PmZ29Vta7Vc
#
import os
import numpy as np
from PIL import Image
import cv2 as cv
import pickle
from PyQt5.QtCore import QObject, pyqtSignal
from threading import Thread
from global_signals import g_emitter
from logger import get_logger
log = get_logger()
class FaceTrainer(QObject, Thread):
# signal for emitting a frame captured from camera
processing_image = pyqtSignal('QString', 'QString')
face_training_finished = pyqtSignal()
def __init__(self, face_cascade_xml, face_images_dataset_dir, parent=None):
super().__init__(parent)
self.face_cascade = cv.CascadeClassifier(face_cascade_xml)
self.recognizer = cv.face.LBPHFaceRecognizer_create()
self.face_images_dataset_dir = face_images_dataset_dir
def run(self):
y_labels = []
x_train = []
cur_id = 0
label_ids = {}
# fetching images from dataset for training
for root, dirs, files in os.walk(self.face_images_dataset_dir):
# FIXME - adding talkative settings in prefs !!!
# if our robot it too talkative, emit this signal
g_emitter().emit_signal_to_feed_mouth(
"checking %s" % os.path.basename(root))
for file in files:
# check file extension for image files
extension = os.path.splitext(file)[1]
if extension in [".jpg", ".jpeg", ".png"]:
full_path = os.path.join(root, file)
label = os.path.basename(root).replace(" ", "-").lower()
if label not in label_ids:
label_ids[label] = cur_id
cur_id += 1
img_id = label_ids[label]
log.debug(
"FaceTrainer :: %s - %s - %s"
% (str(label), str(img_id), str(full_path)))
self.processing_image.emit(label, full_path)
# convert image to grayscale
pil_image = Image.open(full_path).convert("L")
# convery grayscale image to numpy array
image_array = np.array(pil_image, "uint8")
faces = self.face_cascade.detectMultiScale(
image_array, 1.3, 5)
for (x, y, w, h) in faces:
# define roi for eyes detection,ideally,
# we should detect eyes within the rectangular
# bounds of a face
roi = image_array[y:y+h, x:x+w]
x_train.append(roi)
y_labels.append(img_id)
# save trained labels
with open("dataset/face_trainer_labels.pickle", 'wb') as f:
pickle.dump(label_ids, f)
self.recognizer.train(x_train, np.array(y_labels))
self.recognizer.save("dataset/face_trainer.yml")
self.face_training_finished.emit()
if __name__ == "__main__":
# path to Haar face classfier's xml file
face_cascade_xml = './cascades/haarcascades_cuda/haarcascade_frontalface_default.xml'
from local_settings import FACE_IMAGES_DATASET_DIR
ft = FaceTrainer(face_cascade_xml, FACE_IMAGES_DATASET_DIR)
ft.start()
ft.join()
