#!/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>
"""
import sys
from os import path
import cv2
import numpy as np
from PyQt5 import QtCore
from PyQt5 import QtWidgets
from PyQt5 import QtGui
class RecordVideo(QtCore.QObject):
image_data = QtCore.pyqtSignal(np.ndarray)
def __init__(self, camera_port=0, parent=None):
super().__init__(parent)
self.camera = cv2.VideoCapture(camera_port)
self.timer = QtCore.QBasicTimer()
def start_recording(self):
print("[INFO] recording started")
self.timer.start(0, self)
def timerEvent(self, event):
if (event.timerId() != self.timer.timerId()):
return
read, data = self.camera.read()
if read:
self.image_data.emit(data)
class FaceDetectionWidget(QtWidgets.QWidget):
def __init__(self, haar_cascade_filepath, parent=None):
super().__init__(parent)
self.classifier = cv2.CascadeClassifier(haar_cascade_filepath)
self.image = QtGui.QImage()
self._red = (0, 0, 255)
self._width = 2
self._min_size = (30, 30)
def detect_faces(self, image: np.ndarray):
# haarclassifiers work better in black and white
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_image = cv2.equalizeHist(gray_image)
faces = self.classifier.detectMultiScale(gray_image,
scaleFactor=1.3,
minNeighbors=4,
flags=cv2.CASCADE_SCALE_IMAGE,
minSize=self._min_size)
return faces
def image_data_slot(self, image_data):
faces = self.detect_faces(image_data)
for (x, y, w, h) in faces:
cv2.rectangle(image_data,
(x, y),
(x+w, y+h),
self._red,
self._width)
self.image = self.get_qimage(image_data)
if self.image.size() != self.size():
self.setFixedSize(self.image.size())
self.update()
def get_qimage(self, image: np.ndarray):
height, width, colors = image.shape
bytesPerLine = 3 * width
QImage = QtGui.QImage
image = QImage(image.data,
width,
height,
bytesPerLine,
QImage.Format_RGB888)
image = image.rgbSwapped()
return image
def paintEvent(self, event):
painter = QtGui.QPainter(self)
painter.drawImage(0, 0, self.image)
self.image = QtGui.QImage()
class MainWidget(QtWidgets.QWidget):
def __init__(self, haarcascade_filepath, parent=None):
super().__init__(parent)
fp = haarcascade_filepath
self.face_detection_widget = FaceDetectionWidget(fp)
self.record_video = RecordVideo()
image_data_slot = self.face_detection_widget.image_data_slot
self.record_video.image_data.connect(image_data_slot)
layout = QtWidgets.QVBoxLayout()
# adding widget for camera display & facedetection
layout.addWidget(self.face_detection_widget)
# adding start button
self.run_button = QtWidgets.QPushButton('Start')
layout.addWidget(self.run_button)
self.run_button.clicked.connect(self.record_video.start_recording)
self.setLayout(layout)
def main(haar_cascade_filepath):
app = QtWidgets.QApplication(sys.argv)
main_window = QtWidgets.QMainWindow()
main_widget = MainWidget(haar_cascade_filepath)
main_window.setCentralWidget(main_widget)
main_window.show()
sys.exit(app.exec_())
if __name__ == '__main__':
script_dir = path.dirname(path.realpath(__file__))
face_cascade_xml = './cascades/haarcascades_cuda/haarcascade_frontalface_default.xml'
main(face_cascade_xml)
