def get_and_print_total_face_count(self):
while self.perform_human_detection:
time.sleep(5)
Logger.logger().info("[INFO D 1]: {}".format(SendReceiveMessages().get_total_face_detected_count()))
Logger.logger().info("[INFO L 2]: {}".format(SendReceiveMessages().get_face_detected_count_locally()))
Logger.logger().info("[INFO P 3]: {}".format(SendReceiveMessages().get_face_detected_by_peer()))
Logger.logger().info(
"method_for_comparing_local_face_detected_and_global_face_detected: Compute total faces "
"detected by both cameras: {}".format(SendReceiveMessages().get_total_face_detected_count()))
if SendReceiveMessages().get_total_face_detected_count() >= MAX_OCCUPANCY:
Logger.logger().info("Please wait because the occupancy is greater than {}".format(MAX_OCCUPANCY))
def test_occupancy_10(self):
"""
This method validates if occupancy detector can actually correctly calculate the number of people entering the premises on a typical temple day.
:return:
"""
human_detector_inst = HumanDetector(
find_humans_from_video_file_name=
'videos/occupancy_test_videos/occupancy10.mp4',
use_pi_camera=False,
open_display=True)
self.assertEqual(human_detector_inst.perform_job(), None)
human_centroid_dict = human_detector_inst.get_human_centroid_dict()
self.assertEqual(len(human_centroid_dict), 12)
self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[1].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[2].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[3].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[4].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[5].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[6].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[7].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[8].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[9].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[10].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[11].direction, Direction.ENTER)
self.assertEqual(
SendReceiveMessages().get_face_detected_count_locally(), 2)
human_detector_inst.clean_up()
self.__cleanup()
def test_one_person_entering(self):
"""
This method validates if occupancy detector can actually detect one person entering the premises.
:return:
"""
human_detector_inst = HumanDetector(
find_humans_from_video_file_name='videos/one_person_entering.mp4',
use_pi_camera=False,
open_display=False)
self.assertEqual(human_detector_inst.perform_job(), None)
human_centroid_dict = human_detector_inst.get_human_centroid_dict()
self.assertEqual(len(human_centroid_dict), 1)
self.assertEqual(human_centroid_dict[0].direction, Direction.ENTER)
self.assertEqual(
SendReceiveMessages().get_face_detected_count_locally(), 1)
human_detector_inst.clean_up()
self.__cleanup()
def test_one_by_one_crossAbhiSrini(self):
"""
This method validates if occupancy detector can actually detect 1 person entering and teh other exiting the premises.
:return:
"""
human_detector_inst = HumanDetector(
find_humans_from_video_file_name=
'videos/TempleVideos/one_by_one_cross_AbhiSrini.mp4',
use_pi_camera=False,
open_display=True)
self.assertEqual(human_detector_inst.perform_job(), None)
human_centroid_dict = human_detector_inst.get_human_centroid_dict()
self.assertEqual(len(human_centroid_dict), 2)
self.assertEqual(human_centroid_dict[0].direction, Direction.ENTER)
self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)
self.assertEqual(
SendReceiveMessages().get_face_detected_count_locally(), 0)
human_detector_inst.clean_up()
self.__cleanup()
def test_grouped_exit(self):
"""
This method validates if occupancy detector can actually detect four persons grouped together exiting the premises.
:return:
"""
human_detector_inst = HumanDetector(
find_humans_from_video_file_name=
'videos/TempleVideos/grouped_Exit.mp4',
use_pi_camera=False,
open_display=False)
self.assertEqual(human_detector_inst.perform_job(), None)
human_centroid_dict = human_detector_inst.get_human_centroid_dict()
self.assertEqual(len(human_centroid_dict), 4)
self.assertEqual(human_centroid_dict[0].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[1].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[2].direction, Direction.EXIT)
self.assertEqual(human_centroid_dict[3].direction, Direction.EXIT)
self.assertEqual(
SendReceiveMessages().get_face_detected_count_locally(), -4)
human_detector_inst.clean_up()
self.__cleanup()
def clean_up(self):
self.perform_human_detection = False
SendReceiveMessages().cleanup()
# stop the timer and display FPS information
self.fps.stop()
Logger.logger().debug("elapsed time: {:.2f}".format(self.fps.elapsed()))
Logger.logger().debug("approx. FPS: {:.2f}".format(self.fps.fps()))
# Close the log file.
HumanValidator.close_log_file()
# close any open windows
cv2.destroyAllWindows()
# clean up
Logger.logger().debug("cleaning up...")
if self.find_humans_from_video_file_name:
self.video_stream.release()
else:
self.video_stream.stop()
time.sleep(2)
def __init__(self, find_humans_from_video_file_name=None,
use_pi_camera=USE_PI_CAMERA, open_display=OPEN_DISPLAY):
# initialize the frame dimensions (we'll set them as soon as we read
# the first frame from the video)
self.H = None
self.W = None
self.video_stream = None
self.net = None
self.current_time_stamp = None
self.frame = None
self.rgb = None
self.meter_per_pixel = None
self.args = None
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--peer_ip_address", type=str,
help="Provide the IP address of the remote raspberry PI.")
parser.add_argument("-p", "--peer_port", type=int, help="Provide the server port of the remote raspberry PI.",
default=SERVER_PORT)
parser.add_argument('-d', '--debug', type=bool, help='Enable debug logging.', default=False)
self.args = parser.parse_args()
if self.args.debug:
Logger.set_log_level(logging.DEBUG)
self.find_humans_from_video_file_name = find_humans_from_video_file_name
self.use_pi_camera = use_pi_camera
self.open_display = open_display
self.perform_human_detection = True
SendReceiveMessages().perform_job(peer_ip_address=self.args.peer_ip_address)
# Load Model
self.load_model()
# Initialize the camera.
self.initialize_camera()
# start the frames per second throughput estimator
self.fps = FPS().start()
self.centroid_object_creator = CentroidObjectCreator()