def run(self):
if not (self.__run_counting_cars or self.__run_detect_direction
or self.__run_hscd or self.__run_traffic_violation_detection):
print(
"please setup individual modules before running the pipeline")
sys.exit(0)
print("OUTPUT WILL BE AT: " + str(self.__show_output_path()))
print("ALERTS FOR THIS RUN WILL BE AT: " +
str(self.__path_to_alert_poller()))
tf.reset_default_graph()
FLAGS = argHandler()
FLAGS.setDefaults()
FLAGS.demo = self.__video_path # video file to use, or if camera just put "camera"
FLAGS.model = "darkflow/cfg/yolo.cfg" # tensorflow model
FLAGS.load = "darkflow/bin/yolo.weights" # tensorflow weights
FLAGS.threshold = 0.35 # threshold of decetion confidance (detection if confidance > threshold )
FLAGS.gpu = 0.85 # how much of the GPU to use (between 0 and 1) 0 means use cpu
FLAGS.track = True # whether to activate tracking or not
FLAGS.trackObj = "car" # the object to be tracked
FLAGS.saveVideo = True # whether to save the video or not
FLAGS.BK_MOG = False # activate background substraction using cv2 MOG substraction,
# to help in worst case scenarion when YOLO cannor predict(able to detect mouvement, it's not ideal but well)
# helps only when number of detection < 5, as it is still better than no detection.
# (NOTE : deep_sort only trained for people detection )
FLAGS.tracker = "deep_sort" # wich algorithm to use for tracking deep_sort/sort
FLAGS.skip = 0 # how many frames to skipp between each detection to speed up the network
FLAGS.csv = False # whether to write csv file or not(only when tracking is set to True)
FLAGS.display = True # display the tracking or not
# modules
FLAGS.counting_cars = self.__run_counting_cars # to enable counting cars application module
FLAGS.direction_detection = self.__run_detect_direction # run direction detection or skip
FLAGS.speed_estimation = self.__run_hscd # run speed estimation or skip
FLAGS.traffic_signal_violation_detection = self.__run_traffic_violation_detection
# FLAGS.application_dir = os.getcwd()
# FLAGS.user_input_video_name = self.__user_input_video_name
FLAGS.location_name = self.__location_name
FLAGS.path_to_output = self.__path_to_output
FLAGS.start_time = self.__start_time
tfnet = TFNet(FLAGS)
tfnet.camera()
print("End of Demo.")
from darkflow.darkflow.net.build import TFNet
FLAGS = argHandler()
FLAGS.setDefaults()
FLAGS.demo = "camera" # video file to use, or if camera just put "camera"
FLAGS.model = "darkflow/cfg/yolo.cfg" # tensorflow model
FLAGS.load = "darkflow/bin/yolo.weights" # tensorflow weights
# FLAGS.pbLoad = "tiny-yolo-voc-traffic.pb" # tensorflow model
# FLAGS.metaLoad = "tiny-yolo-voc-traffic.meta" # tensorflow weights
FLAGS.threshold = 0.7 # threshold of decetion confidance (detection if confidance > threshold )
FLAGS.gpu = 0.8 #how much of the GPU to use (between 0 and 1) 0 means use cpu
FLAGS.track = False # wheither to activate tracking or not
FLAGS.trackObj = [
'Bicyclist', 'Pedestrian', 'Skateboarder', 'Cart', 'Car', 'Bus'
] # the object to be tracked
#FLAGS.trackObj = ["person"]
FLAGS.saveVideo = True #whether to save the video or not
FLAGS.BK_MOG = True # activate background substraction using cv2 MOG substraction,
#to help in worst case scenarion when YOLO cannor predict(able to detect mouvement, it's not ideal but well)
# helps only when number of detection < 3, as it is still better than no detection.
FLAGS.tracker = "sort" # wich algorithm to use for tracking deep_sort/sort (NOTE : deep_sort only trained for people detection )
FLAGS.skip = 0 # how many frames to skipp between each detection to speed up the network
FLAGS.csv = False #whether to write csv file or not(only when tracking is set to True)
FLAGS.display = True # display the tracking or not
tfnet = TFNet(FLAGS)
tfnet.camera()
exit('Demo stopped, exit.')