from darkflow.darkflow.defaults import argHandler #Import the default arguments
import os
from darkflow.darkflow.net.build import TFNet
FLAGS = argHandler()
FLAGS.setDefaults()
FLAGS.demo = "camera" # 你需要检测的视频文件 默认为你的摄像头 "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 = True # 置为 True 表示启用目标追踪， False 表示仅启用目标检测
#FLAGS.trackObj = ['Bicyclist','Pedestrian','Skateboarder','Cart','Car','Bus'] # the object to be tracked
FLAGS.trackObj = ["person"]
FLAGS.saveVideo = True #是否保存检测后的视频文件到当前目录
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 = "deep_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
