Apply tensorflow object detection on input video stream. One could use webcam (or any other device) stream or send a video file. It is possible to write Output put file with detection boxes.
Clone repo in your working directory
Build docker image:
docker build -t realtime-objectdetection .
Configure script (see bellow)
Launch script:
bash runDocker.sh
Configuration is made in exec.sh at python function call:
python3 my-object-detection.py ...
All possible arguments are:
-n (--num-frames): type=int, default=0: # of frames to loop over for FPS test
-d (--display), type=int, default=0: Whether or not frames should be displayed
-o (--output), type=int, default=0: Whether or not modified videos shall be writen
-on (--output-name), type=str, default="output": Name of the output video file
-I (--input-device), type=int, default=0: Device number input
-i (--input-videos), type=str, default="": Path to videos input, overwrite device input if used
-w (--num-workers), type=int, default=2: Number of workers
-q-size (--queue-size), type=int, default=5: Size of the queue
-l (--logger-debug), type=int, default=0: Print logger debug
Inputs file are in inputs/ folder
Outputs file are in outputs/ folder (.avi)