surviver_dot_IO

A "helper" to the surviv.io game
This program auto fires at other players in game.
So far the AI is simple, the program will pick a player at random, and fires at the center location of it.
For now, the AI do not account for obstacles and the speed of witch plyers are moving.

The objects tracking model is a YOLOv5 implementation made by Ultralytics LLC
Small changes are made to the model to fit my project.
You can find the original model here:
https://github.com/ultralytics/yolov5

SORT is used for target tracking, and its original repo is here:
https://github.com/abewley/sort

Quick Note:

If you are just looking for a quick way to win the game, this project might disappoints you.
It will take some effect to get this program running.
From my testing, it pretty much requires a CUDA device (Nvidia GPU) to run at a reasonable delay, and it also hogs your CPU quite a bit.

Change log

2021-10-02:

Add target selection. When there are multiple potential targets (not blocked by obstacles), firing the closest one.

2021-8-21:

This update is also some house cleaning, but enable some important features:

• Optimized syncing between detection thread and AI thread. Dramatically reduce detection-to-trigger time (about 0.05s on my laptop) in complex scene.
• Fix the video recorder. Now not only you can save video file in detection mode (run only detect.py) but also playing game with screen cap:

python play_game.py --record

The --debug option still works here, too. The recoding is faster than real time for now.

2021-8-7:

Tree as a detection class is finally added.
When deciding which target to shot, the program will now skip the target that don't have a clear shot due to tree in between.
A visual tester is also added under /tester. This program can replace a pre recorded game capture and visualize your mouse actions.

2021-5-23:

Active Data set has since move to a separated repo here
This update are just some house cleaning, no feature updated. Some cleaning is needed for obstacles avoidance feature in the near future.

• Update for use of troch 1.7
• Update YOLOv5 implementation to v5.0

2020-11-22:

Introducing Active Data Set:
This is a seperated GUI program that manage training data and process data generated by active learning.
The program is still in early statge of development with very limited testing, please post in issues if you encounter any bugs.
ADS also consolidates tools before so its all in one place.
Here is a lsit of feartures:

• Data visulization. With images and bonding boxes.
• Data editing:
  • Modify target class
  • Delet targets
  • Delet images
  • Modify BBox positions
• Auto rename
• Training / validation split
• Run active learning

2020-10-11:

Major cleaning up in tracking data structure tracking_list:

• tracking_list is updated every frame
• Only the vision thread (Main thread) modify it
• Synced with AI thread so AI thread only run when the list is updated with all the targets in a frame through a CV
• The list is cleaned every frame so it only contains active targets of the frame

2020-10-03:

Auto termination. When a game round end (when the Battle Results are shown), the program will terminate.
Be sure to restart it if you are to play again.

2020-09-20:

Implementing defelcting shooting: Leading the target if it's moving relitive to you.
In my limited testing, it dose seems to improve accuracy. Becasue this will taking account
the time it takes for bullet to travel and more importantly, the response time of inferencing.

2020-09-12:

Apply SORT real time tracker.
This allows targets to be identified across multiple frames.
This is important because now we know wehther a Player in this frame is the same player from last frame,
this allows us to calculate player's speed and heading later on and many other posibilities.

2020-08-02:

Main thread(target acquiring) and AI thread(firing) is now synced using a conditional variable.
This slightly improve the reaction time and fix the bug of AI thread being overwhelmed by amount of targets.

2020-07-30:

• Upgrating to YOLOv5
• New screen capture mechanism (mss)
  
  With the two new upgrates, the program now is able to update aiming at about 5Hz (Tested on a RTX2070 Max-Q)
  and shown to be enough for game play (idealy, 10Hz would be nice)
  In my test, the program will still not get me to winning but it did scored several kil_ls ;)

2020-07-11:

First attempt of auto firing. Not very good due to 0.5s lag, still very interesting to play.
Once you entered a game session, run:

2020-07-09:

Add screen capture into the model detection source.
The model originally accept still images, video files and
video stream from IP or webcams.
But the game data is none of those.
So we will import the game data as screen capture witch is similar to a web stream.
The LoadStreams class can be modified to accept this.

2020-07-08:

Set up the tracking model.
Import the model from the original author,
making minor changers for data set of class of 1 and
more agrassive learning rate because our target is going to be
man made carton objects.

Download pre-trained weights

Weights trained on 500+ images, Not the best yet, but surely usable:
https://drive.google.com/file/d/1eh_syn9H7KijcB6zEY3bO020aTSdEgpX/view?usp=sharing
Put it under weights/

Instruction

1. Clone the repo

   git clone https://github.com/KevinUTAT/surviver_dot_IO.git
   

2. Download weight file and put it under weights
3. Install all the dependency by:

   pip install -r requirements.txt
   

   If get error due to lack of packge during run, please install them as my list might not cover it all.
   Reconmend using Conda
4. Launch your game.
5. Run the program:

   python play_game.py
   

tips: If you are experencing long reaction time (latency), try lower your display resolution.