Cstrike Detection

Motivation

The project is aimed at constructing automatic aiming system based on object detection algorithm, in order to have fun in CS 1.6.

TODO

• Finish training and test of YOLO v5 and Faster-RCNN.
• Finish deployment pipeline.
• Construct automatic aiming system and test.

Prepare for dataset

Step 1. download dataset

I have published my home made dataset in Kaggle. If you want to launch my project, please download the dataset first and unzip to folder dataset in root path.

root
|--datasets
|--faster-rcnn
|--yolov5
...

step 2. make train data and test data

run python script in console:

$python script/make_meta.py -t 20

-t 20 means size of test dataset is 20. This is the default value.

Usage of detection model

Traning config of two algorithm is almost the same.

YOLO v5

train

$cd yolov5
$python train.py --img 640 --batch 4 --epochs 25 --data "CS.yaml" --weights "models/pretrained/yolov5m.pt"

Where models/pretrained/yolov5m.pt is the pretrained weight and will determine the specific structure of YOLO v5(yolov5m means the middle version of YOLO v5)

detect

$python detect.py --weights "runs/train/exp/weights/best.pt" --img 640 --device 0 --source <media path>

Where runs/train/exp/weights/best.pt is the finetone weight and <media path> can be path of image, video or the folder of both. If <media path> is set to 0, then webcam will be used.

Faster-RCNN

train

python train.py --weights "default" --data "CS.yaml" --epochs 20 --batch 4

weights is set to "default" in default, which means use pretrained faster-rcnn backbone. If you want to use custom weights, please set the argument. Other arguments are the same as YOLO's train.py.

detect

$python detect.py --weights "runs/train/exp/weights/best.pt" --score 0.6 --device cuda --source <media path>

Where runs/train/exp/weights/best.pt is the finetone weight and <media path> can be path of image, video or the folder of both. If <media path> is set to 0, then webcam will be used.

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My Experiment Result

I have tried Faster-RCNN and YOLO v5m to realise the task. Here are the results.

Qualitative Comparsion

I have published a demo video on My bilibili Channel to show the comparsion, there is a screenshot.

Emm, maybe the fact that different color is used on the same class should be mentioned. But I think most of you don't care :D

Time and space consumption

model name	inference time(ms)	model size(MB)
Faster-RCNN	78.51	158
YOLO-v5m	20.2	40.2

mAP

model name	mAP@0.5	weighted mAP@0.5	bbox loss
Faster-RCNN	0.725	0.190	0.014
YOLO-v5m	0.389	0.115	0.017

P-R curve

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How to have fun?

I choose Faster-RCNN for real-time inference, based on which a auto-aim and fire system is constructed.

If you want to use it, cstrike.exe must be installed in your computer (Yeah, my OS is windows11. If you use Unix, please give it a try).

At least, you should ensure that a handler named Counter-Strike can be found in your task list. First, launch cstrike.exe, choose map "ice world" and play the role of counter strike.

I suggest running the game in window mode

Then, train the weight of Faster-RCNN and suppose that the weight file is saved in "./faster-rcnn/runs/train/exp1/weights/best.pth", then run the command in your console:

$python autoaim.py --weights "../faster-rcnn/runs/train/exp1/weights/best.pth" --device cuda --score 0.6 --debug --auto-aim --auto-fire

• --device cuda means we use GPU to accelerate inference.
• --score 0.6 means only bbox whose confidence is greater 0.6 will be displayed or considered in later process.
• --debug means run the program in a debug mode, then an extra window which is rendered with bbox and its corresponding predicted class will be generated.
• --auto-aim means program will move your mouse if it detected enemy.
• --auto-fire means program will open the fire automatically.

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FAQ

Why model's accuary is low when the enemy is counter strike or the map is not "ice world"?

Emm, I only construct the dataset in the view of counter strike in this map, so if you like ...