Automatic Bot for Massive Dungeons Passing based on Windows API, YoloV8 object detection, statistical methods from OpenCV, Tesseract-OCR and spaCy virtualised with Hyper-V (Win11+CUDA)
demo_dung_stage4_5.mp4
demo_idle_metins.mp4
demo_dung_metins.mp4
- Keyboard and Mouse controll:
pynput
- Custom Data Annotation:
Roboflow
- Object detection:
ultralytics
,OpenCV
- Messages handling:
Tesseract-OCR
,spaCy
- Autonomic Dungeon Passing
- Autonomic Metin Stones Destroying among all of the channels
- Idle Exp
All of those modes can handle boundary situations such as game crash or logging out.
Model prepared with Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
Class | Images | Instances | Precision* | Recall* | mAP50* | mAP50-95* |
---|---|---|---|---|---|---|
all | 31 | 36 | 0.993 | 0.939 | 0.974 | 0.828 |
boss_gnoll_cpt | 31 | 11 | 0.983 | 0.818 | 0.931 | 0.739 |
metin_polany | 31 | 12 | 0.996 | 1 | 0.995 | 0.867 |
npc_straznik | 31 | 13 | 0.999 | 1 | 0.995 | 0.877 |
* - Box level
Roboflow Dataset Overview (316 images, 800x600)
- Outputs per training example: 3
- Flip: Horizontal
- Blur: Up to 1px
Grayscale Threshold, Contours Recognition and not triangular shape filter