NiceShot AI is a Python tool powered by computer vision to analyze gameplay videos. With the integration of cutting-edge tools like YOLO, OpenCV, FFmpeg, and matplotlib, NiceShot AI is designed to automatically detect, track and clip key gameplay events as well as create visual report for gameplay session analysis.
Simple demo showcasing tool results: (https://youtu.be/op1GDREXiOg)
- Call of Duty: Black Ops 7 (2025) --> Still in testing
| Key events | Description | Limitations |
|---|---|---|
| Kill | Gun kills | Only face-to-face gun kills |
| Medal | When a medal earned by the player pops up during gameplay | - |
| Death | When player gets eliminated during gameplay | - |
- Call of Duty: Black Ops 6 (2024)
| Key events | Description | Limitations |
|---|---|---|
| Kill | Gun kills | Only face-to-face gun kills |
| Medal | When a medal earned by the player pops up during gameplay | - |
| Death | When player gets eliminated during gameplay | - |
YOLOv8n by Ultralytics. Fine-tuned on custom collected & annotated dataset of gameplay videos under CC license.
- Robust to Scalability: Uses configurable variables enabling it to adapt to different game models and events without massive changes. (In testing)
- Accurate Event Confirmation: Using EasyOCR to prevent counting events occurring in special game scenes (ex.KILLCAMS and SPECTATING).
- Special Events Detection: (Ex. Kill Streaks occurring from the combination of multiple consecutive kills within a time threshold).
- Events Timestamping & CSV Output: Timestamps detected events and dumps into a CSV file with 2 columns [Timestamp, Event] for further gameplay data analysis and inspections.
- Session Analysis: Creates a summary report consisting of multiple charts providing a post-session stats analysis.
- Event Auto-Clipping: Clipping detected events using event's start time and end time.
- Clips Export in 16:9 & TikTok formats
- Creating Highlight Reels: Concatenating all clips within a folder into one compilation video with simple fade in & out transition edits between clips in both vertical & horizontal formats.
- Custom Reel Lengths: Allowing for creating compilations of any length from the extracted clips.
- Analyzing videos in bulk from a Twitch channel: Downloads and analyzes desired game streams from a Twitch channel performing bulk analysis of gameplay videos. (In testing)
- Ranking Special Clips: Ex. (Hot Kill Clips where multiple medals pop up during the event).
- An event may get detected more than one time or not detected at all: From my testing, an event getting detected more than once is more likely than not getting detected at all which means more tuning should be done on the tracker algorithm.
- Only 1 supported game for now
To get started with NiceShot_AI, download & install ffmpeg from the official website: https://www.ffmpeg.org/download.html first and add it to your PATH.
Create a Python virtual environment (optional, but recommended). My Python version is 3.10.11
python -m venv venv
venv\Scripts\activateInstall torch cuda. What worked for me is cuda 12.1:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121Install dependencies:
pip install -r requirements.txtfrom src.niceshot_ai.detector import EventDetector
detector = EventDetector(
"Call of Duty Black Ops 6", # Game name to import proper model and chart configuration
"video1.mp4", # Gameplay video path to analyze
total_hours=100, # Total hours to analyze of the video
save_clips=True, # Auto-clip events and save event clips locally (required for compilation making)
output_dir=".", # Output folder for clips, charts and csv file
max_workers=2, # default for auto-clipping
frame_idx_start=0, # Start frame of the video
frames_to_skip=8, # Frames to skip during analysis (the more, the faster tha analysis)
add_to_csv=True, # Timestamp events and output to csv
create_montage=True, # Create compilation of every clippable event
montage_length_sec=50, # Length of the compilation
max_videos=1,
vertical_format=False, # Auto-clip events in vertical format only
advanced_detection=True, # Confirm some events with OCR
session_analysis=False # Report maker
)
detector.detect_events()
Tested on my new laptop with the following specs:
- CPU: core i9 14th gen
- GPU: RTX5070 8GB
- RAM: 32GB
Tested on my old laptop with the following specs:
- CPU: core i7 10th gen
- GPU: GTX1650 4GB
- RAM: 16GB
| Device | Frame Inference |
|---|---|
| New laptop | Up to 170 FPS with frames_to_skip = 5 |
| Old laptop | Up to 60 FPS with frames_to_skip = 5 |
This is run only to confirm an event after it's detected. Not through the whole video frames. It can cause the processing speed to fall down from 170 FPS to 30 FPS (on new laptop) temporarily until event is confirmed. It can definitely be turned off, however this will cause a kill event during "SPECTATING" to be counted.