Nine-balls-segmentation Computer Vision Model
How to use the Nine-balls-segmentation Segmentation API
Try This Model
Drop an image here or click to upload
Code Snippets
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
result = CLIENT.infer("YOUR_IMAGE.jpg", model_id="nine-balls-segmentation/1")Or, Use Free 1-yellow-ball, 2-blue-ball and 3-red-ball Detection API
Powered by general detection model
pip install inference-sdk# 1. Import the library
from inference_sdk import InferenceHTTPClient
# 2. Connect to your workspace
client = InferenceHTTPClient(
api_url="https://serverless.roboflow.com",
api_key="API_KEY"
)
# 3. Run your workflow on an image
result = client.run_workflow(
workspace_name="<YOUR_WORKSPACE>",
workflow_id="<YOUR_WORKFLOW_ID>",
images={
"image": "YOUR_IMAGE.jpg" # Path to your image file
},
parameters={
"classes": "1-yellow-ball, 2-blue-ball, 3-red-ball, 4-pink-ball, 4-purple-ball"
},
use_cache=True # cache workflow definition for 15 minutes
)
# 4. Get your results
print(result)Run on custom image
Drop an image here or click to upload
About Nine-balls-segmentation Model
Here are a few use cases for this project:
-
Billiards Game Analysis: The model can be employed during pool games for real-time tracking and analysis of shots. This system could help in understanding player strategy, predicting shot possibilities, and providing real-time coaching advice.
-
Virtual Pool Game Development: Developers of virtual or augmented reality pool games could leverage this model to create more realistic simulations with accurate physics based on how the balls are arranged and move on the table in real life.
-
Sports Broadcasting Enhancement: Live broadcasts of billiards matches could use the model to provide enhanced visuals, such as highlighting possible shots or predicting ball movement, thereby improving viewer's understanding and enjoyment of the game.
-
Automated Scoring System: The model can be applied in an automated scoring system where it efficiently keeps track of the game score by identifying which balls have been pocketed and their respective values according to their colors.
-
Practice Improvement for Players: The technology could be used in a training application to help players improve their skills. By analyzing player shots and comparing them with optimal strategies, it can provide feedback and suggestions for better shot selections or techniques.
Tell the agent what you want to build.
Cite This Project
LicenseCC BY 4.0If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{ nine-balls-segmentation_dataset,
title = { Nine-balls-segmentation Dataset },
type = { Open Source Dataset },
author = { wangkq },
howpublished = { \url{ https://universe.roboflow.com/wangkq-zuirn/nine-balls-segmentation } },
url = { https://universe.roboflow.com/wangkq-zuirn/nine-balls-segmentation },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { apr },
note = { visited on 2026-07-29 },
}