pose-as-a-service

CloudPose is a high-performance, containerized pose estimation service built with FastAPI, TensorFlow Lite, and OpenCV. It exposes RESTful endpoints for JSON‐based and image‐based inference, supports load testing via Locust, and can be deployed locally, in Docker, or on Kubernetes.

Prerequisites

• Python 3.10 or newer
• pip package manager
• Docker (optional, for container builds)
• kubectl & a Kubernetes cluster (optional)
• Locust (for load testing)

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Installation

Install dependencies

python -m venv venv            # optional but recommended
source venv/bin/activate       # Linux/macOS
# .\venv\Scripts\activate    # Windows PowerShell
pip install --upgrade pip
pip install -r requirements.txt

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Running Locally

1. Start the API server

   uvicorn app.main:app --host 0.0.0.0 --port 60000

2. Verify endpoints

   curl -X 'POST' \
   'http://207.211.146.117:30000/pose/json' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{"id": <base64-image-string>, "images": <base64-image-string>}'

   curl -X 'POST' \
   'http://207.211.146.117:30000/pose/image' \
   -H 'accept: application/json' \
   -H 'Content-Type: application/json' \
   -d '{"id": <base64-image-string>, "image": <base64-image-string>}'

---

Running Load Tests

1. Run Locust (headless)

   locust -f experiments/locustfile.py \
     --host http://localhost:60000 \
     --users 100 --spawn-rate 10 --run-time 1m

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Docker Usage

1. Build the Docker image

   docker build -t cloudpose:latest .

2. Run the container

   docker run -d -p 60000:60000 --name cloudpose cloudpose:latest

3. Test the service

   curl http://localhost:60000/pose/json

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Kubernetes Deployment

1. Apply manifests

   kubectl apply -f k8s/namespace.yaml
   kubectl apply -f k8s/deployment.yaml
   kubectl apply -f k8s/service.yaml

2. Check rollout status

   kubectl -n cloudpose get pods,svc

3. Access via NodePort

   • The service exposes port 60000 as a NodePort; query any node IP:

     curl http://<NODE_IP>:<NODE_PORT>/pose/json