# 🤖 Video Inference Dashboard Example Roboflow's inference server to analyze video streams. This project extracts insights from video frames at defined intervals and generates informative visualizations and CSV outputs. ## 📦 Use Case: Smart Inventory Monitoring Factories & stores can: - Save time - Count items at intervals, avoiding stockouts. - Restock efficiently using data. - Enhance operations ## 📈 Result This is counting products on shelf, every 5 minutes, categorically and in total.
![alt text](https://storage.googleapis.com/com-roboflow-marketing/objects_by_class_over_time.png "Title")
![alt text](https://storage.googleapis.com/com-roboflow-marketing/objects_over_time_d.png "Title") ## ⚙️ Requirements Make sure you have docker installed. Learn more about building, pulling, and running the Roboflow Inference Docker Image in our [documentation](https://roboflow.github.io/inference/quickstart/docker/). ## 🔍 Installation ### **⌗ 1 Start inference server** x86 CPU: ```bash docker run --net=host roboflow/roboflow-inference-server-cpu:latest ``` NVIDIA GPU ```bash docker run --network=host --gpus=all roboflow/roboflow-inference-server-gpu:latest ``` ### **⌗ 2 Setup and Run** ```python git clone https://github.com/roboflow/inference-dashboard-example.git cd inference-dashboard-example pip install -r requirements.txt ``` ```python python main.py --dataset_id [YOUR_DATASET_ID] --api_key [YOUR_API_KEY] --video_path [PATH_TO_VIDEO] --interval_minutes [INTERVAL_IN_MINUTES] """ --dataset_id: Your dataset name on Roboflow. --version_id: The version ID for inference (default: 1). --api_key: Your API key on Roboflow. --video_path: Path to the video file for analysis. --interval_minutes: Interval in minutes to extract predictions (default: 1). """ ``` ## 🦾 Feedback & Contributions Feel free to open an issue, submit a PR, or share your feedback. All contributions are welcome!