Cattle Activity Tracking | Mastering Streamlit Inference Scripts for Real-Time Insights

Pie Chart Creation: Discover how to visualize cattle activity patterns using Matplotlib to generate meaningful insights like grazing behavior or standing patterns. Streamlit Integration: Learn how to convert your standalone inference script into a fully functional Streamlit app. We cover file upload functionality, real-time video processing updates, and how to display annotated results, pie charts, and heat maps side by side. Session State Management: Understand how Streamlit manages session state to accumulate and display each frame of the video in real-time, providing a more interactive experience for users. 🔍 Key Outputs: Annotated Output Video: View real-time cattle activity with annotations directly within the Streamlit app. Visual Insights: Heat maps and pie charts showing behavior patterns like grazing and standing. User-Friendly Interface: Enable users to upload video files and receive instant feedback on their cattle health and behavior. 💡 Why This Matters: By combining computer vision and Streamlit, we enhance cattle monitoring with intuitive visualizations, helping farmers make informed decisions for better livestock management and proactive care. 🔔 Subscribe for more AI-driven farming tutorials and stay ahead in livestock monitoring! 🔗 Follow My Links: Facebook: https://www.facebook.com/AugmentedStartups/ LinkedIn: https://www.linkedin.com/in/riteshkanjee/ X: https://x.com/augmentstartups Whatsapp AI Community: https://chat.whatsapp.com/JTuIB3eEfDRGo0TL4RzqwB #CattleMonitoring #Streamlit #AIInFarming #HealthAssessment #LivestockManagement #ComputerVision #ActivityRecognition #HeatMap #PieChart