Food Calorie Detection by Image Processing and Diet Assistant

Maintaining a healthy lifestyle requires accurate monitoring of food intake, but traditional calorie tracking methods such as manual logging and food charts are time-consuming and error-prone. This project presents an automated food calorie detection and diet assistant system that leverages computer vision, deep learning, and image processing to estimate calorie content directly from food images. The system captures both top and side views of meals with a reference object for precise size and volume estimation. Faster R-CNN with region proposal networks is used for food detection and classification, while segmentation methods such as GrabCut and Watershed ensure accurate extraction of food objects. The estimated volume is converted into mass using density values and mapped to calorie and nutritional data from trusted databases such as USDA, FooDD, and UECFOOD-256. A lightweight web-based application enables real-time calorie analysis without relying on external servers, ensuring efficiency and accessibility.This system reduces human error, provides instant results, and improves usability compared to traditional methods. Despite challenges like overlapping food items, varied lighting conditions, and the need for wider database coverage, the project demonstrates strong potential in personalized nutrition management. Future enhancements may include recipe suggestions, integration with wearable health devices, and AI-driven dietary recommendations. By combining accuracy, automation, and user-friendliness, this system supports informed dietary decisions, promotes healthy eating habits, and contributes to preventive healthcare and wellness.