Cal-Pol is your personal "Calorie Police," an intelligent kitchen vessel designed to provide a highly accurate, real-time estimate of the calories and macronutrients in your food. Tailored for local cuisines, it goes beyond simple food photography by precisely measuring the weight of ingredients as you add them, identifying them, and calculating their nutritional value on the spot.

The project is a hardware-first system designed for accuracy and ease of use in the kitchen.

The "Smart Analysis Vessel"

• Real-Time Weight Measurement: An integrated, high-precision load cell measures the exact weight of each ingredient as it's added to the vessel.
• Local Ingredient Recognition: Using a combination of an onboard camera and a dataset tailored to local food items, the system identifies ingredients.
• Accurate Calorie Estimation: By combining the precise weight with nutritional data for the recognized ingredient, the system calculates the calorie, protein, carb, and fat content with a high degree of accuracy.
• Cumulative Totals: The device keeps a running total of the nutritional information as you build your meal, perfect for tracking complex recipes.
• Live Display & App Sync: A built-in display shows real-time data, while BLE connectivity syncs the results to a companion mobile app for detailed analysis and logging.

• Hardware (Analysis Vessel):
  • Microcontroller: ESP32 or Raspberry Pi for processing power and connectivity.
  • Sensors: High-precision load cell with an HX711 ADC for weight, and a small camera module for computer vision.
  • Firmware: C/C++ (Arduino Framework) or MicroPython.
  • Display: Small OLED or LCD screen for immediate feedback.
  • Communication: Bluetooth (BLE) to sync with the mobile app.
• Software (AI & App):
  • AI / Machine Learning: Python with TensorFlow Lite or PyTorch Mobile running on the device or a local server for ingredient recognition.
  • Mobile Framework: Flutter or React Native for the user-facing app.
  • Database: A local or cloud-based database of nutritional information, specifically focused on local ingredients.

Current Status: Conceptual Phase

This is a passion project currently in the ideation and research phase. The immediate goal is to develop a hardware prototype that can accurately weigh ingredients and perform basic recognition on a limited set of food items.