Edamam API and dish volume estimation

cloud/microwaveDishAnalyzer.py

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+import cv2
+import numpy as np
+from typing import Dict, Any, Optional
+
+
+class MicrowaveDishAnalyzer:
+    """
+    Estimates food dish volume from top-down camera images and dish height,
+    and cross-validates physical volume against Edamam AI mass estimates.
+    """
+
+    # Constant scale ratio: Centimeters per Pixel.
+    # TODO : Replace this value once your camera calibration is complete.
+    CM_PER_PIXEL: float = 0.05  # Example: 1 pixel = 0.05 cm
+
+    def __init__(self, cm_per_pixel: Optional[float] = None):
+        if cm_per_pixel is not None:
+            self.cm_per_pixel = cm_per_pixel
+        else:
+            self.cm_per_pixel = self.CM_PER_PIXEL
+
+    def calculate_surface_area_cm2(self, image_path: str) -> float:
+        """
+        Segments the food/dish from the background and returns surface area in cm².
+        """
+        image = cv2.imread(image_path)
+        if image is None:
+            raise FileNotFoundError(f"Image could not be loaded from path: {image_path}")
+
+        # 1. Convert to grayscale & blur to reduce noise
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+        # 2. Otsu thresholding to segment foreground (dish) from background (turntable)
+        _, thresh = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+        # 3. Find contours
+        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        if not contours:
+            return 0.0
+
+        # 4. Assume the largest contour corresponds to the dish/food area
+        largest_contour = max(contours, key=cv2.contourArea)
+        area_pixels = cv2.contourArea(largest_contour)
+
+        # 5. Convert pixels² to cm² using scale ratio squared
+        area_cm2 = area_pixels * (self.cm_per_pixel ** 2)
+        return float(area_cm2)
+
+    @staticmethod
+    def _get_shape_factor(food_label: str) -> float:
+        """
+        Selects geometric correction factor (k_shape) based on container/food shape:
+        - Bowls/Soups: ~0.60 (paraboloid)
+        - Drinks/Mugs: ~0.95 (cylinder)
+        - Flat plates/solid foods: ~0.85 (truncated cone / disk)
+        """
+        label = food_label.lower()
+        if any(w in label for w in ["soup", "chili", "stew", "curry", "bowl"]):
+            return 0.60
+        elif any(w in label for w in ["coffee", "tea", "milk", "water", "beverage", "mug"]):
+            return 0.95
+        elif any(w in label for w in ["bread", "cake", "muffin"]):
+            return 0.80
+        return 0.85  # Default factor for plated meals
+
+    def estimate_volume(
+        self, image_path: str, height_cm: float, food_label: str = ""
+    ) -> Dict[str, float]:
+        """
+        Computes total physical volume in cm³ (mL).
+        Volume = Area (cm²) * Height (cm) * Shape Factor
+        """
+        area_cm2 = self.calculate_surface_area_cm2(image_path)
+        k_shape = self._get_shape_factor(food_label)
+        volume_cm3 = area_cm2 * height_cm * k_shape
+
+        return {
+            "surface_area_cm2": round(area_cm2, 2),
+            "height_cm": round(height_cm, 2),
+            "shape_factor": k_shape,
+            "volume_cm3": round(volume_cm3, 2),
+        }
+
+    def reconcile_mass(
+        self, edamam_mass_g: float, volume_cm3: float, food_label: str = ""
+    ) -> Dict[str, Any]:
+        """
+        Cross-validates Edamam's visual mass against physical volume using expected density.
+        Returns the most physically accurate mass estimate in grams.
+        """
+        if volume_cm3 <= 0:
+            return {
+                "final_mass_g": edamam_mass_g,
+                "status": "unvalidated_zero_volume",
+                "calculated_density": None,
+            }
+
+        calculated_density = edamam_mass_g / volume_cm3
+        label = food_label.lower()
+
+        # Expected food densities (g/cm³)
+        if any(w in label for w in ["bread", "popcorn", "cake"]):
+            expected_density = 0.35
+        elif any(w in label for w in ["soup", "beverage", "water", "milk"]):
+            expected_density = 1.0
+        else:
+            expected_density = 0.92  # Average cooked meal (water + fats + carbs)
+
+        # Plausibility bounds (±35% variance around expected density)
+        min_density = expected_density * 0.65
+        max_density = expected_density * 1.35
+
+        if min_density <= calculated_density <= max_density:
+            # Edamam estimate is physically realistic
+            final_mass = edamam_mass_g
+            status = "validated_edamam_mass"
+        else:
+            # Edamam misjudged scale — fallback to Volume * Expected Density
+            final_mass = volume_cm3 * expected_density
+            status = "reconciled_via_volume_density"
+
+        return {
+            "final_mass_g": round(final_mass, 2),
+            "raw_edamam_mass_g": edamam_mass_g,
+            "calculated_density_g_cm3": round(calculated_density, 3),
+            "expected_density_g_cm3": expected_density,
+            "status": status,
+        }