measurement_processor.py

from typing import Dict, Optional
import numpy as np


class MeasurementProcessor:
    NORMALIZATION_FACTORS = {
        "height": 200.0,
        "weight": 100.0,
        "chest": 150.0,
        "waist": 150.0,
        "hips": 150.0,
        "shoulder_width": 60.0,
        "arm_length": 80.0,
        "leg_length": 120.0,
        "inseam": 100.0,
    }
    
    REQUIRED_MEASUREMENTS = [
        "height",
        "weight",
        "chest",
        "waist",
        "hips",
    ]
    
    OPTIONAL_MEASUREMENTS = {
        "shoulder_width": 40.0,
        "arm_length": 60.0,
        "leg_length": 90.0,
        "inseam": 75.0,
    }
    
    @classmethod
    def validate_measurements(cls, measurements: Dict) -> Dict[str, str]:
        errors = []
        
        for field in cls.REQUIRED_MEASUREMENTS:
            if field not in measurements:
                errors.append(f"Missing required measurement: {field}")
            elif not isinstance(measurements[field], (int, float)):
                errors.append(f"Invalid type for {field}: must be number")
            elif measurements[field] <= 0:
                errors.append(f"Invalid value for {field}: must be positive")
        
        for field, default in cls.OPTIONAL_MEASUREMENTS.items():
            if field in measurements:
                if not isinstance(measurements[field], (int, float)):
                    errors.append(f"Invalid type for {field}: must be number")
                elif measurements[field] <= 0:
                    errors.append(f"Invalid value for {field}: must be positive")
        
        return {
            "valid": len(errors) == 0,
            "errors": errors
        }
    
    @classmethod
    def normalize_measurements(cls, measurements: Dict) -> np.ndarray:
        processed = measurements.copy()
        for field, default in cls.OPTIONAL_MEASUREMENTS.items():
            if field not in processed:
                processed[field] = default
        
        normalized = []
        measurement_order = [
            "height", "weight", "chest", "waist", "hips",
            "shoulder_width", "arm_length", "leg_length", "inseam"
        ]
        
        for field in measurement_order:
            value = processed[field]
            factor = cls.NORMALIZATION_FACTORS[field]
            normalized.append(value / factor)
        
        return np.array(normalized, dtype=np.float32)
    
    @classmethod
    def process(cls, measurements: Dict) -> np.ndarray:
        validation = cls.validate_measurements(measurements)
        if not validation["valid"]:
            raise ValueError(f"Invalid measurements: {', '.join(validation['errors'])}")
        
        return cls.normalize_measurements(measurements)


def process_measurements(measurements: Dict) -> np.ndarray:
    return MeasurementProcessor.process(measurements)