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