| import cv2
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| import numpy as np
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| import xgboost as xgb
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| import os
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|
|
| def get_lab_stats(image_path):
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| """Extracts LAB mean and std for each channel from the image."""
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| img = cv2.imread(image_path)
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| if img is None:
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| raise FileNotFoundError(f"Image not found at: {image_path}")
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|
|
| img = cv2.resize(img, (50, 50))
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| lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
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| l, a, b = cv2.split(lab)
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|
|
| return {
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| "L_mean": np.mean(l), "A_mean": np.mean(a), "B_mean": np.mean(b),
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| "L_std": np.std(l), "A_std": np.std(a), "B_std": np.std(b)
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| }
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|
|
| def extract_features(c_path, t_path):
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| """Computes final 15-dim feature vector based on LAB color stats."""
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| c_feat = get_lab_stats(c_path)
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| t_feat = get_lab_stats(t_path)
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|
|
|
|
| L_ratio = t_feat["L_mean"] / (c_feat["L_mean"] + 1e-6)
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| A_ratio = t_feat["A_mean"] / (c_feat["A_mean"] + 1e-6)
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| B_ratio = t_feat["B_mean"] / (c_feat["B_mean"] + 1e-6)
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|
|
|
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| L_diff = t_feat["L_mean"] - c_feat["L_mean"]
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| A_diff = t_feat["A_mean"] - c_feat["A_mean"]
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| B_diff = t_feat["B_mean"] - c_feat["B_mean"]
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|
|
|
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| L_std_ratio = t_feat["L_std"] / (c_feat["L_std"] + 1e-6)
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| A_std_ratio = t_feat["A_std"] / (c_feat["A_std"] + 1e-6)
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| B_std_ratio = t_feat["B_std"] / (c_feat["B_std"] + 1e-6)
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|
|
|
|
| feature_vector = [
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| L_ratio, A_ratio, B_ratio,
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| L_diff, A_diff, B_diff,
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| L_std_ratio, A_std_ratio, B_std_ratio,
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| c_feat["L_std"], c_feat["A_std"], c_feat["B_std"],
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| t_feat["L_std"], t_feat["A_std"], t_feat["B_std"]
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| ]
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|
|
| return np.array(feature_vector)
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|
|
| def quantitative_analysis(c_path, t_path, model_path="xgb_model.json"):
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| """Predicts hCG concentration from C and T line image paths."""
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| if not os.path.exists(model_path):
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| raise FileNotFoundError(f"Model file not found at: {model_path}")
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|
|
|
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| booster = xgb.Booster()
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| booster.load_model(model_path)
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|
|
|
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| features = extract_features(c_path, t_path)
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| dmatrix = xgb.DMatrix(features.reshape(1, -1))
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|
|
|
|
| prediction = booster.predict(dmatrix)[0]
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| return round(prediction, 2)
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|
