File size: 2,462 Bytes
58c7472 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import cv2
import numpy as np
import xgboost as xgb
import os
def get_lab_stats(image_path):
"""Extracts LAB mean and std for each channel from the image."""
img = cv2.imread(image_path)
if img is None:
raise FileNotFoundError(f"Image not found at: {image_path}")
img = cv2.resize(img, (50, 50)) # Optional resize for consistency
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
return {
"L_mean": np.mean(l), "A_mean": np.mean(a), "B_mean": np.mean(b),
"L_std": np.std(l), "A_std": np.std(a), "B_std": np.std(b)
}
def extract_features(c_path, t_path):
"""Computes final 15-dim feature vector based on LAB color stats."""
c_feat = get_lab_stats(c_path)
t_feat = get_lab_stats(t_path)
# Ratios
L_ratio = t_feat["L_mean"] / (c_feat["L_mean"] + 1e-6)
A_ratio = t_feat["A_mean"] / (c_feat["A_mean"] + 1e-6)
B_ratio = t_feat["B_mean"] / (c_feat["B_mean"] + 1e-6)
# Differences
L_diff = t_feat["L_mean"] - c_feat["L_mean"]
A_diff = t_feat["A_mean"] - c_feat["A_mean"]
B_diff = t_feat["B_mean"] - c_feat["B_mean"]
# Std Ratios
L_std_ratio = t_feat["L_std"] / (c_feat["L_std"] + 1e-6)
A_std_ratio = t_feat["A_std"] / (c_feat["A_std"] + 1e-6)
B_std_ratio = t_feat["B_std"] / (c_feat["B_std"] + 1e-6)
# Include raw stds
feature_vector = [
L_ratio, A_ratio, B_ratio,
L_diff, A_diff, B_diff,
L_std_ratio, A_std_ratio, B_std_ratio,
c_feat["L_std"], c_feat["A_std"], c_feat["B_std"],
t_feat["L_std"], t_feat["A_std"], t_feat["B_std"]
]
return np.array(feature_vector)
def quantitative_analysis(c_path, t_path, model_path="xgb_model.json"):
"""Predicts hCG concentration from C and T line image paths."""
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file not found at: {model_path}")
# Load trained model
booster = xgb.Booster()
booster.load_model(model_path)
# Extract features
features = extract_features(c_path, t_path)
dmatrix = xgb.DMatrix(features.reshape(1, -1))
# Make prediction
prediction = booster.predict(dmatrix)[0]
return round(prediction, 2)
# 🔍 Example usage
# pred = quantitative_analysis("path_to_c_image.jpg", "path_to_t_image.jpg")
# print(f"Predicted hCG Concentration: {pred} mIU/mL")
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