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| import joblib | |
| import numpy as np | |
| import pandas as pd | |
| import cv2 | |
| from skimage.color import rgb2hsv | |
| from skimage.measure import shannon_entropy | |
| from scipy.ndimage import generic_filter | |
| # Extract features from the image (same as your previous code) | |
| def extract_features(image_path): | |
| image = cv2.imread(image_path) | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # Extract RGB means | |
| meanr = np.mean(image[:, :, 0]) # Red channel | |
| meang = np.mean(image[:, :, 1]) # Green channel | |
| meanb = np.mean(image[:, :, 2]) # Blue channel | |
| # Convert to HSI and compute HHR | |
| hsv_image = rgb2hsv(image) | |
| hue = hsv_image[:, :, 0] | |
| high_hue_pixels = np.sum(hue > 0.95) | |
| total_pixels = hue.size | |
| HHR = high_hue_pixels / total_pixels | |
| # Convert to Grayscale | |
| gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
| # Compute Entropy | |
| Ent = shannon_entropy(gray_image) | |
| # Compute Brightness | |
| B = np.mean(gray_image) | |
| # Sliding window for gray-level features | |
| def g1_filter(window): | |
| return window[4] - np.min(window) | |
| def g2_filter(window): | |
| return np.max(window) - window[4] | |
| def g3_filter(window): | |
| return window[4] - np.mean(window) | |
| def g4_filter(window): | |
| return np.std(window) | |
| def g5_filter(window): | |
| return window[4] | |
| # Apply filters with 3x3 window | |
| g1 = generic_filter(gray_image, g1_filter, size=3).mean() | |
| g2 = generic_filter(gray_image, g2_filter, size=3).mean() | |
| g3 = generic_filter(gray_image, g3_filter, size=3).mean() | |
| g4 = generic_filter(gray_image, g4_filter, size=3).mean() | |
| g5 = generic_filter(gray_image, g5_filter, size=3).mean() | |
| # Return features | |
| return { | |
| "meanr": meanr, | |
| "meang": meang, | |
| "meanb": meanb, | |
| "HHR": HHR, | |
| "Ent": Ent, | |
| "B": B, | |
| "g1": g1, | |
| "g2": g2, | |
| "g3": g3, | |
| "g4": g4, | |
| "g5": g5, | |
| } | |
| # Function to make predictions | |
| def predict_hemoglobin(age, gender, image_path): | |
| # Extract features from the image | |
| features = extract_features(image_path) | |
| # Add age and gender to the features | |
| features['age'] = age | |
| features['gender'] = 1 if gender.lower() == 'male' else 0 | |
| # Convert features to DataFrame | |
| features_df = pd.DataFrame([features]) | |
| # Load the pre-trained models | |
| svr_model = joblib.load('svr_model.pkl') # SVR model | |
| scaler = joblib.load('minmax_scaler.pkl') # MinMaxScaler | |
| label_encoder = joblib.load('label_encoder.pkl') # LabelEncoder | |
| # Apply MinMaxScaler and LabelEncoder transformations | |
| # For age and gender, you can scale them or leave them as-is, depending on your training procedure | |
| features_df_scaled = scaler.transform(features_df) | |
| # Make the prediction | |
| hemoglobin = svr_model.predict(features_df_scaled)[0] | |
| return hemoglobin | |