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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
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