image_pre_process_model.py

import cv2
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Example function to extract image features
def extract_features(image):
    # Implement feature extraction here (e.g., image sharpness, contrast metrics)
    # Return features as numpy array
    return np.array([])  # Placeholder for actual feature extraction

# Example function to preprocess image based on predicted level
def preprocess_image(image, preprocessing_level):
    # Implement preprocessing based on preprocessing_level
    # Return preprocessed image
    return image  # Placeholder for actual preprocessing

# Example dataset (replace with your own)
def load_dataset():
    # Load and preprocess images
    images = []
    labels = []
    for image_path in image_path:
        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
        preprocessed_image = preprocess_image(image)
        features = extract_features(preprocessed_image)
        images.append(features)
        labels.append(preprocessing_level)
    return np.array(images), np.array(labels)

# Load dataset
X, y = load_dataset()

# Split dataset into train and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train model (example with RandomForestClassifier)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict on validation set
y_pred = model.predict(X_val)

# Evaluate model performance
accuracy = accuracy_score(y_val, y_pred)
print(f"Validation Accuracy: {accuracy:.2f}")

# Example usage in deployment
def predict_preprocessing_level(image):
    preprocessed_image = preprocess_image(image)
    features = extract_features(preprocessed_image)
    preprocessing_level = model.predict([features])[0]
    return preprocessing_level

# Example usage
image_path = 'path_to_new_image.jpg'
new_image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
predicted_level = predict_preprocessing_level(new_image)
preprocessed_new_image = preprocess_image(new_image, predicted_level)