Delete image_classifier.py

image_classifier.py

@@ -1,139 +0,0 @@
-import numpy as np
-import cv2
-from tensorflow.keras.applications import ResNet50
-from tensorflow.keras.applications.resnet50 import preprocess_input
-from tensorflow.keras.preprocessing import image
-from skimage.metrics import structural_similarity as ssim
-import os
-import argparse
-
-class ImageCharacterClassifier:
-    def __init__(self, similarity_threshold=0.7):
-        # Initialize ResNet50 model without top classification layer
-        self.model = ResNet50(weights='imagenet', include_top=False, pooling='avg')
-        self.similarity_threshold = similarity_threshold
-
-    def load_and_preprocess_image(self, image_path, target_size=(224, 224)):
-        # Load and preprocess image for ResNet50
-        img = image.load_img(image_path, target_size=target_size)
-        img_array = image.img_to_array(img)
-        img_array = np.expand_dims(img_array, axis=0)
-        img_array = preprocess_input(img_array)
-        return img_array
-
-    def extract_features(self, image_path):
-        # Extract deep features using ResNet50
-        preprocessed_img = self.load_and_preprocess_image(image_path)
-        features = self.model.predict(preprocessed_img)
-        return features
-
-    def calculate_ssim(self, img1_path, img2_path):
-        # Calculate SSIM between two images
-        img1 = cv2.imread(img1_path)
-        img2 = cv2.imread(img2_path)
-        
-        # Convert to grayscale if images are in color
-        if len(img1.shape) == 3:
-            img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
-        if len(img2.shape) == 3:
-            img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
-        
-        # Resize images to same dimensions
-        img2 = cv2.resize(img2, (img1.shape[1], img1.shape[0]))
-        
-        score = ssim(img1, img2)
-        return score
-
-    def classify_images(self, reference_image_path, image_folder_path):
-        # Extract features from reference image
-        reference_features = self.extract_features(reference_image_path)
-        
-        results = []
-        
-        # Process each image in the folder
-        for image_name in os.listdir(image_folder_path):
-            if image_name.lower().endswith(('.png', '.jpg', '.jpeg')):
-                image_path = os.path.join(image_folder_path, image_name)
-                
-                try:
-                    # Calculate SSIM
-                    ssim_score = self.calculate_ssim(reference_image_path, image_path)
-                    
-                    # Extract features and calculate similarity
-                    image_features = self.extract_features(image_path)
-                    
-                    # Calculate cosine similarity
-                    feature_similarity = np.dot(reference_features.flatten(), 
-                                             image_features.flatten()) / (
-                        np.linalg.norm(reference_features) * np.linalg.norm(image_features))
-                    
-                    # Give more weight to feature similarity
-                    combined_similarity = (0.3 * ssim_score + 0.7 * feature_similarity)
-                    
-                    # Classify based on similarity threshold
-                    is_similar = combined_similarity >= self.similarity_threshold
-                    
-                    results.append({
-                        'image_name': image_name,
-                        'ssim_score': ssim_score,
-                        'feature_similarity': feature_similarity,
-                        'combined_similarity': combined_similarity,
-                        'is_similar': is_similar
-                    })
-                    
-                except Exception as e:
-                    print(f"Error processing {image_name}: {str(e)}")
-                    continue
-        
-        return results
-
-def main():
-    # Create argument parser
-    parser = argparse.ArgumentParser(description='Image Character Classification')
-    parser.add_argument('--reference', '-r', 
-                       type=str, 
-                       required=True,
-                       help='Path to reference image')
-    parser.add_argument('--folder', '-f', 
-                       type=str, 
-                       required=True,
-                       help='Path to folder containing images to compare')
-    parser.add_argument('--threshold', '-t', 
-                       type=float, 
-                       default=0.5,  # Lowered the default threshold
-                       help='Similarity threshold (default: 0.5)')
-    
-    # Parse arguments
-    args = parser.parse_args()
-    
-    # Initialize classifier
-    classifier = ImageCharacterClassifier(similarity_threshold=args.threshold)
-    
-    # Check if paths exist
-    if not os.path.exists(args.reference):
-        print(f"Error: Reference image not found at {args.reference}")
-        return
-    
-    if not os.path.exists(args.folder):
-        print(f"Error: Image folder not found at {args.folder}")
-        return
-    
-    # Perform classification
-    results = classifier.classify_images(args.reference, args.folder)
-    
-    # Sort results by similarity score
-    results.sort(key=lambda x: x['combined_similarity'], reverse=True)
-    
-    # Print results
-    print("\nResults sorted by similarity (highest to lowest):")
-    print("-" * 50)
-    for result in results:
-        print(f"\nImage: {result['image_name']}")
-        print(f"SSIM Score: {result['ssim_score']:.3f}")
-        print(f"Feature Similarity: {result['feature_similarity']:.3f}")
-        print(f"Combined Similarity: {result['combined_similarity']:.3f}")
-        print(f"Is Similar: {result['is_similar']}")
-        print("-" * 30)
-
-if __name__ == "__main__":
-    main()