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image_classifier.py

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+"""
+Simplified Computer Vision Model
+A lightweight image classifier for demonstration
+"""
+
+import random
+
+
+class ImageClassifier:
+    def __init__(self):
+        """
+        Initialize the image classifier
+        In a real implementation, this would load a pre-trained model
+        """
+        # Sample categories for demonstration
+        self.categories = [
+            "person", "bicycle", "car", "motorcycle", "airplane", "bus", 
+            "train", "truck", "boat", "traffic light", "fire hydrant",
+            "stop sign", "parking meter", "bench", "bird", "cat", "dog",
+            "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
+            "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+            "skis", "snowboard", "sports ball", "kite", "baseball bat",
+            "baseball glove", "skateboard", "surfboard", "tennis racket",
+            "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
+            "banana", "apple", "sandwich", "orange", "broccoli", "carrot",
+            "hot dog", "pizza", "donut", "cake", "chair", "couch",
+            "potted plant", "bed", "dining table", "toilet", "tv", "laptop",
+            "mouse", "remote", "keyboard", "cell phone", "microwave",
+            "oven", "toaster", "sink", "refrigerator", "book", "clock",
+            "vase", "scissors", "teddy bear", "hair drier", "toothbrush"
+        ]
+    
+    def classify_image(self, image_path_or_url):
+        """
+        Simulate image classification by returning random top 5 predictions
+        In a real implementation, this would process the image with a neural network
+        """
+        # For demonstration, return random categories with random probabilities
+        # that sum to near 1.0
+        selected_categories = random.sample(self.categories, 5)
+        
+        results = []
+        total_prob = 0
+        for i, category in enumerate(selected_categories):
+            # Generate probabilities that decrease for lower-ranked items
+            prob = max(0.1, 0.8 - (i * 0.15))
+            total_prob += prob
+        
+        # Normalize probabilities to sum to approximately 1.0
+        normalized_results = []
+        for i, category in enumerate(selected_categories):
+            base_prob = max(0.1, 0.8 - (i * 0.15))
+            normalized_prob = (base_prob / total_prob) * 0.9  # Scale to 0.9 to leave room for others
+            normalized_results.append({
+                "label": category,
+                "probability": round(normalized_prob, 4),
+                "category_id": self.categories.index(category)
+            })
+        
+        # Sort by probability descending
+        normalized_results.sort(key=lambda x: x['probability'], reverse=True)
+        
+        return normalized_results
+
+
+def main():
+    # Example usage
+    classifier = ImageClassifier()
+    
+    print("Image Classifier Demo:")
+    print("=" * 50)
+    print("This is a simplified demo. In a real implementation,")
+    print("the model would process actual images using deep learning.")
+    print()
+    
+    results = classifier.classify_image("sample_image.jpg")
+    
+    print("Top 5 predictions:")
+    for i, result in enumerate(results, 1):
+        print(f"{i}. {result['label']}: {result['probability']}")
+
+
+if __name__ == "__main__":
+    main()