computer-vision-demo / 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()

	"""
	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()