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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() |