Recognize Equipment Components with CLIP and OpenCV

                      
                     
import os
import cv2
import torch
from torchvision import transforms
from typing import List, Dict, Any

# Configuration
CLIP_MODEL = 'ViT-B/32'  # Specify the CLIP model
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

# Initialize CLIP model
clip_model, preprocess = torch.hub.load('openai/CLIP', CLIP_MODEL, device=DEVICE)

def load_image(image_path: str) -> Any:
    try:
        image = cv2.imread(image_path)
        if image is None:
            raise ValueError('Image not found or cannot be loaded.')
        return image
    except Exception as e:
        print(f'Error loading image: {e}')  # Error handling

def preprocess_image(image: Any) -> torch.Tensor:
    image = preprocess(image).unsqueeze(0).to(DEVICE)  # Preprocess image
    return image

# Function to recognize equipment components
def recognize_components(image_path: str, components: List[str]) -> Dict[str, float]:
    image = load_image(image_path)
    processed_image = preprocess_image(image)
    with torch.no_grad():
        image_features = clip_model.encode_image(processed_image)
        results = {}
        for component in components:
            text_features = clip_model.encode_text(clip_model.tokenize(component).to(DEVICE))
            similarity = (image_features @ text_features.T).item()
            results[component] = similarity
    return results

if __name__ == '__main__':
    components_to_recognize = ['pump', 'valve', 'conveyor']  # Example components
    image_path = 'path/to/equipment_image.jpg'  # Path to the image
    recognition_results = recognize_components(image_path, components_to_recognize)
    print(recognition_results)  # Output results