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| import os | |
| import cv2 | |
| import numpy as np | |
| import logging | |
| from tensorflow.keras.models import load_model | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| class TBImageProcessor: | |
| """Processes TB images using a trained CNN model for risk assessment.""" | |
| def __init__(self, model_path="tb_cnn_model.h5"): | |
| # Validate model path | |
| if not os.path.exists(model_path): | |
| logger.error(f"Model path '{model_path}' does not exist. Please check the path.") | |
| self.model = None | |
| return | |
| try: | |
| self.model = load_model(model_path) | |
| logger.info("TB Image Processor model loaded successfully.") | |
| except Exception as e: | |
| logger.error(f"Failed to load the TB Image Model: {e}") | |
| self.model = None | |
| def process_image(self, image_path): | |
| """Analyze a TB image and return risk assessment.""" | |
| # Validate the image file | |
| if not os.path.exists(image_path): | |
| logger.error(f"Image path '{image_path}' does not exist.") | |
| return {"error": "Image file not found."} | |
| if self.model is None: | |
| logger.error("Model is not loaded. Cannot process the image.") | |
| return {"error": "Model not loaded."} | |
| try: | |
| # Load and preprocess image | |
| image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) | |
| if image is None: | |
| logger.error(f"Failed to read the image at path '{image_path}'.") | |
| return {"error": "Invalid image format or corrupted file."} | |
| # Resize for CNN input and normalize | |
| if image.shape[0] < 128 or image.shape[1] < 128: | |
| logger.warning("Image dimensions are smaller than expected, resizing may affect accuracy.") | |
| image = cv2.resize(image, (128, 128)) | |
| image = np.expand_dims(image, axis=[0, -1]) / 255.0 | |
| # Make prediction | |
| prediction = self.model.predict(image) | |
| confidence = float(prediction[0][0]) | |
| result = "TB Detected" if confidence > 0.5 else "No TB" | |
| logger.info(f"Prediction result: {result}, Confidence: {confidence:.2f}") | |
| return { | |
| "result": result, | |
| "confidence": confidence | |
| } | |
| except Exception as e: | |
| logger.error(f"Error during image processing: {e}") | |
| return {"error": f"Failed to process image: {str(e)}"} | |
| # Example usage | |
| if __name__ == "__main__": | |
| # Specify the model and image paths | |
| model_path = "path/to/your/tb_cnn_model.h5" | |
| image_path = "path/to/your/tb_image.jpg" | |
| # Instantiate the processor and analyze the image | |
| processor = TBImageProcessor(model_path=model_path) | |
| result = processor.process_image(image_path=image_path) | |
| # Log or print the final result | |
| if "error" in result: | |
| logger.error(f"Processing failed: {result['error']}") | |
| else: | |
| logger.info(f"Final Result: {result['result']}, Confidence: {result['confidence']:.2f}") |