class VisualProcessingAGI:
    def __init__(self):
        self.layer_1 = ImagePreprocessing()
        self.layer_2 = FeatureExtraction()
        self.layer_3 = ObjectDetection()
        self.layer_4 = SemanticSegmentation()
        self.layer_5 = ContextualUnderstanding()

    def preprocess_image(self, image_path):
        """Preprocess the input image for further processing."""
        preprocessed_image = self.layer_1.apply_filters(image_path)
        return preprocessed_image

    def extract_features(self, preprocessed_image):
        """Extract relevant features from the preprocessed image."""
        features = self.layer_2.extract_features(preprocessed_image)
        return features

    def detect_objects(self, features):
        """Detect objects in the image using extracted features."""
        detected_objects = self.layer_3.detect_objects(features)
        return detected_objects

    def segment_semantic(self, preprocessed_image):
        """Perform semantic segmentation to identify regions of interest."""
        segmented_image = self.layer_4.segment_semantic(preprocessed_image)
        return segmented_image

    def understand_context(self, detected_objects, segmented_image):
        """Understand the context and relationships between objects in the image."""
        contextual_info = self.layer_5.get_context(detected_objects, segmented_image)
        return contextual_info

# Define layers
class ImagePreprocessing:
    def apply_filters(self, image_path):
        # Apply filters like resizing, normalization, etc.
        pass

class FeatureExtraction:
    def extract_features(self, preprocessed_image):
        # Extract features such as edges, textures, and other relevant attributes
        pass

class ObjectDetection:
    def detect_objects(self, features):
        # Use a machine learning model to detect objects based on the extracted features
        pass

class SemanticSegmentation:
    def segment_semantic(self, preprocessed_image):
        # Perform semantic segmentation to classify each pixel of the image into categories
        pass

class ContextualUnderstanding:
    def get_context(self, detected_objects, segmented_image):
        # Analyze and understand the context based on detected objects and segmented regions
        pass

# Initialize AGI component
visual_agi = VisualProcessingAGI()

# Example usage
image_path = "path/to/image.jpg"
preprocessed_image = visual_agi.preprocess_image(image_path)
features = visual_agi.extract_features(preprocessed_image)
detected_objects = visual_agi.detect_objects(features)
segmented_image = visual_agi.segment_semantic(preprocessed_image)
contextual_info = visual_agi.understand_context(detected_objects, segmented_image)

# Output the results
print(f"Detected objects: {detected_objects}")
print(f"Sematic segmentation: {segmented_image}")
print(f"Contextual understanding: {contextual_info}")