class VisualProcessing:
    def __init__(self, sensor_input_size=(128, 128), reward_system=None):
        self.sensor_input_size = sensor_input_size
        self.reward_system = reward_system
        self.learned_features = []
        self.attention_map = None
        self.weights = self.initialize_weights()
    
    def initialize_weights(self):
        """Initialize neural network weights for feature extraction."""
        return {
            'conv1': np.random.randn(3, 3, 3) * 0.01,
            'fc1': np.random.randn(64, 256) * 0.01,
            'output': np.random.randn(256, 1) * 0.01
        }
    
    def process_image(self, image):
        """Process visual input and return detected objects."""
        features = self.extract_features(image)
        objects = self.detect_objects(features)
        attention_scores = self.apply_attention(features)
        
        if self.reward_system:
            reward = self.reward_system.calculate_reward(objects)
            self.learn(reward)
        
        return {
            'objects': objects,
            'attention_map': attention_scores,
            'features': features
        }
    
    def extract_features(self, image):
        """Extract features from the input image."""
        # Simulate feature extraction using basic 2D convolution
        channels = []
        for ch in range(3):  # RGB channels
            filtered = np.zeros_like(image[:, :, ch])
            for i in range(0, self.sensor_input_size[0], 3):
                for j in range(0, self.sensor_input_size[1], 3):
                    if np.random.rand() > 0.5:
                        filtered[i:i+2, j:j+2] = image[:, :, ch][i:i+2, j:j+2]
            channels.append(filtered)
        return np.stack(channels, axis=2)
    
    def detect_objects(self, features):
        """Detect objects in the feature map."""
        # Simple object detection using thresholding
        object_probabilities = {}
        for i in range(0, self.sensor_input_size[0], 4):
            for j in range(0, self.sensor_input_size[1], 4):
                region = features[i:i+3, j:j+3]
                avg = np.mean(region)
                if avg > 0.7:
                    object_probabilities[(i,j)] = avg
        return object_probabilities
    
    def apply_attention(self, features):
        """Apply attention mechanism to focus on relevant areas."""
        # Simple attention implementation using learned features
        attention_scores = np.zeros((self.sensor_input_size[0], 
                                  self.sensor_input_size[1]))
        for i in range(self.sensor_input_size[0]):
            for j in range(self.sensor_input_size[1]):
                attention_scores[i,j] = np.dot(
                    self.learned_features, features[i,j].flatten()
                )
        return attention_scores
    
    def learn(self, reward):
        """Update neural network weights based on rewards."""
        if reward > 0:
            # Update weights using simple gradient ascent
            learning_rate = 0.01
            for layer in self.weights:
                self.weights[layer] += learning_rate * np.random.randn(*self.weights[layer].shape)
    
    def get_attention_mask(self):
        """Return current attention map."""
        return self.attention_map
    
    def update_learned_features(self, new_features):
        """Update learned features based on new observations."""
        self.learned_features.extend(new_features)