Computer-Vision.py

# computer_vision.py

"""
Computer Vision Module for Autonomous Vehicle Navigation

This module contains the implementation of computer vision techniques
to detect and classify objects in the vehicle's environment.

Techniques Used:
- Convolutional Neural Networks (CNNs)
- Object Detection
- Semantic Segmentation
"""

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image

class ComputerVision:
    def __init__(self, model_path):
        """
        Initialize the ComputerVision class with the path to the trained model.
        
        :param model_path: str, path to the pre-trained model
        """
        self.model = load_model(model_path)
    
    def preprocess_image(self, img_path, target_size=(224, 224)):
        """
        Preprocess the input image for prediction.
        
        :param img_path: str, path to the input image
        :param target_size: tuple, target size for resizing the image
        :return: preprocessed image array
        """
        img = image.load_img(img_path, target_size=target_size)
        img_array = image.img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array /= 255.0
        return img_array
    
    def predict(self, img_array):
        """
        Make a prediction on the input image.
        
        :param img_array: array, preprocessed image array
        :return: prediction result
        """
        predictions = self.model.predict(img_array)
        return predictions
    
    def object_detection(self, frame):
        """
        Perform object detection on the input frame.
        
        :param frame: array, input frame from the camera
        :return: frame with detected objects
        """
        # Placeholder for object detection implementation
        # Replace with your object detection logic
        return frame
    
    def semantic_segmentation(self, frame):
        """
        Perform semantic segmentation on the input frame.
        
        :param frame: array, input frame from the camera
        :return: segmented frame
        """
        # Placeholder for semantic segmentation implementation
        # Replace with your semantic segmentation logic
        return frame
    
    def process_video(self, video_path, output_path):
        """
        Process a video file for object detection and segmentation.
        
        :param video_path: str, path to the input video file
        :param output_path: str, path to save the output video file
        """
        cap = cv2.VideoCapture(video_path)
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        out = cv2.VideoWriter(output_path, fourcc, 20.0, (640, 480))

        while cap.isOpened():
            ret, frame = cap.read()
            if ret:
                detected_frame = self.object_detection(frame)
                segmented_frame = self.semantic_segmentation(detected_frame)
                out.write(segmented_frame)
                cv2.imshow('Frame', segmented_frame)

                if cv2.waitKey(1) & 0xFF == ord('q'):
                    break
            else:
                break

        cap.release()
        out.release()
        cv2.destroyAllWindows()

if __name__ == "__main__":
    # Example usage
    model_path = 'models/object_detection_model.h5'
    cv_module = ComputerVision(model_path)
    
    # Process a sample image
    img_path = 'data/sample_image.jpg'
    img_array = cv_module.preprocess_image(img_path)
    predictions = cv_module.predict(img_array)
    print("Predictions:", predictions)
    
    # Process a sample video
    video_path = 'data/sample_video.mp4'
    output_path = 'data/output_video.avi'
    cv_module.process_video(video_path, output_path)