online compiler and debugger for c/c++

import cv2 # Load the Haar cascade XML file for object detection object_cascade = cv2.CascadeClassifier('haarcascade_frontalcatface_extended.xml') # Initialize the robot and camera def initialize_robot(): # Code for initializing the robot and camera pass # Capture an image from the camera def capture_image(): # Code for capturing an image from the camera pass # Perform object detection def detect_objects(image): gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) objects = object_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) return objects # Perform obstacle detection def detect_obstacles(image): # Code for obstacle detection using computer vision pass # Move the robot to avoid obstacles def avoid_obstacles(): # Code for avoiding obstacles pass # Main program def main(): # Initialize the robot and camera initialize_robot() while True: # Capture an image from the camera image = capture_image() # Perform object detection objects = detect_objects(image) # Perform obstacle detection obstacles = detect_obstacles(image) if len(objects) > 0: # Handle detected objects # Code for identifying and interacting with objects for (x, y, w, h) in objects: cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 3) cv2.putText(image, 'Object', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) if len(obstacles) > 0: # Avoid obstacles avoid_obstacles() # Display the result cv2.imshow('Object Detection and Obstacle Avoidance', image) # Break the loop when 'q' is pressed if cv2.waitKey(1) & 0xFF == ord('q'): break # Release the camera resources cv2.destroyAllWindows() if __name__ == "__main__": main() ------------------------------------------------------------------------------------------------------------------- import cv2 import numpy as np CAMERA_PORT = 0 # Initialize camera camera = cv2.VideoCapture(CAMERA_PORT) while True: # Read frame from camera ret, frame = camera.read() # Run computer vision model on frame blobs = run_model(frame) # Check for obstacles obstacles = [] for blob in blobs: if blob_is_obstacle(blob): obstacles.append(blob) # Avoid obstacles if obstacles: largest_obstacle = max(obstacles, key=lambda x: x.size) steer_away(largest_obstacle.center) # Output image for debugging cv2.imshow("Camera View", frame) if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows() ------------------------------------------------------------------------------------------------------------------- class Blob: def __init__(self, x, y, w, h, label): self.x = x self.y = y self.width = w self.height = h self.label = label ------------------------------------------------------------------------------------------------------------------- def steer_away(target_x): robot_x = get_robot_x() if target_x < robot_x: turn_left() else: turn_right() -------------------------------------------------------------------------------------------------------------------def blob_is_significant(blob): return blob.width > MIN_SIZE and blob.distance < MAX_DISTANCE -------------------------------------------------------------------------------------------------------------------def planned_path = plan_path(obstacles) def follow_path(): target = get_next_point(planned_path) steer_toward(target) ------------------------------------------------------------------------------------------------------------------- def detect_obstacles(): cv_blobs = run_cv_model() sonar_blobs = run_sonar_scanner() fused_blobs = fuse(cv_blobs, sonar_blobs) return fused_blobs -------------------------------------------------------------------------------------------------------------------def def test_obstacle_filtering(): # test ignoring small blobs