--- title: 'Project documentation template' disqus: hackmd --- [TOC] ## 啟用Docker容器 在本機終端上執行此程式 ```gherkin= sudo docker run -it \ --privileged \ --device /dev/gpiochip0 \ --runtime=nvidia \ --device=/dev/video0:/dev/video0 \ --ipc=host \ -v /home/jetson-nano/Desktop:/data \ -e DISPLAY=$DISPLAY \ -v /tmp/.X11-unix:/tmp/.X11-unix \ ultralytics/ultralytics:latest-jetson-jetpack4 ``` 建立python腳本 --- 自行選擇習慣的腳本編輯器，這裡使用nano ```gherkin= import cv2 from ultralytics import YOLO import Jetson.GPIO as GPIO import time import threading # Load the YOLOv8 model model = YOLO("yolov8n.engine") # Set up GPIO for servo control GPIO.setmode(GPIO.BCM) servo_pin = 13 GPIO.setup(servo_pin, GPIO.OUT) pwm = GPIO.PWM(servo_pin, 50) # 50Hz pwm.start(0) # Define duty cycles for 0 and 90 degrees duty_cycle_90 = 7.5 duty_cycle_0 = 2.5 # Define labels and confidence threshold labels = ["mouse", "keyboard"] confidence_threshold = 0.7 # Function to change servo duty cycle def set_servo_duty_cycle(duty_cycle, duration=2): pwm.ChangeDutyCycle(duty_cycle) time.sleep(duration) # Open the video file cap = cv2.VideoCapture(0) if not cap.isOpened(): print("Error: Could not open webcam.") exit() # Loop through the video frames while cap.isOpened(): # Read a frame from the video success, frame = cap.read() if success: # Run YOLOv8 tracking on the frame, persisting tracks between frames try: results = model.track(frame, persist=True) except Exception as e: print(f"Error during tracking: {e}") break # Visualize the results on the frame annotated_frame = results[0].plot() # Process results and perform actions based on detected labels for result in results: for box in result.boxes: label_index = int(box.cls[0].item()) # Label index confidence = box.conf[0].item() # Confidence score # Ensure result.names is accessible if hasattr(result, 'names') and label_index < len(result.names): label = result.names[label_index] # Label name # Trigger servo motor based on label if label == labels[0] and confidence > confidence_threshold: threading.Thread(target=set_servo_duty_cycle, args=(duty_cycle_90,)).start() elif label == labels[1] and confidence > confidence_threshold: threading.Thread(target=set_servo_duty_cycle, args=(duty_cycle_0,)).start() # Display the annotated frame cv2.imshow("YOLOv8 Tracking", annotated_frame) # Break the loop if 'q' is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break else: # Break the loop if the end of the video is reached break # Release the video capture object and close the display window cap.release() cv2.destroyAllWindows() pwm.stop() GPIO.cleanup() ``` 下載Jetson.GPIO套件 --- ```gherkin apt install Jetson.GPIO ``` ```gherkin Name: Jetson.GPIO Version: 2.1.6 Summary: A module to control Jetson GPIO channels Home-page: https://github.com/NVIDIA/jetson-gpio Author: NVIDIA Author-email: linux-tegra-bugs@nvidia.com License: MIT Location: /usr/local/lib/python3.8/dist-packages Requires: Required-by: ``` 測試馬達用腳本 ```gherkin import Jetson.GPIO as GPIO import time # GPIO.setmode(GPIO.BCM) # servo_pin = 13 # GPIO.setup(servo_pin, GPIO.OUT) # pwm = GPIO.PWM(servo_pin, 50) # 50Hz # pwm.start(0) duty_cycle_90 = 7.5 # 90 duty_cycle_0 = 2.5 # 0 try: # time.sleep(1) pwm.ChangeDutyCycle(duty_cycle_90) time.sleep(2) # # pwm.ChangeDutyCycle(duty_cycle_0) time.sleep(1) # finally: # pwm.stop() GPIO.cleanup() ``` 設定PWM ```gherkin sudo /opt/nvidia/jetson-io/jetson-io.py ```