├── README.md ├── car ├── .ipynb_checkpoints │ ├── Untitled-checkpoint.ipynb │ ├── camera-checkpoint.py │ ├── camera2-checkpoint.py │ ├── controller-checkpoint.py │ └── main-checkpoint.py ├── __pycache__ │ ├── camera.cpython-36.pyc │ ├── controller.cpython-36.pyc │ └── neural_network.cpython-36.pyc ├── camera.py ├── control_data.csv ├── controller.py ├── main.py ├── model │ └── autopilot.pt ├── neural_network.py └── pytorch.py ├── commands ├── media ├── JetsonNanoCarPytorch.gif └── carbg.png ├── stl ├── chassis_platform.stl └── roll_bar_camera.stl └── train ├── .ipynb_checkpoints └── autocar-checkpoint.ipynb ├── autocar.ipynb ├── autopilot.pt ├── csv └── control_data.csv └── images ├── .ipynb_checkpoints ├── 0466151c-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 049aeb3e-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 0547293a-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 05ec496a-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 060a4f8c-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 06194aaa-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 06464b40-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 075bd216-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 0853d0ba-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 08718362-6d3e-11e9-a379-a6029617b541-checkpoint.jpg ├── 08b4f980-6d3e-11e9-a379-a6029617b541-checkpoint.jpg └── 090030a8-6d3e-11e9-a379-a6029617b541-checkpoint.jpg └── TEST_IMAGES.zip /README.md: -------------------------------------------------------------------------------- 1 | # AutoCarJetsonNano 2 | PyTorch Python Neural Network Autonomous 1/10 Car for Nvidia Jetson Nano 3 | 4 | ![Jetson Nano 1/10th RC Car](media/carbg.png) 5 | 6 | ## BOM 7 | 8 | * **System Hardware** 9 | * Servo Driver https://www.amazon.com/dp/B014KTSMLA/ref=cm_sw_r_tw_dp_U_x_.PW4CbAD5YKP4 10 | * Jetson Nano https://developer.nvidia.com/embedded/jetson-nano-developer-kit 11 | * Camera https://leopardimaging.com/product/li-imx219-mipi-ff-nano/ 12 | * Wifi Chip https://www.amazon.com/gp/product/B01MZA1AB2/ref=ppx_yo_dt_b_search_asin_title?ie=UTF8&psc=1 13 | * Wifi Attenna https://www.arrow.com/en/products/2042811100/molex 14 | * PS4 Controller https://www.amazon.com/dp/B01LWVX2RG/ref=cm_sw_r_tw_dp_U_x_USW4CbEXZK23G 15 | 16 | * **Car** 17 | * 1/10 4wd Car chassis https://www.amazon.com/dp/B00NYR8D1O/ref=cm_sw_r_tw_dp_U_x_zIW4Cb721TVKE 18 | * Brushed Motor https://hobbyking.com/en_us/540-6527-brushed-motor-90w.html 19 | * Standard Analog Servo https://hobbyking.com/en_us/hobbykingtm-hk15138-standard-analog-servo-4-3kg-0-17sec-38g.html 20 | * 45A Brushed Car ESC https://hobbyking.com/en_us/hobbyking-x-car-45a-brushed-car-esc.html 21 | * 5000mAh 2S 7.4V 60C https://hobbyking.com/en_us/turnigy-5000mah-2s-7-4v-60c-hardcase-pack-roar-approved.html 22 | 23 | * **Accessories** 24 | * Nylon Standoffs https://www.amazon.com/dp/B015S27EG2/ref=cm_sw_r_tw_dp_U_x_LXW4Cb35VNVK2 25 | * SD card https://www.amazon.com/dp/B06XX29S9Q/ref=cm_sw_r_tw_dp_U_x_cYW4Cb3YZFVXY 26 | * Wires https://www.amazon.com/dp/B01BV3Z342/ref=cm_sw_r_tw_dp_U_x_7YW4CbB36CAF7 27 | 28 | ## How to use 29 | 30 | Install python requirements. Install ds4drv and connect PS4 Bluetooth controller to Ubuntu. Inside AutoCarJetsonNano/car, start main.py and drive car around. Images are captured when speed is > 0. After driving car around, offload images to remote computer for training and copy control_data.csv . Start autocar via Jupyter Lab or Notebook and train model via pytorch. Load model back to Nano after training and start main.py Press X to launch autopilot. 31 | 32 | ![Demo](media/JetsonNanoCarPytorch.gif) 33 | -------------------------------------------------------------------------------- /car/.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /car/.ipynb_checkpoints/camera-checkpoint.py: -------------------------------------------------------------------------------- 1 | import traitlets 2 | from traitlets.config.configurable import SingletonConfigurable 3 | import atexit 4 | import cv2 5 | import threading 6 | import numpy as np 7 | 8 | 9 | class Camera(SingletonConfigurable): 10 | 11 | value = traitlets.Any() 12 | 13 | # config 14 | width = traitlets.Integer(default_value=224).tag(config=True) 15 | height = traitlets.Integer(default_value=224).tag(config=True) 16 | fps = traitlets.Integer(default_value=21).tag(config=True) 17 | capture_width = traitlets.Integer(default_value=3280).tag(config=True) 18 | capture_height = traitlets.Integer(default_value=2464).tag(config=True) 19 | 20 | def __init__(self, *args, **kwargs): 21 | self.value = np.empty((self.height, self.width, 3), dtype=np.uint8) 22 | super(Camera, self).__init__(*args, **kwargs) 23 | 24 | try: 25 | self.cap = cv2.VideoCapture(self._gst_str(), cv2.CAP_GSTREAMER) 26 | 27 | re, image = self.cap.read() 28 | 29 | if not re: 30 | raise RuntimeError('Could not read image from camera.') 31 | 32 | self.value = image 33 | self.start() 34 | except: 35 | self.stop() 36 | raise RuntimeError( 37 | 'Could not initialize camera. Please see error trace.') 38 | 39 | atexit.register(self.stop) 40 | 41 | def _capture_frames(self): 42 | while True: 43 | re, image = self.cap.read() 44 | if re: 45 | self.value = image 46 | else: 47 | break 48 | 49 | def _gst_str(self): 50 | return 'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=%d, height=%d, format=(string)NV12, framerate=(fraction)%d/1 ! nvvidconv ! video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! videoconvert ! appsink' % ( 51 | self.capture_width, self.capture_height, self.fps, self.width, self.height) 52 | 53 | def start(self): 54 | if not self.cap.isOpened(): 55 | self.cap.open(self._gst_str(), cv2.CAP_GSTREAMER) 56 | if not hasattr(self, 'thread') or not self.thread.isAlive(): 57 | self.thread = threading.Thread(target=self._capture_frames) 58 | self.thread.start() 59 | 60 | def stop(self): 61 | if hasattr(self, 'cap'): 62 | self.cap.release() 63 | if hasattr(self, 'thread'): 64 | self.thread.join() 65 | 66 | def restart(self): 67 | self.stop() 68 | self.start() 69 | -------------------------------------------------------------------------------- /car/.ipynb_checkpoints/camera2-checkpoint.py: -------------------------------------------------------------------------------- 1 | # MIT License 2 | # Copyright (c) 2019 JetsonHacks 3 | # See license 4 | # Using a CSI camera (such as the Raspberry Pi Version 2) connected to a 5 | # NVIDIA Jetson Nano Developer Kit using OpenCV 6 | # Drivers for the camera and OpenCV are included in the base image 7 | 8 | import cv2 9 | from datetime import datetime 10 | 11 | # gstreamer_pipeline returns a GStreamer pipeline for capturing from the CSI camera 12 | # Defaults to 1280x720 @ 60fps 13 | # Flip the image by setting the flip_method (most common values: 0 and 2) 14 | # display_width and display_height determine the size of the window on the screen 15 | 16 | def gstreamer_pipeline (capture_width=640, capture_height=360, display_width=640, display_height=360, framerate=60, flip_method=2) : 17 | return ('nvarguscamerasrc ! ' 18 | 'video/x-raw(memory:NVMM), ' 19 | 'width=(int)%d, height=(int)%d, ' 20 | 'format=(string)NV12, framerate=(fraction)%d/1 ! ' 21 | 'nvvidconv flip-method=%d ! ' 22 | 'video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! ' 23 | 'videoconvert ! ' 24 | 'video/x-raw, format=(string)BGR ! appsink' % (capture_width,capture_height,framerate,flip_method,display_width,display_height)) 25 | 26 | def show_camera(): 27 | # To flip the image, modify the flip_method parameter (0 and 2 are the most common) 28 | print(gstreamer_pipeline(flip_method=2)) 29 | cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=2), cv2.CAP_GSTREAMER) 30 | if cap.isOpened(): 31 | window_handle = cv2.namedWindow('CSI Camera', cv2.WINDOW_AUTOSIZE) 32 | # Window 33 | count = 0 34 | while cv2.getWindowProperty('CSI Camera',0) >= 0: 35 | ret_val, img = cap.read(); 36 | cv2.imshow('CSI Camera',img) 37 | count = 0 38 | while ret_val: 39 | cv2.imwrite("frame%d.jpg" % count, img) # save frame as JPEG file 40 | success,image = cap.read() 41 | print('Read a new frame: ', success) 42 | count += 1 43 | # This also acts as 44 | keyCode = cv2.waitKey(30) & 0xff 45 | # Stop the program on the ESC key 46 | if keyCode == 27: 47 | break 48 | cap.release() 49 | cv2.destroyAllWindows() 50 | else: 51 | print('Unable to open camera') 52 | 53 | def save_camera(): 54 | print(gstreamer_pipeline(flip_method=2)) 55 | cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=2), cv2.CAP_GSTREAMER) 56 | success,image = cap.read() 57 | while success: 58 | now = datetime.now() 59 | cv2.imwrite("video_img/%s.jpg" % now, image) # save frame as JPEG file 60 | success,image = cap.read() 61 | print('Read a new frame: ', success) 62 | 63 | 64 | 65 | if __name__ == '__main__': 66 | save_camera() 67 | -------------------------------------------------------------------------------- /car/.ipynb_checkpoints/controller-checkpoint.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # 4 | # This file presents an interface for interacting with the Playstation 4 Controller 5 | # in Python. Simply plug your PS4 controller into your computer using USB and run this 6 | # script! 7 | # 8 | # NOTE: I assume in this script that the only joystick plugged in is the PS4 controller. 9 | # if this is not the case, you will need to change the class accordingly. 10 | # 11 | # Copyright © 2015 Clay L. McLeod 12 | # 13 | # Distributed under terms of the MIT license. 14 | 15 | import os 16 | import pprint 17 | import pygame 18 | 19 | class PS4Controller(object): 20 | """Class representing the PS4 controller. Pretty straightforward functionality.""" 21 | 22 | def init(self): 23 | """Initialize the joystick components""" 24 | pygame.init() 25 | pygame.joystick.init() 26 | self.controller = pygame.joystick.Joystick(0) 27 | self.controller.init() 28 | self.axis_data = None 29 | self.button_data = None 30 | self.hat_data = None 31 | 32 | print('Pygame init complete') 33 | 34 | def listen(self): 35 | """Listen for events to happen""" 36 | 37 | if not self.axis_data: 38 | self.axis_data = {0:0.0,1:0.0,2:0.0,3:-1.0,4:-1.0,5:0.0} #default 39 | 40 | if not self.button_data: 41 | self.button_data = {} 42 | for i in range(self.controller.get_numbuttons()): 43 | self.button_data[i] = False 44 | 45 | if not self.hat_data: 46 | self.hat_data = {} 47 | for i in range(self.controller.get_numhats()): 48 | self.hat_data[i] = (0, 0) 49 | 50 | for event in pygame.event.get(): 51 | if event.type == pygame.JOYAXISMOTION: 52 | self.axis_data[event.axis] = round(event.value,2) 53 | elif event.type == pygame.JOYBUTTONDOWN: 54 | self.button_data[event.button] = True 55 | elif event.type == pygame.JOYBUTTONUP: 56 | self.button_data[event.button] = False 57 | elif event.type == pygame.JOYHATMOTION: 58 | self.hat_data[event.hat] = event.value 59 | 60 | return self.button_data, self.axis_data, self.hat_data 61 | 62 | # Insert your code on what you would like to happen for each event here! 63 | # In the current setup, I have the state simply printing out to the screen. 64 | 65 | #os.system('clear') 66 | #pprint.pprint(self.button_data) 67 | #pprint.pprint(self.axis_data) 68 | #pprint.pprint(self.hat_data) 69 | 70 | 71 | if __name__ == "__main__": 72 | ps4 = PS4Controller() 73 | ps4.init() 74 | ps4.listen() 75 | -------------------------------------------------------------------------------- /car/.ipynb_checkpoints/main-checkpoint.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from board import SCL, SDA 3 | import busio 4 | from adafruit_pca9685 import PCA9685 5 | from adafruit_motor import servo 6 | from adafruit_motor import motor 7 | from controller import PS4Controller 8 | #import camera 9 | 10 | #init controller 11 | ps4 = PS4Controller() 12 | 13 | #init i2c 14 | i2c = busio.I2C(SCL, SDA) 15 | 16 | #init PCA 17 | pca = PCA9685(i2c) 18 | pca.frequency = 50 19 | 20 | servo_steer = servo.Servo(pca.channels[0]) 21 | esc = servo.ContinuousServo(pca.channels[1]) 22 | 23 | def scale_servo(x): 24 | y = round((30-70)*x+1/1+1+70,2) #used to scale -1,1 to 0,180 25 | return y 26 | 27 | def scale_esc(x): 28 | y = round((x+1)/8,2) #used to scale -1,1 to 0,180 29 | return y 30 | 31 | def drive(axis_data): 32 | servo_steer.angle = scale_servo(axis_data[0]) 33 | sum_inputs = round(-scale_esc(axis_data[4]) + scale_esc(axis_data[3]),2) 34 | esc.throttle = sum_inputs 35 | #print(sum_inputs) 36 | 37 | def toggle(x): 38 | return not x 39 | 40 | #init vars 41 | train = False 42 | trig = True 43 | 44 | #init Camera 45 | #camera = Camera.instance(width=224, height=224) 46 | 47 | try: 48 | while True: 49 | button_data, axis_data, hat_data = ps4.listen() 50 | 51 | if button_data[1] == True and trig: 52 | train = toggle(train) 53 | trig = False 54 | print(train) 55 | elif button_data[1] == False: 56 | trig = True 57 | else: 58 | pass 59 | 60 | if train: 61 | print('Training Mode!') 62 | drive(axis_data) 63 | csave_camera 64 | else: 65 | print('Auto Pilot Mode!') 66 | 67 | 68 | 69 | except KeyboardInterrupt: 70 | pca.deinit() 71 | sys.exit(0) 72 | 73 | -------------------------------------------------------------------------------- /car/__pycache__/camera.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/car/__pycache__/camera.cpython-36.pyc -------------------------------------------------------------------------------- /car/__pycache__/controller.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/car/__pycache__/controller.cpython-36.pyc -------------------------------------------------------------------------------- /car/__pycache__/neural_network.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/car/__pycache__/neural_network.cpython-36.pyc -------------------------------------------------------------------------------- /car/camera.py: -------------------------------------------------------------------------------- 1 | import atexit 2 | import cv2 3 | import threading 4 | import numpy as np 5 | 6 | 7 | class Camera(): 8 | 9 | def __init__(self): 10 | self.value = np.empty((224, 224, 3), dtype=np.uint8) 11 | #super(Camera, self).__init__(*args, **kwargs) 12 | self.capture_width=224 13 | self.capture_height=224 14 | self.fps=21 15 | self.width=224 16 | self.height=224 17 | self.count = 0 18 | 19 | try: 20 | self.cap = cv2.VideoCapture(self._gst_str(), cv2.CAP_GSTREAMER) 21 | #self.cap = cv2.VideoCapture(self._gstreamer_pipeline(flip_method=2), cv2.CAP_GSTREAMER) 22 | 23 | re, image = self.cap.read() 24 | 25 | if not re: 26 | raise RuntimeError('Could not read image from camera.') 27 | 28 | self.value = image 29 | self.start() 30 | 31 | except: 32 | self.stop() 33 | raise RuntimeError('Could not initialize camera. Please see error trace.') 34 | 35 | atexit.register(self.stop) 36 | 37 | def _capture_frames(self): 38 | while True: 39 | re, image = self.cap.read() 40 | if re: 41 | self.value = image 42 | self.count += 1 43 | else: 44 | break 45 | '''def _gst_str(self): 46 | return 'nvarguscamerasrc ! video/x-raw(memory:NVMM), width=%d, height=%d, format=(string)NV12, framerate=(fraction)%d/1 ! nvvidconv ! video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! videoconvert ! appsink' % ( 47 | self.capture_width, self.capture_height, self.fps, self.width, self.height)''' 48 | 49 | 50 | def _gst_str(self): 51 | return ('nvarguscamerasrc ! ' 52 | 'video/x-raw(memory:NVMM), ' 53 | 'width=(int)%d, height=(int)%d, ' 54 | 'format=(string)NV12, framerate=(fraction)%d/1 ! ' 55 | 'nvvidconv flip-method=%d ! ' 56 | 'video/x-raw, width=(int)%d, height=(int)%d, format=(string)BGRx ! ' 57 | 'videoconvert ! ' 58 | 'video/x-raw, format=(string)BGR ! appsink' % (self.capture_width,self.capture_height,self.fps,2,self.capture_width,self.capture_height)) 59 | 60 | def start(self): 61 | if not self.cap.isOpened(): 62 | self.cap.open(self._gst_str(), cv2.CAP_GSTREAMER) 63 | if not hasattr(self, 'thread') or not self.thread.isAlive(): 64 | self.thread = threading.Thread(target=self._capture_frames) 65 | self.thread.start() 66 | 67 | def stop(self): 68 | if hasattr(self, 'cap'): 69 | self.cap.release() 70 | if hasattr(self, 'thread'): 71 | self.thread.join() 72 | 73 | def restart(self): 74 | self.stop() 75 | self.start() 76 | 77 | if __name__ == "__main__": 78 | import sys 79 | 80 | cam = Camera() 81 | 82 | try: 83 | while True: 84 | #print(type(cam.value)) 85 | print(cam.count) 86 | 87 | except KeyboardInterrupt: 88 | sys.exit(0) 89 | -------------------------------------------------------------------------------- /car/control_data.csv: 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12cf76d0-6dd8-11e9-a80b-a6029617b541,0.0,1.0 514 | 12d71372-6dd8-11e9-a80b-a6029617b541,0.0,0.99 515 | 12de65be-6dd8-11e9-a80b-a6029617b541,0.0,0.87 516 | 12e6030a-6dd8-11e9-a80b-a6029617b541,0.0,0.79 517 | 12ed7676-6dd8-11e9-a80b-a6029617b541,0.0,0.79 518 | 12f54162-6dd8-11e9-a80b-a6029617b541,0.0,0.81 519 | 12fc815c-6dd8-11e9-a80b-a6029617b541,0.0,1.0 520 | 1303fe32-6dd8-11e9-a80b-a6029617b541,0.0,1.0 521 | 130b6fd2-6dd8-11e9-a80b-a6029617b541,0.0,1.0 522 | 1312f1e4-6dd8-11e9-a80b-a6029617b541,0.0,1.0 523 | 131a989a-6dd8-11e9-a80b-a6029617b541,0.0,1.0 524 | 13223f96-6dd8-11e9-a80b-a6029617b541,0.0,0.76 525 | -------------------------------------------------------------------------------- /car/controller.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | # 4 | # This file presents an interface for interacting with the Playstation 4 Controller 5 | # in Python. Simply plug your PS4 controller into your computer using USB and run this 6 | # script! 7 | # 8 | # NOTE: I assume in this script that the only joystick plugged in is the PS4 controller. 9 | # if this is not the case, you will need to change the class accordingly. 10 | # 11 | # Copyright © 2015 Clay L. McLeod 12 | # 13 | # Distributed under terms of the MIT license. 14 | 15 | import os 16 | import pprint 17 | import pygame 18 | 19 | class PS4Controller(object): 20 | """Class representing the PS4 controller. Pretty straightforward functionality.""" 21 | 22 | def init(self): 23 | """Initialize the joystick components""" 24 | pygame.init() 25 | pygame.joystick.init() 26 | self.controller = pygame.joystick.Joystick(0) 27 | self.controller.init() 28 | self.axis_data = False 29 | self.button_data = False 30 | self.hat_data = False 31 | 32 | print('Pygame init complete') 33 | 34 | def listen(self): 35 | """Listen for events to happen""" 36 | 37 | if not self.axis_data: 38 | self.axis_data = {0:0.0,1:0.0,2:0.0,3:-1.0,4:-1.0,5:0.0} #default 39 | 40 | if not self.button_data: 41 | self.button_data = {} 42 | for i in range(self.controller.get_numbuttons()): 43 | self.button_data[i] = False 44 | 45 | if not self.hat_data: 46 | self.hat_data = {} 47 | for i in range(self.controller.get_numhats()): 48 | self.hat_data[i] = (0, 0) 49 | 50 | for event in pygame.event.get(): 51 | if event.type == pygame.JOYAXISMOTION: 52 | self.axis_data[event.axis] = round(event.value,2) 53 | elif event.type == pygame.JOYBUTTONDOWN: 54 | self.button_data[event.button] = True 55 | elif event.type == pygame.JOYBUTTONUP: 56 | self.button_data[event.button] = False 57 | elif event.type == pygame.JOYHATMOTION: 58 | self.hat_data[event.hat] = event.value 59 | 60 | return self.button_data, self.axis_data, self.hat_data 61 | 62 | # Insert your code on what you would like to happen for each event here! 63 | # In the current setup, I have the state simply printing out to the screen. 64 | 65 | #os.system('clear') 66 | #pprint.pprint(self.button_data) 67 | #pprint.pprint(self.axis_data) 68 | #pprint.pprint(self.hat_data) 69 | 70 | 71 | if __name__ == "__main__": 72 | ps4 = PS4Controller() 73 | ps4.init() 74 | ps4.listen() 75 | -------------------------------------------------------------------------------- /car/main.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import torch 3 | import torchvision 4 | import numpy as np 5 | from board import SCL, SDA 6 | import busio 7 | from adafruit_pca9685 import PCA9685 8 | from adafruit_motor import servo 9 | from adafruit_motor import motor 10 | from controller import PS4Controller 11 | import camera 12 | import cv2 13 | import csv 14 | from uuid import uuid1 15 | import neural_network 16 | 17 | 18 | class Autocar(): 19 | 20 | def __init__(self): 21 | # init i2c 22 | i2c = busio.I2C(SCL, SDA) 23 | 24 | # init PCA 25 | self.pca = PCA9685(i2c) 26 | self.pca.frequency = 50 27 | 28 | self.servo_steer = servo.Servo(self.pca.channels[0]) 29 | self.esc = servo.ContinuousServo(self.pca.channels[1]) 30 | 31 | # init model 32 | 33 | model = neural_network.Net() 34 | self.model = model.eval() 35 | self.model.load_state_dict(torch.load('model/autopilot.pt')) 36 | self.device = torch.device('cuda') 37 | self.model.to(self.device) 38 | 39 | # init vars 40 | self.temp = 0 41 | mean = 255.0 * np.array([0.485, 0.456, 0.406]) 42 | stdev = 255.0 * np.array([0.229, 0.224, 0.225]) 43 | self.normalize = torchvision.transforms.Normalize(mean, stdev) 44 | self.angle_out = 0 45 | 46 | # init Camera 47 | self.cam = camera.Camera() 48 | 49 | # initial content 50 | with open('control_data.csv','w') as f: 51 | f.write('date,steering,speed\n') 52 | 53 | def scale_servo(self, x): 54 | 55 | # used to scale -1,1 to 0,180 56 | y = round((30-70)*x+1/1+1+70,2) 57 | 58 | return y 59 | 60 | def scale_esc(self, x): 61 | 62 | # used to scale -1,1 to 0,180 63 | y = round((x+1)/12,2) 64 | 65 | return y 66 | 67 | def drive(self, axis_data): 68 | 69 | self.servo_steer.angle = self.scale_servo(-axis_data[0]) 70 | sum_inputs = round(-self.scale_esc(axis_data[4]) + self.scale_esc(axis_data[3]),2) 71 | self.esc.throttle = sum_inputs 72 | 73 | def save_data(self, axis_data): 74 | 75 | count = self.cam.count 76 | img = self.cam.value 77 | 78 | if count!= self.temp: 79 | 80 | num = uuid1() 81 | cv2.imwrite('images/'+str(num)+".jpg", img) 82 | 83 | # append inputs to csv 84 | with open('control_data.csv','a',newline='') as f: 85 | writer=csv.writer(f) 86 | writer.writerow([num,axis_data[0],axis_data[4]]) 87 | 88 | self.temp = count 89 | 90 | print('Save data!') 91 | 92 | else: 93 | pass 94 | 95 | 96 | def preprocess(self, camera_value): 97 | 98 | x = camera_value 99 | x = cv2.resize(x, (224, 224)) 100 | x = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) 101 | x = x.transpose((2, 0, 1)) 102 | x = torch.from_numpy(x).float() 103 | x = self.normalize(x) 104 | x = x.to(self.device) 105 | x = x[None, ...] 106 | 107 | return x 108 | 109 | def autopilot(self): 110 | 111 | img = self.preprocess(self.cam.value) 112 | count = self.cam.count 113 | 114 | if count!= self.temp: 115 | print('RUN!') 116 | self.model.eval() 117 | with torch.no_grad(): 118 | output = self.model(img) 119 | outnump = output.cpu().data.numpy() 120 | 121 | if outnump >= 1: 122 | self.angle_out = [[1]] 123 | 124 | elif outnump <= -1: 125 | self.angle_out = [[-1]] 126 | else: 127 | self.angle_out = outnump 128 | 129 | print(self.angle_out[0][0]) 130 | 131 | self.temp = count 132 | 133 | 134 | else: 135 | pass 136 | 137 | self.drive({0:self.angle_out[0][0],1:0.0,2:0.0,3:-1.0,4:1,5:0.0}) 138 | 139 | 140 | if __name__ == "__main__": 141 | 142 | car = Autocar() 143 | 144 | # init controller 145 | ps4 = PS4Controller() 146 | ps4.init() 147 | 148 | # Start in training mode 149 | train = True 150 | trig = True 151 | 152 | def toggle(x): 153 | return not x 154 | 155 | try: 156 | while True: 157 | 158 | button_data, axis_data, hat_data = ps4.listen() 159 | 160 | if button_data[1] == True and trig: 161 | train = toggle(train) 162 | trig = False 163 | 164 | elif button_data[1] == False: 165 | trig = True 166 | else: 167 | pass 168 | 169 | if train: 170 | car.drive(axis_data) 171 | 172 | if axis_data[4] >= 0.12: 173 | car.save_data(axis_data) 174 | else: 175 | print('Not saving img') 176 | else: 177 | car.autopilot() 178 | 179 | 180 | except KeyboardInterrupt: 181 | car.pca.deinit() 182 | sys.exit(0) 183 | 184 | -------------------------------------------------------------------------------- /car/model/autopilot.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/car/model/autopilot.pt -------------------------------------------------------------------------------- /car/neural_network.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | class Flatten(nn.Module): 5 | def forward(self, input): 6 | return input.view(input.size(0), -1) 7 | 8 | class Net(nn.Module): 9 | def __init__(self): 10 | super(Net, self).__init__() 11 | # convolutional layer (sees 224x224x3 image tensor) 12 | self.flatten = Flatten() 13 | self.conv1 = nn.Conv2d(3, 24, 5, stride=2, padding=1) 14 | self.conv2 = nn.Conv2d(24, 32, 5, stride=1, padding=1) 15 | self.conv3 = nn.Conv2d(32, 64, 5, stride=1, padding=1) 16 | self.conv4 = nn.Conv2d(64, 64, 3, stride=1, padding=1) 17 | self.conv5 = nn.Conv2d(64, 64, 3, stride=1, padding=1) 18 | self.pool = nn.MaxPool2d(2, 2) 19 | self.fc1 = nn.Linear(64*3*3, 144) 20 | self.fc2 = nn.Linear(144, 1) 21 | self.dropout = nn.Dropout(0.15) 22 | 23 | def forward(self, x): 24 | # add sequence of convolutional and max pooling layers 25 | x = self.pool(F.relu(self.conv1(x))) 26 | x = self.pool(F.relu(self.conv2(x))) 27 | x = self.pool(F.relu(self.conv3(x))) 28 | x = self.pool(F.relu(self.conv4(x))) 29 | x = self.pool(F.relu(self.conv5(x))) 30 | # flatten image input 31 | x = self.flatten(x) 32 | x = self.dropout(x) 33 | x = F.relu(self.fc1(x)) 34 | x = self.dropout(x) 35 | x = self.fc2(x) 36 | return x 37 | -------------------------------------------------------------------------------- /car/pytorch.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch.nn.functional as F 3 | 4 | # define the CNN architecture 5 | class Net(nn.Module): 6 | def __init__(self): 7 | super(Net, self).__init__() 8 | # convolutional layer (sees 32x32x3 image tensor) 9 | self.conv1 = nn.Conv2d(3, 16, 3, padding=1) 10 | # convolutional layer (sees 16x16x16 tensor) 11 | self.conv2 = nn.Conv2d(16, 32, 3, padding=1) 12 | self.pool = nn.MaxPool2d(2, 2) 13 | self.fc1 = nn.Linear(32 * 4 * 4, 256) 14 | self.fc2 = nn.Linear(256, 1) 15 | self.dropout = nn.Dropout(0.25) 16 | 17 | def forward(self, x): 18 | x = self.pool(F.relu(self.conv1(x))) 19 | x = self.pool(F.relu(self.conv2(x))) 20 | # flatten image input 21 | x = x.view(-1, 32 * 4 * 4) 22 | x = self.dropout(x) 23 | x = F.relu(self.fc1(x)) 24 | x = self.dropout(x) 25 | x = self.fc2(x) 26 | return x 27 | -------------------------------------------------------------------------------- /commands: -------------------------------------------------------------------------------- 1 | # For deleting jpegs in directory folder 2 | find . -type f -iname \*.jpg -delete 3 | 4 | # Move captured images to USB stick 5 | sudo cp -r /home/ENTER_NAME_HERE/AutoCarJetsonNano/car/images /media/joe/usb 6 | 7 | # Move CSV data to USB stick 8 | sudo cp -r /home/ENTER_NAME_HERE/AutoCarJetsonNano/car/control_data.csv /media/joe/usb 9 | -------------------------------------------------------------------------------- /media/JetsonNanoCarPytorch.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/media/JetsonNanoCarPytorch.gif -------------------------------------------------------------------------------- /media/carbg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/media/carbg.png -------------------------------------------------------------------------------- /stl/chassis_platform.stl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/stl/chassis_platform.stl -------------------------------------------------------------------------------- /stl/roll_bar_camera.stl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/stl/roll_bar_camera.stl -------------------------------------------------------------------------------- /train/autopilot.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bastulli/AutoCarJetsonNano/f379b0f934e36eef3c6a2bd4439bfbffaa1f2cf2/train/autopilot.pt 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