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# If not running interactively, don't do anything
case $- in
    *i*) ;;
      *) return;;
esac

# don't put duplicate lines or lines starting with space in the history.
# See bash(1) for more options
HISTCONTROL=ignoreboth

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Step 1: Setup CUDA on Jetson Nano

• Setup some environment variables so nvcc is on $PATH. Add the following lines to your ~/.bashrc file.

# Add this to your .bashrc file
export CUDA_HOME=/usr/local/cuda
# Adds the CUDA compiler to the PATH
export PATH=$CUDA_HOME/bin:$PATH
# Adds the libraries

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Installation Notes

Make sure you set -D BUILD_opencv_cudacodec=OFF.

cmake -D CMAKE_BUILD_TYPE=RELEASE \
	-D WITH_CUDA=ON \
	-D CUDA_ARCH_PTX="" \
        -D BUILD_opencv_cudacodec=OFF \ # 
	-D CMAKE_INSTALL_PREFIX=/usr/local \

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Install the Backend

sudo apt-get install tk-dev python-tk python3-tk

Install Matplotlib

pip install matplotlib

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#VEHICLE
DRIVE_LOOP_HZ = 50

#CAMERA
CAMERA_TYPE = "LEOPARD"
IMAGE_W = 224
IMAGE_H = 224
IMAGE_DEPTH = 3
CAMERA_FRAMERATE = DRIVE_LOOP_HZ
CAMERA_VFLIP = False

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#VEHICLE
DRIVE_LOOP_HZ = 50      # the vehicle loop will pause if faster than this speed.
MAX_LOOPS = None        # the vehicle loop can abort after this many iterations, when given a positive integer.
 
# #CAMERA
CAMERA_TYPE = "LEOPARD" # (PICAM|WEBCAM|CVCAM|CSIC|V4L|MOCK|LEOPARD)
IMAGE_W = 224
IMAGE_H = 224
IMAGE_DEPTH = 3         # default RGB=3, make 1 for mono
CAMERA_FRAMERATE = DRIVE_LOOP_HZ

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#!/usr/bin/env python3
'''
Scripts to train a keras model using tensorflow.
Basic usage should feel familiar: python train_v2.py --model models/mypilot

Usage:
    train.py [--tubs=tub1,tub2] (--model=<model>) [--type=(linear|inferred|tensorrt_linear|tflite_linear)]

Options:
    -h --help              Show this screen.

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import numpy as np
from fastai2.vision.all import *
from fastai2.distributed import *

def train():
    path = untar_data(URLs.CAMVID_TINY)

    def label_func(fn): 
        return path/"labels"/f"{fn.stem}_P{fn.suffix}"

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from pathlib import Path

import cv2
import numpy as np

import depthai as dai

if __name__ == "__main__":
    model_path = Path(__file__).parent / 'custom_ops/out'
    pipeline = dai.Pipeline()