mcarletti

#!/bin/bash

sudo apt remove --purge -y 'cuda*' 'nvidia-*'
sudo add-apt-repository --remove -y ppa:graphics-drivers/ppa

sudo apt update
sudo reboot

mcarletti

#!/bin/bash
## This gist contains step by step instructions to install cuda v9.0 and cudnn 7.2 in ubuntu 18.04

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

mcarletti

#!/bin/bash

sudo apt update

# Tested on:
# ubuntu 18.04 (kernel 4.15.0-58-generic x86_64 GNU/Linux)
# gcc (Ubuntu 5.5.0-12ubuntu1) 5.5.0 20171010
# cuda 8.0 (/usr/local/cuda-8.0)

git clone https://github.com/zju3dv/pvnet.git

mcarletti

# ========================================================
# Assumo che la struttura del progetto sia la seguente:
#
# .
# ├── include
# │   └── engine
# │       └── ...
# ├── sources
# │       └── ...
# ├── examples

mcarletti

#!/bin/bash
#
# Install CUDA 10.0 on a "fresh" Ubuntu 18.04 LTS machine.
#
# update: July 2019
# author: Marco Carletti
#
# Deep Learning frameworks requirements:
# PyTorch 1.1.0 -- CUDA 10.x
# TensorFlow-pgu 1.13.x or higher -- CUDA 10.0, cuDNN 7.3

mcarletti

[Desktop Entry]
Name=Name of Application
GenericName=Application
GenericName[it]=Applicazione
Comment=Cool app, very useful
Comment[it]=Applicazione molto utile e intrigante
Categories=cat1;cat2;...
Keywords=kw1;kw2;...
Encoding=UTF-8
Exec=/path/to/executable

mcarletti

#!/bin/sh

if [[ $(/usr/bin/id -u) -ne 0 ]]; then
    echo "Not running as root"
    exit 1
fi

apt update
apt upgrade -y

mcarletti

# Samsung A3
# Front camera: 8 MP, f/2.4, 31mm (standard), AF

width:  4128
height: 2322
focal: 3.6

rms: 1.6153361028439144

camera_mtx:

mcarletti

#!/bin/bash

# Tested on
# ------------------------------
# OS                Ubuntu 18.04
# Platform          x86_64, aarch64
# Python            3.6

if [ $# != 1 ]; then
	echo -e "Usage:\n\t./build_opencv.sh VERSION\n\nExample:\n\t./build_opencv.sh 4.2.0"

mcarletti

def load_nv12_as_bgr(filename, shape, dtype=np.uint8):
    with open(filename, "rb") as fp:
        data = fp.read()
    image = np.frombuffer(data, dtype=dtype).reshape(shape)
    image = cv2.cvtColor(image, cv2.COLOR_YUV2BGR_NV12)
    return image