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import argparse 
import pyexiv2 
import cv2 
import os.path
import numpy as np


ap = argparse.ArgumentParser() 
ap.add_argument("-i", "--input", required=True, help="input image name.") 
args = vars(ap.parse_args())

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covariance matrix

numpy http://wiki.scipy.org/Tentative_NumPy_Tutorial

http://stackoverflow.com/a/21759340 Python b1 = np.array([21,78,45,133,12,245,19,148,35,19,149,251,210,168,47,153]).reshape(4, 4) b2 = np.array([41,105,210,148,94,15,32,179,36,14,195,75,84,158,157,255]).reshape(4, 4) b3 = np.array([97,152,87,211,35,147,153,25,98,78,123,14,52,210,96,128]).reshape(4, 4)

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# gets read from a database, hardwired here
NDVI_COLORMAP="006600 008800 00BB00 00FF00 CCFF00 FFFF00 FFCC00 FF8800 FF0000 EE0000 DD0000 CC0000 BB0000 AA0000 990000 880000 770000"

NIR_INPUT=nir_input.tif
NIR_OUTPUT=nir_output.tif

RED_INPUT=red_input.tif
RED_OUTPUT=red_output.tif

NDVI_IMAGE=new_ndvi.tif

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source: https://stackoverflow.com/questions/37987839/how-can-i-run-tensorboard-on-a-remote-server#40413202

ssh -L 16006:127.0.0.1:6006 user@my_server_ip

$ tensorboard --logdir=/path/

* open browser on local machine. >> localhost:16006

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Run PDAL on docker

docker run -it --rm -v $(pwd):/data pdal/pdal pdal pipeline /path/to/json/file/simple_crop.json

Simple_crop.json - using WKT point location and a distance of 50ft

{
  "pipeline":[
    "path/to/original/las/file/lidar.las",

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sudo add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
sudo sh -c 'echo "deb https://qgis.org/ubuntugis-ltr xenial main" >> /etc/apt/sources.list'  
sudo sh -c 'echo "deb-src https://qgis.org/ubuntugis-ltr xenial main " >> /etc/apt/sources.list'  
sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-key 51F523511C7028C3
sudo apt-get update
sudo apt-get install -y qgis qgis-plugin-grass 

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# 1. Open excel file
# 2. Make sure there is a column of file paths to files. Eg. C://directory1/direcotry1/image01.jpg
# 3. Make sure the column to the right of the file paths is open for the images to be inserted.
# 4. Go to Developer > Visual Basic. A new window will open. In the right hand menu under "Microsoft Excel Objects", right click the 
# sheet name you are working with (eg. "Sheet1"). Right click it > Insert > Module.
# 5. A New window will pop up for your code. Copy and past the code below into the window and Run.
# 6. It will prompt you to select the entire column and rows containing the file path to the images. Select it and click ok.

Sub InsertPicFromFile()
    Dim xRg As Range

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# Create CSV from a list of files in a given directory. It is the companion file of export_photos.py. It doesnt take into account multiple photos associated with the same ID. Meant to be used with "export_photos.py"

import csv
import os
import argparse
import itertools

parser = argparse.ArgumentParser()
parser.add_argument('-i','--input folder', type=str, required=True, help='path location of images')
parser.add_argument('-o','--output folder', type=str, help='path location for csv output, leave blank if same as input.')

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source: https://towardsdatascience.com/jupyter-notebooks-i-getting-started-with-jupyter-notebooks-f529449797d2

This way of installing Jupyter Lab allows you to install it once and any virtual env has access to Jupyter through ipykernel.

1. Install pyenv (w/ virtualenv)
2. Install a new version of python pyenv install <a new python version>
3. Set a global version of python pyenv global <new python version>
4. Install pipx

Once the above are installaed.

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find /path/to/directory/ -type f -print0 | awk -F/ 'BEGIN { RS="\0" } { n=$NF } k[n]==1 { print p[n]; } k[n] { print $0 } { p[n]=$0; k[n]++ }' > duplicate_files.txt
source: https://unix.stackexchange.com/a/468461/363004