Đỗ Thành Long soiqualang

Image download from ftp --> upload to AWS s3 bucket
Pull to AWS EC2 machine. Save off SWIR band ratios as tif with appropriate contrast. The SWIR data is an 8-band image. You need to first grab onnly the bands you need for visualization (8-3-1 or 6-3-1 make good combos)

import rasterio
import numpy as np

raster='18NOV09185526-A2AS-058669488010_01_P001.TIF'. #your file

Story

In needed include base64 encoded simple image into HTML page in two formats:

1st as PNG format has length 196 characters and looks like this:

iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAWElEQVR42mNkwA/qgbgRnwJGAgb8BwI7RkbGw5QYUAs0oGXUAPwGgKKqgYF0ANLTyAi1xhZI2WOYzsjYDJTbC2QewGHIwcERBsPcgHqgAX8pMQAcxfhyIwATTkxL+hgX2QAAAABJRU5ErkJggg==

2nd as CUR format has length 1536 characters and looks like this:

`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

	import cv2
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import csv

	try:
	from PIL import Image
	except ImportError:
	import Image

	#!/usr/bin/env python

	from fiona.transform import transform_geom
	from rasterstats import point_query
	import mercantile


	def make_dem_url(lon: float, lat: float, z: int = 14) -> str:
	"""Returns a URL referencing the GeoTiff Digitial Elevation Model (DEM)
	for the given point at a zoom level (default, max is 14).

	"""
	Zonal Statistics
	Vector-Raster Analysis

	Copyright 2013 Matthew Perry

	Usage:
	zonal_stats.py VECTOR RASTER
	zonal_stats.py -h \| --help
	zonal_stats.py --version

	<?php
	/ini settings/
	set_time_limit(0);
	ini_set('memory_limit', '512M');

	//DOWNLOAD SCRIPT
	$filePath = "G:/Software/versions/..PATH TO DOWNLOAD FILE...zip"; // set your download file path here.
	download($filePath); // calls download function
	function download($filePath)
	{

	// Copy big file from somewhere else
	$src_filepath = 'http://example.com/all_the_things.txt'; $src = fopen($src_filepath, 'r');
	$tmp_filepath = '...'; $tmp = fopen($tmp_filepath, 'w');
	$buffer_size = 1024;

	while (!feof($src)) {
	$buffer = fread($src, $buffer_size); // Read big file/data source/etc. in small chunks
	fwrite($tmp, $buffer); // Write in small chunks
	}

	SINGLE FILE CONVERT
	gdal_translate input_nysdop_ortho.jp2 output.tif -b 1 -b 2 -b 3 -mask 4 -co COMPRESS=JPEG -co JPEG_QUALITY=25 -co PHOTOMETRIC=YCBCR --config GDAL_TIFF_INTERNAL_MASK YES -a_srs EPSG:2260

	BATCH CONVERT
	for /r %g in (*.jp2) do gdal_translate -of GTiff "%g" "%~dpng.tif" -b 1 -b 2 -b 3 -mask 4 -co COMPRESS=JPEG -co JPEG_QUALITY=25 -co PHOTOMETRIC=YCBCR --config GDAL_TIFF_INTERNAL_MASK YES -a_srs EPSG:2260

	BUILT VIRTUAL RASTER (CATALOG)
	gdalbuildvrt catalog.vrt e_06811416_06_05000_4bd_2011.jp2 e_06811414_06_05000_4bd_2011.jp2 e_06781416_06_05000_4bd_2011.jp2 e_06781414_06_05000_4bd_2011.jp2

	gdalbuildvrt -allow_projection_difference -hidenodata -vrtnodata "255 255 255" nysdop.vrt *.jp2 -a_srs EPSG:2260

	<!doctype html>
	<html>
	<head>
	<script src="//ajax.googleapis.com/ajax/libs/jquery/2.1.1/jquery.min.js"></script>
	<script type="text/javascript">
	var spData = null;
	function doData(json) {
	spData = json.feed.entry;
	}

	-- Get Max ID from table
	SELECT MAX(id) FROM table;

	-- Get Next ID from table
	SELECT nextval('table_id_seq');

	-- Set Next ID Value to MAX ID
	SELECT setval('table_id_seq', (SELECT MAX(id) FROM table));