Tom Vercauteren tvercaut

Spectral information

https://omlc.org/spectra/hemoglobin/summary.html https://omlc.org/spectra/icg/

A simple PyTorch implementation of plane fitting with RANSAC. Some ideas are taken from: https://github.com/leomariga/pyRANSAC-3D.

In each (batched-)RANSAC iteration, a batch of triplets is sampled and assessed in parallel. A cross-product is used to get plane equations from triplets. The refinement, post identification of inliers, is done with an eigen decomposition of P.T @ P where P is the matrix of points in homogeneous coordinates. Classicially, an SVD decomposition of P is used but this can be slow and memory intensive with large number of points. We thus opted for the eigen decomposition approach despite it being less numerically stable.

	#!/usr/bin/env python
	import numpy as np
	import scipy, scipy.linalg
	import warnings

	# Define some useful function first
	# see statsmodels/tsa/tsatools.py

	def vec(mat):
	"""

	#!/usr/bin/env python
	from __future__ import absolute_import, division, print_function
	import tensorflow as tf
	from tensorflow.python.framework import ops
	from tensorflow.python.ops import array_ops
	from tensorflow.python.ops import sparse_ops

	import numpy as np
	import scipy.linalg

	<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN">
	<html>
	<head>

	<!-- Get jquery -->
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>

	<link href="https://fonts.googleapis.com/css?family=Lato%3A300%2C300italic%2C400%2C400italic%2C700%2C700italic" rel="stylesheet" type="text/css"/>
	<link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700\|Source+Code+Pro:400,700" rel="stylesheet" type="text/css">
	<link rel="stylesheet" href="https://www.gstatic.com/_/atari/_/ss/k=atari.vw.-2q1obhhypir7.L.W.O/d=1/rs=AGEqA5mToJSdsBqoIabxpABkzJkjFu3hJg"/>

	#!/bin/sh

	# Sanity checks
	if ! [ -x "$(command -v gs)" ]; then
	echo "gs is not installed -> exit"
	exit 1
	fi

	# Define some handy options to use with ghostscript
	export PDF2PDFFLAGS="-dCompatibilityLevel=1.5 -dPDFSETTINGS=/ebook -dPrinted=false -dColorConversionStrategy=/UseDeviceIndependentColor -dDownsampleColorImages=true -dDownsampleGrayImages=true -dDownsampleMonoImages=true -dMaxSubsetPct=100 -dSubsetFonts=true -dEmbedAllFonts=true -dOptimize=true -dUseFlateCompression=true -dNOPAUSE -dBATCH -sDEVICE=pdfwrite"

	#EXTM3U
	#EXTINF:0,Radio France - FIP (AAC+Metadata)
	http://radio-metadata.fr:8000/fip.m3u
	#EXTINF:0,Radio France - FIP Reggae (AAC+Metadata)
	http://radio-metadata.fr:8000/fipreggae.m3u
	#EXTINF:0,Radio France - FIP World (AAC+Metadata)
	http://radio-metadata.fr:8000/fipmonde.m3u
	#EXTINF:0,Radio France - FIP Nouveautés (AAC+Metadata)
	http://radio-metadata.fr:8000/fipnouveaute.m3u
	#EXTINF:0,Radio France - FIP Electro (AAC+Metadata)

	import numpy as np
	import matplotlib.pyplot as plt
	import imageio
	url = 'https://archive.org/download/10thframekixxc64800dpi48bit/10th%20Frame%20%28Kixx%29_C64_Cassette_800dpi_48bit.tif'
	im0 = imageio.imread(url)
	print('im0:',np.min(im0),np.max(im0),im0.shape,im0.dtype)

	im0gray = 0.299np.float32(im0[:,:,0]) + 0.587np.float32(im0[:,:,1]) + 0.114*np.float32(im0[:,:,2])
	im0gray = im0gray / np.max(im0gray)
	im1 = np.uint16(np.round( im0gray*((1<<10)-1) ))