Install ImageMagick for image conversion:
brew install imagemagick
Install tesseract for OCR:
brew install tesseract --all-languages
Or install without --all-languages
and install them manually as needed.
Install ImageMagick for image conversion:
brew install imagemagick
Install tesseract for OCR:
brew install tesseract --all-languages
Or install without --all-languages
and install them manually as needed.
#!/usr/bin/env python | |
# see http://socialdatablog.com/extract-pdf-annotations.html | |
myxkfolder="/home/steve/xk/" #you need to set this to where you want your to-dos to appear | |
import poppler, os.path, os, time, datetime | |
for root, dirs, files in os.walk('./'): | |
for lpath in files: |
This is a guide for Scala and Java development on Windows, using Windows Subsystem for Linux, although a bunch of it is applicable to a VirtualBox / Vagrant / Docker subsystem environment. This is not complete, but is intended to be as step by step as possible.
Read the entire Decent Security guide, and follow the instructions, especially:
As a developer, it bothers me when someone sends me a large pdf file compared to the number of pages. Recently, I recieved a 12MB scanned document for just one letter-sized page... so I got to googlin, like I usually do, and found ghostscript!
to learn more abot ghostscript (gs): https://www.ghostscript.com/
What we are interested in, is the gs command line tool, which provides many options for manipulating PDF, but we are interested in compressign those large PDF's into small yet legible documents.
credit goes to this answer on askubuntu forum: https://askubuntu.com/questions/3382/reduce-filesize-of-a-scanned-pdf/3387#3387?newreg=bceddef8bc334e5b88bbfd17a6e7c4f9
""" I was writing a dataloader from a video stream. I ran some numbers. | |
# in a nutshell. | |
-> np.transpose() or torch.permute() is faster as uint8, no difference between torch and numpy | |
-> np.uint8/number results in np.float64, never do it, if anything cast as np.float32 | |
-> convert to pytorch before converting uint8 to float32 | |
-> contiguous() is is faster in torch than numpy | |
-> contiguous() is faster for torch.float32 than for torch.uint8 | |
-> convert to CUDA in the numpy to pytorch conversion, if you can. | |
-> in CPU tensor/my_float is > 130% more costly than tensor.div_(myfloat), however tensor.div_() | |
does not keep track of gradients, so be careful using it. |
I bought M1 MacBook Air. It is the fastest computer I have, and I have been a GNOME/GNU/Linux user for long time. It is obvious conclusion that I need practical Linux desktop environment on Apple Silicon.
Fortunately, Linux already works on Apple Silicon/M1. But how practical is it?