I have a pet project I work on, every now and then. CNoEvil.
The concept is simple enough.
What if, for a moment, we forgot all the rules we know. That we ignore every good idea, and accept all the terrible ones. That nothing is off limits. Can we turn C into a new language? Can we do what Lisp and Forth let the over-eager programmer do, but in C?
This is a collection of the things I believe about software development. I have worked for years building backend and data processing systems, so read the below within that context.
Agree? Disagree? Feel free to let me know at @JanStette.
Keep it simple, stupid. You ain't gonna need it.
| Mute these words in your settings here: https://twitter.com/settings/muted_keywords | |
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This week NN Group released a video by Jakob Nielsen in which he attempts to help designers deal with the problem of customers being resistant to their new site/product redesign. The argument goes thusly:
There's slightly more to it than that, he caveats his argument with requiring you to have of course followed their best practices on product design, and allows for a period of customers being able to elect to continue to use the old site, although he says this is obviously only a temporary solution as you don't want to support both.
| # Create a new caption file | |
| ~~~~~~~~ | |
| ffmpeg -i captions.srt captions.ass | |
| ~~~~~~~~ | |
| # Add subtitles to main video without changing it | |
| ~~~~~~~~ | |
| ffmpeg -i video.mp4 -vf "subtitles=captions.ass:force_style='OutlineColour=&H80000000,BorderStyle=4,Outline=1,Shadow=0,MarginV=20'" subtitled-video.mp4 |
| ### JHW 2018 | |
| import numpy as np | |
| import umap | |
| # This code from the excellent module at: | |
| # https://stackoverflow.com/questions/4643647/fast-prime-factorization-module | |
| import random |
| """ Enhancing Intent Classification with the Universal Sentence Encoder: | |
| https://medium.com/scalableminds/enhancing-intent-classification-with-the-universal-sentence-encoder-ecbcd7a3005c | |
| """ | |
| from rasa_nlu.featurizers import Featurizer | |
| import tensorflow_hub as hub | |
| import tensorflow as tf | |
| import cv2 | |
| image = cv2.imread('test.jpg') | |
| overlay = image.copy() | |
| x, y, w, h = 10, 10, 10, 10 # Rectangle parameters | |
| cv2.rectangle(overlay, (x, y), (x+w, y+h), (0, 200, 0), -1) # A filled rectangle | |
| alpha = 0.4 # Transparency factor. |
| import numpy as np | |
| import cv2 | |
| image=cv2.imread('MNIST/test/3647.png') | |
| gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)## for 9 problem with 4665,8998,73,7 | |
| #I faced a problem with the deskewing and the solution suggested by @zindarod "https://stackoverflow.com/questions/51237834/deskewing-mnist-dataset-images-using-minarearect-of-opencv" | |
| #really improved the deskewing | |
| #gray=cv2.bitwise_not(gray) | |
| Gblur=cv2.blur(gray,(5,5)) | |
| thresh=cv2.threshold(Gblur,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] |
