Prabhu Kumar prabhuiitdhn

About YUV formats

First of all: YUV pixel formats and Recommended 8-Bit YUV Formats for Video Rendering. Chromium's source code contains good documentation about those formats too: chromium/src/media/base/video_types.h and chromium/src/media/base/video_frame.cc (search for RequiresEvenSizeAllocation(), NumPlanes() and those kinds of functions).

YUV?

You can think of an image as a superposition of several planes (or layers in a more natural language). YUV formats have three planes: Y, U, and V.

Y is the luma plane, and can be seen as the image as grayscale. U and V are reffered to as the chroma planes, which are basically the colours. All the YUV formats have these three planes, and differ by the different orderings of them.

	import argparse
	from PIL import Image

	def nv12_to_rgb(nv12: bytes \| bytearray, size: tuple[int, int]) -> Image:
	w, h = size
	n = w * h
	y, u, v = nv12[:n], nv12[n + 0::2], nv12[n + 1::2]
	yuv = bytearray(3 * n)
	yuv[0::3] = y
	yuv[1::3] = Image.frombytes('L', (w // 2, h // 2), u).resize(size).tobytes()

	# Print most common words in a corpus collected from Twitter
	#
	# Full description:
	# http://marcobonzanini.com/2015/03/02/mining-twitter-data-with-python-part-1/
	# http://marcobonzanini.com/2015/03/09/mining-twitter-data-with-python-part-2/
	# http://marcobonzanini.com/2015/03/17/mining-twitter-data-with-python-part-3-term-frequencies/
	#
	# Run:
	# python twitter_most_common_words.py <filename.jsonl>

	# You need to install scikit-learn:
	# sudo pip install scikit-learn
	#
	# Dataset: Polarity dataset v2.0
	# http://www.cs.cornell.edu/people/pabo/movie-review-data/
	#
	# Full discussion:
	# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn

	"""
	Based on: http://www.tannerhelland.com/4435/convert-temperature-rgb-algorithm-code/
	Comments resceived: https://gist.github.com/petrklus/b1f427accdf7438606a6
	Original pseudo code:

	Set Temperature = Temperature \ 100

	Calculate Red:

	If Temperature <= 66 Then