R0 D2-03 Easy contributable i18n process with Sphinx - Takayuki Shimizukawa (PyCon APAC 2015)
Speaker: Takayuki Shimizukawa
Sphinx can extract paragraphs from sphinx document and store them into gettext format translation catalog files. Gettext format translation catalog is easy to translate from one language to other languages. Also Sphinx support internationalization by using such catalog files. You can use your favolite editors or services to translate your sphinx docs. In this session, I'll explain 3 things; (1) entier process to translate sphinx docs. (2) automation mechanism for the process. (3) tips, tricks and traps for wrinting docs and translating.
About the speaker
* Sphinx co-maintainer since 2011 * "Let's start Sphinx" Japanese ebook author * Sphinx-users.jp account * PyCon JP organizer
Day 2, 13:00-13:45
Abstract
在現代社群媒體興起下,許多網站、app都能允許使用者上傳圖片與文字發布貼文,然而圖片/照片大小不一很容易使頁面排版混亂,要求使用者每次上傳時都手動裁切也非常耗時,而通常圖片都隱含著重點區域,本演講將講述如何以python實作、應用已開源的深度學習模型來做出圖片自動裁切系統。本演講將會簡單引入深度學習,接著分享三個開源的相關模型(圖片重點、臉、文字)細節、單一使用的缺點、如何疊加三個模型來達到最好的效果,而過程中也會說明實作方法。
Description
整體而言,本演講將會介紹三個以python開發的模型(或有python接口)以及其開源專案,並舉出單一使用缺點及如何同時使用達到最佳效果
Salient object detection (https://github.com/sairajk/PyTorch-Pyramid-Feature-Attention-Network-for-Saliency-Detection)
Zhao, T., & Wu, X. (2019). Pyramid feature attention network for saliency detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3085-3094).
Face recognition (https://github.com/ageitgey/face_recognition)
Chinese OCR (https://github.com/DayBreak-u/chineseocr_lite)
with PSENet (Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., & Shao, S. (2019). Shape robust text detection with progressive scale expansion network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9336-9345).)
以上模型皆已訓練完畢並也提供了所有參數權重,使用者僅須從github clone下來或是從PyPI安裝使用,不須花時間重新訓練模型,故可以很快將整個架構建起來。
演講過程首先先提出許多社群媒體有圖片裁切需求但不可能在使用者上傳圖片時都有人工幫忙裁切,因此有一個自動裁切系統將會有所助益。然而若單一使用Salient object detection模型(previous work),那結果會忽略許多文字且著重奇怪的物件,故後續會需要加入臉部與文字偵測,藉由三者的輸出以不同權重疊加,最後利用dynamic programming計算疊加權重最大的矩陣區域,來得到最終裁切結果,另外會分享在不同情境下三個權重該如何調整。
本演講也會在介紹每個模型時說明如何以python實作及使用,整個大專案屆時將會提供code置於github上讓會眾可以直接使用或參考。
Slides not uploaded by the speaker.
HackMD: https://hackmd.io/@pycontw/2021/%2F%40pycontw%2FBkzKJVqMt
Speaker: 何明洋
A passionate data scientist and full stack developer who excels at solving practical problems, especially in 2D/3D CV, audio, and medical signal, by designing ML/DL algorithms and building
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https://www.youtube.com/watch?v=iRGEG-Lft40
摘要 Abstract:
To simplify the work to do, CPython leverages a global big lock to serialize execution of threads. The big lock results in wasting computing power for lock contention in truly parallel environment and will become the bottleneck when the system getting larger.
In the previous work "Global Interpreter Lock: Episode I - Break the Seal" in PyCon APAC 2015[1], we focus on how to live along with CPython's GIL well. In this work, we are going to nullify the effects of GIL by giving each thread a private GIL.
Without modifications of the OS kernel and CPython, dynamic linker would be the easiest way to separate memory namespaces within a process. We will show examples on how to use it and discuss the limitations.
Slide Link:
https://www.slideshare.net/penvirus/global-interpreter-lock-episode-iii-cat-lt-devzero-gil
PyCon Taiwan 2017 official: https://tw.pycon.org/2017/
PyCon Taiwan 2017 Facebook Fan Page: https://www.facebook.com/pycontw/
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https://www.youtube.com/watch?v=gmCAc1e_ANo
Speaker: Takayuki Shimizukawa
Using the automated documentation feature of Sphinx, you can make with ease the extensive documentation of Python program.
You just write python function documents (docstrings), Sphinx organizes them into the document, can be converted to a variety of formats.
In this session, I'll explain a documentation procedure that uses with sphinx autodoc and autosummary extensions.
About the speaker
* Sphinx co-maintainer since 2011
* "Let's start Sphinx" Japanese ebook author
* Sphinx-users.jp account
* PyCon JP organizer
個人網頁連結 http://about.me/shimizukawa
Twitter @shimizukawa
組織/公司 BePROUD corp / PyCon JP / Sphinx-usres.jp
頭銜 Python developer
https://tw.pycon.org/2015apac/zh/program/69
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https://www.youtube.com/watch?v=RQL04qfsPy8
PyCon APAC 2022|一般演講 Talks|國泰金控 Cathay Financial Holdings / 美光科技 Micron 冠名贊助
✏️ 共筆 Note:https://hackmd.io/@pycontw/r1O1wpmyo
?? Slido:https://app.sli.do/event/mkwQmSfgPv8JCsh8Jny9xm
? 語言 Language:英文 English
? 層級 Level:中階 Intermediate
? 分類 Category:科學 Science
? 摘要 Abstract ?
Scanning Tunneling Microscope is widely used for 2D material characterization and even more, for building quantum computer at the cutting edge of science. A typical surface science experiment can generate hundreds of STM images each containing multiple structures to be identified. Since each investigation is different, it needs to draw on highly flexible methods algorithmically and integrate several image processing techniques. In this talk I’ll present an end-to-end Pythonic solution: reading raw STM data, implementing various STM artifact, noise filtering schemes and flattening. Then I’ll discuss how to search for, identify, count and measure different surface structures, particularly some epitaxial islands. Finally I’ll show statistical results of size and angular distributions, and apply some similar image processing, feature recognition and image interpolation algorithms to extract angular distributions from electron diffraction measurements and compare the results.
? 說明 Description ?
Scanning Tunneling Microscope (STM) is a kind of microscope used to resolve surface structures under sub-nano scale such as features of sizes, shapes and orientations and their electronic band structures as well. It also allows for precise atomic manipulation based on which creation of quantum bits is achieved and propels building of a quantum computer. STM tip scanning is regularly affected by thermal drift, atoms getting picked up by, and moving around on the tip. I’ll use both straightforward NumPy and where possible introduce STM analysis libraries to show how these effects can be mitigated. Once that is done, I’ll first illustrate how edge detection and alignment with respect to surface atoms can be implemented directly in NumPy in various ways, then introduce OpenCV. Since the shapes can merge partially as they grow, I’ll describe how overlapping shapes can still be distinguished and measured. While OpenCV is powerful, it is necessary to do a significant amount of image pre-processing before reliable and repeatable shape identification is working smoothly. Finally, I'll move to Low Energy Electron Diffraction (LEED), which is also quite commonly used for determination of surface structure. LEED images can basically be viewed as 2D Fourier-transformed images of their real-space counterparts (STM images here). After showing how to use numpy's map coordinate to get real space coordinates, I'll show how to fit Gaussian spots and arcs, find them on the screen and once again get distributions from them.
? 講者介紹 About Speaker - Hsu-Kai Cheng ?
I am currently a research assistant at Center for Condensed Matter Sciences in National Taiwan University (NTU). I received my bachelor's degree in the Department of Physics, NTU and my master’s degree in the Graduate Institute of Applied Physics, NTU. My scientific interests include surface science device development and characterization and my current research focuses on post-graphene materials such as black phosphorus and transition metal dichalcogenides. Especially, I aim to explore the properties of topological materials in which the interplay between topology and functions is so overwhelming and renders materials of this kind really promising for future application.
#pycontw #pyconapac2022 #python #scanning #tunneling #microscopy #imageprocessing #featurerecognition #featureextraction
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https://www.youtube.com/watch?v=8g6_Q2zMJ0g