#BoundingBox
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wisepl · 7 months ago
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Data Annotation Services For Plans & Elevations
Accurate Data Annotation for Architectural Plans & Elevations
We specialize in precise and efficient data annotation for architectural designs, ensuring your machine learning models are trained with high-quality, labeled data. Whether you need annotations for floor plans, elevations, or blueprints, we deliver accuracy that drives innovation in the construction and real estate industries.
Accuracy: Detailed and error-free annotations to enhance your AI’s understanding of architectural elements. Speed: Fast turnaround times to keep your project on schedule. Expertise: Years of experience in machine learning and computer vision make us the perfect partner for your AI projects. Scalability: Flexible services tailored to projects of any size, from small developments to large-scale city planning. Confidentiality: We prioritize data security to safeguard your sensitive information.
Elevate your AI-driven designs with Wisepl Pvt Ltd's world-class data annotation services
Contact us today to get started! www.wisepl.com | [email protected]
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labelerdata · 3 years ago
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The building sector is changing thanks to computer vision! PPE detection is one frequent use scenario. When employees are working without the proper safety equipment on the jobsite, use computer vision to send out alarms. Get the Demo Now! Visit: https://www.datalabeler.com/ Mail: [email protected]
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citrustreedesigns · 7 years ago
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If you use photoshop for your photography or graphic design...this is a helpful trick. Sometimes, in moving things around, you completely loose track of the bounding box for an image, shape or text. If your bounding box has managed to drift off the visible work area...
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madraynesims · 3 years ago
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Re: Screaming Death Bonfire, moving chair slots alone might not work; the associated bones might have to be moved too (not totally sure). In "Slots" tab, expand "Bones" row, select a bone, then click + drag the little arrows *for the bone* in the viewport to move slot + bone simultaneously. In "Mesh" tab, expand "Visual Proxy" row, click on "Boundingbox" row, and adjust box in viewport. Thx for persevering with TS3 (I love the quirky spin-off objects too) and good luck!
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Hmm, I'm wondering if something is missing here. There's only one bone listed, but it's greyed out and I don't see an arrow to drag. I'm realizing that most of the time I see objects have 0xCD68F001 assigned to them, which I make sure to do in Milkshape. I thought maybe I forgot to assign it, but the campfire I cloned did not have this either, so I didn't do it. Now I'm wondering if the campfire I cloned was a bad choice lol.
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So here's the one I cloned. The bounding box seems to surround the campfire itself.
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And here's the bounding box for mine. I didn't do this myself, it must have updated to this when I clicked a pop up, not sure. But I'm seeing it's HUGE compared to the original. So you're saying I should make it more far apart, thus increasing the box? Kind of seems like (based on the other) that I should decrease the box instead? I swear I remember getting a pop up that asked me about the bounding box and I told it not to adjust it. How weird! I had assumed it was unchanged.
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Alright, after adjusting the box, I can now see chairs highlighted when placing it down!
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Chairs now spawn properly as it should. The Sims 3 version of the Screaming Death Bonfire is updated on the main post, I'd recommend redownloading! I’ll just throw the update here as well: Screaming Death Bonfire Sims 3 chair fix
Thank you so much for the help anon, I appreciate it!
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arkh-dwg-blog · 5 years ago
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XL - Process
Experimenting with different commands and topography types.
Patch. Sweep. Loft. Extrude. Contour. Boundingbox. Drape
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rachaelboudreau · 5 years ago
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Task 3 reflection
 New Cartographies, Emergent Landscapes
 ARCN2105A Digital Practice & Theory                                                       Rachael Boudreau 101102884                                                                   Tuesday January 28 2020
Troubleshooting
Rhino is completely new to me and therefore having to try and use the tools taught during the lab was difficult to remember and it often took several tries to get them to work. I had the most difficulty with the rebuild command and creating a surface with several points for some reason, I am still unsure of how to do it. The step model took the longest to build because I had to create a separate surface at each step and use the split command because I didn't know an easier way to do it, although I am sure there is one. 
Workflow
After this exercise I now feel far more comfortable using commands like sweep, drape, patch, split, boundingbox and contour and being able to export files to illustrator. Knowing this can help with future studio projects and I may be able to use Rhino for creating typographic models in a few different forms. I especially find being able to export a Rhino model to illustrator to be extremely helpful because illustrator creates high quality line drawings. 
For Next Time
Next time I get the chance to use Rhino I will definitely be able to get the project going a lot faster than this time and I plan to practice the commands and tools I am still having trouble with like; Rebuild, creating a surfaces from a curve and I’ll be sure to layer as I go along as opposed to doing it all at the end.
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longzip · 3 years ago
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Ps to pdf lettersize
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#Ps to pdf lettersize how to
#Ps to pdf lettersize pdf
Click Size, and choose one of the following options: Fit Scales small pages up and large pages down to fit the paper.
#Ps to pdf lettersize pdf
In the PDF, select the Print Production tool. Acrobat can automatically scale a PDF to fit the paper, or you can resize pages by setting a specifc percentage. When the status change to Done click the Download PDF button. dimensions of your PDF will be bigger than the size you want your final prints to be. Click the Convert to PDF button to start the conversion.
#Ps to pdf lettersize how to
They do not encode information that is specific to the application software, hardware, or operating system used to create or view the document. How to Convert XPS to PDF Click the Choose Files button to select your XPS files. A PDF file can be any length, contain any number of fonts and images and is designed to enable the creation and transfer of printer-ready output.Įach PDF file encapsulates a complete description of a 2D document (and, with the advent of Acrobat 3D, embedded 3D documents) that includes the text, fonts, images and 2D vector graphics that compose the document. PDF is a file format developed by Adobe Systems for representing documents in a manner that is separate from the original operating system, application or hardware from where it was originally created. Microsoft Office binary file format specifications.The file basically contains a set of instructions that tell the printer what to print and provide the co-ordinates on the page of where an object or text should be printed. The code works fine, but I imagine there is a more straightforward way of doing this. Now a legacy file format, most printers and programs continue to support this format, with the only pre-requisite being a printer utility that supports PostScript. I have postscript PS images and have written a macro to convert to PDF and then crop to their boundingbox size. Typically files are created by applications such as Adobe Acrobat or Quark Xpress. PostScript is actually a programming language as well as a file type. Although no longer as popular or common as it was historically, it is still an important file type. This file type revolutionised publishing allowing certain curved items to be printed that previously could only be printed on specialist CAD printers. Postscript addressed this by defining a language that allowed applications to instruct the printer how to display objects on the page. Prior to the development of PostScript, it was not possible to print images and text on the same page. ps extension is a file type used by the publishing industry.
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annotationsupport · 3 years ago
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Number Identification Annotation Services
Annotation support offers Number identification-bounding box annotation services for #Machine Learning  and #deep learning. 
Want to know more:https://annotationsupport.com/boundingbox-annotation.php
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gtssidata4 · 3 years ago
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Few Question For Training Dataset For AI
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An image annotation is performed for various machine learning models. A bounding frame is used to annotate pictures for computer vision programs. AI computers can recognize and learn items from the real world using this box-type of annotation. Rectangular shapes are formed around a video frame or image in the boundingbox image annotation. This helps with object detection learning.
Every ML Engineer aspires for an accurate and reliable AI model. Data scientists dedicate almost of the time labeling and adding data. It is because the model's performance relies on the quality Speech Recognition Dataset used to train them.
Our business clients have a number of questions. We have created a guide to show how our skilled team trains ML models accurately using gold-standard data.
Questions frequently asked
As promised, you'll find a complete reference to all your questions as well as the errors you can avoid in any stage during the development lifecycle.
1.What can you do to make sense of all the data?
Your company may have accumulated an enormous amount of data. Now you want key insights or valuable information.
The training data will not be useful if there is no understanding of your business requirements or project requirements. So don't try to discover patterns or meaning in your data. You should instead go into it with a specific purpose in mind so you don’t find the wrong solutions.
2.Is the data from the training a good representation of the production data. How do I identify it?
Although you might not realize it, the labeled dataset you are using for training your model could be vastly different from the actual production environment.
How do I recognize? These are the indicators to look for. Your model performed admirably in a test environment but did not perform well during production.
3.How can we reduce bias?
Avoiding bias in your models is the best way to mitigate it.
Data bias may take many forms, including non-representative AI Training Datasets and feedback loop issues. It is vital to keep abreast with new developments and establish strong process standards and frameworks to counter various forms of bias.
4.How can I prioritize my training data annotation process?
This is one of the most commonly asked questions. When annotating a dataset, which part of it should you prioritize? This is a valid query, especially when working with large datasets. You don’t have to annotation the entire set.
Advanced techniques enable you to choose the right part of your data set and cluster it, so that only the relevant data is sent for annotation. This allows you send the most relevant information about your model’s success.
5.Are there any labels that I should be aware about?
While you may feel the need to give images as much detail-oriented labels as possible, this might not be always necessary or optimal. It is expensive and takes a lot of time to ensure that every image has the right level of detail.
Avoid being too strict or asking for the most precise data annotation.
6.How can you account in edge cases?
When creating your data annotation strategy, you must consider edge cases. You need to understand that not every edge case can be predicted. Instead, you can choose to have a range of variables and a strategy so that edge cases can be identified as they arise. This will allow you to address them quickly.
7.How do I manage data uncertainty?
Ambiguity is common in data sets and annotations should address it. An image of a half-ripe orange could be labeled as either a green apple (or a red one).
Clare instructions from the beginning will be key to eliminating such ambiguity. First, ensure that the annotators and subject matter experts communicate regularly. Prevent ambiguity by creating a standard rule and making it easy to implement across the workforce.
Specify the Training Dataset Needs
Before the machine learns, it is vital to set up the labelling classes and training data classes. Machine learning models typically have a supervisor, unsupervised, or reinforcement. The supervision of learning data is helpful in the detection different objects. They are then calculated by various ML algorithms using an annotation box. The vast majority of supervised ML algorithmic learning relies on learning from annotated datasets.
An ML engineer selects the data categorization classification labels or classes based upon which annotation can be utilised. This is done after determining which algorithm/machine learning model would best suit the business challenge. While it may be easy to prepare learning data using this method, data collection should still be precise. Data accuracy is vital for annotation in order to produce high-fidelity, precise results. The performance of the machine learning model and its outputs will be determined by the quality of the training dataset. After deciding which algorithm or machinelearning model is best for the business problem, an ML Engineer decides what data categorization labels and classes can be used.
Last but not less
Every firm must be ready to change at the touch of a button in the twentyfirst century. Innovations, disruptions, or requirements should be implemented quickly and on a large scale. It is also a competitive field and it can be difficult to thrive when you have the best offers. Therefore, any business challenge that requires classified data and/or a machine learning model to determine a viable solution should be practiced in order to perform quality checks and confidently provide high-quality Audio Transcripiton service. GTS therefore provides high-quality image datasets to train AI/ML models.
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wisepl · 1 year ago
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Image Annotation For Person Detection
Introducing cutting-edge image annotation technology for precise person detection. Keep a watchful eye on your surroundings with unparalleled accuracy and efficiency. Our system employs state-of-the-art algorithms to identify individuals swiftly and reliably, ensuring maximum security and peace of mind.
Experience the power of advanced person detection today and elevate your surveillance capabilities to new heights! Contact us now for a personalized demo and take the first step towards a safer tomorrow.
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albaellabeauty · 3 years ago
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AlbaElla Beauty Studio
AlbaElla Beauty Studio provides top-notch beauty services, plus online and live training courses in microblading and permanent makeup for both new and established PMU artists. Services include microblading, microshading, laser tattoo removal, teeth whitening, and more.
https://www.mapquest.com/search/result?query=AlbaElla%20Beauty%20Studio&boundingBox=46.5286346952717,-120.9814453125,29.49698759653577,-75.0146484375&page=0&mqId=431736687&index=0
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nihaozin · 6 years ago
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@PreciseBPOSol: RT @PreciseBPOSol: Interested in leveraging BPO resources for labeling (image annotation, video labeling, etc.) get in touch with us. #AI #ML #machinelearning #computervision #AutonomousVehicles #driverless #labeling #boundingboxes #DataScience #ArtificialIntelligence [email protected] https://t.co/7ubU5BVjKb
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321arka · 6 years ago
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SketchUp is simple yet powerful software full of special and useful extensions and TrueBend from Thom is another one that allows users to bend geometry within a single click.
About TrueBend:
This extension can be found in SketchUp’s Extension Warehouse and it bends instances for a given degree, saving the original length of the reference segment and in recent this reference segment is the bottom front of the boundingbox. As this works at the bottom front bounding box edge so it is recommended that the instance axis line up with the bend.
To see the detail tutorial follow:www.youtube.com/watch?v=k-e0wjINFr4
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jguertl · 6 years ago
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Custom Dataset and DataLoader in Gluon
One of the most critical steps for model training and inference is loading the data: without data you can’t do machine learning. In this post I want to show you how to use the Gluon API to define a custom Dataset and iterate through it by using a custom DataLoader.
Dataset
Objects of type Dataset are used to represent collections of data, and include methods to load and parse the data. Gluon has a number of different Dataset classes for working with image data straight out-of-the-box, for example ArrayDataset or ImageFolderDataset. Sometimes you have data that doesn’t quite fit the format expected by the included Datasets. You might be able to preprocess your data to fit the expected format, but it is easy to create your own dataset to do this. All you need to do is create a class that implements a getitem method, that returns a sample (e.g. an image/boundingbox pair). Below you can find a custom Dataset class used in the MXNet CycleGan implementation.
DataLoader
A DataLoader is used to create mini-batches of samples from a Dataset, and provides a convenient iterator interface for looping these batches. It’s typically much more efficient to pass a mini-batch of data through a neural network than a single sample at a time, because the computation can be performed in parallel. A required parameter of DataLoader is the size of the mini-batches you want to create, called batch_size. Before Gluon’s DataLoader, MXNet used DataIter objects for loading data for training and testing. Note to take care of pre-processing steps such as augmentations in a transform function when switching to the new DataLoader.
In this example, we can see mini-batches of data (and labels), each with 5 samples. When comparing the shape of the batches to the samples returned by the Dataset, we’ve gained an extra dimension at the start which is sometimes called the batch axis. Our data_loader loop will stop when every sample of dataset has been returned as part of a batch. Sometimes the dataset length isn’t divisible by the mini-batch size, leaving a final batch with a smaller number of samples.
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graphicartsunit · 8 years ago
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オブジェクトを正確に整列させるIllustrator用スクリプトを作りました
Illustratorには標準で整列機能がありますが、テキストオブジェクトについては「見た目」としての正確な整列ができません。これは昔からの仕様で、今後変更される可能性はかなり低いと思っています。そこで、テキストオブジェクトの正確な整列をサポートするスクリプトを作りました。うまく揃わないテキストに悩まされている方は、ぜひ使ってみてください。
更新履歴
GitHubのリポジトリをご確認ください
対応バージョン
Illustrator CS5/CS6/CC/CC 2014/CC 2015/CC 2015.3/CC 2017
ダウンロード
スクリプトをダウンロードする
インストール方法
ダウンロードしたファイルを解凍します。
所定の場所に「オブジェクトを整列Plus.jsx」をコピーします。Windows版ではお使いのIllustratorの種類によって保存する場所が異なりますのでご注意ください。
Illustratorを再起動します。
ファイル → スクリプト → オブジェクトを整列Plusと表示されていればインストール成功です。
ファイルをコピーする場所
OS バージョン フォルダの場所 Mac 全 /Applications/Adobe Illustrator (ver)/Presets/ja_JP/スクリプト/ 32bit Win CS5まで C:\Program Files\Adobe\Adobe Illustrator (ver)\Presets\ja_JP\スクリプト\ 64bit Win CS5, CS6(32bit版) C:\Program Files (x86)\Adobe\Adobe Illustrator (ver)\Presets\ja_JP\スクリプト\ 64bit Win CS6(64bit版)以降 C:\Program Files\Adobe\Adobe Illustrator (ver) (64 Bit)\Presets\ja_JP\スクリプト\
(ver)にはお使いのIllustratorのバージョンが入ります
本スクリプトは、CS4以前では動作を検証しておりません
標準の整列機能ではテキストオブジェクトが正確に揃わない
Illustrator標準の整列機能を使ってテキストオブジェクトを揃えようとすると、以下2つの問題にぶつかります。
横書きテキストの場合、下に隙間ができてしまう(期待する位置より上に揃う)
トラッキングされた文字の場合、右端(下端)にトラッキング分の隙間ができてしまう(期待するより左、または上に揃う)
いずれも、テキストのバウンディングボックス基準で整列位置が計算される仕様に基づくもので、Illustratorの整列がデタラメに処理されているわけではないのですが、おそらく現実として期待する結果になってないと感じることが多いのではないでしょうか。
このスクリプトを使うと
文字の(仮想)ボディのサイズを想定してテキストオブジェクトの大きさを再計算し、それに基づいて整列を行います。バウンディングボックスで文字の下に大きな隙間ができてしまうフォント(源ノ角ゴシックなど)や、トラッキングが設定されたテキストオブジェクトでも比較的正確な整列が可能です。
なお、Illustratorのテキストとバウンディングボックスの関係について興味のある方は、ものかのさんさんのブログ記事を参考にしてみてください。
IllustratorでSource Han Sansのバウンディングボックスが縦長になる理由
使い方
オブジェクトを選択してスクリプトを実行すると、整列のダイアログが開きます
[水平方向]、[垂直方向]で、整列したい位置のチェックボックスをクリックします。整列をしたくないときは、オンになっているチェックボックスを再度クリックして、チェックをオフにします
[基準の範囲]から、整列の基準としたい対象を選択します。アートボード、選択範囲、最背面オブジェクト、最前面オブジェクトの4つから選択可能です
整列基準の範囲を確認したいときは整列の範囲をハイライトのチェックをオンにします。対象範囲がピンクにハイライトされます
実行をクリックすると整列を実行します
基準となる範囲の詳細
項目 説明 アートボード 現在アクティブなアートボードの大きさ 選択範囲 選択されたオブジェクト全体の範囲 最前面オブジェクト 選択された中で最も前面にあるオブジェクトの大きさ 最背面オブジェクト 選択された中で最も背面にあるオブジェクトの大きさ
ダイアログについて
スクリプトは最後に実行したダイアログの状態を記憶しますので、スクリプトを起動したあとにすぐreturn(enter)キーを押せ���前回と同じ設定で整列を実行できます。さらに、スクリプトの設定をカスタマイズすれば、ダイアログを省略して整列を実行することも可能です(次項参照)。
カスタマイズ
10行目あたりにあるsettingsの各値を変更することで、ダイアログを省略した整列ができます。ダイアログを省略するには、showDialogをfalseにしたうえで、その他の値を希望する挙動に合わせておきます。また、withAltKeyをtrueにしておくと、ダイアログ省略設定時でもoption(Alt)キーを押しながらスクリプトを実行することで一時的にダイアログを使った整列が可能です。下記を参考に、テキストエディタでスクリプトを開いて直接編集してください。
キー 型 説明 値 vertical Number 水平方向の揃え -1:移動なし|0:左|1:中央|2:右 horizontal Number 垂直方向の揃え -1:移動なし|0:上|1:中段|2:下 base Number 整列の基準 0:アートボード|1:選択範囲|2:最前面オブジェクト|3:最背面オブジェクト previewArea Boolean 整列基準範囲のハイライト表示 false:しない|true:する showDialog Boolean ダイアログを表示 false:しない|true:する withAltKey Boolean option(Alt)キーを押しながら実行でダイアログを強制表示 false:しない|true:する
ダイアログ表示をカットしたい場合は、showDialogをfalseにしてください
withAltKeyの値はshowDialogがfalseのときのみ有効です
キーボードショートカットのすすめ
ダイアログを省略して実行するときは特にですが、スクリプトをキーボードショートカットで実行できるようにしておくことをおすすめします。Illustrator標準ではスクリプトにショートカットキーを割り当てできないので、Macの方は、ものかのさんの「SPAi」や、Keyboard Maestroなどのツールを使うといいでしょう。Keyboard Maestroは、三階ラボさんの記事が詳しくて参考になります。Windowsの方は……なんとか頑張ってください!(ツールがあるのかどうか知らないです。すみません)
SPAi | Script Panel for Illstrator
3flab inc. | Keyboard Maestro の使い方 – 基本機能の説明編
なお、ショートカットキーの中にoption(Alt)キーが含まれる場合、強制的にダイアログが表示されます。強制表示を無効にしたいときは、カスタマイズでwithAltKeyをfalseに設定してください。ただし、この場合はダイアログの強制表示機能は使えませんのでご注意ください。
その他
クリッピングマスクされたオブジェクトは、クリッピングパスの大きさを基準とします
エリア内文字はテキストエリアを整列の基準とします
注意
今のところ、グループ編集モードなどで整列を実行しようとしたときエラーが出ることがあります。時間がとれたら修正する予定です
CS6ではダイアログ表示直後のプレビューが乱れることがありますが、何かの設定を一度変更すれば正常に戻ります(原因不明)
ネスト(入れ子)になった複合シェイプなどで、一部のオブジェクトしか移動しない現象が確認されています。原因がわかり次第対応予定です (0.5.8で対応済)
必要なオブジェクトが選択されていないときは、警告を表示して処理を中断します
オブジェクトの種類や構造によって意図しない結果になる可能性もゼロではありません
大量のオブジェクトでは処理に時間がかかることがあります
免責事項
このスクリプトを使って起こったいかなる現象についても制作者は責任を負えません。すべて自己責任にてお使いください。
CS5からCC 2017(いずれもMac版)で動作の確認はしましたが、OSのバージョンやその他の状況によって実行できないことがあるかもしれません。もし動かなかったらごめんなさい。
ライセンス
オブジェクトを整列Plus.jsx
Copyright (c) 2017 Toshiyuki Takahashi
Released under the MIT license
http://opensource.org/licenses/mit-license.php
Created by Toshiyuki Takahashi (Graphic Arts Unit)
Twitter
Illustratorの書籍を執筆しています。基本機能を重点的に紹介している内容で、初心者がつまづきやすいポイントは特に詳しく解説しています。ベジェ曲線の基本となる、ペンツールについても取り上げていますので、ぜひチェックしてみてください!
Illustratorプロフェッショナルの教科書 デザイン作成とパス・文字を正しく扱う技術 CC 2014/CC/CS6対応版
著者:高橋としゆき
出版社:エムディエヌコーポレーション/サイズ:B5判/ページ:208P/本体価格:本体2,800円+税
ISBN:978-4-8443-6473-3
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annotationsupport · 3 years ago
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Bounding box annotations in Retail & E-commerce industries
Annotation support provides high quality #boundingbox annotations for various industries like Autonomous vehicles, Retail, Machine learning & Robotics. More details at:https://annotationsupport.com/boundingbox-annotation.php… 
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