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Merge pull request #73712 from PatrickFarley/comvis-build-seo
[Cog Svcs] Comvis build seo
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‎.openpublishing.redirection.json

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"redirect_url": "/azure/cognitive-services/computer-vision/home",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/cognitive-services/Computer-vision/QuickStarts/quickstart-summary.md",
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"redirect_url": "/azure/cognitive-services/computer-vision/home",
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"redirect_document_id": false
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},
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{
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"source_path": "articles/media-services/previous/media-services-hyperlapse-content.md",
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"redirect_url": "/azure/media-services/previous/media-services-analytics-overview",

‎articles/cognitive-services/Computer-vision/Category-Taxonomy.md

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ms.service: cognitive-services
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ms.subservice: computer-vision
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ms.topic: reference
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ms.date: 03/21/2019
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ms.date: 04/17/2019
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ms.author: kefre
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ms.custom: seodec18
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---
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# 86-Category Taxonomy
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# Computer Vision 86-category taxonomy
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abstract_
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‎articles/cognitive-services/Computer-vision/QuickStarts/javascript-analyze.md

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---
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title: "Quickstart: Analyze a remote image - REST, JavaScript"
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title: "Quickstart: Analyze remote image - REST, JavaScript"
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titleSuffix: "Azure Cognitive Services"
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description: In this quickstart, you analyze an image using the Computer Vision API with JavaScript.
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services: cognitive-services
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ms.service: cognitive-services
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ms.subservice: computer-vision
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ms.topic: quickstart
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ms.date: 03/11/2019
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ms.date: 04/17/2019
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ms.author: pafarley
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---

‎articles/cognitive-services/Computer-vision/QuickStarts/php-domain.md

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---
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title: "Quickstart: Domain-specific image content - REST, PHP"
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title: "Quickstart: Domain-specific content - REST, PHP"
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titleSuffix: "Azure Cognitive Services"
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description: In this quickstart, you use a domain model to identify landmarks in an image using the Computer Vision API with PHP.
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services: cognitive-services
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ms.service: cognitive-services
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ms.subservice: computer-vision
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ms.topic: quickstart
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ms.date: 03/11/2019
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ms.date: 04/17/2019
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---

‎articles/cognitive-services/Computer-vision/QuickStarts/python-analyze.md

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- You must have [Python](https://www.python.org/downloads/) installed if you want to run the sample locally.
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- You must have a subscription key for Computer Vision. You can get a free trial key from [Try Cognitive Services](https://azure.microsoft.com/try/cognitive-services/?api=computer-vision). Or, follow the instructions in [Create a Cognitive Services account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) to subscribe to Computer Vision and get your key.
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- You must have the following Python packages installed. You can use [pip](https://packaging.python.org/tutorials/installing-packages/) to install Python packages.
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- [requests](http://docs.python-requests.org/en/master/)
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- requests
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- [matplotlib](https://matplotlib.org/)
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- [pillow](https://python-pillow.org/)
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‎articles/cognitive-services/Computer-vision/QuickStarts/python-disk.md

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- You must have [Python](https://www.python.org/downloads/) installed if you want to run the sample locally.
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- You must have a subscription key for Computer Vision. You can get a free trial key from [Try Cognitive Services](https://azure.microsoft.com/try/cognitive-services/?api=computer-vision). Or, follow the instructions in [Create a Cognitive Services account](https://docs.microsoft.com/azure/cognitive-services/cognitive-services-apis-create-account) to subscribe to Computer Vision and get your key.
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- You must have the following Python packages installed. You can use [pip](https://packaging.python.org/tutorials/installing-packages/) to install Python packages.
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- [requests](http://docs.python-requests.org/en/master/)
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- requests
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- [matplotlib](https://matplotlib.org/)
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- [pillow](https://python-pillow.org/)
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‎articles/cognitive-services/Computer-vision/QuickStarts/python-domain.md

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---
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title: "Quickstart: Domain-specific image content - REST, Python"
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title: "Quickstart: Domain-specific content - REST, Python"
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titleSuffix: "Azure Cognitive Services"
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description: In this quickstart, you use domain models to identify celebrities and landmarks in an image using the Computer Vision API with Python.
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ms.service: cognitive-services
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ms.subservice: computer-vision
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ms.topic: quickstart
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ms.date: 02/21/2019
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ms.date: 04/17/2019
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‎articles/cognitive-services/Computer-vision/QuickStarts/python-hand-text.md

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You can run this quickstart in a step-by step fashion using a Jupyter notebook on [MyBinder](https://mybinder.org). To launch Binder, select the following button:
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[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
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[![The launch Binder button](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Microsoft/cognitive-services-notebooks/master?filepath=VisionAPI.ipynb)
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If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/try/cognitive-services/) before you begin.
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‎articles/cognitive-services/Computer-vision/QuickStarts/quickstart-summary.md

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‎articles/cognitive-services/Computer-vision/Tutorials/CSharpTutorial.md

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---
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title: "Sample: Explore an image processing app in C#"
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titleSuffix: Computer Vision - Cognitive Services - Azure
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description: Explore a basic Windows app that uses the Computer Vision API in Microsoft Cognitive Services. Perform OCR, create thumbnails, and work with visual features in an image.
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titleSuffix: Azure Cognitive Services
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description: Explore a basic Windows app that uses the Computer Vision API in Azure Cognitive Services. Perform OCR, create thumbnails, and work with visual features in an image.
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services: cognitive-services
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author: PatrickFarley
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manager: nolachar
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ms.service: cognitive-services
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ms.topic: sample
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ms.date: 02/08/2019
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ms.date: 04/17/2019
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---

‎articles/cognitive-services/Computer-vision/Tutorials/storage-lab-tutorial.md

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title: "Tutorial: Generate metadata for Azure Storage images"
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title: "Tutorial: Generate metadata for Azure images"
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description: In this tutorial, you will learn how to integrate the Azure Computer Vision service into a web app to generate metadata for images.
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#Customer intent: As a developer of an image-intensive web app, I want to be able to automatically generate captions and search keywords for each of my images.
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1. Return to the menu for your resource group and click the Computer Vision API subscription that you just created. Copy the URL under **Endpoint** to somewhere you can easily retrieve it in a moment. Then click **Show access keys**.
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![Viewing the access keys](../Images/copy-vision-endpoint.png)
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![Azure portal page with the endpoint URL and access keys link outlined](../Images/copy-vision-endpoint.png)
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1. In the next window, copy the value of **KEY 1** to the clipboard.
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![Copying the access key](../Images/copy-vision-key.png)
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![Manage keys dialog, with the copy button outlined](../Images/copy-vision-key.png)
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## Add Computer Vision credentials
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To view all of the attached metadata, use the Azure Storage Explorer to view the storage container you're using for images. Right-click any of the blobs in the container and select **Properties**. In the dialog, you'll see a list of key-value pairs. The computer-generated image description is stored in the item "Caption," and the search keywords are stored in "Tag0," "Tag1," and so on. When you're finished, click **Cancel** to close the dialog.
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![Blob metadata](../Images/blob-metadata.png)
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![Image properties dialog window, with metadata tags listed](../Images/blob-metadata.png)
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## Clean up resources
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‎articles/cognitive-services/Computer-vision/Vision-API-How-to-Topics/HowtoAnalyzeVideo_Vision.md

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In most modes, there will be a visible delay between the live video on the left, and the visualized analysis on the right. This delay is the time taken to make the API call. The exception to this is in the "EmotionsWithClientFaceDetect" mode, which performs face detection locally on the client computer using OpenCV, before submitting any images to Cognitive Services. By doing this, we can visualize the detected face immediately, and then update the emotions later once the API call returns. This demonstrates the possibility of a "hybrid" approach, where some simple processing can be performed on the client, and then Cognitive Services APIs can be used to augment this with more advanced analysis when necessary.
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![Screenshot of the LiveCameraSample app showing an image with tags displayed](../../Video/Images/FramebyFrame.jpg)
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![Screenshot of LiveCameraSample app showing image with tags displayed](../../Video/Images/FramebyFrame.jpg)
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### Integrating into your codebase
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## Summary
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In this guide, you learned how to run near-real-time analysis on live video streams using the Face, Computer Vision, and Emotion APIs, and how you can use our sample code to get started. You can get started building your app with free API keys at the [Microsoft Cognitive Services sign-up page](https://azure.microsoft.com/try/cognitive-services/).
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In this guide, you learned how to run near-real-time analysis on live video streams using the Face, Computer Vision, and Emotion APIs, and how you can use our sample code to get started. You can get started building your app with free API keys at the [Azure Cognitive Services sign-up page](https://azure.microsoft.com/try/cognitive-services/).
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Please feel free to provide feedback and suggestions in the [GitHub repository](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/), or for more broad API feedback, on our [UserVoice site](https://cognitive.uservoice.com/).
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‎articles/cognitive-services/Computer-vision/concept-brand-detection.md

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---
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# Brand detection
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# Detect popular brands in images
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Brand detection is a specialized mode of [object detection](concept-object-detection.md) that uses a database of thousands of global logos to identify commercial brands in images or video. You can use this feature, for example, to discover which brands are most popular on social media or most prevalent in media product placement.
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‎articles/cognitive-services/Computer-vision/concept-categorizing-images.md

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# Categorize images by subject matter
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In addition to tags and a description, Computer Vision returns the taxonomy-based categories detected in an image. Unlike tags, categories are organized in a parent/child hereditary hierarchy, and there are fewer of them (86, as opposed to thousands of tags). All category names are in English. Categorization can be done by itself or alongside the newer tags model.
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‎articles/cognitive-services/Computer-vision/concept-detecting-color-schemes.md

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The following example illustrates the JSON response returned by Computer Vision when detecting the color scheme of the example image. In this case, the example image is not a black and white image, but the dominant foreground and background colors are black, and the dominant colors for the image as a whole are black and white.
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![Outdoor Mountain](./Images/mountain_vista.png)
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![Outdoor Mountain at sunset, with a person's silhouette](./Images/mountain_vista.png)
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```json
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{

‎articles/cognitive-services/Computer-vision/concept-detecting-domain-content.md

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![Satya Nadella standing, smiling](./images/satya.jpeg)
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```json
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‎articles/cognitive-services/Computer-vision/concept-detecting-faces.md

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‎articles/cognitive-services/Computer-vision/concept-generating-thumbnails.md

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The generated thumbnail can vary widely depending on what you specify for height, width, and smart cropping, as shown in the following image.
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![Thumbnails](./Images/thumbnail-demo.png)
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![A mountain image next to various cropping configurations](./Images/thumbnail-demo.png)
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| Image | Thumbnail |
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|-------|-----------|
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|![A person standing on a mountain rock at sunset](./Images/mountain_vista.png) | ![Outdoor Mountain thumbnail](./Images/mountain_vista_thumbnail.png) |
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|![Outdoor Mountain at sunset, with a person's silhouette](./Images/mountain_vista.png) | ![Thumbnail of Outdoor Mountain at sunset, with a person's silhouette](./Images/mountain_vista_thumbnail.png) |
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|![A white flower with a green background](./Images/flower.png) | ![Vision Analyze Flower thumbnail](./Images/flower_thumbnail.png) |
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|![A woman on the roof of an apartment building](./Images/woman_roof.png) | ![Woman Roof thumbnail](./Images/woman_roof_thumbnail.png) |
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|![A woman on the roof of an apartment building](./Images/woman_roof.png) | ![thumbnail of a woman on the roof of an apartment building](./Images/woman_roof_thumbnail.png) |
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## Next steps
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‎articles/cognitive-services/Computer-vision/concept-object-detection.md

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# Detect common objects in images
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Object detection is similar to [tagging](concept-tagging-images.md), but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
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The Detect API applies tags based on the objects or living things identified in the image. At this point, there is no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
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The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can't be localized with bounding boxes.
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## Object detection example
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