Merge pull request #203815 from PatrickFarley/freshness-pass

[cog svcs] Freshness pass

Author: v-stsavell

articles/cognitive-services/Computer-vision/concept-brand-detection.md

@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: conceptual
-ms.date: 01/05/2022
+ms.date: 07/05/2022
 ms.author: pafarley
 ---
 

articles/cognitive-services/Computer-vision/concept-categorizing-images.md

@@ -1,24 +1,24 @@
 ---
 title: Image categorization - Computer Vision
 titleSuffix: Azure Cognitive Services
-description: Learn concepts related to the image categorization feature of the Computer Vision API.
+description: Learn concepts related to the image categorization feature of the Image Analysis API.
 services: cognitive-services
 author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: conceptual
-ms.date: 04/17/2019
+ms.date: 07/05/2022
 ms.author: pafarley
 ms.custom: seodec18
 ---
 
 # Categorize images by subject matter
 
-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.
+In addition to tags and a description, Image Analysis can return the taxonomy-based categories detected in an image. Unlike tags, categories are organized in a parent/child 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.
 
-## The 86-category concept
+## The 86-category hierarchy
 
 Computer vision can categorize an image broadly or specifically, using the list of 86 categories in the following diagram. For the full taxonomy in text format, see [Category Taxonomy](category-taxonomy.md).
 

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

@@ -9,7 +9,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: conceptual
-ms.date: 10/01/2019
+ms.date: 07/05/2022
 ms.author: pafarley
 ms.custom: seodec18
 ---
@@ -18,9 +18,6 @@ ms.custom: seodec18
 
 Computer Vision can detect adult material in images so that developers can restrict the display of these images in their software. Content flags are applied with a score between zero and one so developers can interpret the results according to their own preferences.
 
-> [!NOTE]
-> Much of this functionality is offered by the [Azure Content Moderator](../content-moderator/overview.md) service. See this alternative for solutions to more rigorous content moderation scenarios, such as text moderation and human review workflows.
-
 Try out the adult content detection features quickly and easily in your browser using Vision Studio.
 
 > [!div class="nextstepaction"]

articles/cognitive-services/Computer-vision/how-to/analyze-video.md

@@ -8,7 +8,7 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: computer-vision
 ms.topic: how-to
-ms.date: 09/09/2019
+ms.date: 07/05/2022
 ms.devlang: csharp
 ms.custom: [seodec18, devx-track-csharp, cogserv-non-critical-vision]
 ---
@@ -135,7 +135,7 @@ while (true)
 
 ## Implement the solution
 
-### Get started quickly
+### Get sample code
 
 To help get your app up and running as quickly as possible, we've implemented the system that's described in the preceding section. It's intended to be flexible enough to accommodate many scenarios, while being easy to use. To access the code, go to the [Video frame analysis sample](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/) page on GitHub.
 
@@ -221,7 +221,7 @@ By using this approach, you can visualize the detected face immediately. You can
 
 ![The LiveCameraSample app displaying an image with tags](../../Video/Images/FramebyFrame.jpg)
 
-### Integrate the samples into your codebase
+### Integrate samples into your codebase
 
 To get started with this sample, do the following:
 
@@ -239,9 +239,10 @@ When you're ready to integrate the samples, reference the VideoFrameAnalyzer lib
 
 The image-, voice-, video-, and text-understanding capabilities of VideoFrameAnalyzer use Azure Cognitive Services. Microsoft receives the images, audio, video, and other data that you upload (via this app) and might use them for service-improvement purposes. We ask for your help in protecting the people whose data your app sends to Azure Cognitive Services.
 
-## Summary
+## Next steps
 
 In this article, you learned how to run near real-time analysis on live video streams by using the Face and Computer Vision services. You also learned how you can use our sample code to get started.
 
 Feel free to provide feedback and suggestions in the [GitHub repository](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/). To provide broader API feedback, go to our [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858) site.
 
+- [Call the Image Analysis API (how to)](call-analyze-image.md)

articles/cognitive-services/Computer-vision/how-to/identity-analyze-video.md

@@ -3,36 +3,36 @@ title: "Example: Real-time video analysis - Face"
 titleSuffix: Azure Cognitive Services
 description: Use the Face service to perform near-real-time analysis on frames taken from a live video stream.
 services: cognitive-services
-author: SteveMSFT
+author: PatrickFarley
 manager: nitinme
 
 ms.service: cognitive-services
-ms.subservice: face-api
+ms.subservice: computer-vision
 ms.topic: how-to
-ms.date: 03/01/2018
-ms.author: sbowles
+ms.date: 07/05/2022
+ms.author: pafarley
 ms.devlang: csharp
 ms.custom: devx-track-csharp
 ---
 
-# Example: How to Analyze Videos in Real-time
+# Example: How to analyze videos in real time
 
 [!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
 
-This guide will demonstrate how to perform near-real-time analysis on frames taken from a live video stream. The basic components in such a system are:
+This guide will demonstrate how to perform near-real-time analysis on frames taken from a live video stream. The basic steps in this system are:
 
 - Acquire frames from a video source
 - Select which frames to analyze
 - Submit these frames to the API
 - Consume each analysis result that is returned from the API call
 
-These samples are written in C# and the code can be found on GitHub here: [https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/).
+These samples are written in C# and the code can be found [on GitHub](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/).
 
-## The Approach
+## Methods
 
 There are multiple ways to solve the problem of running near-real-time analysis on video streams. We will start by outlining three approaches in increasing levels of sophistication.
 
-### A Simple Approach
+### Using infinite loop
 
 The simplest design for a near-real-time analysis system is an infinite loop, where each iteration grabs a frame, analyzes it, and then consumes the result:
 
@@ -71,7 +71,7 @@ while (true)
 
 This code launches each analysis in a separate Task, which can run in the background while we continue grabbing new frames. With this method we avoid blocking the main thread while waiting for an API call to return, but we have lost some of the guarantees that the simple version provided. Multiple API calls might occur in parallel, and the results might get returned in the wrong order. This could also cause multiple threads to enter the ConsumeResult() function simultaneously, which could be dangerous, if the function is not thread-safe. Finally, this simple code does not keep track of the Tasks that get created, so exceptions will silently disappear. Therefore, the final step is to add a "consumer" thread that will track the analysis tasks, raise exceptions, kill long-running tasks, and ensure that the results get consumed in the correct order.
 
-### A Producer-Consumer Design
+### Producer-consumer design
 
 In our final "producer-consumer" system, we have a producer thread that looks similar to our previous infinite loop. However, instead of consuming analysis results as soon as they are available, the producer simply puts the tasks into a queue to keep track of them.
 
@@ -134,13 +134,13 @@ while (true)
 }
 ```
 
-## Implementing the Solution
+## Implementation
 
-### Getting Started
+### Get sample code
 
 To get your app up and running as quickly as possible, you will use a flexible implementation of the system described above. To access the code, go to [https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis).
 
-The library contains the class FrameGrabber, which implements the producer-consumer system discussed above to process video frames from a webcam. The user can specify the exact form of the API call, and the class uses events to let the calling code know when a new frame is acquired or a new analysis result is available.
+The library contains the class **FrameGrabber**, which implements the producer-consumer system discussed above to process video frames from a webcam. The user can specify the exact form of the API call, and the class uses events to let the calling code know when a new frame is acquired or a new analysis result is available.
 
 To illustrate some of the possibilities, there are two sample apps that use the library. The first is a simple console app, and a simplified version of it is reproduced below. It grabs frames from the default webcam, and submits them to the Face service for face detection.
 
@@ -152,7 +152,7 @@ In most modes, there will be a visible delay between the live video on the left,
 
 ![HowToAnalyzeVideo](../../Video/Images/FramebyFrame.jpg)
 
-### Integrating into your codebase
+### Integrate into your codebase
 
 To get started with this sample, follow these steps:
 
@@ -167,13 +167,12 @@ To get started with this sample, follow these steps:
     - For LiveCameraSample, the keys should be entered into the Settings pane of the app. They will be persisted across sessions as user data.
 
 
-When you're ready to integrate, **reference the VideoFrameAnalyzer library from your own projects.** 
+When you're ready to integrate, reference the **VideoFrameAnalyzer** library from your own projects.
 
-## Summary
+## Next steps
 
 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 to use our sample code to get started.
 
 Feel free to provide feedback and suggestions in the [GitHub repository](https://github.com/Microsoft/Cognitive-Samples-VideoFrameAnalysis/) or, for broader API feedback, on our [UserVoice](https://feedback.azure.com/d365community/forum/09041fae-0b25-ec11-b6e6-000d3a4f0858) site.
 
-## Related Topics
 - [Call the detect API](identity-detect-faces.md)

articles/cognitive-services/Custom-Vision-Service/export-model-python.md

@@ -9,15 +9,15 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: how-to
-ms.date: 01/05/2022
+ms.date: 07/05/2022
 ms.author: pafarley
 ms.devlang: python
 ms.custom: devx-track-python
 ---
 
 # Tutorial: Run a TensorFlow model in Python
 
-After you have [exported your TensorFlow model](./export-your-model.md) from the Custom Vision Service, this quickstart will show you how to use this model locally to classify images.
+After you've [exported your TensorFlow model](./export-your-model.md) from the Custom Vision Service, this quickstart will show you how to use this model locally to classify images.
 
 > [!NOTE]
 > This tutorial applies only to models exported from "General (compact)" image classification projects. If you exported other models, please visit our [sample code repository](https://github.com/Azure-Samples/customvision-export-samples).

articles/cognitive-services/Custom-Vision-Service/export-your-model.md

@@ -9,13 +9,13 @@ manager: nitinme
 ms.service: cognitive-services
 ms.subservice: custom-vision
 ms.topic: how-to
-ms.date: 10/27/2021
+ms.date: 07/05/2022
 ms.author: pafarley
 ---
 
 # Export your model for use with mobile devices
 
-Custom Vision Service allows classifiers to be exported to run offline. You can embed your exported classifier into an application and run it locally on a device for real-time classification.
+Custom Vision Service lets you export your classifiers to be run offline. You can embed your exported classifier into an application and run it locally on a device for real-time classification.
 
 ## Export options