Merge pull request #3590 from davidbritch/dabritch-xamarin-remove

[Azure AI Svcs] Remove Xamarin references

Author: denrea

.openpublishing.redirection.json

@@ -264,6 +264,11 @@
       "source_path_from_root": "/articles/ai-services/openai/concepts/provisioned-reservation-update.md",
       "redirect_url": "/azure/ai-services/openai/concepts/provisioned-migration",
       "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/custom-vision-service/logo-detector-mobile.md",
+      "redirect_url": "/azure/ai-services/custom-vision-service",
+      "redirect_document_id": false
     }
   ]
 }

articles/ai-services/custom-vision-service/custom-vision-onnx-windows-ml.md

@@ -40,7 +40,7 @@ The example application is available at the [Azure AI services ONNX Custom Visio
 
 To use your own image classifier model, follow these steps:
 
-1. Create and train a classifier with the Custom Vision Service. For instructions on how to do this, see [Create and train a classifier](./getting-started-build-a-classifier.md). Use one of the **compact** domains such as **General (compact)**. 
+1. Create and train a classifier with the Custom Vision Service. For instructions on how to do this, see [Create and train a classifier](./getting-started-build-a-classifier.md). Use one of the **compact** domains such as **General (compact)**.
    * If you have an existing classifier that uses a different domain, you can convert it to **compact** in the project settings. Then, re-train your project before continuing.
 1. Export your model. Switch to the Performance tab and select an iteration that was trained with a **compact** domain. Select the **Export** button that appears. Then select **ONNX**, and then **Export**. Once the file is ready, select the **Download** button. For more information on export options, see [Export your model](./export-your-model.md).
 1. Open the downloaded *.zip* file and extract the *model.onnx* file from it. This file contains your classifier model.
@@ -59,6 +59,5 @@ To discover other ways to export and use a Custom Vision model, see the followin
 * [Export your model](./export-your-model.md)
 * [Use exported TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
 * [Use exported CoreML model in a Swift iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
-* [Use exported CoreML model in an iOS application with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)
 
 For more information on using ONNX models with Windows ML, see [Integrate a model into your app with Windows ML](/windows/ai/windows-ml/integrate-model).

articles/ai-services/custom-vision-service/export-model-python.md

@@ -32,7 +32,7 @@ This guide shows you how to use an [exported TensorFlow model](./export-your-mod
     pip install numpy
     pip install opencv-python
     ```
-    
+
 ## Load your model and tags
 
 The downloaded _.zip_ file from the export step contains a _model.pb_ and a _labels.txt_ file. These files represent the trained model and the classification labels. The first step is to load the model into your project. Add the following code to a new Python script.
@@ -64,19 +64,19 @@ with open(labels_filename, 'rt') as lf:
 There are a few steps you need to take to prepare an image for prediction. These steps mimic the image manipulation performed during training.
 
 1. Open the file and create an image in the BGR color space
-    
+
     ```Python
     from PIL import Image
     import numpy as np
     import cv2
-    
+
     # Load from a file
     imageFile = "<path to your image file>"
     image = Image.open(imageFile)
-    
+
     # Update orientation based on EXIF tags, if the file has orientation info.
     image = update_orientation(image)
-    
+
     # Convert to OpenCV format
     image = convert_to_opencv(image)
     ```
@@ -108,40 +108,40 @@ There are a few steps you need to take to prepare an image for prediction. These
     with tf.compat.v1.Session() as sess:
         input_tensor_shape = sess.graph.get_tensor_by_name('Placeholder:0').shape.as_list()
     network_input_size = input_tensor_shape[1]
-    
+
     # Crop the center for the specified network_input_Size
     augmented_image = crop_center(augmented_image, network_input_size, network_input_size)
-    
+
     ```
 
 1. Define helper functions. The steps above use the following helper functions:
-    
+
     ```Python
     def convert_to_opencv(image):
         # RGB -> BGR conversion is performed as well.
         image = image.convert('RGB')
         r,g,b = np.array(image).T
         opencv_image = np.array([b,g,r]).transpose()
         return opencv_image
-    
+
     def crop_center(img,cropx,cropy):
         h, w = img.shape[:2]
         startx = w//2-(cropx//2)
         starty = h//2-(cropy//2)
         return img[starty:starty+cropy, startx:startx+cropx]
-    
+
     def resize_down_to_1600_max_dim(image):
         h, w = image.shape[:2]
         if (h < 1600 and w < 1600):
             return image
-    
+
         new_size = (1600 * w // h, 1600) if (h > w) else (1600, 1600 * h // w)
         return cv2.resize(image, new_size, interpolation = cv2.INTER_LINEAR)
-    
+
     def resize_to_256_square(image):
         h, w = image.shape[:2]
         return cv2.resize(image, (256, 256), interpolation = cv2.INTER_LINEAR)
-    
+
     def update_orientation(image):
         exif_orientation_tag = 0x0112
         if hasattr(image, '_getexif'):
@@ -158,7 +158,7 @@ There are a few steps you need to take to prepare an image for prediction. These
                     image = image.transpose(Image.FLIP_LEFT_RIGHT)
         return image
     ```
-    
+
 ## Classify an image
 
 Once the image is prepared as a tensor, we can send it through the model for a prediction.
@@ -201,4 +201,3 @@ The results of running the image tensor through the model will then need to be m
 Next, learn how to wrap your model into a mobile application:
 * [Use your exported TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
 * [Use your exported CoreML model in a Swift iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
-* [Use your exported CoreML model in an iOS application with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)

articles/ai-services/custom-vision-service/export-programmatically.md

@@ -32,13 +32,13 @@ trainer = CustomVisionTrainingClient(ENDPOINT, credentials)
 ```
 
 > [!IMPORTANT]
-> Remember to remove the keys from your code when youre done, and never post them publicly. For production, consider using a secure way of storing and accessing your credentials. For more information, see the Azure AI services [security](../security-features.md) article.
+> Remember to remove the keys from your code when you're done, and never post them publicly. For production, consider using a secure way of storing and accessing your credentials. For more information, see the Azure AI services [security](../security-features.md) article.
 
 ## Call the export method
 
 Call the **export_iteration** method.
-* Provide the project ID, iteration ID of the model you want to export. 
-* The *platform* parameter specifies the platform to export to: allowed values are `CoreML`, `TensorFlow`, `DockerFile`, `ONNX`, `VAIDK`, and `OpenVino`. 
+* Provide the project ID, iteration ID of the model you want to export.
+* The *platform* parameter specifies the platform to export to: allowed values are `CoreML`, `TensorFlow`, `DockerFile`, `ONNX`, `VAIDK`, and `OpenVino`.
 * The *flavor* parameter specifies the format of the exported model: allowed values are `Linux`, `Windows`, `ONNX10`, `ONNX12`, `ARM`, `TensorFlowNormal`, and `TensorFlowLite`.
 * The *raw* parameter gives you the option to retrieve the raw JSON response along with the object model response.
 
@@ -92,5 +92,4 @@ Integrate your exported model into an application by exploring one of the follow
 * [Use your ONNX model with Windows Machine Learning](custom-vision-onnx-windows-ml.md)
 * See the sample for [CoreML model in an iOS application](https://go.microsoft.com/fwlink/?linkid=857726) for real-time image classification with Swift.
 * See the sample for [TensorFlow model in an Android application](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample) for real-time image classification on Android.
-* See the sample for [CoreML model with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel) for real-time image classification in a Xamarin iOS app.
 * See the sample for how to use the exported model [(VAIDK/OpenVino)](https://github.com/Azure-Samples/customvision-export-samples)

articles/ai-services/custom-vision-service/export-your-model.md

@@ -46,7 +46,7 @@ To convert the domain of an existing model, follow these steps:
 
     :::image type="content" source="media/export-your-model/gear-icon.png" alt-text="Screenshot that shows the gear icon.":::
 
-1. In the **Domains** section, select one of the **compact** domains. Select **Save Changes** to save the changes. 
+1. In the **Domains** section, select one of the **compact** domains. Select **Save Changes** to save the changes.
 
     > [!NOTE]
     > For Vision AI Dev Kit, the project must be created with the **General (Compact)** domain, and you must specify the **Vision AI Dev Kit** option under the **Export Capabilities** section.
@@ -76,4 +76,3 @@ To integrate your exported model into an application, explore one of the followi
 * [Use your ONNX model with Windows Machine Learning](custom-vision-onnx-windows-ml.md)
 * See the Swift sample for [CoreML model in an iOS application](https://go.microsoft.com/fwlink/?linkid=857726)
 * See the Android sample for [TensorFlow model in an Android app](https://github.com/Azure-Samples/cognitive-services-android-customvision-sample)
-* See the Xamarin iOS sample for [CoreML model with Xamarin](https://github.com/xamarin/ios-samples/tree/master/ios11/CoreMLAzureModel)

articles/ai-services/custom-vision-service/index.yml

@@ -17,7 +17,7 @@ metadata:
 landingContent:
 # Cards and links should be based on top customer tasks or top subjects
 # Start card title with a verb
-  # Card 
+  # Card
   - title: About Custom Vision
     linkLists:
       - linkListType: overview
@@ -61,10 +61,6 @@ landingContent:
         links:
           - text: Use the prediction API
             url: use-prediction-api.md
-      - linkListType: tutorial
-        links:
-          - text: Logo detector for mobile
-            url: logo-detector-mobile.md
 
   - title: Test and improve models
     linkLists:
@@ -89,12 +85,6 @@ landingContent:
             url: custom-vision-onnx-windows-ml.md
           - text: Run TensorFlow model in Python
             url: export-model-python.md
-      - linkListType: tutorial
-        links:
-          #- text: IoT Visual Alerts app
-          #  url: iot-visual-alerts-tutorial.md
-          - text: Logo detector for mobile
-            url: logo-detector-mobile.md
 
   - title: Reference
     linkLists: