Fixes

Author: alexbuckgit

articles/azure-video-analyzer/video-analyzer-docs/deploy-on-stack-edge.md

@@ -433,7 +433,7 @@ To connect to your IoT hub by using the Azure IoT Tools extension, do the follow
 
         Kubernetes supports [pod affinity](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#inter-pod-affinity-and-anti-affinity), which can schedule pods on the same node. To achieve co-location, you can add to the inference pod manifest, a podAffinity section that references the Video Analyzer module.
 
-        ```json
+        ```yaml
         // Example Video Analyzer module deployment match labels
         selector:
           matchLabels:
@@ -454,8 +454,8 @@ To connect to your IoT hub by using the Azure IoT Tools extension, do the follow
                 topologyKey: "kubernetes.io/hostname"
         ```
 
-* **You get a 404 error code when you use the *rtspsim* module**  
-    
+* **You get a 404 error code when you use the *rtspsim* module**
+
     The container reads videos from exactly one folder within the container. If you map/bind an external folder into a folder that already exists within the container image, Docker hides the files present in the container image.
  
     For example, with no bindings, the container might have these files:  

articles/azure-video-analyzer/video-analyzer-docs/troubleshoot.md

@@ -114,13 +114,13 @@ Video Analyzer via the pipeline extension processors can extend the pipeline to
 
   As an example, here is a Yolo v3 container that's running on local machine with an IP address of 172.17.0.3.
 
-  ```
+  ```shell
   curl -X POST http://172.17.0.3/score -H "Content-Type: image/jpeg" --data-binary @<fullpath to jpg>
   ```
 
   Result returned:
 
-  ```
+  ```json
   {"inferences": [{"type": "entity", "entity": {"tag": {"value": "car", "confidence": 0.8668569922447205}, "box": {"l": 0.3853073438008626, "t": 0.6063712999658677, "w": 0.04174524943033854, "h": 0.02989496027381675}}}]}
   ```
 
@@ -135,7 +135,7 @@ Video Analyzer via the pipeline extension processors can extend the pipeline to
 
 Video Analyzer provides a direct method-based programming model that allows you to set up multiple topologies and multiple pipelines. As part of the topology and pipeline setup, you invoke multiple direct method calls on the IoT Edge module. If you invoke these multiple method calls in parallel, especially the ones that start and stop the pipelines, you might experience a timeout failure such as the following:
 
-Assembly Initialization method Microsoft.Media.VideoAnalyzer.Test.Feature.Edge.AssemblyInitializer.InitializeAssemblyAsync threw exception. Microsoft.Azure.Devices.Common.Exceptions.IotHubException: <br/> `{"Message":"{\"errorCode\":504101,\"trackingId\":\"55b1d7845498428593c2738d94442607-G:32-TimeStamp:05/15/2020 20:43:10-G:10-TimeStamp:05/15/2020 20:43:10\",\"message\":\"Timed out waiting for the response from device.\",\"info\":{},\"timestampUtc\":\"2020-05-15T20:43:10.3899553Z\"}","ExceptionMessage":""}. Aborting test execution. `
+Assembly Initialization method Microsoft.Media.VideoAnalyzer.Test.Feature.Edge.AssemblyInitializer.InitializeAssemblyAsync threw exception. Microsoft.Azure.Devices.Common.Exceptions.IotHubException: <br/> `{"Message":"{\"errorCode\":504101,\"trackingId\":\"55b1d7845498428593c2738d94442607-G:32-TimeStamp:05/15/2020 20:43:10-G:10-TimeStamp:05/15/2020 20:43:10\",\"message\":\"Timed out waiting for the response from device.\",\"info\":{},\"timestampUtc\":\"2020-05-15T20:43:10.3899553Z\"}","ExceptionMessage":""}. Aborting test execution.`
 
 We recommend that you _not_ call direct methods in parallel. Call them sequentially (that is, make one direct method call only after the previous one is finished).
 
@@ -158,7 +158,7 @@ To gather the relevant logs that should be added to the ticket, follow the instr
 
    On the IoT Edge device, use the following command after replacing `<avaedge>` with the name of your Video Analyzer edge module :
 
-   ```cmd
+   ```shell
    sudo iotedge restart <avaedge>
    ```
 
@@ -208,7 +208,7 @@ When you need to gather logs from an IoT Edge device, the easiest way is to use
 
 1. Run the `support-bundle` command with the _--since_ flag to specify how much time you want your logs to cover. For example, 2h will get logs for the last two hours. You can change the value of this flag to include logs for different periods.
 
-   ```
+   ```shell
    sudo iotedge support-bundle --since 2h
    ```
 

articles/cognitive-services/Computer-vision/spatial-analysis-operations.md

@@ -90,32 +90,33 @@ This is an example of the DETECTOR_NODE_CONFIG parameters for all Spatial Analys
 ### Camera calibration node parameter settings
 This is an example of the `CAMERACALIBRATOR_NODE_CONFIG` parameters for all spatial analysis operations.
 
-```
+```json
 {
-"gpu_index": 0,
-"do_calibration": true,
-"enable_breakpad": false,
-"enable_orientation": true
+  "gpu_index": 0,
+  "do_calibration": true,
+  "enable_breakpad": false,
+  "enable_orientation": true
 }
 ```
 
-| Name | Type| Description|
+| Name | Type | Description |
 |---------|---------|---------|
 | `do_calibration` | string | Indicates that calibration is turned on. `do_calibration` must be true for **cognitiveservices.vision.spatialanalysis-persondistance** to function properly. `do_calibration` is set by default to `True`. |
 | `enable_breakpad`| bool | Indicates whether to enable breakpad, which is used to generate a crash dump for debug use. It is `false` by default. If you set it to `true`, you also need to add `"CapAdd": ["SYS_PTRACE"]` in the `HostConfig` part of container `createOptions`. By default, the crash dump is uploaded to the [RealTimePersonTracking](https://appcenter.ms/orgs/Microsoft-Organization/apps/RealTimePersonTracking/crashes/errors?version=&appBuild=&period=last90Days&status=&errorType=all&sortCol=lastError&sortDir=desc) AppCenter app, if you want the crash dumps to be uploaded to your own AppCenter app, you can override the environment variable `RTPT_APPCENTER_APP_SECRET` with your app's app secret.
 | `enable_orientation` | bool | Indicates whether you want to compute the orientation for the detected people or not. `enable_orientation` is set by default to `True`. |
 
 ### Calibration config
+
 This is an example of the `CALIBRATION_CONFIG` parameters for all spatial analysis operations.
 
-```
+```json
 {
-"enable_recalibration": true,
-"calibration_quality_check_frequency_seconds": 86400,
-"calibration_quality_check_sample_collect_frequency_seconds": 300,
-"calibration_quality_check_one_round_sample_collect_num": 10,
-"calibration_quality_check_queue_max_size": 1000,
-"calibration_event_frequency_seconds": -1
+  "enable_recalibration": true,
+  "calibration_quality_check_frequency_seconds": 86400,
+  "calibration_quality_check_sample_collect_frequency_seconds": 300,
+  "calibration_quality_check_one_round_sample_collect_num": 10,
+  "calibration_quality_check_queue_max_size": 1000,
+  "calibration_event_frequency_seconds": -1
 }
 ```
 
@@ -128,10 +129,11 @@ This is an example of the `CALIBRATION_CONFIG` parameters for all spatial analys
 | `calibration_quality_check_queue_max_size` | int | Maximum number of data samples to store when camera model is calibrated. Default is `1000`. Only used when `enable_recalibration=True`.|
 | `calibration_event_frequency_seconds` | int | Output frequency (seconds) of camera calibration events. A value of `-1` indicates that the camera calibration should not be sent unless the camera calibration info has been changed. Default is `-1`.|
 
-
 ### Camera calibration output
+
 This is an example of the output from camera calibration if enabled. Ellipses indicate more of the same type of objects in a list.
-```
+
+```json
 {
   "type": "cameraCalibrationEvent",
   "sourceInfo": {
@@ -231,25 +233,30 @@ You can configure the speed computation through the tracker node parameter setti
 |---------|---------|---------|
 | `enable_speed` | bool | Indicates whether you want to compute the speed for the detected people or not. `enable_speed` is set by default to `True`. It is highly recommended that you enable both speed and orientation to have the best estimated values. |
 
-
 ## Spatial Analysis operations configuration and output
+
 ### Zone configuration for cognitiveservices.vision.spatialanalysis-personcount
 
- This is an example of a JSON input for the SPACEANALYTICS_CONFIG parameter that configures a zone. You may configure multiple zones for this operation.
+This is an example of a JSON input for the SPACEANALYTICS_CONFIG parameter that configures a zone. You may configure multiple zones for this operation.
 
 ```json
 {
-"zones":[{
-       "name": "lobbycamera",
-       "polygon": [[0.3,0.3], [0.3,0.9], [0.6,0.9], [0.6,0.3], [0.3,0.3]],
-       "events":[{
-              "type": "count",
-              "config":{
-                     "trigger": "event",
+  "zones": [
+    {
+      "name": "lobbycamera",
+      "polygon": [[0.3,0.3], [0.3,0.9], [0.6,0.9], [0.6,0.3], [0.3,0.3]],
+      "events": [
+        {
+          "type": "count",
+          "config": {
+            "trigger": "event",
             "threshold": 16.00,
             "focus": "footprint"
-      }
-       }]
+          }
+        }
+      ]
+    }
+  ]
 }
 ```