add Rust Wasm operator viconverter (#145)

## Purpose
<!-- Describe the intention of the changes being proposed. What problem
does it solve or functionality does it add? -->
* Add a Wasm operator example to transform and flatten video indexer
insights to specific format that can be further processed and stored by
Azure Event Hub and Fabric Lakehouse.

## Does this introduce a breaking change?
<!-- Mark one with an "x". -->
```
[ ] Yes
[x] No
```

## Pull Request Type
What kind of change does this Pull Request introduce?

<!-- Please check the one that applies to this PR using "x". -->
```
[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, local variables)
[ ] Refactoring (no functional changes, no api changes)
[x] Documentation content changes
[x] Other... Please describe: Add a new Wasm operator example
```

## How to Test
*  Get the code

```
git clone https://github.com/Azure-Samples/explore-iot-operations.git
```

* Test the code
<!-- Add steps to run the tests suite and/or manually test -->
```
cd explore-iot-operations.git/samples/wasm/rust/examples/viconverter
docker run --rm -v "$(pwd):/workspace" ghcr.io/azure-samples/explore-iot-operations/rust-wasm-builder --app-name viconverter
```

## What to Check
Verify that the following are valid
* viconverter.wasm is built successfully.
```
Build complete! Output: bin/x86_64/release/viconverter.wasm
```

## Other Information
<!-- Add any other helpful information that may be needed here. -->

Author: zhen9910

samples/wasm/rust/README.md

@@ -76,6 +76,7 @@ The `examples/` directory contains complete examples:
 - **collection**: Data collection and batching
 - **enrichment**: Data enrichment and annotation
 - **window**: Time-based windowing operations
+- **viconverter**: Azure AI Video Indexer insights conversion
 
 ## Building Operators
 

samples/wasm/rust/examples/README.md

@@ -95,6 +95,17 @@ docker run --rm -v "$(pwd):/workspace" ghcr.io/azure-samples/explore-iot-operati
   - Time-based data processing
 - Key Features: Configurable delay intervals, preserves data ordering
 
+### Azure AI Video Indexer insights processing
+
+#### viconverter - insights conversion processing
+- Path: `viconverter/`
+- Operator Type: Map
+- Purpose: transform and flatten video indexer insights to specific format that can be further processed and stored by Azure Event Hub and Fabric Lakehouse.
+- Use Cases:
+  - Computer vision pipelines
+  - Image processing
+  - surveillance systems
+
 ## Architecture Patterns
 
 ### Operator Type Implementations

samples/wasm/rust/examples/viconverter/Cargo.toml

@@ -0,0 +1,22 @@
+[package]
+name = "viconverter"
+version = "0.2.0"
+authors = ["TinyKube Devs"]
+license = "MIT"
+edition = "2021"
+
+[dependencies]
+wit-bindgen = "0.22"
+tinykube_wasm_sdk = { version = "0.2.0", registry="azure-vscode-tinykube" }
+serde = { version = "1", default-features = false, features = [
+	"derive",
+] }
+serde_json = { version = "1", default-features = false, features = [
+	"alloc", # "serde_json requires that either `std` (default) or `alloc` feature is enabled"
+] }
+jsonschema = { version = "0.8", default-features = false}
+once_cell = "1.17.1"
+
+[lib]
+crate-type = ["cdylib"]
+path = "src/lib.rs"

samples/wasm/rust/examples/viconverter/src/lib.rs

@@ -0,0 +1,131 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
+// Generated by `wit_bindgen::generate` expansion.
+#![allow(clippy::missing_safety_doc)]
+
+use serde::{Deserialize, Serialize};
+use tinykube_wasm_sdk::logger::{self, Level};
+use tinykube_wasm_sdk::macros::map_operator;
+
+/// Minimal view of the input: we only deserialize what we need.
+#[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
+struct Input {
+    id: usize,
+    height: u32,
+    width: u32,
+    #[serde(default)]
+    detections: Vec<Detection>,
+}
+
+#[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
+struct Detection {
+    #[serde(rename = "Id")]
+    id: String,
+    insight_name: String,
+    model_type: String,
+    instances: Vec<Instance>,
+}
+
+#[derive(Debug, Deserialize)]
+#[serde(rename_all = "camelCase")]
+struct Instance {
+    x: f32,
+    y: f32,
+    width: f32,
+    height: f32,
+    start: f64,
+    end: f64,
+    start_time: String,
+    end_time: String,
+    confidence: f32,
+}
+
+/// The flattened row you can store in your DB.
+#[derive(Debug, Serialize)]
+#[serde(rename_all = "snake_case")]
+struct DetectionRow {
+    // Root (frame) context
+    id: usize,
+    frame_width: u32,
+    frame_height: u32,
+
+    // Parent detection metadata
+    detection_id: String,
+    insight_name: String,
+    model_type: String,
+
+    // Instance fields
+    x: f32,
+    y: f32,
+    width: f32,
+    height: f32,
+    start: f64,
+    end: f64,
+    start_time: String,
+    end_time: String,
+    confidence: f32,
+}
+
+fn map_init(_configuration: ModuleConfiguration) -> bool {
+    // Add code here to process the module init properties and module schemas from the configuration
+
+    true
+}
+
+#[map_operator(init = "map_init")]
+fn map(input: DataModel) -> DataModel {
+    logger::log(Level::Info, "viconverter/map", "Get a map request");
+    // create result object from input
+    let DataModel::Message(mut result) = input else {
+        panic!("Unexpected input type");
+    };
+
+    // Extract payload from message to process
+    let staged_payload = match &result.payload {
+        BufferOrBytes::Buffer(buffer) => buffer.read(),
+        BufferOrBytes::Bytes(bytes) => bytes.clone(),
+    };
+
+    let payload_str = std::str::from_utf8(&staged_payload).unwrap();
+    let Input {
+        id,
+        height,
+        width,
+        detections,
+    } = serde_json::from_str(payload_str).unwrap();
+
+    let total_instances = detections.iter().map(|d| d.instances.len()).sum();
+    let mut rows = Vec::with_capacity(total_instances);
+
+    for det in detections {
+        for inst in det.instances {
+            rows.push(DetectionRow {
+                id,
+                frame_width: width,
+                frame_height: height,
+
+                detection_id: det.id.clone(),
+                insight_name: det.insight_name.clone(),
+                model_type: det.model_type.clone(),
+
+                x: inst.x,
+                y: inst.y,
+                width: inst.width,
+                height: inst.height,
+                start: inst.start,
+                end: inst.end,
+                start_time: inst.start_time,
+                end_time: inst.end_time,
+                confidence: inst.confidence,
+            });
+        }
+    }
+
+    let serialized_rows = serde_json::to_vec(&rows).unwrap();
+    result.payload = BufferOrBytes::Bytes(serialized_rows);
+    // return result
+    DataModel::Message(result)
+}