# Person Detection ML Application

## Description

The UC Person Detection application is designed to identify and locate persons within its field of view. It leverages object detection techniques to generate bounding boxes around detected individuals and assigns confidence scores to indicate the reliability of each detection. The output includes the precise location of each person in the image along with a confidence value, enabling accurate and efficient person recognition for various embedded vision applications. This example supports both WQVGA(480x270) and VGA(640x480) resolutions.

## Build Instructions

### Prerequisites
- [GCC/AC6 build environment setup](../developer_guide/build_env.rst)
- [Astra MCU SDK VS Code Extension installed and configured](../developer_guide/Astra_MCU_SDK_VSCode_Extension_Userguide.rst)

### Configuration and Build Steps

### 1. Using Astra MCU SDK VS Code extension
   - Navigate to **IMPORTED REPOS** → **Build and Deploy** in the Astra MCU SDK VS Code Extension.
   - Select the **Build Configurations** checkbox, then select the necessary options.
   - Select **person_detection** in the **Application** dropdown. This will apply the defconfig.
   - Select the appropriate build and clean options from the checkboxes. Then click **Run**. This will build the SDK generating the required `.elf` or `.axf` files for deployment using the installed package.

   For detailed steps refer to the [Astra MCU SDK VS Code Extension Userguide](../developer_guide/Astra_MCU_SDK_VSCode_Extension_Userguide.rst).

   ![Build Configurations](../_static/Assets/Images/user_guide/person_detection/image_1.png)

### 2. Native build in the terminal
1. **Select Default Configuration and build sdk + example**
   This will apply the defconfig, then build and install the SDK package, generating the required `.elf` or `.axf` files for deployment using the installed package.
   ```bash
   make cm55_person_detection_defconfig BOARD=SR110_RDK BUILD=SRSDK
   ```
   This configuration uses WQVGA resolution by default.

2. **Edit default configs and build sdk + example**

   >💡Tip: Run `make cm55_person_detection_defconfig BOARD=SR110_RDK BUILD=SRSDK EDIT=1` to modify the configuration via a GUI and proceed with build.

   | Configuration | Menu Navigation | Action |
   |---------------|-----------------|---------|
   | **VGA Resolution** | `COMPONENTS CONFIGURATION → Off Chip Components → Display Resolution` | Change to `VGA(640x480)` |
   | **WQVGA in LP Sense** | `COMPONENTS CONFIGURATION → Drivers` | Enable `MODULE_LP_SENSE_ENABLED` |
   | **Static Image** | `COMPONENTS CONFIGURATION → Off Chip Components` | Disable `MODULE_IMAGE_SENSOR_ENABLED` |

3. **Rebuild the Application using pre-built package**
   The build process will produce the necessary .elf or .axf files for deployment with the installed package.
   ```bash
   make cm55_person_detection_defconfig BOARD=SR110_RDK or make
   ```
   **Note:** We need to have the pre-built Astra MCU SDK package before triggering the example alone build.

## Deployment and Execution

### Setup and Flashing

1. **Open the VSCode Astra MCU SDK Extension and connect to the Debug IC USB port on the Astra Machina Micro Kit.**
   For detailed steps refer to the [Astra MCU SDK User Guide](../quickstart/Astra_MCU_SDK_User_Guide.md).

2. **Generate Binary Files**
   - FW Binary generation
      - Navigate to **IMPORTED REPOS** → **Build and Deploy** in Astra MCU SDK VSCode Extension.
      - Select the **Image Conversion** option, browse and select the required .axf or .elf file. If the usecase is built using the VS Code extension, the file path will be automatically populated.

      ![Binary Conversion](../_static/Assets/Images/user_guide/person_detection/image_2.png)
      - Click **Run** to create the binary files.
      - Refer to [Astra MCU SDK VSCode Extension User Guide](../developer_guide/Astra_MCU_SDK_VSCode_Extension_Userguide.rst) for more detailed instructions.
   - Model Binary generation (to place the Model in Flash)
      - To generate `.bin` file for TFLite models, please refer to the [Vela compilation guide](Astra_MCU_SDK_vela_compilation_tflite_model.md).

3. **Flash the Application**

   To flash the application:

   * Select the **Image Flashing** option in the **Build and Deploy** view in the Astra MCU SDK VSCode Extension.
   * Select **SWD/JTAG** as the Interface.
   * Choose the respective image bins and click **Run**.

   ![Image Flashing](../_static/Assets/Images/user_guide/person_detection/image_3.png)

   **For WQVGA resolution:**
   - Flash the generated `B0_flash_full_image_GD25LE128_67Mhz_secured.bin` file directly to the device.

   > Note: Model weights is placed in SRAM.

   **For VGA resolution:**

   - For VGA resolution, flash the **model binary first**, and then proceed to flash the **generated use case binary**.

   - **Steps:**
   1. Flash the pre-generated model binary: `person_detection_flash(448x640).bin`.
      Due to memory constraints, the model weights need to be stored in Flash.
      Browse and select this binary from the location: `examples/vision_examples/uc_person_detection/models/`
      Select the **Model Binary** checkbox and enter the specified flash address in the **"Flash Offset"** field and start flashing.
      - **Flash address:** `0x629000`
      - **Calculation Note:**
         The flash address is determined by adding the `host_image` size and the `image_offset_SDK_image_B_offset` parameter (defined in `NVM_data.json`).
         Ensure the resulting address is aligned to a sector boundary (a multiple of 4096 bytes).
         This calculated address should then be assigned to the `image_offset_Model_A_offset` macro in your `NVM_data.json` file.

      ![Model Flashing](../_static/Assets/Images/user_guide/person_detection/image_4.png)
   2. Flash the generated `B0_flash_full_image_GD25LE128_67Mhz_secured.bin` file.

   > Note: By default, flashing a binary performs a sector erase based on the binary size. To erase the entire flash memory, enable the **Full Flash Erase** checkbox. When this option is selected along with a binary file, the tool first performs a full flash erase before flashing the binary. If the checkbox is selected without specifying a binary, only a full flash erase operation will be executed.

   Refer to the [Astra MCU SDK VSCode Extension User Guide](../developer_guide/Astra_MCU_SDK_VSCode_Extension_Userguide.rst) for detailed instructions on flashing.

4. **Device Reset**

   Reset the target device after flashing is complete.

### Note:

The placement of the model (in **SRAM** or **FLASH**) is determined by its memory requirements. Models that exceed the available **SRAM** capacity, considering factors like their weights and the necessary **tensor arena** for inference, will be stored in **FLASH**.

### Running the Application using VS Code extension

1. After successfully flashing the usecase and model binaries, click on **Video Streamer** option in the side panel. This will open the Video Streamer webview.

   ![Video Streamer](../_static/Assets/Images/user_guide/person_detection/image_5.png)

2. Before running the application, make sure to connect a USB cable to the Application SR110 USB port on the Astra Machina Micro board and then press the reset button
   - Select the newly enumerated COM port in the dropdown.
   - For logging output, click on **SERIAL MONITOR** and connect to the DAP logger port.

3. Select **PERSON_DETECTION** from the **UC ID** dropdown. Select **RGB Demosaic**: BayerRGGB

   ![Video Streamer Options](../_static/Assets/Images/user_guide/person_detection/image_6.png)

4. Click **Create Use Case** button. Then click the **Start Use Case** button. A python window will be opened and video stream will be displayed as shown below. Logs can be viewed through the Serial Monitor

   ![Video Stream Window](../_static/Assets/Images/user_guide/person_detection/image_7.png)

5. **Autorun usecases:** If the usecase is built with autorun enabled, after flashing the binary and completing step 3 (selecting the usecase from the **UC ID** dropdown), click on the **Connect Image Source** button. This will open the video stream pop-up.

## Adapting Pipeline for Custom Object Detection Models

This person detection pipeline can be adapted to work with custom object detection models. However, certain validation steps and potential modifications are required to ensure compatibility.

### Prerequisites for Model Compatibility

Before adapting this pipeline for another object detection model, you must verify the following:

#### 1. Model Format Requirements
- Your object detection model should be in `.tflite` format
- The model should produce similar output tensor structure (bounding boxes, confidence scores)

#### 2. Vela Compiler Compatibility Check

**Step 1: Analyze Original Model**
1. Load your `object_detection_model.tflite` file in [Netron](https://netron.app/)
2. Document the output tensors:
   - Tensor names
   - Tensor identifiers/indexes
   - Quantization parameters (scale and offset values)
   - Tensor dimensions

**Step 2: Compile with Vela**
1. Pass your model through the Vela compiler to generate `model_vela.bin` or `model_vela.tflite`
2. Analyze the Vela-compiled model in Netron using the same steps as above

**Step 3: Compare Outputs**
Compare the following between original and Vela-compiled models:
- **Output tensor indexes/identifiers**: Verify if they remain in the same order
- **Quantization parameters**: Check if scale and offset values are preserved
- **Tensor dimensions**: Ensure dimensions match your expected output format

### Pipeline Adaptation Process

#### Case 1: No Changes Required
If the Vela compilation preserves:
- ✅ Output tensor indexes in the same order
- ✅ Same quantization scale and offset values

**Result**: You can proceed with the existing pipeline without modifications.

#### Case 2: Modifications Required
If the Vela compilation changes:
- ❌ Output tensor index order
- ❌ Quantization parameters

**Required Actions**: Modify the pipeline code as described below.

### Code Modifications

If your model's output tensor indexes change after Vela compilation, you need to update the tensor parameter assignments in `uc_person_detection.c`:

#### Location: `detection_post_process` function

**Original Code:**
```c
g_box1_params = &g_all_tens_params[0];
g_box2_params = &g_all_tens_params[1];
g_cls_params  = &g_all_tens_params[2];
```

**Modified Code:**
Update the array indexes according to your Vela-compiled model's output tensor identifiers:
```c
// Example: If your model_vela output has different tensor order
g_box1_params = &g_all_tens_params[X];  // Replace X with actual index from Netron
g_box2_params = &g_all_tens_params[Y];  // Replace Y with actual index from Netron
g_cls_params  = &g_all_tens_params[Z];  // Replace Z with actual index from Netron
```