Person Classification ML Application

Description

The Person Classification application is a machine learning-powered computer vision solution designed to perform real-time object detection and classification. The application analyzes input from camera feeds and intelligently categorizes detected objects into two distinct classes: person and non-person. This binary classification system enables accurate identification and tracking of human presence within the camera’s field of view. This example supports both WQVGA(480x270) and VGA(640x480) resolutions.

Build Instructions

Prerequisites

• GCC/AC6 build environment setup

• Astra SRSDK VS Code Extension

• SynaToolkit

Configuration and Build Steps

1. Select Default Configuration

   make cm55_person_classification_defconfig
   

   This configuration uses WQVGA resolution by default.

2. Optional Configuration:

   💡Tip: Run make menuconfig to modify the configuration via a GUI.

   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. Build the Application The build process will generate the required .elf or .axf files for deployment.

   make build or make
   

Deployment and Execution

Setup and Flashing

1. Open the Astra SRSDK VSCode Extension and connect to the Debug IC USB port on the Astra Machina Micro Kit. For detailed steps refer to the Quick Start Kit.

2. Generate Binary Files

   • FW Binary generation

     • Navigate to AXF/ELF TO BIN → Bin Conversion in Astra SRSDK VSCode Extension

     • Load the generated sr110_cm55_fw.elf or sr110_cm55_fw.axf file

     • Click Run Image Generator to create the binary files

     • Refer to Astra SRSDK VSCode Extension User Guide.

   • Model Binary generation (to place the Model in Flash)

     • To generate .bin file for TFLite models, please refer to the Vela compilation guide.

3. Flash the Application

   To flash the application:

   • Navigate to IMAGE LOADING in the Astra SRSDK VSCode Extension.

   • Select SWD/JTAG as the service type.

   • Choose the respective image bins and click Flash Execute.

   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:

   • Flash the pre-generated model binary: person_classification_flash(448x640).bin. Due to memory constraints, need to burn the Model weights to Flash.

     • Location: examples/vision_examples/uc_person_classification/models/

     • Flash address: 0x629000

     • Calculation Note: Flash address is determined by the sum of the host_image size and the image_offset_SDK_image_B_offset (parameter, which is defined within NVM_data.json). It’s crucial that the resulting address is aligned to a sector boundary (a multiple of 4096 bytes).This calculated resulting address should then be assigned to the image_offset_Model_A_offset macro in your NVM_data.json file.

   • Flash the generated B0_flash_full_image_GD25LE128_67Mhz_secured.bin file

   Refer to the Astra SRSDK VSCode Extension User Guide 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

1. Open SynaToolkit_2.5.0

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

   • Connect to the newly enumerated COM port

   • For logging output, connect to DAP logger port

   

3. The example logs will then appear in the logger window.

   

4. Next, navigate to Tools → Video Streamer in SynaToolkit to run the application.

   

5. Video Streamer

   • Configure the following settings:

     • UC ID: PERSON_CLASSIFICATION

     • RGB Demosaic: BayerRGGB

   

   • Click Create Usecase

   • Connect the image source

   • Click Start Usecase to begin real-time classification

   

6. After starting the use case, Person Classification will begin streaming video as shown below.