Edge Impulse

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### Setup Hailo RPi5 Examples Environment

Source: https://docs.edgeimpulse.com/projects/expert-network/vehicle-detection-raspberry-pi-ai-kit

Clone the example repository, navigate into the directory, and set up the environment. Install the supervision library.

```bash
$ git clone https://github.com/hailo-ai/hailo-rpi5-examples.git
$ cd hailo-rpi5-examples
$ source setup_env.sh
$./compile_postprocess.sh
$ pip3 install supervision
```

--------------------------------

### Clone and Run Quickstart Script

Source: https://docs.edgeimpulse.com/projects/expert-network/opencode-offline-coding-assistant

Clone the starter repository and execute the main setup script. This script automates the installation of build tools, cloning and building llama.cpp, downloading models, and installing OpenCode.

```bash
git clone https://github.com/eoinjordan/arduino-edgeai-opencode-starter.git
cd arduino-edgeai-opencode-starter
chmod +x scripts/*.sh
bash scripts/quickstart.sh
```

--------------------------------

### Set up ESP32 OTA Example Project

Source: https://docs.edgeimpulse.com/tutorials/topics/lifecycle-management/ota-arduino

Clone the ESP OTA example, set the target to ESP32, and configure project settings using menuconfig. Ensure you have the ESP-IDF toolchain installed.

```bash
mkdir ~/ota-esp32
cd ~/ota-esp32
cp -r $IDF_PATH/examples/system/ota .
idf.py set-target esp32
idf.py menuconfig
```

--------------------------------

### Setup ESP IDF OTA Example and Edge Impulse Inferencing Project

Source: https://docs.edgeimpulse.com/tutorials/topics/lifecycle-management/ota-espressif-idf

Create a directory for OTA updates, copy the ESP IDF OTA example, and clone a fresh version of the Edge Impulse C++ inferencing example repository.

```bash
mkdir ~/ota-esp32
cd ~/ota-esp32
cp -r $IDF_PATH/examples/system/ota .
git clone https://github.com/edgeimpulse/example-standalone-inferencing-espressif-esp32
```

--------------------------------

### Install and Start SSH Server

Source: https://docs.edgeimpulse.com/hardware/boards/arduino-uno-q

These commands install, enable, and start the OpenSSH server on your Arduino UNO Q. This is necessary for remote access via SSH.

```bash
sudo apt install openssh-server -y
sudo systemctl enable ssh
sudo systemctl stop sshd
sudo ssh-keygen -A
sudo systemctl start sshd
```

--------------------------------

### Install Linux Dependencies for Audio and Camera Examples

Source: https://docs.edgeimpulse.com/tools/libraries/sdks/inference/linux/cpp

Install necessary libraries for audio (libasound2) and camera (OpenCV) functionalities on Linux systems.

```bash
sudo apt install libasound2
sh build-opencv-linux.sh          # only needed if you want to run the camera example
```

--------------------------------

### Install SDK Dependencies (Optional)

Source: https://docs.edgeimpulse.com/docs/tools/edge-impulse-for-linux/linux-python-sdk

Installs additional Python dependencies required for camera or microphone examples from the cloned repository.

```bash
cd linux-sdk-python
pip install -r requirements.txt
```

--------------------------------

### Data Forwarder Output Example

Source: https://docs.edgeimpulse.com/tools/clis/edge-impulse-cli/data-forwarder

An example of the interactive prompts and connection status messages displayed by the data forwarder during setup.

```text
Edge Impulse data forwarder v1.5.0
? What is your user name or e-mail address (edgeimpulse.com)? jan@edgeimpulse.com
? What is your password? [hidden]
Endpoints:
    Websocket: wss://remote-mgmt.edgeimpulse.com
    API:       https://studio.edgeimpulse.com
    Ingestion: https://ingestion.edgeimpulse.com

[SER] Connecting to /dev/tty.usbmodem401203
[SER] Serial is connected
[WS ] Connecting to wss://remote-mgmt.edgeimpulse.com
[WS ] Connected to wss://remote-mgmt.edgeimpulse.com
? To which project do you want to add this device? accelerometer-demo-1
? 3 sensor axes detected. What do you want to call them? Separate the names with ",": accX, accY, accZ
? What name do you want to give this device? Jan's DISCO-L475VG
[WS ] Authenticated
```

--------------------------------

### Install Dependencies and Setup Python Environment

Source: https://docs.edgeimpulse.com/projects/expert-network/workplace-organizer-nvidia-jetson-nano

Installs required system packages and Python libraries, then creates and activates a virtual environment for the project. Ensure you are in the project directory before running.

```bash
mkdir EdgeImpulse
cd EdgeImpulse
sudo apt-get install python3-pip libatlas-base-dev libportaudio2 libportaudiocpp0 portaudio19-dev
sudo apt-get install python3 -m pip install --upgrade pip
pip3 install virtualenv six
python3 -m virtualenv -p python3 env
source env/bin/activate
python3 -m pip install edge_impulse_linux
```

--------------------------------

### Install and Start arduino-mcp

Source: https://docs.edgeimpulse.com/projects/expert-network/opencode-offline-coding-assistant

Install the arduino-mcp package globally using npm and then start the REST server. This server enables OpenCode tools to interact directly with Arduino IDE sketches.

```bash
npm install -g arduino-claude-mcp
arduino-claude-mcp          # starts REST server on port 3080
```

--------------------------------

### Clone Example Repository

Source: https://docs.edgeimpulse.com/tutorials/topics/feature-extraction/use-embeddings-sensor-fusion

Clone the example repository to get started with sensor fusion using embeddings. This repository contains the necessary scripts and configurations.

```bash
git clone https://github.com/edgeimpulse/example-sensor-fusion-using-embeddings.git
```

--------------------------------

### Serial Daemon Output Example

Source: https://docs.edgeimpulse.com/tools/clis/edge-impulse-cli/serial-daemon

This is an example of the output when the serial daemon runs, showing the login process, device configuration, and WiFi setup.

```bash
Edge Impulse serial daemon v1.1.0
? What is your user name or e-mail address? jan@edgeimpulse.com
? What is your password? [hidden]
Endpoints:
    Websocket: wss://remote-mgmt.edgeimpulse.com
    API:       https://studio.edgeimpulse.com
    Ingestion: https://ingestion.edgeimpulse.com

[SER] Connecting to /dev/tty.usbmodem401203
[SER] Serial is connected, trying to read config...
[SER] Retrieved configuration
? To which project do you want to add this device? accelerometer-demo-1
Configuring API key in device... OK
Configuring HMAC key in device... OK
? What name do you want to give this device? Jan's DISCO-L475VG
Setting upload host in device... OK
Configuring remote management settings... OK
? WiFi is not connected, do you want to set up a WiFi network now? Yes
Scanning WiFi networks... OK
? Select WiFi network SSID: edgeimpulse-office, Security: WPA2 (3), RSSI: -60 dBm
? Enter password for network "edgeimpulse-office" 0624710192
Connecting to "edgeimpulse-office"... OK
[SER] Device is connected over WiFi to remote management API, no need to run the daemon. Exiting...

```

--------------------------------

### Install Node.js and Edge Impulse CLI

Source: https://docs.edgeimpulse.com/tutorials/topics/inference/run-qualcomm-device-cloud

Run this script on your QDC image to install Node.js and the Edge Impulse CLI. This is a one-time setup per session.

```bash
wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh
sh setup-edge-impulse-qc-linux.sh
```

--------------------------------

### Download and Run Jetson Setup Script

Source: https://docs.edgeimpulse.com/hardware/boards/nvidia-jetson

Download and execute the Edge Impulse setup script for Jetson devices. This script installs necessary dependencies for Edge Impulse integration.

```bash
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/jetson.sh | bash
```

--------------------------------

### Start Jetson Nano Trainer Application

Source: https://docs.edgeimpulse.com/projects/expert-network/api-sample-application-jetson-nano

Execute the main Python script from the project root directory to begin the Edge Impulse data ingestion and project setup process.

```bash
python3 ei_jetson_trainer.py
```

--------------------------------

### Start Keywords Noise Job

Source: https://docs.edgeimpulse.com/tools/libraries/api-bindings/studio/python/edgeimpulse_api/api/jobs_api

Starts a job to add keywords and noise data to a project, typically used for initial project setup or the getting started guide.

```python
edgeimpulse_api.api.jobs_api.JobsApi.start_keywords_noise_job(
	self,
	project_id: Annotated[int, Strict(strict=True), FieldInfo(annotation=NoneType, required=True, description='Project ID')],
	**kwargs
) ‑> edgeimpulse_api.models.start_job_response.StartJobResponse
```

--------------------------------

### Clone Example Repository

Source: https://docs.edgeimpulse.com/hardware/deployments/run-cpp-zephyr-nordic

Clone the example repository containing a basic Zephyr application to get started with standalone inferencing.

```bash
git clone https://github.com/edgeimpulse/example-standalone-inferencing-zephyr.git
```

--------------------------------

### Clone and Initialize Example Project

Source: https://docs.edgeimpulse.com/tutorials/topics/zephyr/zephyr-module-microphone-kws

Clone the example repository, navigate into the directory, and initialize/update west modules to set up the project.

```bash
git clone https://github.com/edgeimpulse/ei-zephyr-mic-kws-inference.git
cd ei-zephyr-mic-kws-inference
west init --local .
west update

```

--------------------------------

### Start Keywords Noise Job

Source: https://docs.edgeimpulse.com/tools/libraries/api-bindings/studio/python/edgeimpulse_api/api/jobs_api

Adds keywords and noise data to a project, typically used for the getting started guide.

```APIDOC
## start_keywords_noise_job

### Description
Add keywords and noise data to a project (for getting started guide)

### Method
POST

### Endpoint
/api/projects/{projectId}/jobs/keywords-noise

### Parameters
#### Path Parameters
- **projectId** (int) - Required - The ID of the project to add keywords and noise to.

### Response
#### Success Response (200)
- **id** (int) - The ID of the created job.
- **created** (string) - The timestamp when the job was created.
- **type** (string) - The type of the job.
- **project_id** (int) - The ID of the project associated with the job.
- **user_id** (int) - The ID of the user who initiated the job.
- **finished** (string) - The timestamp when the job was finished (if applicable).
- **succeeded** (boolean) - Whether the job succeeded.
- **failed** (boolean) - Whether the job failed.
- **error** (string) - Any error message if the job failed.
- **started** (string) - The timestamp when the job was started (if applicable).
- **progress** (float) - The progress of the job.
- **output** (object) - Additional output from the job.

### Response Example
```json
{
  "id": 2,
  "created": "2023-10-27T10:05:00Z",
  "type": "keywords-noise",
  "project_id": 456,
  "user_id": 789,
  "finished": null,
  "succeeded": false,
  "failed": false,
  "error": null,
  "started": null,
  "progress": 0.0,
  "output": {}
}
```
```

--------------------------------

### Set Up Python Virtual Environment and Install Dependencies

Source: https://docs.edgeimpulse.com/tutorials/topics/model-cascades/ocr

Create a Python virtual environment, activate it, and install the required packages from the requirements.txt file. Make the model files executable.

```bash
python3 -m venv .venv
source .venv/bin/activate

pip install -r requirements.txt
chmod +x models/*/*.eim
```

--------------------------------

### Clone Android Inferencing Repository

Source: https://docs.edgeimpulse.com/tutorials/topics/android/keyword-spotting

Clone the example Android inferencing repository to get started. Navigate into the specific keyword spotting example directory.

```bash
git clone https://github.com/edgeimpulse/example-android-inferencing.git
cd example-android-inferencing/example_kws
```

--------------------------------

### Full Configuration Output Example

Source: https://docs.edgeimpulse.com/tools/protocols/remote-management/serial

Displays all configuration options, grouped by category. Configuration values are listed after a key-value pair format.

```text
===== Device info =====
ID:         C4:7F:51:94:4A:38
Type:       DISCO_L475VG_IOT01A
AT Version: 1.6.0
Data Transfer Baudrate: 921600

===== Sensors ======
Name: Built-in accelerometer, Max sample length: 300s, Frequencies: [62.50Hz, 100.00Hz]
Name: Built-in microphone, Max sample length: 60s, Frequencies: [16000.00Hz]

===== Snapshot ======
Has snapshot:    1
Supports stream: 1
Color depth:     Grayscale
Resolutions:     [ 128x96, 320x240, 640x480 ]

===== WIFI =====
Present:   1
SSID:      Edge Impulse Wifi
Password:  machinelearning
Security:  3
MAC:       C4:7F:51:94:4A:38
Connected: 1
Present:   1

===== Sampling parameters =====
Label:     noise
Interval:  10.00 ms.
Length:    1000 ms.
HMAC key:  cde1e82eec38e086db7b9c6d2f60c97e

===== Upload settings =====
Api Key:   ei_9231b10d97b18c6065868c0729400a0945df0d2715429495396678863d778f5c
Host:      http://ingestion.edgeimpulse.com
Path:      /api/training/data

===== Remote management =====
URL:        ws://remote-mgmt.edgeimpulse.com
Connected:  1
Last error:
```

--------------------------------

### Clone Edge Impulse Example Repository

Source: https://docs.edgeimpulse.com/tutorials/topics/lifecycle-management/ota-particle-workbench

Clone the example repository to get a small application for your Particle device. This is the starting point for integrating your Edge Impulse project.

```bash
git clone https://github.com/edgeimpulse/example-standalone-inferencing-photon2
```

--------------------------------

### Clone the example repository

Source: https://docs.edgeimpulse.com/hardware/deployments/run-cpp-sony-spresense

Import the example repository containing a small application for Sony's Spresense to classify sensor data.

```bash
$ git clone https://github.com/edgeimpulse/example-standalone-inferencing-spresense
```

--------------------------------

### Example wpa_supplicant.conf Configuration

Source: https://docs.edgeimpulse.com/hardware/devices/jmo-triple-vision-camera

An example configuration file for wpa_supplicant, showing how to set up WPA-PSK authentication with SSID and password.

```text
# Only WPA-PSK is used. Any valid cipher combination is accepted.
ctrl_interface=/var/run/sockets

network={
#Open
#       ssid="example open network"
#       key_mgmt=NONE
#WPA-PSK-Configuration
#  Update the SSID to match that of the Wi-Fi SSID of your router.
ssid="QSoftAP"
#       proto=WPA RSN
#       key_mgmt=WPA-PSK
#       pairwise=TKIP CCMP
#       group=TKIP CCMP
# Update the password to match that of the Wi-Fi password of your router.
psk="1234567890"
#WEP-Configuration
#       ssid="example wep network"
#       key_mgmt=NONE
#       wep_key0="abcde"
#       wep_key1=0102030405
#       wep_tx_keyidx=0
}

```

--------------------------------

### Add keywords and noise to a project

Source: https://docs.edgeimpulse.com/apis/studio/jobs/add-keywords-and-noise

This endpoint adds keywords and noise data to a specified project. It is typically used to populate a project for the getting started guide.

```APIDOC
## POST /api/{projectId}/jobs/keywords-noise

### Description
Add keywords and noise data to a project (for getting started guide)

### Method
POST

### Endpoint
/api/{projectId}/jobs/keywords-noise

### Parameters
#### Path Parameters
- **projectId** (integer) - Required - Project ID

### Response
#### Success Response (200)
- **success** (boolean) - Whether the operation succeeded
- **error** (string) - Optional error description (set if 'success' was false)
- **id** (integer) - Job identifier. Status updates will include this identifier.

### Response Example
{
  "success": true,
  "id": 12873488112
}
```

--------------------------------

### Initiate WiFi Setup on Thingy53

Source: https://docs.edgeimpulse.com/hardware/boards/nordic-semi-thingy53

Respond 'Yes' when prompted by the daemon to set up a WiFi network connection for the Thingy53.

```bash
? WiFi is not connected, do you want to set up a WiFi network now? Yes
Scanning WiFi networks...
```

--------------------------------

### Initiate Hermes AI Agent Setup

Source: https://docs.edgeimpulse.com/projects/expert-network/mini-figurine-cataloger-and-listing-tracker-with-hermes-agent

Run this command in the terminal to start the Hermes AI agent installation and configuration process. You will be prompted to select an LLM provider.

```bash
hermes model
```

--------------------------------

### Build the eimclassify Example

Source: https://docs.edgeimpulse.com/tools/libraries/sdks/inference/linux/go

Navigate to the eimclassify example directory and build the executable using Go.

```bash
cd cmd/eimclassify
go build
```

--------------------------------

### Launch getting started wizard

Source: https://docs.edgeimpulse.com/llms.txt

Launches the getting started wizard for a project.

```APIDOC
## Launch getting started wizard

### Description
Launches the getting started wizard for a project.

### Method
POST

### Endpoint
/api/v1/projects/{projectId}/launch-getting-started-wizard
```

--------------------------------

### Create Python Virtual Environment and Install Dependencies

Source: https://docs.edgeimpulse.com/tutorials/topics/model-cascades/ocr

Set up a Python virtual environment and install the necessary dependencies from the requirements.txt file for creating representative datasets.

```bash
cd source_models && python3 -m venv .venv && source .venv/bin/activate && pip3 install -r requirements.txt && cd ..
```

--------------------------------

### Get npm Installation Directory

Source: https://docs.edgeimpulse.com/docs/cli-installation

Retrieves the directory where npm installs global packages. This is used to verify or adjust the installation path.

```bash
npm config get prefix
```

--------------------------------

### Install Node.js 22.x on Debian/Ubuntu

Source: https://docs.edgeimpulse.com/docs/cli-installation

Installs Node.js version 22.x using NodeSource repository. Ensures Node.js is installed before proceeding with CLI setup.

```bash
curl -sL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs
node -v
```

--------------------------------

### Set up Database Structure

Source: https://docs.edgeimpulse.com/projects/expert-network/bluetooth-fall-detection-arduino-nano-33

Run this command to create the necessary database structure for the fall detection server. Ensure Python 3 is installed.

```bash
$ sudo python3 databaseSetup.py
```

--------------------------------

### Install and Enable SSH Server

Source: https://docs.edgeimpulse.com/projects/expert-network/cv-punchcards-lattepanda

Installs the OpenSSH server for remote console access and enables it to start on boot.

```bash
sudo apt install openssh-server -y
sudo systemctl enable ssh
sudo systemctl start ssh
```

--------------------------------

### Clone Edge Impulse Linux SDK Python Examples

Source: https://docs.edgeimpulse.com/projects/expert-network/azure-machine-learning-kubernetes-compute-ei

Downloads the example applications for the Edge Impulse Python SDK. These examples can be used as a starting point for your own applications.

```bash
$ git clone https://github.com/edgeimpulse/linux-sdk-python
```

--------------------------------

### Clone Repository and Navigate

Source: https://docs.edgeimpulse.com/tutorials/topics/android/static-buffer-inference

Clone the example Android inferencing repository and navigate to the static buffer example directory.

```bash
git clone https://github.com/edgeimpulse/example-android-inferencing.git
cd example-android-inferencing/example_static_buffer
```

--------------------------------

### Build Application with Make

Source: https://docs.edgeimpulse.com/hardware/deployments/run-cpp

Navigate to your project directory and use the `make` command to build your application. The `-j [jobs]` option can be used to speed up the build process by utilizing multiple threads.

```bash
cd my-motion/
make -j 4
```

--------------------------------

### Create Project Directory

Source: https://docs.edgeimpulse.com/projects/expert-network/renesas-rzv2l-monitoring-retail-checkout-lines

Navigate to your project directory to prepare for downloading the model.

```bash
mkdir monitoring_retail_checkout_lines && \
cd monitoring_retail_checkout_lines
```

--------------------------------

### GET / Block Information

Source: https://docs.edgeimpulse.com/studio/organizations/custom-blocks/custom-processing-blocks

Example of a plain text response for the GET / endpoint, providing basic information about the custom processing block.

```text
Custom processing block by Awesome Developer.
```

--------------------------------

### Install Braccio Libraries and Compile Firmware

Source: https://docs.edgeimpulse.com/tutorials/topics/robotics-series/robotic-arm-ros2-integration

Use the Arduino CLI to install necessary libraries, core, compile the UNO Q Braccio firmware, and upload it to the device.

```bash
arduino-cli lib install Braccio
arduino-cli core install arduino:zephyr
arduino-cli compile --fqbn arduino:zephyr:unoq firmware/unoq_braccio_firmware
arduino-cli upload -p /dev/ttyACM0   --fqbn arduino:zephyr:unoq firmware/unoq_braccio_firmware
```

--------------------------------

### Install Python SDK Dependencies

Source: https://docs.edgeimpulse.com/projects/expert-network/face-tracking-yolo-pro-rubik-pi

Installs the Python package dependencies required by the cloned Linux SDK examples. This is typically done using a requirements.txt file.

```bash
pip install -r requirements.txt
```

--------------------------------

### Build and Flash Firmware

Source: https://docs.edgeimpulse.com/tutorials/topics/lifecycle-management/acquire-process-golioth

Build the application firmware for the nRF9160 Thingy board using west and then flash it to the device.

```bash
west build -p -b thingy91_nrf9160_ns app/
west flash

```

--------------------------------

### Install Tensorflow (for Part 2)

Source: https://docs.edgeimpulse.com/projects/expert-network/eeg-data-machine-learning-part-1

Install the Tensorflow library, which is required for Part 2 of the tutorial involving machine learning.

```bash
pip install tensorflow
```

--------------------------------

### Specify Target Platform for Quickstart

Source: https://docs.edgeimpulse.com/projects/expert-network/opencode-offline-coding-assistant

Override the default platform detection by specifying the target system when running the quickstart script. Use 'pi' for Raspberry Pi, 'jetson' for NVIDIA Jetson, or 'cpu' for generic Linux/macOS.

```bash
bash scripts/quickstart.sh pi       # Raspberry Pi — CPU only
```

```bash
bash scripts/quickstart.sh jetson   # Jetson — CUDA build (sm_87)
```

```bash
bash scripts/quickstart.sh cpu      # Generic Linux / macOS CPU
```

--------------------------------

### Test Display Environment

Source: https://docs.edgeimpulse.com/hardware/devices/jmo-triple-vision-camera

Run this command to verify that your display environment is set up correctly by launching a test video source.

```bash
gst-launch-1.0 -v videotestsrc ! autovideosink
```

--------------------------------

### Install Hermes AI Agent

Source: https://docs.edgeimpulse.com/projects/expert-network/mini-figurine-cataloger-and-listing-tracker-with-hermes-agent

Installs the Hermes AI agent into the user partition using a provided script, skipping the setup wizard. Updates environment paths afterward.

```bash
curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash -s -- \
  --dir /home/arduino/hermes-agent \
  --skip-setup
  
source ~/.bashrc
```

--------------------------------

### Multi-Label Annotation

Source: https://docs.edgeimpulse.com/tools/specifications/data-annotation/ei-labels

Example of a JSON structure for multi-label annotation with start and end indices.

```json
{
    "version": 1,
    "files": [
        {
            "path": "testing/sample_1.json",
            "name": "sample_1",
            "category": "testing",
            "label": {
                "type": "multi-label",
                "labels": [
                    {
                        "label": "first_label",
                        "startIndex": 0,
                        "endIndex": 451
                    },
                    {
                        "label": "second_label",
                        "startIndex": 452,
                        "endIndex": 1847
                    }
                ]
            },
            "metadata": {}
        }
    ]
}
```

--------------------------------

### Create Project Directory

Source: https://docs.edgeimpulse.com/projects/expert-network/renesas-rzv2l-counting-retail-inventory

Navigate into a new directory to store your project files.

```bash
mkdir monitoring_retail_inventory && \
cd monitoring_retail_inventory
```

--------------------------------

### Get Raw Sample Slice

Source: https://docs.edgeimpulse.com/apis/studio/dsp/get-raw-sample-slice

Retrieves a slice of raw sample data with specified start and end indices and an associated label. The `endIndex` is inclusive. For example, `{ startIndex: 10, endIndex: 13, label: 'running' }` means that the values at indices 10, 11, 12, and 13 are labeled 'running'. Time codes can be obtained by multiplying by the sample's `intervalMs` property.

```APIDOC
## GET /api/v2/projects/{projectId}/datasets/{datasetId}/samples/{sampleId}/raw-data-slice

### Description
Retrieves a slice of raw sample data with specified start and end indices and an associated label.

### Method
GET

### Endpoint
/api/v2/projects/{projectId}/datasets/{datasetId}/samples/{sampleId}/raw-data-slice

### Parameters
#### Query Parameters
- **startIndex** (integer) - Required - Start index of the label (e.g. 0)
- **endIndex** (integer) - Required - End index of the label (e.g. 3). This value is inclusive, so { startIndex: 0, endIndex: 3 } covers 0, 1, 2, 3.
- **label** (string) - Required - The label for this section.

### Response
#### Success Response (200)
- **startIndex** (integer) - Start index of the label.
- **endIndex** (integer) - End index of the label (inclusive).
- **label** (string) - The label for this section.
- **labelMap** (object) - Optional - Structured sample labels in the form of a key-value map. Only defined for samples with key-value labels.
  - **type** (string) - Enum: "key-values"
  - **labels** (object) - Additional properties are strings representing key-value pairs.
```

--------------------------------

### Initialize Portal API Client

Source: https://docs.edgeimpulse.com/studio/organizations/upload-portals

Sets up authentication tokens and portal ID from environment variables. Exits if essential tokens are missing, providing instructions on how to set them.

```python
import requests
import json
import os
import hashlib

PORTAL_TOKEN = os.environ.get('EI_PORTAL_TOKEN')
PORTAL_ID = os.environ.get('EI_PORTAL_ID')
JWT_TOKEN = os.environ.get('EI_JWT_TOKEN')

if not PORTAL_TOKEN:
    print('Missing EI_PORTAL_TOKEN environmental variable.')
    print('Go to a portal, and copy the part after "?token=" .')
    print('Then run:')
    print('    export EI_PORTAL_TOKEN=ec61e...')
    print('')
    print('You can add the line above to your ~/.bashrc or ~/.zshrc file to automatically load the token in the future.')
    exit(1)

if not PORTAL_ID:
    print('Missing EI_PORTAL_ID environmental variable.')
    print('Go to a portal, open the browser console, and look for "portalId" in the "Hello world from Edge Impulse" object to find it.')
    print('Then run:')
    print('    export EI_PORTAL_ID=122')
    print('')
    print('You can add the line above to your ~/.bashrc or ~/.zshrc file to automatically load the token in the future.')
    exit(1)

if not JWT_TOKEN:
    print('WARN: Missing EI_JWT_TOKEN environmental variable, you will only have write-only access to the portal')
    print('Run `python3 get_jwt_token.py` for instructions on how to set the token')
    print('(this requires access to the organization that owns the portal)')
```

--------------------------------

### Download Example Image Files

Source: https://docs.edgeimpulse.com/tutorials/tools/sdks/studio/python/upload-download-data

Download sample image files to a local 'dataset' directory. These files will be used as an example dataset for uploading.

```python
# Download image files to use as an example dataset
!mkdir -p dataset
!wget -P dataset -q \
  https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/capacitor.01.png \
  https://raw.githubusercontent.com/edgeimpulse/notebooks/main/.assets/images/capacitor.02.png
```

--------------------------------

### Initialize Camera and Edge Impulse Runner

Source: https://docs.edgeimpulse.com/projects/expert-network/avnet-rzboard-object-counting

Sets up the camera and initializes the Edge Impulse runner. It includes logic to find available webcams and select one, or use a specified camera port. Raises an exception if no cameras are found or initialization fails.

```python
show_camera = True
if (sys.platform == 'linux' and not os.environ.get('DISPLAY')):
    show_camera = False

def now():
    return round(time.time() * 1000)

def get_webcams():
    port_ids = []
    for port in range(5):
        print("Looking for a camera in port %s:" %port)
        camera = cv2.VideoCapture(port)
        if camera.isOpened():
            ret = camera.read()[0]
            if ret:
                backendName =camera.getBackendName()
                w = camera.get(3)
                h = camera.get(4)
                print("Camera %s (%s x %s) found in port %s " %(backendName,h,w, port))
                port_ids.append(port)
            camera.release()
    return port_ids

def sigint_handler(sig, frame):
    print('Interrupted')
    if (runner):
        runner.stop()
    sys.exit(0)

signal.signal(signal.SIGINT, sigint_handler)


print('MODEL: ' + modelfile)



with ImageImpulseRunner(modelfile) as runner:
    try:
        model_info = runner.init()
        print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
        labels = model_info['model_parameters']['labels']
        if camera_port:
            videoCaptureDeviceId = int(args[1])
        else:
            port_ids = get_webcams()
            if len(port_ids) == 0:
                raise Exception('Cannot find any webcams')
            if len(port_ids)> 1:
                raise Exception("Multiple cameras found. Add the camera port ID as a second argument to use to this script")
            videoCaptureDeviceId = int(port_ids[0])

        camera = cv2.VideoCapture(videoCaptureDeviceId)
        ret = camera.read()[0]
        if ret:
            backendName = camera.getBackendName()
            w = camera.get(3)
            h = camera.get(4)
            print("Camera %s (%s x %s) in port %s selected." %(backendName,h,w, videoCaptureDeviceId))
            camera.release()
        else:
            raise Exception("Couldn't initialize selected camera.")

        next_frame = 0 # limit to ~10 fps here

        # Define the top of the image and the number of columns
        TOP_Y = 100
        NUM_COLS = 5
        COL_WIDTH = int(w / NUM_COLS)
        # Define the factor of the width/height which determines the threshold
        # for detection of the object's movement between frames:
        DETECT_FACTOR = 1.5

        # Initialize variables
        count = [0] * NUM_COLS
        countsum = 0
        previous_blobs = [[] for _ in range(NUM_COLS)]



        for res, img in runner.classifier(videoCaptureDeviceId):
            # Initialize list of current blobs
            current_blobs = [[] for _ in range(NUM_COLS)]

            if (next_frame > now()):
                time.sleep((next_frame - now()) / 1000)

            if "bounding_boxes" in res["result"].keys():
                print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
                for bb in res["result"]["bounding_boxes"]:
                    print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                    img = cv2.rectangle(img, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)

                        # Check which column the blob is in
                    col = int(bb['x'] / COL_WIDTH)
                    # Check if blob is within DETECT_FACTOR*h of a blob detected in the previous frame and treat as the same object
                    for blob in previous_blobs[col]:
                        print(abs(bb['x'] - blob[0]) < DETECT_FACTOR * (bb['width'] + blob[2]))
                        print(abs(bb['y'] - blob[1]) < DETECT_FACTOR * (bb['height'] + blob[3]))
                        if abs(bb['x'] - blob[0]) < DETECT_FACTOR * (bb['width'] + blob[2]) and abs(bb['y'] - blob[1]) < DETECT_FACTOR * (bb['height'] + blob[3]):
                        # Check this blob has "moved" across the Y threshold
                            if blob[1] >= TOP_Y and bb['y'] < TOP_Y:
                                # Increment count for this column if blob has left the top of the image
                                count[col] += 1
                                countsum += 1
                    # Add current blob to list
                    current_blobs[col].append((bb['x'], bb['y'], bb['width'], bb['height']))

            # Update previous blobs
            previous_blobs = current_blobs

            if (show_camera):
                im2 = cv2.resize(img, dsize=(800,800))
                cv2.putText(im2, f'{countsum} items passed', (15,750), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
                cv2.imshow('edgeimpulse', cv2.cvtColor(im2, cv2.COLOR_RGB2BGR))
```

--------------------------------

### Clone Example Standalone Inferencing Linux Repository

Source: https://docs.edgeimpulse.com/hardware/porting/linux-inference/linux-inference-cpp

Clone the example repository to start building your C++ application. This repository contains the necessary structure for integrating the Edge Impulse SDK.

```bash
git clone https://github.com/edgeimpulse/example-standalone-inferencing-linux
```

--------------------------------

### launch_getting_started_wizard

Source: https://docs.edgeimpulse.com/tools/libraries/api-bindings/studio/python/edgeimpulse_api/api/projects_api

Launches the getting started wizard for a project. This action is irreversible and clears all project data.

```APIDOC
## launch_getting_started_wizard

### Description
Launch getting started wizard. This clears out *all data in your project*, and is irrevocable. This function is only available through a JWT token, and is not available to all users.

### Method
POST

### Endpoint
/api/v1/projects/{projectId}/getting-started

### Parameters
#### Path Parameters
- **projectId** (int) - Required - The ID of the project.

#### Query Parameters
- **kwargs** - Optional - Additional keyword arguments.

### Response
#### Success Response (200)
- **success** (boolean) - Indicates if the wizard launch was successful.
- **error** (string) - An error message if the operation failed.
```

--------------------------------

### Install Akida and Edge Impulse Tooling

Source: https://docs.edgeimpulse.com/projects/expert-network/brainchip-akida-xray-classification

Execute these commands in order to update the system, install prerequisites, and install the Edge Impulse CLI, Akida CLI, Edge Impulse Linux SDK, and Akida PCIe driver. Remember to replace '<your-username-here>' with your actual username in step 8.

```bash
1. sudo apt-get update && sudo apt-get upgrade
2. sudo reboot
3. sudo su
4. curl -sL https://deb.nodesource.com/setup_12.x | sudo bash -
5. apt-get install build-essential linux-headers-$(uname -r) git gcc g++ make nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps libatlas-base-dev libportaudio2 libportaudiocpp0 portaudio19-dev python3-opencv python3-pip
6. npm config set user root && sudo npm install edge-impulse-linux -g --unsafe-perm
7. reboot
8. export PATH=/home/<your-username-here>/.local/bin:$PATH  # (or add to `.profile` file to make permanent)
9. pip install --upgrade pip
10. pip install --upgrade akida
11. pip install edge_impulse_linux -i https://pypi.python.org/simple
12. pip install pyaudio
13. git clone https://github.com/edgeimpulse/linux-sdk-python
14. git clone https://github.com/Brainchip-Inc/akida_dw_edma
15. cd akida_dw_edma/
16. ./install.sh
17. akida devices
```

--------------------------------

### Download and Run Orin Setup Script

Source: https://docs.edgeimpulse.com/hardware/boards/nvidia-jetson

Download and execute the Edge Impulse setup script for NVIDIA Jetson Orin devices. This script prepares the device for Edge Impulse integration.

```bash
wget -q -O - https://cdn.edgeimpulse.com/firmware/linux/orin.sh | bash
```

--------------------------------

### Install Edge Impulse Linux CLI

Source: https://docs.edgeimpulse.com/hardware/boards/quectel-pi-sg565d

Download and execute the setup script to install the Edge Impulse CLI and necessary dependencies on the Quectel PI-SG565D. This is a prerequisite for connecting the device to Edge Impulse.

```bash
wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh
sh setup-edge-impulse-qc-linux.sh
```

--------------------------------

### Install Edge Impulse Linux CLI

Source: https://docs.edgeimpulse.com/docs/edge-ai-hardware/cpu-%2B-ai-accelerators/thundercomm-rubikpi3

Download and execute the setup script for the Edge Impulse Linux CLI and its dependencies. Ensure you run the `source ~/.profile` command after installation for subsequent sessions.

```bash
$ wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh
$ sh setup-edge-impulse-qc-linux.sh
```

--------------------------------

### Unzip and Navigate Application Directory

Source: https://docs.edgeimpulse.com/tutorials/hardware/renesas-rzv2l-object-detection-x2

Unzips the downloaded application archive and navigates into the created directory. Verify the contents using 'ls -l'.

```bash
unzip classify-camera-finer-control.zip
```

```bash
cd edge-impulse-linux-cli-classify-camera-finer-control
```

```bash
ls -l
```

--------------------------------

### Install Edge Impulse CLI and Dependencies

Source: https://docs.edgeimpulse.com/hardware/boards/advantech-aom2721-osm

Download and run the setup script to install the Edge Impulse CLI and necessary dependencies on the AOM-2721 OSM. This script is specifically for Qualcomm Linux devices.

```bash
$ wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh
$ sh setup-edge-impulse-qc-linux.sh
```

--------------------------------

### Install Akida Driver

Source: https://docs.edgeimpulse.com/projects/expert-network/brainchip-akida-industrial-inspection

Navigate to the cloned repository and execute the install script to compile and install the Akida PCIe driver. This makes the Akida hardware accessible to the system.

```bash
cd akida_dw_edma
sudo ./install.sh
```

--------------------------------

### Start Edge Impulse Daemon

Source: https://docs.edgeimpulse.com/hardware/boards/nordic-semi-nrf9160-dk

Run this command to start the Edge Impulse daemon. It will guide you through logging in and selecting an Edge Impulse project. Use the --clean flag to switch projects.

```bash
edge-impulse-daemon
```

```bash
edge-impulse-daemon --clean
```

--------------------------------

### Initialize Sample Class Object and Database Connection

Source: https://docs.edgeimpulse.com/projects/expert-network/food-irradiation-dose-detection-dfrobot-beetle-esp32c3

Includes the necessary class file and initializes a 'sample' class object with database connection details. This object is used for managing data samples.

```php
    include_once "assets/class.php";

    // Define the new 'sample' object:
    $sample = new sample();
    $sample->__init__($conn, $server["table"]);
```

--------------------------------

### Setup Edge Impulse on Rubik Pi 3

Source: https://docs.edgeimpulse.com/projects/expert-network/anomaly-detection-n8n-rubik-pi

Run these commands to update the system, download the Edge Impulse setup script, install necessary utilities, and execute the script for Edge Impulse integration.

```bash
sudo apt update
wget https://cdn.edgeimpulse.com/firmware/linux/setup-edge-impulse-qc-linux.sh
sudo apt install selinux-utils
source ~/.profile
chmod +x setup-edge-impulse-qc-linux.sh
./setup-edge-impulse-qc-linux.sh
```

--------------------------------

### Run the eimclassify Example

Source: https://docs.edgeimpulse.com/tools/libraries/sdks/inference/linux/go

Execute the built eimclassify program to start classifying custom sensor data.

```bash
./eimclassify
```

--------------------------------

### Install OpenVINO Prerequisites

Source: https://docs.edgeimpulse.com/projects/expert-network/robotic-arm-sorting-arduino-braccio

Sets up a Python virtual environment and installs OpenVINO development tools and blobconverter. This is required for converting ONNX models to MyriadX blob format.

```bash
$ virtualenv --python=python3.8 ~/openvino_env
$ source ~/openvino_env/bin/activate
$ python3 -m pip install --upgrade pip
$ pip3 install openvino-dev==2022.1
$ pip3 install blobconverter
```
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