qwiic_huskylens_py

SparkFun Qwiic HUSKYLENS - Python Package

The DFRobot Huskylens Module provides a simple and cost effective solution for adding AI Camera capabilities to your project. Implementing an I2C interface, these sensors can be rapidly added to any project with boards that are part of the SparkFun Qwiic ecosystem.

This repository implements a Python package for the DFRobot Huskylens. This package works with Python, MicroPython and CircuitPython.

Contents

• About
• Installation
• Supported Platforms
• Documentation
• Examples

About the Package

This python package enables the user to access the features of the HUSKYLENS via a single Qwiic cable. This includes face recognition, object tracking, tag tracking, line tracking, classification and more. The capabilities of the HUSKYLENS are each demonstrated in the included examples.

New to qwiic? Take a look at the entire SparkFun qwiic ecosystem.

Supported SparkFun Products

This Python package supports the following SparkFun qwiic products on Python, MicroPython and Circuit python.

• SparkFun AI Camera Sensor - HUSKYLENS

Supported Platforms

Python	Platform	Boards
Python	Linux	Raspberry Pi , NVIDIA Jetson Orin Nano via the SparkFun Qwiic SHIM
MicroPython	Raspberry Pi - RP2, ESP32	SparkFun Pro Micro RP2350, SparkFun IoT RedBoard ESP32, SparkFun IoT RedBoard RP2350
CircuitPython	Raspberry Pi - RP2, ESP32	SparkFun Pro Micro RP2350, SparkFun IoT RedBoard ESP32, SparkFun IoT RedBoard RP2350

‍[!NOTE] The listed supported platforms and boards are the primary platform targets tested. It is fully expected that this package will work across a wide variety of Python enabled systems.

Installation

The first step to using this package is installing it on your system. The install method depends on the python platform. The following sections outline installation on Python, MicroPython and CircuitPython.

Python

PyPi Installation

The package is primarily installed using the pip3 command, downloading the package from the Python Index - "PyPi".

Note - the below instructions outline installation on a Linux-based (Raspberry Pi) system.

First, setup a virtual environment from a specific directory using venv:

python3 -m venv path/to/venv

You can pass any path as path/to/venv, just make sure you use the same one for all future steps. For more information on venv click here.

Next, install the qwiic package with:

path/to/venv/bin/pip3 install sparkfun-qwiic-huskylens

Now you should be able to run any example or custom python scripts that have import qwiic_huskylens by running e.g.:

path/to/venv/bin/python3 example_script.py

MicroPython Installation

If not already installed, follow the instructions here to install mpremote on your computer.

Connect a device with MicroPython installed to your computer and then install the package directly to your device with mpremote mip.

mpremote mip install github:sparkfun/qwiic_huskylens_py

If you would also like to install the examples for this repository, issue the following mip command as well:

mpremote mip install github:sparkfun/qwiic_huskylens_py@examples

CircuitPython Installation

If not already installed, follow the instructions here to install CircUp on your computer.

Ensure that you have the latest version of the SparkFun Qwiic CircuitPython bundle.

circup bundle-add sparkfun/Qwiic_Py

Finally, connect a device with CircuitPython installed to your computer and then install the package directly to your device with circup.

circup install --py qwiic_huskylens

If you would like to install any of the examples from this repository, issue the corresponding circup command from below. (NOTE: The below syntax assumes you are using CircUp on Windows. Linux and Mac will have different path seperators. See the CircUp "example" command documentation for more information)

circup example qwiic_huskylens\qwiic_huskylens_ex1_face_recognition_2d
circup example qwiic_huskylens\qwiic_huskylens_ex2_face_recognition_3d
circup example qwiic_huskylens\qwiic_huskylens_ex3_object_tracking
circup example qwiic_huskylens\qwiic_huskylens_ex4_object_recognition
circup example qwiic_huskylens\qwiic_huskylens_ex5_line_tracking
circup example qwiic_huskylens\qwiic_huskylens_ex6_color_recognition
circup example qwiic_huskylens\qwiic_huskylens_ex7_tag_recogntion
circup example qwiic_huskylens\qwiic_huskylens_ex8_object_classification
circup example qwiic_huskylens\qwiic_huskylens_ex9_write_to_screen

Example Use

Below is a quickstart program to print readings from the HUSKYLENS.

See the examples directory for more detailed use examples and examples/README.md for a summary of the available examples.

import qwiic_huskylens 
import sys
import time
 
def runExample():
    print("\nQwiic Huskylens Example 1 - 2D Face Recognition\n")
 
    myHuskylens = qwiic_huskylens.QwiicHuskylens() 
 
    if myHuskylens.is_connected() == False:
        print("The device isn't connected to the system. Please check your connection",
            file=sys.stderr)
        return
 
    if myHuskylens.begin() == False:
        print("Failed to initialize the device. Please check your connection", file=sys.stderr)
        return
 
    myHuskylens.forget() # Forget all the faces that the device has already learned
    myHuskylens.set_algorithm(myHuskylens.kAlgorithmFaceRecognition) # The device has several algorithms, we want to use face recognition
 
    print("Lets teach the HuskyLens the first 2D face.")
    newName = input("Enter the name of the 2D face you are about to teach the lens: ")
    print("Please show the face to the camera. Will attempt to learn the face in 3 seconds.")
    time.sleep(3)
 
    myHuskylens.wait_for_objects_of_interest()
 
    myHuskylens.learn_new()
    myHuskylens.name_last(newName)
    print("Face learned!")
 
    print("Lets teach the HuskyLens the second 2D face.")
    newName = input("Enter the name of the 2D face you are about to teach the lens: ")
    print("Please show the face to the camera. Will attempt to learn the face in 3 seconds.")
    time.sleep(3)
 
    myHuskylens.wait_for_objects_of_interest()
 
    myHuskylens.learn_new()
    myHuskylens.name_last(newName)
    print("Face learned!")
 
    nScans = 0
    while True:
        myFaces = myHuskylens.get_objects_of_interest()
        if len(myFaces) == 0:
            print("No faces found")
        else:
            print("----------------------New Faces Scan #{}----------------------".format(nScans))
 
            for face in myFaces:
                name = myHuskylens.get_name_for_id(face.id) # The myHuskylens object keeps track of the names we have assigned this program run
                if name:
                    print("Face Name: " + name)
                print ("Face ID: " + str(face.id))
                print ("Face X: " + str(face.xCenter))
                print ("Face Y: " + str(face.yCenter))
                print ("Face Width: " + str(face.width))
                print ("Face Height: " + str(face.height))
                print("\n")
 
                nScans += 1
 
        time.sleep(2)
 
if __name__ == '__main__':
    try:
        runExample()
    except (KeyboardInterrupt, SystemExit) as exErr:
        print("\nEnding Example")
        sys.exit(0)