SparrowMap Tutorial: Detect Government Vehicles to counter Flock/AXON ALPRs

Building a project with computer vision sounds intimidating, but it is really just connecting a few pre-built Lego bricks. Here is the absolute ground-floor guide to getting the vision engine for SparrowMap running on your computer, assuming you are starting from zero.

1.Download the Tools:Python and a code editor.

You need two things installed on your computer before writing any code:

1. Python: This is the programming language that powers the AI. Go to python.org and download the latest version. Crucial Windows Step: During the installation screen, check the box that says “Add Python to PATH” before clicking Install. If you miss this, nothing else will work.

2. VS Code: This is the text editor where you will write and run your code. Download it from code.visualstudio.com.

2.Create Your Project Folder and Virtual Environment:Keep your project isolated.

We need a clean sandbox for SparrowMap so its files don’t mess with anything else on your computer.

1. Create a new folder on your Desktop and name it SparrowMap.

2. Open VS Code. Go to the top menu, click File > Open Folder, and select your new SparrowMap folder.

3. Open the terminal inside VS Code by clicking Terminal > New Terminal at the top. A command-line box will pop up at the bottom of the screen.

4. Create a “Virtual Environment” (a private bubble for this project’s code) by typing this into the terminal and hitting Enter:

   • Windows: python -m venv venv

   • Mac/Linux: python3 -m venv venv

5. Activate the environment so your computer knows to use it:

   • Windows: .\venv\Scripts\activate

   • Mac/Linux: source venv/bin/activate

You will know it worked if you see (venv) appear at the start of your terminal line.

3.Install the AI and Camera Libraries:Downloading YOLO and OpenCV.

Now we download the actual brains of the operation into your virtual environment.

In your activated terminal, type the following command and hit Enter:

Bash

pip install ultralytics opencv-python

• ultralytics gives you YOLOv8, the AI that detects vehicles.

• opencv-python allows your code to turn on your webcam and read the video.

Let it run. It might take a minute or two to download everything.

4.Write the Vision Script:Connecting the camera to the AI.

1. In the left panel of VS Code, click the New File icon next to your folder name. Name the file main.py.

2. Copy and paste the following code exactly as it is into main.py. This script turns on your webcam, passes the video to YOLOv8, and draws boxes around anything it recognizes (like cars, people, or dogs).

Python

import cv2
from ultralytics import YOLO

# 1. Load the pre-trained YOLOv8 Nano model (fastest version)
model = YOLO('yolov8n.pt')

# 2. Connect to the webcam (0 is your primary camera)
cap = cv2.VideoCapture(0)

print("Press 'q' on your keyboard to quit the stream.")

while True:
    # 3. Read a single frame from the camera
    success, frame = cap.read()
    
    if not success:
        print("Failed to grab camera frame.")
        break

    # 4. Have YOLOv8 scan the frame for objects
    results = model.predict(frame, stream=True)

    # 5. Draw the detection boxes on the frame
    for result in results:
        annotated_frame = result.plot()

    # 6. Show the video feed with the boxes on screen
    cv2.imshow("SparrowMap - Live Feed", annotated_frame)

    # 7. Listen for the 'q' key to quit the program
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 8. Clean up when done
cap.release()
cv2.destroyAllWindows()

3. Press Ctrl + S (or Cmd + S on Mac) to save the file.

5.Run the Program:See it in action.

Make sure your webcam is plugged in or uncovered. In your VS Code terminal (making sure (venv) is still showing), type:

Bash

python main.py

The first time you run this, it will take a few seconds to automatically download the YOLOv8 AI model file (yolov8n.pt). Once it finishes, a new window will pop up showing your webcam feed.

Point the camera at yourself, or hold up a picture of a car on your phone to the webcam. You will see brightly colored boxes snap around objects with labels and confidence percentages.

To stop it, click on the video window and press q.

This base script proves your hardware and software are talking to each other. Out of the box, it knows what a generic “car” or “truck” is.