Free AI Laptimer

Setup

Now it's time to set up: first, we'll test the camera, then we'll load and convert the AI model.

Camera Test

Accessing the camera is extremely easy; OpenCV provides easy-to-use methods for this purpose. The only important thing is to find out the camera number: the “VideoCapture” method requires the index of the camera to be used. Normally, every built-in camera (in a laptop or tablet) has the index 0, while an external camera (e.g., USB camera) has the index 1.

import cv2
from ultralytics import YOLO

# Open the video stream
cam = cv2.VideoCapture(0)

# Loop through the video frames
while cam.isOpened():
    # Read a frame from the video
    success, frame = cam.read()

    if success:
        # Display the annotated frame
        cv2.imshow("TEST", frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cam.release()
cv2.destroyAllWindows

First YOLO Test

This is the standard test for YOLO: You load the "bus image" from the Ultralytics website and let YOLO recognize the existing objects:

from ultralytics import YOLO
# Load the pretrained model
model = YOLO("yolo11n.pt")

# recognition of the objects
results = model("https://ultralytics.com/images/bus.jpg")

# Visualize the results
for result in results:
    result.show()

The result should look like this:

Conversion To NCNN

NCNN is a highly efficient inference framework optimized for mobile and embedded devices. When combined with YOLO (You Only Look Once), it offers several advantages: The combination with NCNN enables YOLO models to run in real-time on mobile devices without relying on high-performance GPUs. NCNN is platform-independent and does not require special runtime dependencies like CUDA. This makes deploying YOLO on devices without GPUs or specialized hardware more straightforward.

# Load a YOLO11n PyTorch model
model = YOLO("yolo11n.pt")

# Export the model to NCNN format
model.export(format="ncnn")  # creates 'yolo11n_ncnn_model'

# Load the exported NCNN model
ncnn_model = YOLO("yolo11n_ncnn_model")

PyQt5 GUI API

The setup is now almost complete. We just need an API for programming the user interface, for which we will use PyQT5. PyQt5, a set of Python bindings for the versatile Qt application framework, has emerged as a powerful tool for developers seeking to craft cross-platform applications with ease and elegance. Combining Python's simplicity with Qt's comprehensive features, PyQt5 offers a seamless way to build engaging user interfaces.

The GPL version of PyQt5 can be installed from PyPI:

pip install PyQt5