Potential solution

The task involves creating a Python script that uses OpenCV and PyTorch to detect and count cars from images captured by a Luxonis OAK-D camera. The script will increment a counter each time the front of a car crosses a predefined line in the center of the image. The C# application will then start this Python script as a separate process and update the UI with the car count received from the script's output.

How to implement

The implementation will be divided into two parts: the Python script for car detection and the C# code to integrate this script into the existing application.

Python Script (EduTester/CarDetection.py)

import cv2
import torch
import depthai as dai

# Initialize the camera
def init_camera():
    # Code to initialize Luxonis OAK-D camera
    pass

# Load the car detection model
def load_model():
    # Code to load a pre-trained car detection model
    pass

# Process and detect cars in the image
def detect_cars(frame, model):
    # Code to process the frame and detect cars
    pass

# Check if the car crosses the line
def car_crosses_line(car, line_position):
    # Code to check if the car crosses the line
    pass

# Main function to start the car detection process
def main():
    camera = init_camera()
    model = load_model()
    iCount = 0
    line_position = camera.get_frame_height() // 2  # Assuming a method to get frame height

    while True:
        # Capture frame-by-frame
        ret, frame = camera.read()

        if not ret:
            break

        # Detect cars
        cars = detect_cars(frame, model)

        # Count cars
        for car in cars:
            if car_crosses_line(car, line_position):
                iCount += 1

        # Output the current count
        print(iCount)

        # Display the resulting frame (optional for debugging)
        cv2.imshow('Frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # When everything done, release the capture
    camera.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

C# Integration (EduTester/MainWindow.xaml.cs)

using System;
using System.Diagnostics;
using System.IO;
using System.Windows;
using System.Windows.Input;

namespace EduTester
{
    public partial class MainWindow : Window
    {
        // ... Existing code ...

        private Process carDetectionProcess;
        private int carCount = 0;

        public MainWindow()
        {
            InitializeComponent();
            DataContext = MTester;
            StartCarDetectionScript();
        }

        private void StartCarDetectionScript()
        {
            carDetectionProcess = new Process();
            carDetectionProcess.StartInfo.FileName = "python";
            carDetectionProcess.StartInfo.Arguments = "CarDetection.py"; // Adjust the path if necessary
            carDetectionProcess.StartInfo.UseShellExecute = false;
            carDetectionProcess.StartInfo.RedirectStandardOutput = true;
            carDetectionProcess.StartInfo.CreateNoWindow = true;
            carDetectionProcess.OutputDataReceived += CarDetectionProcess_OutputDataReceived;
            carDetectionProcess.Start();
            carDetectionProcess.BeginOutputReadLine();
        }

        private void CarDetectionProcess_OutputDataReceived(object sender, DataReceivedEventArgs e)
        {
            if (!string.IsNullOrEmpty(e.Data))
            {
                // Assuming the Python script sends the updated count as a line of output
                int.TryParse(e.Data, out carCount);
                Dispatcher.Invoke(() =>
                {
                    // Update the UI with the new car count
                    // Assuming there is a Label or TextBlock with x:Name="CarCountLabel"
                    CarCountLabel.Content = $"Cars Detected: {carCount}";
                });
            }
        }

        protected override void OnClosed(EventArgs e)
        {
            base.OnClosed(e);
            if (!carDetectionProcess.HasExited)
            {
                carDetectionProcess.Kill();
            }
        }

        // ... Existing code ...

        // Add a UI element to display the car count
        // Example: <Label x:Name="CarCountLabel" Content="Cars Detected: 0" />
    }
}

Please ensure that the Python environment is set up with the necessary dependencies, and the path to the Python executable and the CarDetection.py script is correctly specified in the C# code. The C# application will need to handle the output from the Python script to update the UI with the car count.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on EduTester/EduTester.csproj

To update the `EduTester.csproj` file to include the new `CarDetection.py` script and ensure that all necessary Python dependencies are properly referenced, you would typically follow these steps: 1. Add the `CarDetection.py` script to the project: - You can include the Python script in the project by adding an `` that references the script file. This will ensure that the script is recognized by the project and included in any deployments or distributions. 2. Ensure Python dependencies are referenced: - Since the project is a C# project, it does not natively handle Python dependencies. You would need to set up a Python environment separately, install the required packages (like OpenCV and PyTorch), and then make sure that the C# application can start the Python script within this environment. Here's how you could modify the `.csproj` file to include the Python script: ```xml ``` For handling Python dependencies, you would typically not modify the `.csproj` file. Instead, you would: - Use a Python virtual environment where you install the necessary packages. - Ensure that the C# application knows the path to the Python executable within this virtual environment. - Start the Python script from the C# application using `System.Diagnostics.Process` or a similar mechanism, pointing to the correct Python environment. Since the `.csproj` file does not directly handle Python dependencies, you would document the setup process for the Python environment separately, perhaps in a README file or a setup script that prepares the environment for the user. Remember, the actual implementation of starting the Python script and handling dependencies would be done in the C# code, not in the `.csproj` file. The `.csproj` file change here is simply to include the Python script as part of the project for organizational purposes.

Changes on EduTester/MainWindow.xaml.cs

To integrate car detection functionality into the `MainWindow.xaml.cs` file, you will need to follow these steps: 1. **Add Necessary Namespaces**: Import the necessary namespaces for process management and inter-process communication. 2. **Define Variables**: Define variables to manage the Python script process and to keep track of the car count. 3. **Start Python Script**: Create a method to start the Python script as a separate process and establish a communication channel (e.g., standard input/output or a socket). 4. **Receive Data**: Implement a method to receive the car count updates from the Python script. 5. **Update UI**: Modify the UI to display the car count and update it whenever a new count is received. 6. **Handle Process Exit**: Ensure that the Python script process is properly terminated when the application closes. Here's a practical proposal for modifying the `MainWindow.xaml.cs` file: ```csharp using System; using System.Diagnostics; using System.IO; using System.Windows; using System.Windows.Input; namespace EduTester { public partial class MainWindow : Window { Matematyka MTester = new Matematyka(20, 20, 10); private Process carDetectionProcess; private int carCount = 0; public MainWindow() { InitializeComponent(); DataContext = MTester; StartCarDetectionScript(); } private void StartCarDetectionScript() { carDetectionProcess = new Process(); carDetectionProcess.StartInfo.FileName = "python"; carDetectionProcess.StartInfo.Arguments = "CarDetection.py"; // Adjust the path if necessary carDetectionProcess.StartInfo.UseShellExecute = false; carDetectionProcess.StartInfo.RedirectStandardOutput = true; carDetectionProcess.StartInfo.CreateNoWindow = true; carDetectionProcess.OutputDataReceived += CarDetectionProcess_OutputDataReceived; carDetectionProcess.Start(); carDetectionProcess.BeginOutputReadLine(); } private void CarDetectionProcess_OutputDataReceived(object sender, DataReceivedEventArgs e) { if (!string.IsNullOrEmpty(e.Data)) { // Assuming the Python script sends the updated count as a line of output int.TryParse(e.Data, out carCount); Dispatcher.Invoke(() => { // Update the UI with the new car count // Assuming there is a Label or TextBlock with x:Name="CarCountLabel" CarCountLabel.Content = $"Cars Detected: {carCount}"; }); } } protected override void OnClosed(EventArgs e) { base.OnClosed(e); if (!carDetectionProcess.HasExited) { carDetectionProcess.Kill(); } } // ... Existing code ... // Add a UI element to display the car count // Example: } } ``` Please note that you will need to add a UI element (e.g., a `Label` or `TextBlock`) to the XAML file to display the car count. The example assumes that there is a `Label` with `x:Name="CarCountLabel"`. Also, ensure that the path to the `CarDetection.py` script is correct and that Python is installed and accessible from the system PATH. The script should be designed to send the car count as a line of output, which this C# code will read and use to update the UI.

Changes on EduTester/CarDetection.py

To solve the task described in the ticket for the `EduTester/CarDetection.py` file, we need to implement an image processing and car detection algorithm using Python, OpenCV, and PyTorch. Here's a concrete practical proposal for implementing the required functionality: 1. **Set up the Python environment**: - Ensure Python is installed on the system. - Install the necessary libraries: `opencv-python`, `torch`, and `depthai` (for Luxonis OAK-D camera support). 2. **Initialize the camera**: - Use the `depthai` library to initialize the Luxonis OAK-D camera and set it up to capture images. 3. **Implement the car detection algorithm**: - Use a pre-trained car detection model or train a custom model using PyTorch. - Load the model into the script. - Process each frame captured by the camera using OpenCV to convert it into a format suitable for the model. - Use the model to detect cars in each frame. 4. **Implement the counting logic**: - Define a line in the center of the image (vertical axis). - For each detected car, determine if the front of the car crosses the line. - If a car crosses the line, increment the `iCount` variable. 5. **Output the count**: - The script should continuously output the current car count, which can be read by the C# application. Here is a skeleton of the Python script that outlines the steps above: ```python import cv2 import torch import depthai as dai # Initialize the camera def init_camera(): # Code to initialize Luxonis OAK-D camera pass # Load the car detection model def load_model(): # Code to load a pre-trained car detection model pass # Process and detect cars in the image def detect_cars(frame, model): # Code to process the frame and detect cars pass # Main function to start the car detection process def main(): camera = init_camera() model = load_model() iCount = 0 while True: # Capture frame-by-frame ret, frame = camera.read() if not ret: break # Detect cars cars = detect_cars(frame, model) # Count cars for car in cars: if car_crosses_line(car): iCount += 1 # Output the current count print(iCount) # Display the resulting frame (optional for debugging) cv2.imshow('Frame', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break # When everything done, release the capture camera.release() cv2.destroyAllWindows() if __name__ == "__main__": main() ``` This script is a starting point and will need to be fleshed out with the actual logic for initializing the camera, loading the model, processing the images, detecting the cars, and implementing the counting logic. The `car_crosses_line` function will need to be implemented to check if a detected car's front passes the specified line. Remember to handle any exceptions and edge cases, such as poor lighting conditions or occlusions, to ensure the car counting is as accurate as possible.

pgarbacz / EduTester

Car counting #2

Potential solution

How to implement

Python Script (EduTester/CarDetection.py)

C# Integration (EduTester/MainWindow.xaml.cs)