Creating a Pingpong Robot with a PyQt User Interface and OpenCV Ball Detection

In this tutorial, we will be creating a Pingpong Robot that uses a PyQt user interface (UI) and an OpenCV ball detector to track a ping pong ball and move the robot accordingly to hit the ball back.

To begin, make sure you have the following installed on your system:

• Python
• PyQt
• OpenCV
• Numpy

Step 1: Setting up the UI with PyQt
First, we will create the user interface using PyQt. Create a new Python file and import the necessary libraries:

import sys
from PyQt5.QtWidgets import QApplication, QMainWindow, QPushButton, QLabel, QWidget

Next, create a class for the main window and initialize the UI elements:

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        self.init_ui()

    def init_ui(self):
        self.setWindowTitle("Pingpong Robot")
        self.setGeometry(100, 100, 500, 500)

        self.start_button = QPushButton("Start", self)
        self.start_button.move(50, 50)
        self.start_button.clicked.connect(self.start_robot)

        self.stop_button = QPushButton("Stop", self)
        self.stop_button.move(150, 50)
        self.stop_button.clicked.connect(self.stop_robot)

        self.status_label = QLabel("", self)
        self.status_label.move(50, 100)

    def start_robot(self):
        # Code to start the robot
        pass

    def stop_robot(self):
        # Code to stop the robot
        pass

Finally, create an application instance and run the main window:

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    sys.exit(app.exec_())

Step 2: Implementing the Ball Detector with OpenCV
Next, we will use OpenCV to detect the ping pong ball. Create a new Python file and import the necessary libraries:

import cv2
import numpy as np

Define a function to detect the ball using a color threshold:

def detect_ball(frame):
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    lower_bound = np.array([0, 192, 60])
    upper_bound = np.array([20, 255, 255])

    mask = cv2.inRange(hsv, lower_bound, upper_bound)

    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    if len(contours) > 0:
        c = max(contours, key=cv2.contourArea)
        ((x, y), radius) = cv2.minEnclosingCircle(c) 
        return (int(x), int(y))

    return None

Step 3: Integrating the UI with the Ball Detector
Now, we will integrate the UI with the ball detector. Modify the start_robot method in the MainWindow class:

def start_robot(self):
    video = cv2.VideoCapture(0)

    while True:
        ret, frame = video.read()

        ball_pos = detect_ball(frame)

        if ball_pos is not None:
            x, y = ball_pos
            # Code to move the robot based on ball position

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    video.release()

In the above code, we capture frames from the video feed, detect the ball using the detect_ball function, and move the robot based on the ball’s position.

Step 4: Controlling the Robot
To control the robot, you would need to interface with the robot’s hardware. This could involve sending commands to actuators or motors based on the ball’s position.

Step 5: Testing the Pingpong Robot
Run the application and test the Pingpong Robot by starting the robot, tracking the ping pong ball, and moving the robot accordingly to hit the ball back.

This tutorial covered the basics of creating a Pingpong Robot using a PyQt user interface and an OpenCV ball detector. You can further enhance the robot by adding features such as speed control, angle adjustment, and more advanced ball tracking algorithms. Have fun experimenting with different functionalities and improving the performance of your Pingpong Robot!