Bebop_window

In this task, We are trying to pass the bebop through the window which has been colored.Bebop must perform the following sub-tasks respectively.

Features

• Window Detection
• pid Controller (decreasing errors in x,y,z dimensions)
• pid controller (adjusting Drone speed)
• passing through the window

Window detection

Drone can detect special objects aiming its camera or sensors.

Color Detection with OpenCV in HSV Color Space

This Python script detects a specific color in real-time video using OpenCV and the HSV (Hue-Saturation-Value) color space. It draws contours around the detected color in the video feed.

Prerequisites

• Python 3.x
• OpenCV (pip install opencv-python)

Usage

1. Run the color_detection.py script.
2. Adjust the HSV range parameters to match the color you want to detect:
   • hue_min, hue_max: Range of hue values
   • saturation_min, saturation_max: Range of saturation values
   • value_min, value_max: Range of value (brightness) values
3. The script will open a video window showing the original video feed with contours drawn around the detected color.

Customization

  import cv2
import numpy as np

# Define the range of HSV values for the specific color
lower_color = np.array([hue_min, saturation_min, value_min])
upper_color = np.array([hue_max, saturation_max, value_max])

# Open the video capture
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the video capture
    ret, frame = cap.read()
    if not ret:
        break

    # Convert the frame to HSV color space
    hsv_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Create a binary mask for the specified color
    mask = cv2.inRange(hsv_frame, lower_color, upper_color)

    # Find contours in the mask
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Draw contours around the detected color
    for contour in contours:
        cv2.drawContours(frame, [contour], -1, (0, 255, 0), 2)

    # Show the original frame with contours
    cv2.imshow("Color Detection", frame)

    # Press 'q' to exit
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

# Release the video capture and destroy OpenCV windows
cap.release()
cv2.destroyAllWindows()

You can modify the script to work with a different video source (e.g., a video file) or implement additional features based on your specific use case.

License

This project is licensed under the MIT License.

Acknowledgments

This script was created for educational purposes and is based on the capabilities of OpenCV.

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For more information on OpenCV, visit the OpenCV Documentation.

If you have any questions or suggestions, please feel free to contribute or reach out!

Full tutorial :

https://www.linkedin.com/feed/update/urn:li:activity:7030598976512892928?utm_source=share&utm_medium=member_desktop

the Proportional-Integral-Derivative (PID) controller

A PID (Proportional-Integral-Derivative) controller is a widely used feedback control mechanism in control systems. It is used to regulate a process based on feedback by adjusting a control output.

Overview

The PID controller consists of three main components:

1. Proportional (P) Term: This term provides an output proportional to the current error. It helps bring the system closer to the desired setpoint but may result in steady-state error if used alone.

2. Integral (I) Term: This term accumulates past errors over time and helps eliminate steady-state error. It is useful when there's a constant bias or offset in the system.

3. Derivative (D) Term: This term considers the rate of change of the error. It helps improve the stability of the system and can prevent overshooting.

Usage

In this repository, you'll find a simple PID controller implementation in [language]. The controller can be used for [describe the application, e.g., controlling a motor speed, temperature, etc.].

Prerequisites

• [List any required libraries or tools]

Installation

1. [Step-by-step installation instructions]

Usage Example

// Import the PID controller module
const PIDController = require('pid-controller');

// Create a new PID controller instance
const pid = new PIDController(Kp, Ki, Kd);

// Set the desired setpoint
pid.setSetpoint(desiredValue);

// Loop to control the process
while (true) {
  // Get the current value from the process
  const currentValue = getCurrentValue();

  // Compute the control output
  const controlOutput = pid.compute(currentValue);

  // Apply the control output to the process
  applyControlOutput(controlOutput);

  // Add a delay or use the appropriate timing mechanism
}
Full Tutorial :
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiAtZnK372AAxWDTaQEHUZ6DGMQFnoECBcQAw&url=https%3A%2F%2Fwww.omega.com%2Fen-us%2Fresources%2Fpid-controllers&usg=AOvVaw0uxGPDA38LGDhXTbVxHkeA&opi=89978449