Car licence plate detection_GUi

[NitinRwt]

Car License Plate Detection GUI

Overview

The Car License Plate Detection GUI is a user-friendly interface for performing license plate detection on images or video streams. It utilizes advanced computer vision techniques to identify and highlight license plates within the provided media.

Features

• Input Options:

  • Supports image and video input for license plate detection.
  • Users can either upload an image or provide the path to a video file.

• Detection Results:

  • Displays the original input alongside the detected license plates.
  • Highlights detected license plates with bounding boxes for easy identification.

• Real-time Processing:

  • For video input, the GUI provides real-time processing capabilities.
  • Users can observe license plate detection results in live video streams.

• Accuracy and Confidence:

  • Provides information about the accuracy and confidence level of each detected license plate.
  • Users can interpret the reliability of the detection results.

• Save and Export:

  • Allows users to save the processed images or video with annotated license plates.
  • Provides an option to export the detection results for further analysis.

• User Interaction:

  • Simple and intuitive controls for users to navigate through the application.
  • Clear buttons for initiating the detection process and managing input files.

How to Use

1. Upload Image/Video:

   • Click on the "Upload" button to choose an image or video for license plate detection.

2. Perform Detection:

   • Press the "Detect" button to initiate the license plate detection process.

3. View Results:

   • Observe the original input alongside the detected license plates with bounding boxes.

4. Save/Export:

   • Use the "Save" or "Export" button to save the processed media with annotations.

5. Real-time Video Processing:

   • If processing a video, real-time detection results will be displayed.

System Requirements

• Python version: [Specify the required Python version]
• Dependencies: [List any external libraries or frameworks needed]

How to Install

1. Clone the repository from NitinRwt/TFODCourse.
2. Install the necessary dependencies using the provided requirements file.
3. Run the GUI application script.

[smith-kyle]

I noticed you all aren't using a notebook review tool and wanted to invite you to review this pull request with GitNotebooks: https://gitnotebooks.com/nicknochnack/TFODCourse/pull/156

It lets you do things like comment on rendered markdown and code cells, so might be an easy win for your PR reviews.