/interpret-function-curve

Interpret the function curve within the image using the Canny Edge Detection algorithm.

Primary LanguageJavaScriptMIT LicenseMIT

Interpret the function curve within the image using the Canny Edge Detection algorithm

Overview

This application allows users to upload local images, interpret the function curve within the image using the Canny Edge Detection algorithm, and download the results as a CSV file. The application utilizes a third-party library, CannyJS.min.js, for edge detection.

Setup

To set up this project, you need to have the following files and dependencies:

  1. The git repository containing the following files:

    • index.html (HTML file)
    • script.js (JavaScript file)
    • CannyJS.min.js (JavaScript library for Canny Edge Detection)

  2. A local image file that you wish to upload and process.

Features

This web page has the following features:

  1. Image Upload: A file input field allows the user to upload a local image.

  2. Canny Edge Detection: The JavaScript library CannyJS.min.js is used for implementing the Canny Edge Detection algorithm.

  3. Display Output: The results of the Canny Edge Detection are displayed on the web page.

  4. Parameter Adjustment: Users can adjust the algorithm parameters using four input fields:

    • htInput: Height Threshold
    • ltInput: Low Threshold
    • sigmmaInput: Sigma
    • kernelSizeInput: Kernel Size

  5. Download CSV: A button allows users to download the edge detection results as a CSV file.

  6. Download CSV (Spline Interpolation): Smooth the data using interpolation algorithms.

Usage

To use the web application and get the coordinates from the function curve in an image file:

  1. Open the Application: Access the web application through the browser by visiting the URL /index.html.

  2. Select an Image File: Click the file input element and select an image file containing a function curve.

  3. Adjust Algorithm Parameters: Use the input fields to adjust the Canny Edge Detection parameters (Height Threshold, Low Threshold, Sigma, Kernel Size). These parameters can be tweaked to improve the edge detection results.

  4. Process the Image: Click the "Get Coordinates" to trigger the edge detection. The function will process the uploaded image and display the result on the canvas.

  5. Download the Results: Click the "Download CSV" to trigger the download function, which saves the output as a CSV file containing the coordinates of the function curve's points.

  6. Download CSV (Spline Interpolation): Optional. Similar to "Download CSV", smooth the data using interpolation algorithms and trigger the download function.

Contributions and Issues

If you encounter any issues or would like to contribute to this project, please open an issue on Gitee with a detailed description and relevant code snippets.