Approximate Contour

This function block is designed to simplify complex shapes detected in images by approximating their contours. It reduces the number of points in the contour while maintaining the overall shape of the contour, allowing for more efficient shape analysis.

📥 Inputs

Image Any The original image in which the contours exist.

Contour The contour data that you want to approximate.

📤 Outputs

Image Any The output image that displays both the original contour and the approximated contour.

Contour The approximated contour simplified to use the minimum number of edges.

Center The coordinates of the center of the approximated contour.

Perimeter The length of the perimeter of the approximated contour.

Number of corners The number of corners in the approximated contour.

🕹️ Controls

Epsilon A slider that determines the degree of approximation. A smaller epsilon value results in a contour that closely resembles the original, while a larger epsilon will yield a simpler shape.

🎨 Features

Contour Simplification The block reduces the number of vertices in the contour, simplifying shape recognition tasks.

Visual Representation Both the original and the approximated contours are drawn on the output image for clear visual comparison.

Feature Extraction Outputs important shape metrics such as perimeter, center, and corner count.

📝 Usage Instructions

1. Input Images: Connect the original image with contours to the Image Any input and the contour to be approximated to the Contour input.

2. Set Epsilon: Adjust the Epsilon slider to set how closely you want the approximated contour to match the original.

3. Evaluate: Execute the block to obtain and visualize the approximated contour along with additional shape metrics.

📊 Evaluation

When executed, this function block processes the input to return the approximate contour, along with an output image that overlays this approximation on the original contour.

💡 Tips and Tricks

Finding the Right Epsilon

We recommend starting with a lower epsilon value and gradually increasing it to see how it affects your results. You can find a balance between simplicity and maintaining critical features of the contour.

Contour Detection Preparation

For best results, consider preprocessing your input image with noise reduction techniques using Blur before contour extraction.

Visualizing Contours

To visualize contours better, consider using Histogram On Curve to analyze the line characteristics if working along defined edges.

🛠️ Troubleshooting

Contour Not Detected

If the contour is not being detected, ensure that the image input is a binary or correctly thresholded image, as contours cannot be extracted well from noisy images.

Invalid Epsilon Value

Check that the epsilon value is within an acceptable range. If you notice unexpected behavior in the approximation, this is a good place to start debugging.