Image Adaptive Threshold

This function block is designed to apply adaptive thresholding techniques to an image, helping to highlight features based on local pixel intensity, making it an effective tool for binary segmentation in images.

📥 Inputs

Image Any The input image on which adaptive thresholding will be applied.

📤 Outputs

Image Gray The output will be a grayscale image after applying adaptive thresholding.

🕹️ Controls

Threshold Type A dropdown menu to select the type of adaptive thresholding method to use: either mean or Gaussian.

Threshold A slider to set the maximum threshold value. This value determines the cutoff for pixel values when resulting in a binary image.

Kernel Size A slider to adjust the size of the Gaussian kernel used in the adaptive thresholding process. The kernel size should be odd and determines how much local neighborhood of each pixel is taken into account.

🎨 Features

Adaptive Thresholding Methods Choose between different adaptive threshold methods to find the most suitable one for your image.

Dynamic Adjustments The slider controls allow for real-time changes to the threshold and kernel size, enabling immediate feedback for better adjustments.

📝 Usage Instructions

1. Input Image: Connect an image source to the Image Any input.

2. Select Threshold Type: Choose the desired adaptive thresholding method from the Threshold Type dropdown.

3. Set Threshold Value: Adjust the Threshold slider to set the maximum value for pixels.

4. Configure Kernel Size: Use the Kernel Size slider to specify the size of the kernel (this will be multiplied by 2 and subtracted by 1 to ensure it is odd).

5. Evaluate: Run the function block to apply adaptive thresholding to the input image.

📊 Evaluation

When executed, this function block applies the selected adaptive thresholding method to the input image, returning a resultant binary image that highlights significant features based on the local pixel intensity.

💡 Tips and Tricks

Ensure Proper Kernel Size

The kernel size must be an odd number. If you set it to an even value, you may not get the expected results. Use the Kernel Size slider to control this.

Preprocessing with Denoising

If you notice noise affecting your thresholding results, consider applying a Denoising function block or a Blur function block before adaptive thresholding to clean up the image.

Experiment with Both Methods

Try both threshold types—mean and Gaussian—to see which works best with your specific image. The method's performance may vary depending on the content of the image.

🛠️ Troubleshooting

Output Not As Expected

If the resulting binary image does not highlight features effectively, try adjusting the Threshold and Kernel Size controls. Experimenting with different values can lead to better segmentation results.

Kernel Size Warning

If you encounter a warning about kernel size or threshold, ensure that both these parameters meet the defined limits and correctly adhere to the specifications (kernel size as an odd number, threshold value between 0-255).