Edge Filter

This function block applies the Canny edge detection algorithm to the input image, highlighting the edges based on specified thresholds. It allows for dynamic adjustment of the edge detection sensitivity using a slider.

📥 Inputs

Image Any This input expects an image, typically in grayscale, upon which edge detection will be performed.

📤 Outputs

Image Gray The output is a grayscale image that contains the detected edges.

🕹️ Controls

Filter Coefficient This slider allows you to adjust the sensitivity of edge detection. Moving the slider will change the thresholds used in the Canny edge detection algorithm.

🎨 Features

Dynamic Threshold Adjustment The edge detection thresholds can be modified in real time using the slider for immediate feedback.

Robust Edge Detection Utilizes the Canny algorithm, well-known for its accuracy in edge detection, making this an effective tool for identifying object boundaries.

📝 Usage Instructions

1. Input Image: Connect a grayscale image to the input. If the image is in color, it may be necessary to convert it to grayscale before input.

2. Adjust Threshold: Use the Filter Coefficient slider to modify the sensitivity of edge detection.

3. Evaluate: Run the block to perform edge detection, and view the resulting output image highlighting the detected edges.

📊 Evaluation

When executed, this function block produces an output image where the edges detected in the input are marked, allowing you to easily visualize the object's contours.

💡 Tips and Tricks

Preprocessing for Better Results

Before applying the edge filter, consider preprocessing the input image with a Bilateral Filter or a Gaussian Blur. This helps reduce noise and improve edge detection clarity.

Adjusting Sensitivity

Moving the slider can make a substantial difference. Lower values will create less strict detection conditions, while higher values will require more intense edges to be detected.

Using Along with Other Filters

Combine the Edge Filter with other filtering techniques, such as Threshold or Image ROI, to focus edge detection on specific regions or features of the input image.

🛠️ Troubleshooting

No Edges Detected

Make sure that the input image is not completely uniform (like completely white or completely black). Additionally, adjusting the slider to a lower value may help detect more edges.

Input Image Issues

Ensure that the input image is properly connected and is in a format (grayscale) suitable for edge detection. If necessary, convert it to grayscale using a previous function block.