AugeLab Studio Manual
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  • 👋Welcome to AugeLab Studio User Manual
  • 📘Introduction
    • AugeLab Studio
    • Key Features
    • Use Cases
    • System Requirements
  • 🚀Getting Started
    • Signing up
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    • First Look
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      • Detection
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    • Further Reading
  • đŸ–Ĩī¸AugeLab Studio Interface
    • Detailed Look
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      • Leverage AI with Module Downloader
  • 🧱Function Blocks
    • Block Structures
    • Sockets
    • Blocks Column
    • Connections
    • All Function Blocks
      • AI Blocks
        • Face Detection
        • Mask Detection
        • Object Detection - Custom
        • Object Detection
        • Pose Estimation
        • Safety Equipment Detection
        • Social Distance Detector
        • Super Resolution
        • Text Detection
        • OCR
      • CNN Blocks
        • Average Pooling 2D
        • Batch Normalization
        • Choose Folder 2D
        • Compile Model
        • Conv. Sep. Layer 2D
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        • Convolutional Layer 2D
        • Dropout Layer
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        • Fully Connected
        • Global Average Pooling 2D
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        • Input Layer 2D
        • Loss CCE
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        • Metrics Accuracy
        • Model EfficientNet
        • Model MobileNet
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        • Model VGG
        • Optimizer Adadelta
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        • Optimizer Adam
        • Optimizer Adamax
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        • ReLU Layer
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        • Training Parameters
      • Data/Logic
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        • logic
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        • Signal Operators
          • Delay Step
          • Edge Falling
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          • OFF Delay
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      • Image/Transformations
        • Analysis
          • Color Density Percentage
          • Get Dimension
          • Histogram On Curve
          • Histogram On Line
          • Image Color Match
          • Image Memory
          • Image Resolution and Channel Value
          • Maximum Images
          • Mean Value of Image
          • Measure Position Distance
          • Minimum Images
          • Non-zero of Image
          • Std. of Image
          • Structural Similarity
        • Transformation Filters
          • Auto Alignment
          • Auto Contrast
          • Color Quantizer and Clustering
          • Color Space
          • Contrast-Brightness-Gamma
          • Contrast Optimization
          • Deconvolution
          • Denoising
          • Distance Transformation
          • FloodFill
          • Grab Cut Algorithm
        • Color Filters
          • 2D Filter
          • Apply Mask
          • Bilateral Filter
          • Blur
          • Edge Filter
          • HSV Filter
          • Image Adaptive Threshold
          • Image Threshold
          • Invert Image
          • Morphological Transformations
          • Normalize Image
          • RGB Mask
          • RGB Set
          • Sobel Filter
        • Operations
          • Add Images Weighted
          • Add Images
          • Collage Images
          • Divide Images
          • Flip Image
          • Image AutoRotator
          • Image Concatenate
          • Image Resize
          • Image Resizer
          • Merge Channels
          • Multiply Images
          • Polar Transform
          • Rotate Image Angle
          • Slice Image
          • Split Image
          • Subtract Images
      • Detections/Shapes
        • Detectors
          • Barcode Reader
          • Blob Detector
          • Blur Detector
          • Circle Detector
          • Corner Detector
          • Custom CNN Model
          • Data Matrix Reader
          • Detect Reference
          • Feature Detector
          • Find Object - Multiple Image
          • Find Object
          • Find Reference
          • Harris Corner Filter
          • Line Detector
          • Match Shapes
          • Measure Object Distance
          • Shape Detector
        • Draw
          • Draw Detections
          • Draw Line
          • Draw Point
          • Draw Rectangle
          • Draw Result On Image
          • Write Date On Image
          • Write Text On Image
        • Roi Processing
          • Check Area (Polygon)
          • Check Area
          • Get Pixel Mouse
          • Get Pixel
          • Get ROI
          • Image ROI Center
          • Image ROI Polygon
          • Image ROI Select Multi
          • Image ROI Select
          • Image ROI
          • Perspective Transform
          • Rectangles in Rectangle
        • Shape Analysis
          • Approximate Contour
          • Choose Line
          • Contour to Image
          • Fill Contour
          • Find Contour
          • Hull Convex
          • Minimum Circle
          • Minimum Ellipse
          • Minimum Rectangle
          • Minimum Rotated Rectangle
          • Most Similar Shape
          • Point Polygon Test
      • Input/Output
        • Communication
          • Modbus Connect
          • Modbus Read
          • Modbus Write
          • MQTT Publish
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          • OPC UA Client
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          • REST API - Get
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        • Data Inputs
          • Date-Time List
          • Date-Time
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          • Keyboard/Barcode Reader
          • Logic Input
          • Number Input
          • Number Range
          • PWM (Pulse Width Modulation)
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          • String Input
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        • Image Inputs
          • Camera IP (ONVIF)
          • Camera IP
          • Camera USB External
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          • Camera USB
          • Load Image From Path
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          • Make Image
          • Pixel
          • Video
        • Outputs/Exports
          • CSV Export
          • Cycle Timer
          • File/Folder Operations
          • GPU Statistics
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          • Led Output
          • Multi Image Write
          • Output
          • Scope
          • Show Image
          • Stop
  • 📡Devices and Communication
    • Camera Usage
    • Communication Protocols
    • Further Reading
  • 🧩Example Projects
    • Demo Projects
    • Circumference Measurement
    • Object Counting
    • Tile Width Measurement
    • Human Detection
    • Object Detection
  • 🔑Key Features
    • Deploy Custom HMI Applications
    • Annotate Data for Object Detection
    • Train Custom AI Models
      • Choosing the Right Database
      • When to Stop Training
    • Create Plugins
      • Components
      • Coding Reference
    • Share Your Solutions with Community
    • Instal Python Packages
  • 📑FAQ
    • Contact Us
    • FAQ
    • Setting up a full project
  • Additional Resources
    • Training Schedule
    • Training Materials
    • AugeLab Experts
  • Appendix
    • Dictionary
    • References
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  • đŸ“Ĩ Inputs
  • 📤 Outputs
  • đŸ•šī¸ Controls
  • 🎨 Features
  • 📝 Usage Instructions
  • 📊 Evaluation
  • 💡 Tips and Tricks
  • đŸ› ī¸ Troubleshooting

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  1. Function Blocks
  2. All Function Blocks
  3. Image/Transformations
  4. Transformation Filters

FloodFill

This function block utilizes the flood fill algorithm to fill enclosed shapes within a grayscale image. It is particularly useful for isolating and modifying specific regions based on connected pixel values.

đŸ“Ĩ Inputs

Image Gray A grayscale image that will be processed to fill enclosed areas.

📤 Outputs

Image Gray The output is a modified grayscale image where enclosed shapes have been filled.

đŸ•šī¸ Controls

This function block does not have additional user controls beyond the input and output connections.

🎨 Features

Shape Filling Efficiently fills connected regions in the image, allowing for various applications in image processing and analysis.

Automatic Handling The algorithm identifies and fills shapes based on the specified starting point.

📝 Usage Instructions

  1. Connect Input: Link a grayscale image to the Image Gray input.

  2. Run Evaluation: Initiate the block to fill enclosed shapes in the input image. The result will be output as a new filled image.

📊 Evaluation

When executed, the flood fill algorithm will process the input image, filling enclosed shapes with the specified fill color (in this case, white).

💡 Tips and Tricks

Custom Fill Color

To modify the fill color, you would need to adapt the starting point in the flood fill function. The default starting point is at the top left corner (0, 0), and it fills with white. Changing the color requires additional configuration, which may involve modifying algorithm implementation.

Using Masks for Specific Regions

You can create a mask beforehand if you want to fill only specific areas. This can be achieved by combining or creating regions utilizing other function blocks like Image ROI.

Performance Optimization

Flood filling can be computationally intensive for large images. Consider resizing images or cropping unnecessary areas before processing.

đŸ› ī¸ Troubleshooting

Flood Fill Not Filling Shapes

If the flood fill does not appear to fill any shapes, check whether the starting point (0, 0) is within the desired region. You may need to change the starting point or ensure the area is indeed enclosed.

Image Size Issues

Ensure that the image provided to the input socket is a valid grayscale image. If the image dimensions are incorrect or the format is not compatible, the flood fill may not work as expected.

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Last updated 8 months ago

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