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
    • Simple Tour
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      • Basics
      • Detection
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    • Further Reading
  • đŸ–Ĩī¸AugeLab Studio Interface
    • Detailed Look
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    • Managing Projects
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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
        • Conv. Trans. Layer 2D
        • Convolutional Layer 2D
        • Dropout Layer
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        • Fully Connected
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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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        • Training Parameters
      • Data/Logic
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        • logic
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      • Image/Transformations
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          • Color Density Percentage
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          • Image Color Match
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          • Image Resolution and Channel Value
          • Maximum Images
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          • Minimum Images
          • Non-zero of Image
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        • Transformation Filters
          • Auto Alignment
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          • Color Space
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          • Contrast Optimization
          • Deconvolution
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        • Color Filters
          • 2D Filter
          • Apply Mask
          • Bilateral Filter
          • Blur
          • Edge Filter
          • HSV Filter
          • Image Adaptive Threshold
          • Image Threshold
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          • Morphological Transformations
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          • RGB Mask
          • RGB Set
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        • Operations
          • Add Images Weighted
          • Add Images
          • Collage Images
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          • Flip Image
          • Image AutoRotator
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          • Merge Channels
          • Multiply Images
          • Polar Transform
          • Rotate Image Angle
          • Slice Image
          • Split Image
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      • 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
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          • MQTT Publish
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          • OPC UA Client
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          • REST API - Get
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          • Send Mail
          • Siemens S7 Connect
          • Siemens S7 Read
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        • Data Inputs
          • Date-Time List
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          • Keyboard/Barcode Reader
          • Logic Input
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          • PWM (Pulse Width Modulation)
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        • Image Inputs
          • Camera IP (ONVIF)
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          • Camera USB External
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          • Load Image From Path
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          • Make Image
          • Pixel
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        • 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
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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. Detections/Shapes
  4. Detectors

Circle Detector

This function block is utilized to detect circular patterns within an input image. It employs the Hough Circle Transform technique to locate and outline circles within images effectively.

đŸ“Ĩ Inputs

Image Any This input accepts any image data that may contain circular patterns for detection.

📤 Outputs

Result The output image showcasing the detected circles, marked for easy identification.

Positions The coordinates of the detected circles, allowing for further processing or analysis.

Number of Circles The total count of detected circles in the image.

Circles Detailed information about the detected circles in the format specified by the Circle type, which can include various attributes of the detected circles.

đŸ•šī¸ Controls

dp A slider used to adjust the inverse ratio of the accumulator resolution to the image resolution used during circle detection.

Edge Detection A slider to set the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller).

Threshold This slider sets the center threshold for the circle detection method.

Min Distance A parameter that determines the minimum distance between the centers of detected circles.

Min Radius A slider to specify the minimum radius of circles to be detected, defined as a percentage of the input image's width.

Max Radius A slider to set the maximum radius of circles to detect, also defined as a percentage of the input image's width.

🎨 Features

Flexible Parameters Users can fine-tune several parameters to optimize circle detection for various kinds of input images.

Visual Feedback The resulting output image clearly marks the detected circles, offering real-time visual feedback on the detection process.

📝 Usage Instructions

  1. Connect Input: Link your input image, which may contain circles, to the Image Any input.

  2. Adjust Parameters: Use the sliders to set appropriate parameters to optimize circle detection according to the specific characteristics of your image.

  3. Evaluate: Run the block to detect circles. The output will include an image with detected circles and relevant data about the positions and counts.

📊 Evaluation

On execution, this function block analyzes the input image and returns an output with the identified circles drawn on it along with their positions and counts.

💡 Tips and Tricks

Adjusting Edge Detection

If you are struggling to detect circles, try adjusting the Edge Detection parameter higher or lower based on the image clarity.

Testing Different Radius Values

Experiment with different values for Min Radius and Max Radius based on the expected sizes of circles in the input image for optimal results.

Using Preprocessing

You can use preprocessing techniques like Blur or Image Threshold blocks to enhance edges in the image, making it easier to detect circular patterns.

More Accurate Detection

Using a higher Min Distance value can help to reduce the detection of false positives by preventing overlapping detections.

đŸ› ī¸ Troubleshooting

No Circles Detected

If no circles are detected, try adjusting the parameters, especially the Threshold and the Min Radius settings to find a suitable configuration based on your input image.

Soft or Blurred Circles

If circles appear soft or are not well-defined, consider applying a preprocessing method such as a Gaussian Blur to your input image before it reaches this block.

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Last updated 8 months ago

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