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
    • Installation
    • First Look
    • Simple Tour
    • Your Very First Project
      • Basics
      • Detection
      • Wrapping Up
    • More Local Examples
    • Further Reading
  • đŸ–Ĩī¸AugeLab Studio Interface
    • Detailed Look
    • Scenario Area
    • Menu and Toolbar
    • Managing Projects
    • Installing AI and much more
      • 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
        • Flatten Layer 2D
        • Fully Connected
        • Global Average Pooling 2D
        • Global Max Pooling 2D
        • Input Layer 2D
        • Loss CCE
        • Max Pooling 2D
        • Metrics Accuracy
        • Model EfficientNet
        • Model MobileNet
        • Model ResNet
        • Model VGG
        • Optimizer Adadelta
        • Optimizer Adagrad
        • Optimizer Adam
        • Optimizer Adamax
        • Optimizer FTRL
        • Optimizer Nadam
        • Optimizer RMSProp
        • Optimizer SGD
        • ReLU Layer
        • Softmax Layer
        • Training Parameters
      • Data/Logic
        • Flow Control
          • Batch Concatenation
          • Batch Processing
          • Debatch
          • Get Batch Size
          • HMI Background
          • Subsystem Enabled
          • Subsystem In
          • Subsystem Loop
          • Subsystem Out
          • Subsystem
        • logic
          • All True
          • And
          • Demux
          • Equals
          • Greater
          • Logic Operations
          • Mux
          • Not
          • Or
          • Set - Reset
          • Smaller
        • Mathmetical Operations
          • Add
          • Counter
          • Divide
          • Math Operations
          • Maximum
          • Minimum
          • Multiply
          • Not Equals
          • Round
          • Square Root
          • Subtract
          • Trigonometry
        • Data Operations
          • Data Memory
          • Data to JSON
          • Data Type Converter
          • Datetime Compare
          • Dictionary Operations
          • Exclude Nones
          • Find Substring
          • Get Element
          • Is None
          • List Operations
          • Parse Data Dictionary
          • Replace None
          • String Merge
          • String Operations
        • Referencing
          • Data Read Global
          • Data Read Local
          • Data Write Global
          • Data Write Local
          • Debug Input
          • Tag From
          • Tag To
        • Signal Operators
          • Delay Step
          • Edge Falling
          • Edge Rising
          • Multi Port Switch
          • OFF Delay
          • ON Delay
      • 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
          • MQTT Subscribe
          • OPC UA Client
          • OPC UA Read
          • OPC UA Write
          • REST API - Get
          • REST API - Post
          • Send Mail
          • Siemens S7 Connect
          • Siemens S7 Read
          • Siemens S7 Write
        • Data Inputs
          • Date-Time List
          • Date-Time
          • Headless Check
          • Keyboard/Barcode Reader
          • Logic Input
          • Number Input
          • Number Range
          • PWM (Pulse Width Modulation)
          • Rising Edge
          • String Input
          • Text
        • Image Inputs
          • Camera IP (ONVIF)
          • Camera IP
          • Camera USB External
          • Camera USB Vidgear
          • Camera USB
          • Load Image From Path
          • Load Image
          • Make Image
          • Pixel
          • Video
        • Outputs/Exports
          • CSV Export
          • Cycle Timer
          • File/Folder Operations
          • GPU Statistics
          • Image Logger
          • Image Write
          • 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
  • đŸ› ī¸ Troubleshooting
  • 💡 Tips and Tricks

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  1. Function Blocks
  2. All Function Blocks
  3. CNN Blocks

Choose Folder 2D

This function block is used to select a directory containing image data organized into structured folders. It is designed to facilitate the preparation of data for training a 2D convolutional neural network (CNN).

đŸ“Ĩ Inputs

This function block does not have any inputs.

📤 Outputs

PNNModel This output provides the configuration of the model including the path to the training data and the classes derived from the dataset.

đŸ•šī¸ Controls

Channel Size A dropdown menu that allows you to specify the color mode for reading images (e.g., Grayscale or RGB).

Data Augmentation A checkbox that enables or disables data augmentation during the training process. This can help improve model robustness.

Choose Folder A button that, when clicked, opens a dialog for selecting the folder containing the structured dataset.

🎨 Features

Structured Folder Support The block checks for a specific folder structure (train, validation, and test) to ensure the data is organized correctly for model training.

Class Logging Automatically logs the class names found in the training data directory for user reference.

📝 Usage Instructions

  1. Select Folder: Click on the Choose Folder button to locate the directory containing your images. The folder should have subdirectories for train, validation, and optionally test.

  2. Configure Channel Size: Select the desired image reading mode (Grayscale or RGB) using the dropdown menu.

  3. Toggle Data Augmentation: Check or uncheck the Data Augmentation option based on whether you want to apply augmentation during training.

  4. Evaluate: Run the function block to return a model configuration based on the selected dataset.

📊 Evaluation

When executed, this function block checks the chosen folder structure, logs the relevant classes, and provides the necessary configurations for training a CNN, returning a structured output for further processing.

đŸ› ī¸ Troubleshooting

Folder Not Found

Ensure the selected folder is valid and that it contains the required subfolders: train, validation, and test. If any are missing, the block will log errors accordingly.

No Classes Found

If the folder structure is correct but classes are missing, check that you have populated directories within the train folder. Each subdirectory within train should represent a class.

Invalid Input for Channel Size

Make sure that the choice for channel size selected matches the formats of images in the directory. The images should be consistent with either RGB or Grayscale.

💡 Tips and Tricks

Organizing Data Efficiently

Always structure your dataset with proper class directories within the train, validation, and test folders. This helps automatically manage the data flow for training and evaluation.

Using Data Augmentation

Using data augmentation can greatly improve model performance by providing variability in the dataset. However, ensure that the augmentation settings are appropriate for your specific model needs.

Debugging Folder Issues

Utilize the debug feature in your environment by setting the DEBUG variable to true, which can log additional information about the folder structure and class detection.

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Last updated 8 months ago

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