Object Detection
This function block is designed to detect specific objects in an image using a YOLOv4-based object detection model. Users can select the object class they wish to detect and set a confidence threshold for the detection process.
π₯ Inputs
Image Any
The input image in which objects will be detected.
π€ Outputs
Image Any
The output shows the modified image with detected objects highlighted by rectangles.
Object Count
This output returns the total number of detected objects in the input image.
Object Center Locations
Returns the center points of the detected objects, with the ability to track multiple detections.
Object Sizes (w, h)
The sizes of detected objects represented by their widths and heights.
Rectangles
Coordinates of rectangles that bound the detected objects, allowing for multiple detections.
πΉοΈ Controls
Confidence Threshold %
A slider to adjust the confidence threshold for detections. Adjusting this value helps filter out less certain detections.
Select Detection Class
A dropdown menu to choose the specific object classes to be detected from the available options.
π¨ Features
Multiple Detection Classes
Users can select from predefined classes, such as "All", "Human", "Animals", "Indoor", and "Outdoor".
Dynamic Confidence Adjustment
The threshold slider allows for real-time adjustments to the sensitivity of the detection algorithm.
Comprehensive Outputs
Multiple outputs giving detailed feedback about detected objects, including image modifications, counts, and bounding rectangle information.
π Usage Instructions
Connect Input: Attach an image containing potential objects to the input.
Select Class: Choose a desired class from the
Select Detection Class
dropdown to specify what the object detection algorithm should search for.Set Confidence: Adjust the
Confidence Threshold %
slider to set how confident the model should be to consider an object detected.Evaluate: Run the block to perform object detection. The modified image and detection results will be provided as outputs.
π Evaluation
When executed, this function block analyzes the input image for the presence of the selected objects, returning modifications of the original image along with quantitative detection data.
π‘ Tips and Tricks
π οΈ Troubleshooting
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