This task is used to crop the best object detected in an image, based on the various configurable criteria.
In the input parameters it is expected:
Only detections with a confidence higher than the one set in this parameter will be used for the rest of the processes.
Accuracy 0, implies that all detections will be evaluated, this may introduce erroneous detections in the process.
Accuracy very close to 1, implies that only the clearest detections for the model will be evaluated, this may bias the detections of valid objects.
By default, the value of 0.2 dramatically decreases anomalous detections and allows enough flexibility to maximize the number of detections.
In the advanced parameters we can configure:
This value allows you to filter out-of-focus images.
The default value is 100, it is appropriate to perform several tests to determine which value best suits the type of images evaluated.
If the value of this parameter is 0, the threshold is not applied.
The method we use to determine the quality of an image is the Laplacian operator, which allows us to obtain a mathematical parameter of image sharpness by studying the edges of objects. Thus a poorly focused image will have a lower Laplacian value than one in which the edges are clearly distinguishable.
Process by parts: This option allows processing the image by parts, intended for very large images.
Window Width: Measure in pixels of the width of the frame, when processing by parts.
Window Height: Measure in pixels of the height of the frame, when processing by parts.
Overlap value: Measurement in pixels, when processed in parts.
Area Weight: Weighting value to be used to determine the best object detected.
Score Weight: Weighting value that will be used to determine the best detected object.