Classic Forward
Description
Execute the forward of your Network. Type : polymorphic.
First we’ll set input by resizing it to (Width, Height) then we normalizing pixel value by multiplying by “Normalize” value.
If Width or Height = 0, we keep image size.
If Normalize = 0, we don’t normalize.
NB : original image reference isn’t modify because we work with a copy.
Input parameters
DNN Src : class
Image Src : class, type accepted U8 and RGB.
Preproc Image Parameters : cluster, these parameters create a copy of the resized and normalized image to be sent to the model input.
Width : integer, image width expected by the model. Height : integer, image height expected by the model.
Normalize : float, multiplier for image values.
Output parameters
DNN Dst : class
Forward Outputs : array,
Node Name : string, name of output layer.
Data : array, data of output layer.
Dimensions : array, output layer data shape.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install TIGR library to run it).