<aiNeuralNetworkModel variable>.Detect (Function)

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<aiNeuralNetworkModel variable>.Detect (Function)

In french: <Variable iaModèleRéseauDeNeurones>.Détecte

Runs a trained neural network model on an image. This function allows you to use artificial intelligence to detect objects in an image.

Caution: This function is an advanced function. To use this function, it is recommended to read the documentation of the neural network used (specifics, expected results and interpretation).

Example

// Image declaration
MyImage is Image
MyImage = IMG_Test

// Declare a model
MyAINNModel is aiNeuralNetworkModel

MyAINNModel.Configuration = "MyModel.cfg"
MyAINNModel.TrainedWeights = "MyModel.weights.pb"
MyAINNModel.PixelScaleFactor = 1.0

// Caution: the X and Y dimensions depend on the model.
// If the specified dimensions do not match the model,
// AIDetectModel returns an error.
MyAINNModel.XDimension = 300
MyAINNModel.YDimension = 300

MyAINNModel.AverageIntensityR = 104
MyAINNModel.AverageIntensityG = 117
MyAINNModel.AverageIntensityB = 113
MyAINNModel.RGBColor = True

myMatrixArray is array of 1 array of 1 by 1 by 200 by 7 real

// Run model
myMatrixArray = MyAINNModel.Detect(MyImage)

Syntax

<Result> = <Model>.Detect(<Image>)

<Result>: Array of array

Array of matrices containing the result of the execution of the model.
This array is specific to each model and must be known by the developer.

<Model>: aiNeuralNetworkModel variable

Name of the aiNeuralNetworkModel variable describing the characteristics of the neural network used.
Caution: The different characteristics of this variable are specific to each model and must be known by the developer.

<Image>: String, Image or Image control

Image to be analyzed. The image can correspond to:
a variable of type Image,
the name and path of the image,
the name and path of a PDF file,
an Image memo item,
an Image control.

Remarks

The AI engine used by <aiNeuralNetworkModel variable>.Detect is OpenCV. This engine reads the AI models and executes them.
The supported neural network models are:
- caffe,
- tensorflow,
- darknet,
- New in version 28
  onnx
The model must be trained (weights are known).
The expected extensions according to the models are:
- Configuration:
  - Caffe: *.prototxt
  - Tensorflow: *.pbtxt
  - Darknet: *.cfg
- Weights:
  - Caffe: *.caffemodel
  - Tensorflow: *.pb
  - Darknet: *.weights
  - New in version 28
    Open Neural Network Exchange (ONNX): *.onnx

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