RasterObjectDetectionModelTrainer

Input

The transformer accepts any feature but the contents are largely ignored. A possible input would be the feature output from RasterObjectDetectorSamplePreparer which will contain a number of input parameters required for translation.

Output

This port returns the original feature and the _out_detection_model_name attribute, containing the path to the produced custom detection model.

This port returns a feature with all its original contents and an fme_rejection_message containing the error message if any occur.

Rejected Feature Handling: can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a default FME option and as a workspace parameter.

Input Samples

Positive Samples File	Positive .vec file. Can be produced by either RasterObjectDetectorSamplePreparer or RasterObjectDetectorSampleGenerator. See their documentation for further details. Alternatively, this file can be produced by manually calling the opencv_createsamples app.
Background Description File	Negative samples description file. Must contain absolute paths to your negative sample images, one image per line of the file. Can be produced by either RasterObjectDetectorSamplePreparer or RasterObjectDetectorSampleGenerator. See their documentation for further details.
Number of Positive Samples	Number of positive samples contained in the input Positive Samples File.
Number of Negative Samples	Number of negative samples contained in the input Background Description File.

Training Parameters

Training Model Type

HAAR: Trains detection model using Haar-like features, which are rectangular features that represent a difference of the sum of pixels within the area. The model generally takes longer to train and has slower detection time (speed heavily depends on model complexity and not a general truth about the model types). However, usually this model type is more accurate than LBP features.
LBP: Model will use Local Binary Patterns for object detection. The opposite (compared to HAAR-feature) is true regarding performance. LBP models are considerably faster to train but may lack in accuracy in some applications.

Sample Width & Height

Width and Height of the resulting detection window. Must be the same as the size of positive samples specified in the input .vec file. These parameters can be acquired from RasterObjectDetectorSamplePreparer or RasterObjectDetectorSampleGenerator.

Value Buffer Size

Size of buffer for feature values in Mb (Megabytes). The more memory is given, the faster the training process is going to be. Ensure that the combined memory specified for Value and Index buffer sizes does not exceed the total system memory.

Index Buffer Size

Size of buffer for feature indices in Mb (Megabytes). The more memory is given, the faster the training process is going to be. Ensure that the combined memory specified for Value and Index buffer sizes does not exceed the total system memory.

Advanced

Number of Stages	Number of cascade stages to be trained.
Acceptance Ratio Break Value	Parameter determines the precision to which the model will be trained to. A good guideline is not to train the model past 10e-5 (0.000001), to ensure the model isn't overtrained on the data. -1 disables the feature.
Parallel Processing	NO PARALLELISM \| MINIMAL \| MODERATE \| AGGRESSIVE \| EXTREME. This parameter determines the number of threads to use for processing. NO PARALLELISM means single-threaded processing. On a 16 (virtual) core machine, the rest of the options will map to 8, 16, 24, 32 threads respectively.

Output

Intermediate Working Directory

Working directory where the intermediate steps and parameters for detection model training will be placed.
Sometimes the training can take days and even weeks. In case of an interruption, the training may be continued with the same parameters from the last known "good" training stage which will be saved in this directory.
The training parameters must match exactly, otherwise the training will be restarted from the beginning. Additionally, if you want to retrain with the same parameters, but maybe your samples have changed, you would need to delete the outputs in the intermediate directory.

Detection Model

Path to the file which will contain the trained detection model.

Boosted Classifier Parameters

From OpenCV: A common machine learning task is supervised learning. In supervised learning, the goal is to learn the functional relationship F: y = F(x) between the input x and the output y. Predicting the qualitative output is called classification, while predicting the quantitative output is called regression.

You can read more about Boosting parameters in the official OpenCV Documentation and OpenCV Boosting Documentation

How to Set Parameter Values

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

RasterObjectDetectionModelTrainer

Typical Uses

Configuration

Input Ports

Output Ports

Parameters

Editing Transformer Parameters

FME Community