Accepts a raster input and outputs rectangular geometries outlining the detected object(s). The transformer uses OpenCV’s Cascade Classifier for object detection and allows for selection of various object types and detection models or classifiers. Each classifier is trained to detect a specific object, for instance: human bodies, faces and eyes. Multiple classifiers can to be used in the same transformer on the same source raster(s) to produce different sets of results, grouped by detection model.

Detection models use a detection kernel window that is moved across the entire raster. If the pixel pattern in a specific area of the raster matches the kernel “sufficiently”, that area is treated as a detected object. For the purposes of matching, the kernel and source raster are scaled up and down, respectively, to detect smaller and larger objects.

A rough bounding box of the detected object will be individually attached to a feature and output via the Detected port. The detection parameters, scaling factor, minimum number of neighbors and detection object sizes work together to help balance the number of objects detected, processing speed and detection accuracy. See the parameters section for more details.

Input Ports

Output Ports



Detection Model

These parameters allow the user to choose multiple detection models under a single detection type.

The transformer offers two broad approaches towards object detection: Haar feature-based cascade classifiers and Local Binary Patterns or LBP.

Haar feature-based cascade classifiers is an object detection method, where a cascade function is trained from a large sample of positive and negative images, from which features are extracted that describe the image. In this context, the word “cascade” indicates that the classifiers consist of a number of chained simpler classifiers. A very large set of defining features is required to classify or detect an object, therefore this method is generally slightly slower than LBP.



Local Binary Pattern utilizes differences between a particular cell and the surrounding neighbors, at a specified window size. For each cell, all the neighbors around the center cell are analyzed (first 1 cell away, then 2, etc.) and their difference with the center is calculated. The results are put in a histogram of frequency of each neighboring value occurring.


Categorized list of built in detection models:


Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

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. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Dialog Options - Tables

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

FME Community

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