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							import imhr
							roi = imhr.eyetracking.ROI(isMultiprocessing=False, isDebug=True,
								# roi detection method and contour shape
								detection='manual', shape='straight',
								# path to read and export data
								image_path='./images', output_path='./output', metadata_source='metadata.xlsx',
								# stimuli size and screen size
								screensize=[1920,1080], recenter=[(1920*.50),(1080*.50)])
Python function to create Regions of Interest

imhr.eyetracking.ROI() can identify Region/Area of Interest using machine learning and image coding techniques.

 

Defining Region of Interest

With the imhr library, there are two methods of creating regions of interest using manually highlighted images or haar cascades.

 

Manual highlighting

The first method is manually highlighting each region of interest by using a image media file that is able to store multiple layers of content. The two file types supported here are Adobe Photoshop Document (psd) and Digital Imaging and Communications in Medicine (dcm) filetypes. Each ROI can be stored as a seperate layer, which can then processed for exporting.

 

Manually identified ROI in Adobe Photoshop

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Haar Cascades

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Face, eye, and smile identfication using haar cascades.

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Bounds

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Original images with manual contours added.
Contour Approximation (polygon) that will most closely match the orginal shape of the ROI.
Generated Convex Hull (hull), which is similar to but not as complex as a Contour Approximation and will include bulges for areas that are convex.
Circle creates a mininum enclosing circle, with the center equal to that of the ROI.
Bounding Rectangle (rotated), with the position matching that of the general shape of the ROI.
Bounding Rectangle (straight) parallel to the shape of the background image.

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Output

Two types of data are exported, contour shape and boundaries. Boundaries are saved in SR Research DataViewer (ias), and Microsoft Office Open XML Workbook (xlsx) formats, while contour shapes are saved in Hierarchical Data Format (HDF5; h5). Exported data can read either by SR Research DataViewer (ias) or any other data processing tool (xlsx, h5).

 

Both xlsx and HDF5 provide all metadata associated with each image, while ias files matches the format required by DataViewer. Of the three filetypes, HDF5 files are the most detailed providing both metadata for each ROI as well as exact pixel coordinates associated with each ROI.