imhr.eyetracking._roi¶

Classes

class imhr.eyetracking._roi.ROI(isMultiprocessing=False, detection='manual', image_path=None, output_path=None, metadata_source=None, roi_format='both', shape='box', roicolumn='roi', uuid=None, filetype='psd', **kwargs)[source]¶

Bases: object

Generate regions of interest that can be used for data processing and analysis.

Methods

Methods

draw_contours(filepath[, img])	[summary]
export_data(df, path, filename[, uuid, …])	[summary]
extract_contours(image, imagename, roiname)	[summary]
extract_metadata(imagename, imgtype, layer)	Extract metadata for each region of interest.
finished(df[, errors])	Process bounds for all images.
format_contours(imagename, metadata, …)	[summary]
format_image([image, imgtype, isRaw, …])	Resize image and reposition image, relative to screensize.
haarcascade(directory[, core, queue])	[summary]
manual_detection(directory[, core, queue])	[summary]
process()	[summary]

draw_contours(filepath[, img])	[summary]
export_data(df, path, filename[, uuid, …])	[summary]
extract_contours(image, imagename, roiname)	[summary]
extract_metadata(imagename, imgtype, layer)	Extract metadata for each region of interest.
finished(df[, errors])	Process bounds for all images.
format_contours(imagename, metadata, …)	[summary]
format_image([image, imgtype, isRaw, …])	Resize image and reposition image, relative to screensize.
haarcascade(directory[, core, queue])	[summary]
manual_detection(directory[, core, queue])	[summary]
process()	[summary]

classmethod extract_metadata(imagename, imgtype, layer)[source]¶

Extract metadata for each region of interest.

Parameters:	imagename : [type] [description] imgtype : [type] [description] layer : [type] [description]
Returns:	[type] [description] [type] [description]

classmethod format_image(image=None, imgtype='psd', isRaw=False, isPreprocessed=False, isNormal=False, isHaar=False)[source]¶

Resize image and reposition image, relative to screensize.

Parameters:	IMG : None or Can be either: psd_tools.PSDImage Photoshop PSD/PSB file object. The file should include one layer for each region of interest, by default None imgtype : str {‘psd’,’dcm’,’tiff’, ‘bitmap’} Image type. isRaw : None or ###, optional If True, the image will be returned without resizing or placed on top of a background image. Default is False. isPreprocessed : None or ###, optional If True, the image will be returned with resizing and placed on top of a background image. Default is False.
Attributes:	image : PIL.Image.Image PIL image object class.
Returns:	image, background : PIL.Image.Image PIL image object class.

classmethod extract_contours(image, imagename, roiname)[source]¶

[summary]

Parameters:	image : [type] [description] imagename : [type] [description] roiname : [type] [description]
Returns:	[type] [description]
Raises:	Exception [description] Exception [description]

classmethod format_contours(imagename, metadata, roiname, roinumber, bounds, coords)[source]¶

[summary]

Parameters:	imagename : [type] [description] metadata : [type] [description] roiname : [type] [description] roinumber : [type] [description] roilabel : [type] [description] bounds_ : [type] [description] contours_ : [type] [description]
Returns:	[type] [description] [type] [description]
Raises:	Exception [description]

classmethod draw_contours(filepath, img=None)[source]¶

[summary]

Parameters:	filepath : [type] [description] data : [type] [description] fig : [type] [description] source : str, optional [description], by default ‘bounds’

classmethod export_data(df, path, filename, uuid=None, newcolumn=None, level='image')[source]¶

[summary]

Parameters:	df : [type] Bounds. path : [type] [description] filename : [type] [description] uuid : [type], optional [description], by default None newcolumn : [type], optional [description], by default None nested : string {image,`all`} Nested order, either image or all. Default is image.
Returns:	[type] [description]

classmethod manual_detection(directory, core=0, queue=None)[source]¶

[summary]

Parameters:	directory : list [description] core : int (if isMultiprocessing) Core used for this function. Default is 0. queue : queue.Queue Constructor for a multiprocessing ‘first-in, first-out’ queue. Note: Queues are thread and process safe.
Returns:	[type] [description]

classmethod haarcascade(directory, core=0, queue=None)[source]¶

[summary]

Parameters:	directory : [type] [description] core : int, optional [description], by default 0 queue : [type], optional [description], by default None
Returns:	[type] [description]
Raises:	Exception [description]

classmethod process()[source]¶

[summary]

Returns:	[type] [description]

classmethod finished(df, errors=None)[source]¶

Process bounds for all images.

Parameters:	df : [type] [description] errors : [type], optional [description], by default None