calibration Module¶

eyetracker.analysis.calibration.get_resolution()[source]¶

Checks current screen resolution

Only to be used in Linux and when PySide is not in use, as there the size of the screen can be queried from app = QtGui.QApplication(sys.argv) with app.dektop().screenGeometry().

Function uses Linux command “xrandr” to query the system about current resoulution.

Returns:	size : tuple of ints (width, height) size of screen in pixels

eyetracker.analysis.calibration.where_circles(resolution=False, rad=30)[source]¶

Counts where the circles should be drawn.

Parameters:	resolution : tuple ints (width, height) rad : int radius of the biggest circle that will be drawn
Returns:	coordinates : tuples coordinates of nine places where the circles will be drawn

detect Module¶

eyetracker.analysis.detect.glint(image, maxCorners=2, quality=0.0001, minDist=20, mask=None, blockSize=3)[source]¶

Function detects glint on the retina.

Based on the funccv2.goodFeaturesToTrack. For more info on parameters check the docs for opencv and goofFeaturesToTrack.

Parameters:

image : np.array image of the eye where the glints are supposed to be detected maxCorners : int how many glints should it detect, default is 2 quality : : minimal accepted quality of image corners minDist : : minimum distance between the detected glints, default is 20 mask : : area of the image that should be used for glint detection default is None so it looks through the whole picture blockSize : : size of an average block for computing a derivative covariation : : matrix over each pixel neighborhood

Returns:

where : array ccoordinates(a list [x, y]) for found glints

eyetracker.analysis.detect.pupil(image, dp=1, minDist=100, param1=50, param2=10, minRadius=20, maxRadius=70)[source]¶

Function detects pupil on the image of an eye.

Based on the funccv2.HoughCircles. for more info on parameters check the docs for opencv and HoughCircles.

Parameters:

image - np.array : image of the eye where the pupil is supposed to be detected dp : int inverse ratio of the accumulator resolution to the image’s, minDist : int minimum distance between found circles param1 : int higher threshold for the Canny edge detector param2 : int accumulator threshold for the circles centers, smaller it is, more false positives minRadius : int minimal radius of the detected circle maxRadius : int maximal radius of the detected circle

Returns:

where : array coordinates and the radius of the circle (a list [x, y, z]) for found pupils

processing Module¶

eyetracker.analysis.processing.adaptiveThreshold(image, max_v=255, adaptiveMethod='gaussian', thresh_type='bin', blockSize=33, subtConstant=10)[source]¶

Threshold the image using adaptive methods.

For corresponding adaptive methods see docs.opencv.org: {‘gaussian’ : cv2.ADAPTIVE_THRESH_GAUSSIAN_C, ‘mean’ : cv2.ADAPTIVE_THRESH_MEAN_C}

Parameters:

image : np.array 2D array depicting an image in one-scale color(ex. grayscale) max_v : int maximal value to be used in threshold adaptiveMethod : string method used for thresholding, possible ‘gaussian’ or ‘mean’ thresh_type : string prethresholding, possible thresholds ‘bin’(binary) or ‘bin_inv’(inversed binary) blockSize : int Size of a pixel neighborhood that is used to calculate a threshold value, the size must be an odd number. subtConstant : int a constant that will be subtracted from mean or weighted mean (depending on adaptiveMethod chosen)

Returns:

thresholded : np.array image array processed accordingly.

eyetracker.analysis.processing.bgr2gray(imageBGR)[source]¶

Convert color image(BGR) to gray image.

Parameters:	image : np.array 2d 24-bit array depicting an image in three-channel color: Blue,Green,Red
Returns:	image : np.array 2d 8-bit array depicting a given image converted to gray scale.

eyetracker.analysis.processing.find_purkinje(purkinje1, purkinje2)[source]¶

Find virtual purkinje image in a two IR LED setting.

Simple finding of the middle between two points.

Parameters:	purkinje1 : tuple of (int, int) the coordinates of first purkinje image purkinje2 : tuple of (int, int) the coordinates of second purkinje image
Returns:	middle : tuple of (int, int) which are the coordinates of the middle between purkinje1 and purkinje2.

eyetracker.analysis.processing.gray2bgr(imageGRAY)[source]¶

Convert gray image to color image(BGR).

Parameters:	image : np.array 2d 8-bit array depicting an image in one-channel color (greyscale)
Returns:	image : np.array 2d 24-bit array depicting a given image converted to three-channel color scale: Blue,Green,Red.

eyetracker.analysis.processing.imageFlipMirror(im, mirrored, flipped)[source]¶

Flip and/or mirror the given image.

Parameters:	im : np.array 2D array depicting an image as an numpy array mirrored : np.array self explanatory boolean parameter (left - right) fliped np.array : self explanatory boolean parameter (top - bottom)
Returns:	im : np.array image array processed accordingly.

eyetracker.analysis.processing.mark(image, where, radius=10, color='red', thickness=3)[source]¶

Mark object with a circle.

Parameters:

image : np.array 3d array depicting the original image that is to be marked, needs to be in three scale color where : np.array array of sets of coordinates (x, y or x, y, radius) of the object radius : int set same radius for all objects, if a set of coordinates has a third value this will be overruled color : string color of circles marking the object, possible: ‘blue’, ‘green’ or ‘red’ thickness : int thickness of the circle

Returns:

true : True

eyetracker.analysis.processing.runningAverage(image, average, alpha)[source]¶

Calculates running average of given pictures stream.

Using cv2.accumulateWeighted.

Parameters:	image : np.array new image to be averaged along with past image stream, average : np.array past averaged image, alpha : int control parameter of the running average, it describes how fast previous images would be forgotten, 1 - no average, 0 - never forget anything.
Returns:	image : np.array averaged image as numpy array.

eyetracker.analysis.processing.threshold(image, thresh_v=30, max_v=255, thresh_type='trunc')[source]¶

Threshold the image.

For corresponding threshold types description see docs.opencv.org: {‘otsu’ : cv2.THRESH_OTSU, ‘bin’ : cv2.THRESH_BINARY, ‘bin_inv’ : cv2.THRESH_BINARY_INV, ‘zero’ : cv2.THRESH_TOZERO, ‘zero_inv’ : cv2.THRESH_TOZERO_INV, ‘trunc’ : cv2.THRESH_TRUNC}

Parameters:	image : np.array 2d 8-bit array depicting an image in one-channel color(ex. grayscale) thresh_v : int value of threshold cut-off max_v : int maximal value when thresholding, relevant only if thresh_type is ‘bin’ or ‘bin_inv’ thresh_type : string type of thresholding, possible ‘otsu’, ‘bin’, ‘bin_inv’, ‘zero’, ‘zero_inv’, ‘trunc’
Returns:	thresholded_image : np.array given image after aplication of a given threshold.