imgProcessor.measure.SNR package¶

Submodules¶

imgProcessor.measure.SNR.SNR module¶

imgProcessor.measure.SNR.SNR.SNR(img1, img2=None, bg=0, noise_level_function=None, constant_noise_level=False, imgs_to_be_averaged=False)[source]¶

Returns a signal-to-noise-map uses algorithm as described in BEDRICH 2016 JPV (not jet published)

Parameters:	constant_noise_level – True, to assume noise to be constant imgs_to_be_averaged – True, if SNR is for average(img1, img2)

imgProcessor.measure.SNR.SNR.getBackgroundLevel(img)[source]¶

imgProcessor.measure.SNR.SNR_IEC module¶

imgProcessor.measure.SNR.SNR_IEC.SNR_IEC(i1, i2, ibg=0, allow_color_images=False)[source]¶

Calculate the averaged signal-to-noise ratio SNR50 as defined by IEC NP 60904-13

needs 2 reference EL images and one background image

imgProcessor.measure.SNR.SNRaverage module¶

imgProcessor.measure.SNR.SNRaverage.SNRaverage(snr, method='X75', excludeBackground=True, checkBackground=True, backgroundLevel=None)[source]¶: average a signal-to-noise map :param method: [‘average’,’X75’, ‘RMS’, ‘median’] - X75: this SNR will be exceeded by 75% of the signal :type method: str :param checkBackground: check whether there is actually a background level to exclude :type checkBackground: bool :returns: averaged SNR as float