libcamera/jmondi/libcamera.git - Jacopo Mondi's clone of libcamera

Jacopo Mondi's clone of libcamera

git repository hosting on libcamera.org

Age	Commit message (Expand)	Author
2021-11-24	cam: Convert to pragma once	Kieran Bingham
2021-08-05	cam: Add support for viewfinder through DRM/KMS	Laurent Pinchart
2021-05-06	cam: Add option to print the Request metadata	Jacopo Mondi
2020-08-03	cam: Add --monitor option	Umang Jain
2020-07-27	cam: Add option to disallow adjusting of requested formats	Niklas Söderlund
2020-04-26	cam: Add an option to list camera controls	Laurent Pinchart
2020-04-26	cam: Rename OptProps to OptListProperties	Laurent Pinchart
2020-03-06	cam: Add option to list camera properties	Jacopo Mondi
2019-06-19	cam: Add --info option to print information about stream(s)	Niklas Söderlund
2019-05-25	cam: capture: Break out capture to a new class	Niklas Söderlund

str"> noise std = a + b*sqrt(pixel mean) """ def noise(Cam, Img, plot): Cam.log += '\nProcessing image: {}'.format(Img.name) stds = [] means = [] """ iterate through macbeth square patches """ for ch_patches in Img.patches: for patch in ch_patches: """ renormalise patch """ patch = np.array(patch) patch = (patch-Img.blacklevel_16)/Img.againQ8_norm std = np.std(patch) mean = np.mean(patch) stds.append(std) means.append(mean) """ clean data and ensure all means are above 0 """ stds = np.array(stds) means = np.array(means) means = np.clip(np.array(means), 0, None) sq_means = np.sqrt(means) """ least squares fit model """ fit = np.polyfit(sq_means, stds, 1) Cam.log += '\nBlack level = {}'.format(Img.blacklevel_16) Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1])) Cam.log += ' slope = {:.3f}'.format(fit[0]) """ remove any values further than std from the fit anomalies most likely caused by: > ucharacteristically noisy white patch > saturation in the white patch """ fit_score = np.abs(stds - fit[0]*sq_means - fit[1]) fit_std = np.std(stds) fit_score_norm = fit_score - fit_std anom_ind = np.where(fit_score_norm > 1) fit_score_norm.sort() sq_means_clean = np.delete(sq_means, anom_ind) stds_clean = np.delete(stds, anom_ind) removed = len(stds) - len(stds_clean) if removed != 0: Cam.log += '\nIdentified and removed {} anomalies.'.format(removed) Cam.log += '\nRecalculating fit' """ recalculate fit with outliers removed """ fit = np.polyfit(sq_means_clean, stds_clean, 1) Cam.log += '\nNoise profile: offset = {}'.format(int(fit[1])) Cam.log += ' slope = {:.3f}'.format(fit[0]) """ if fit const is < 0 then force through 0 by dividing by sq_means and fitting poly order 0 """ corrected = 0 if fit[1] < 0: corrected = 1 ones = np.ones(len(means)) y_data = stds/sq_means fit2 = np.polyfit(ones, y_data, 0) Cam.log += '\nOffset below zero. Fit recalculated with zero offset' Cam.log += '\nNoise profile: offset = 0' Cam.log += ' slope = {:.3f}'.format(fit2[0]) # print('new fit') # print(fit2) """ plot fit for debug """ if plot: x = np.arange(sq_means.max()//0.88) fit_plot = x*fit[0] + fit[1] plt.scatter(sq_means, stds, label='data', color='blue') plt.scatter(sq_means[anom_ind], stds[anom_ind], color='orange', label='anomalies') plt.plot(x, fit_plot, label='fit', color='red', ls=':') if fit[1] < 0: fit_plot_2 = x*fit2[0] plt.plot(x, fit_plot_2, label='fit 0 intercept', color='green', ls='--') plt.plot(0, 0) plt.title('Noise Plot\nImg: {}'.format(Img.str)) plt.legend(loc='upper left') plt.xlabel('Sqrt Pixel Value') plt.ylabel('Noise Standard Deviation') plt.grid() plt.show() """ End of plotting code """ """ format output to include forced 0 constant """ Cam.log += '\n' if corrected: fit = [fit2[0], 0] return fit else: return fit