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author	Jacopo Mondi <jacopo@jmondi.org>	2020-06-30 17:24:53 +0200
committer	Jacopo Mondi <jacopo@jmondi.org>	2020-08-03 11:16:17 +0200
commit	ed9fcf29e7d836fa1bd0b44931bfbe6369654a83 (patch)
tree	808ff4fdd723276e4617f0dd213acc991546ba8d /src/ipa/raspberrypi/controller/rpi/sharpen.cpp
parent	e0f8ce8454764ed27d5b1c2fcf22383f67def36b (diff)

libcamera: ipu3: Remove streams from IPU3CameraConfiguration

The IPU3CameraConfiguration::streams_ field was used to keep an association between the StreamConfiguration and the assigned streams before CameraConfiguration::setStream() was called at configure() time. The stream assignment was based on the order in which elements were inserted in the vector, implementing a fragile association between streams and their intended configurations. As it is now possible to assign streams at validation time, there is no need to keep that association in place, and the streams_ vector is now unused. Remove it and the associated accessor method from the IPU3CameraConfiguration class. Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Niklas Söderlund <niklas.soderlund@ragnatech.se> Signed-off-by: Jacopo Mondi <jacopo@jmondi.org>

Diffstat (limited to 'src/ipa/raspberrypi/controller/rpi/sharpen.cpp')

0 files changed, 0 insertions, 0 deletions

ixel 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