libcamera/vivid.git - libcamera pipeline handler for VIVID

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author	Jacopo Mondi <jacopo@jmondi.org>	2021-01-05 13:12:38 +0100
committer	Jacopo Mondi <jacopo@jmondi.org>	2021-01-18 10:39:14 +0100
commit	db6a3bfa6e92081c33463ce58c51ac8fae7f854c (patch)
tree	1858a24c92b20c417becbf5d943a5e281ccf8f96 /src/qcam/assets/feathericons/wifi-off.svg
parent	a1a20998aca365a5835d534c49fc3cd4de7246ec (diff)

libcamera: camera_sensor: Initialize VIMC properties

The VIMC driver does not yet support all the features required for all sensor drivers. As it is the main testing platforms and the driver changes might take a long time to land in the developments and testing platforms, temporary close the gap by skipping driver validation and initializing properties with static information such as the sensor resolution. Reviewed-by: Niklas Söderlund <niklas.soderlund@ragnatech.se> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Jacopo Mondi <jacopo@jmondi.org>

Diffstat (limited to 'src/qcam/assets/feathericons/wifi-off.svg')

0 files changed, 0 insertions, 0 deletions

# SPDX-License-Identifier: BSD-2-Clause # # Copyright (C) 2019, Raspberry Pi (Trading) Limited # # ctt_geq.py - camera tuning tool for GEQ (green equalisation) from ctt_tools import * import matplotlib.pyplot as plt import scipy.optimize as optimize """ Uses green differences in macbeth patches to fit green equalisation threshold model. Ideally, all macbeth chart centres would fall below the threshold as these should be corrected by geq. """ def geq_fit(Cam, plot): imgs = Cam.imgs """ green equalisation to mitigate mazing. Fits geq model by looking at difference between greens in macbeth patches """ geqs = np.array([geq(Cam, Img)*Img.againQ8_norm for Img in imgs]) Cam.log += '\nProcessed all images' geqs = geqs.reshape((-1, 2)) """ data is sorted by green difference and top half is selected since higher green difference data define the decision boundary. """ geqs = np.array(sorted(geqs, key=lambda r: np.abs((r[1]-r[0])/r[0]))) length = len(geqs) g0 = geqs[length//2:, 0] g1 = geqs[length//2:, 1] gdiff = np.abs(g0-g1) """ find linear fit by minimising asymmetric least square errors in order to cover most of the macbeth images. the philosophy here is that every macbeth patch should fall within the threshold, hence the upper bound approach """ def f(params): m, c = params a = gdiff - (m*g0+c) """ asymmetric square error returns: 1.95 * a**2 if a is positive 0.05 * a**2 if a is negative """ return(np.sum(a**2+0.95*np.abs(a)*a)) initial_guess = [0.01, 500] """ Nelder-Mead is usually not the most desirable optimisation method but has been chosen here due to its robustness to undifferentiability (is that a word?) """ result = optimize.minimize(f, initial_guess, method='Nelder-Mead') """ need to check if the fit worked correectly """ if result.success: slope, offset = result.x Cam.log += '\nFit result: slope = {:.5f} '.format(slope) Cam.log += 'offset = {}'.format(int(offset)) """ optional plotting code """ if plot: x = np.linspace(max(g0)*1.1, 100) y = slope*x + offset plt.title('GEQ Asymmetric \'Upper Bound\' Fit') plt.plot(x, y, color='red', ls='--', label='fit') plt.scatter(g0, gdiff, color='b', label='data') plt.ylabel('Difference in green channels') plt.xlabel('Green value') """ This upper bound asymmetric gives correct order of magnitude values. The pipeline approximates a 1st derivative of a gaussian with some linear piecewise functions, introducing arbitrary cutoffs. For pessimistic geq, the model parameters have been increased by a scaling factor/constant. Feel free to tune these or edit the json files directly if you belive there are still mazing effects left (threshold too low) or if you think it is being overcorrected (threshold too high). We have gone for a one size fits most approach that will produce acceptable results in most applications. """ slope *= 1.5 offset += 201 Cam.log += '\nFit after correction factors: slope = {:.5f}'.format(slope) Cam.log += ' offset = {}'.format(int(offset)) """ clamp offset at 0 due to pipeline considerations """ if offset < 0: Cam.log += '\nOffset raised to 0' offset = 0 """ optional plotting code """ if plot: y2 = slope*x + offset plt.plot(x, y2, color='green', ls='--', label='scaled fit') plt.grid() plt.legend() plt.show() """ the case where for some reason the fit didn't work correctly Transpose data and then least squares linear fit. Transposing data makes it robust to many patches where green difference is the same since they only contribute to one error minimisation, instead of dragging the entire linear fit down. """ else: print('\nError! Couldn\'t fit asymmetric lest squares') print(result.message) Cam.log += '\nWARNING: Asymmetric least squares fit failed! ' Cam.log += 'Standard fit used could possibly lead to worse results' fit = np.polyfit(gdiff, g0, 1) offset, slope = -fit[1]/fit[0], 1/fit[0] Cam.log += '\nFit result: slope = {:.5f} '.format(slope) Cam.log += 'offset = {}'.format(int(offset)) """ optional plotting code """ if plot: x = np.linspace(max(g0)*1.1, 100) y = slope*x + offset plt.title('GEQ Linear Fit') plt.plot(x, y, color='red', ls='--', label='fit') plt.scatter(g0, gdiff, color='b', label='data') plt.ylabel('Difference in green channels') plt.xlabel('Green value') """ Scaling factors (see previous justification) The model here will not be an upper bound so scaling factors have been increased. This method of deriving geq model parameters is extremely arbitrary and undesirable. """ slope *= 2.5 offset += 301 Cam.log += '\nFit after correction factors: slope = {:.5f}'.format(slope) Cam.log += ' offset = {}'.format(int(offset)) if offset < 0: Cam.log += '\nOffset raised to 0' offset = 0 """ optional plotting code """ if plot: y2 = slope*x + offset plt.plot(x, y2, color='green', ls='--', label='scaled fit') plt.legend() plt.grid() plt.show() return round(slope, 5), int(offset) """" Return green channels of macbeth patches returns g0, g1 where > g0 is green next to red > g1 is green next to blue """ def geq(Cam, Img): Cam.log += '\nProcessing image {}'.format(Img.name) patches = [Img.patches[i] for i in Img.order][1:3] g_patches = np.array([(np.mean(patches[0][i]), np.mean(patches[1][i])) for i in range(24)]) Cam.log += '\n' return(g_patches)


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