libcamera/vivid.git - libcamera pipeline handler for VIVID

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author	David Plowman <david.plowman@raspberrypi.com>	2021-06-28 13:23:22 +0100
committer	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2021-06-28 19:33:42 +0300
commit	bdf04cca086eb72a9e0528ba90a5c53d96e52a01 (patch)
tree	dbe4f65e6e61fe36cfdf445907983718c070aa05 /src/qcam/assets/feathericons/cloud-lightning.svg
parent	8738d539f4a350d51bc1f6b780cc1fdfd62cf4ec (diff)

libcamera: Add support for monochrome sensors

This commit adds support for monochrome (greyscale) raw sensors. These are sensors that have no colour filter array, so all pixels are the same and there are no distinct colour channels. These sensors still require many of an ISP's processing stages, such as denoise, tone mapping, but not those that involve colours (such as demosaic, or colour matrices). Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>

Diffstat (limited to 'src/qcam/assets/feathericons/cloud-lightning.svg')

0 files changed, 0 insertions, 0 deletions

# SPDX-License-Identifier: BSD-2-Clause # # Copyright (C) 2019, Raspberry Pi Ltd # # camera tuning tool for CCM (colour correction matrix) from ctt_image_load import * from ctt_awb import get_alsc_patches import colors from scipy.optimize import minimize from ctt_visualise import visualise_macbeth_chart import numpy as np """ takes 8-bit macbeth chart values, degammas and returns 16 bit """ ''' This program has many options from which to derive the color matrix from. The first is average. This minimises the average delta E across all patches of the macbeth chart. Testing across all cameras yeilded this as the most color accurate and vivid. Other options are avalible however. Maximum minimises the maximum Delta E of the patches. It iterates through till a minimum maximum is found (so that there is not one patch that deviates wildly.) This yields generally good results but overall the colors are less accurate Have a fiddle with maximum and see what you think. The final option allows you to select the patches for which to average across. This means that you can bias certain patches, for instance if you want the reds to be more accurate. ''' matrix_selection_types = ["average", "maximum", "patches"] typenum = 0 # select from array above, 0 = average, 1 = maximum, 2 = patches test_patches = [1, 2, 5, 8, 9, 12, 14] ''' Enter patches to test for. Can also be entered twice if you would like twice as much bias on one patch. ''' def degamma(x): x = x / ((2 ** 8) - 1) # takes 255 and scales it down to one x = np.where(x < 0.04045, x / 12.92, ((x + 0.055) / 1.055) ** 2.4) x = x * ((2 ** 16) - 1) # takes one and scales up to 65535, 16 bit color return x def gamma(x): # Take 3 long array of color values and gamma them return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x] """ FInds colour correction matrices for list of images """ def ccm(Cam, cal_cr_list, cal_cb_list, grid_size): global matrix_selection_types, typenum imgs = Cam.imgs """ standard macbeth chart colour values """ m_rgb = np.array([ # these are in RGB [116, 81, 67], # dark skin [199, 147, 129], # light skin [91, 122, 156], # blue sky [90, 108, 64], # foliage [130, 128, 176], # blue flower [92, 190, 172], # bluish green [224, 124, 47], # orange [68, 91, 170], # purplish blue [198, 82, 97], # moderate red [94, 58, 106], # purple [159, 189, 63], # yellow green [230, 162, 39], # orange yellow [35, 63, 147], # blue [67, 149, 74], # green [180, 49, 57], # red [238, 198, 20], # yellow [193, 84, 151], # magenta [0, 136, 170], # cyan (goes out of gamut) [245, 245, 243], # white 9.5 [200, 202, 202], # neutral 8 [161, 163, 163], # neutral 6.5 [121, 121, 122], # neutral 5 [82, 84, 86], # neutral 3.5 [49, 49, 51] # black 2 ]) """ convert reference colours from srgb to rgb """ m_srgb = degamma(m_rgb) # now in 16 bit color. # Produce array of LAB values for ideal color chart m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb] """ reorder reference values to match how patches are ordered """ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3)) m_lab = np.array([m_lab[i::6] for i in range(6)]).reshape((24, 3)) m_rgb = np.array([m_rgb[i::6] for i in range(6)]).reshape((24, 3)) """ reformat alsc correction tables or set colour_cals to None if alsc is deactivated """ if cal_cr_list is None: colour_cals = None else: colour_cals = {} for cr, cb in zip(cal_cr_list, cal_cb_list): cr_tab = cr['table'] cb_tab = cb['table'] """ normalise tables so min value is 1 """ cr_tab = cr_tab / np.min(cr_tab) cb_tab = cb_tab / np.min(cb_tab) colour_cals[cr['ct']] = [cr_tab, cb_tab] """ for each image, perform awb and alsc corrections. Then calculate the colour correction matrix for that image, recording the ccm and the colour tempertaure. """ ccm_tab = {} for Img in imgs: Cam.log += '\nProcessing image: ' + Img.name """ get macbeth patches with alsc applied if alsc enabled. Note: if alsc is disabled then colour_cals will be set to None and no the function will simply return the macbeth patches """ r, b, g = get_alsc_patches(Img, colour_cals, grey=False, grid_size=grid_size) """ do awb Note: awb is done by measuring the macbeth chart in the image, rather than from the awb calibration. This is done so the awb will be perfect and the ccm matrices will be more accurate. """ r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4] r_g = np.mean(r_greys / g_greys) b_g = np.mean(b_greys / g_greys) r = r / r_g b = b / b_g """ normalise brightness wrt reference macbeth colours and then average each channel for each patch """ gain = np.mean(m_srgb) / np.mean((r, g, b))


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