libcamera/libcamera.git - libcamera official repository

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author	Paul Elder <paul.elder@ideasonboard.com>	2022-07-21 01:40:04 +0900
committer	Laurent Pinchart <laurent.pinchart@ideasonboard.com>	2022-11-25 10:52:06 +0200
commit	4c30ed47f656d67b619470b9091ebf6661b8a778 (patch)
tree	d40635f78715e841a0ea58d048ce313dbc6808d5 /include/linux/v4l2-mediabus.h
parent	72721611fa5d772a3a2b7cd037e38c5db13dc265 (diff)

pipeline: rkisp1: Query the driver for formats

Query the driver for the output formats and sizes that it supports, instead of hardcoding them. This allows future-proofing for formats that are supported by some but not all versions of the driver. As the rkisp1 driver currently does not support VIDIOC_ENUM_FRAMESIZES, fallback to the hardcoded list of supported formats and framesizes. This feature will be added to the driver in parallel, though we cannot guarantee that users will have a new enough kernel for it. Signed-off-by: Paul Elder <paul.elder@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Jacopo Mondi <jacopo@jmondi.org>

Diffstat (limited to 'include/linux/v4l2-mediabus.h')

0 files changed, 0 insertions, 0 deletions

# SPDX-License-Identifier: GPL-2.0-or-later # Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com> # # Debayering code from PiCamera documentation from numpy.lib.stride_tricks import as_strided import libcamera as libcam import libcamera.utils import numpy as np def demosaic(data, r0, g0, g1, b0): # Separate the components from the Bayer data to RGB planes rgb = np.zeros(data.shape + (3,), dtype=data.dtype) rgb[r0[1]::2, r0[0]::2, 0] = data[r0[1]::2, r0[0]::2] # Red rgb[g0[1]::2, g0[0]::2, 1] = data[g0[1]::2, g0[0]::2] # Green rgb[g1[1]::2, g1[0]::2, 1] = data[g1[1]::2, g1[0]::2] # Green rgb[b0[1]::2, b0[0]::2, 2] = data[b0[1]::2, b0[0]::2] # Blue # Below we present a fairly naive de-mosaic method that simply # calculates the weighted average of a pixel based on the pixels # surrounding it. The weighting is provided by a byte representation of # the Bayer filter which we construct first: bayer = np.zeros(rgb.shape, dtype=np.uint8) bayer[r0[1]::2, r0[0]::2, 0] = 1 # Red bayer[g0[1]::2, g0[0]::2, 1] = 1 # Green bayer[g1[1]::2, g1[0]::2, 1] = 1 # Green bayer[b0[1]::2, b0[0]::2, 2] = 1 # Blue # Allocate an array to hold our output with the same shape as the input # data. After this we define the size of window that will be used to # calculate each weighted average (3x3). Then we pad out the rgb and # bayer arrays, adding blank pixels at their edges to compensate for the # size of the window when calculating averages for edge pixels. output = np.empty(rgb.shape, dtype=rgb.dtype) window = (3, 3) borders = (window[0] - 1, window[1] - 1) border = (borders[0] // 2, borders[1] // 2) rgb = np.pad(rgb, [ (border[0], border[0]), (border[1], border[1]), (0, 0), ], 'constant') bayer = np.pad(bayer, [ (border[0], border[0]), (border[1], border[1]), (0, 0), ], 'constant') # For each plane in the RGB data, we use a nifty numpy trick # (as_strided) to construct a view over the plane of 3x3 matrices. We do # the same for the bayer array, then use Einstein summation on each # (np.sum is simpler, but copies the data so it's slower), and divide # the results to get our weighted average: for plane in range(3): p = rgb[..., plane] b = bayer[..., plane] pview = as_strided(p, shape=( p.shape[0] - borders[0], p.shape[1] - borders[1]) + window, strides=p.strides * 2) bview = as_strided(b, shape=( b.shape[0] - borders[0], b.shape[1] - borders[1]) + window, strides=b.strides * 2) psum = np.einsum('ijkl->ij', pview) bsum = np.einsum('ijkl->ij', bview) output[..., plane] = psum // bsum return output def to_rgb(fmt, size, data): w = size.width h = size.height if fmt == libcam.formats.YUYV:


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