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

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author	David Plowman <david.plowman@raspberrypi.com>	2020-11-23 07:38:00 +0000
committer	Kieran Bingham <kieran.bingham@ideasonboard.com>	2020-11-23 14:24:32 +0000
commit	23ada683db6a33ee848dd9d3061d252e6c9e5dfd (patch)
tree	9876905f00fe65b4c729a6c8bc1473d80df72538 /src/ipa/raspberrypi/controller
parent	f185a168335edaebb5f661070d579b819bbc4ad2 (diff)

libcamera: ipa: raspberrypi: awb: Add SwitchMode method to output AWB status

The Awb class now implements a SwitchMode method which outputs its AwbStatus for other algorithms to read, should they be interested. Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>

Diffstat (limited to 'src/ipa/raspberrypi/controller')

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src/ipa/raspberrypi/controller/rpi/awb.cpp

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src/ipa/raspberrypi/controller/rpi/awb.hpp

2 files changed, 15 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 io import BytesIO from numpy.lib.stride_tricks import as_strided from PIL import Image from PIL.ImageQt import ImageQt from PyQt5 import QtCore, QtGui, QtWidgets import libcamera as libcam import libcamera.utils import numpy as np import sys def rgb_to_pix(rgb): img = Image.frombuffer('RGB', (rgb.shape[1], rgb.shape[0]), rgb) qim = ImageQt(img).copy() pix = QtGui.QPixmap.fromImage(qim) return pix 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: # YUV422 yuyv = data.reshape((h, w // 2 * 4)) # YUV444 yuv = np.empty((h, w, 3), dtype=np.uint8) yuv[:, :, 0] = yuyv[:, 0::2] # Y yuv[:, :, 1] = yuyv[:, 1::4].repeat(2, axis=1) # U yuv[:, :, 2] = yuyv[:, 3::4].repeat(2, axis=1) # V m = np.array([ [1.0, 1.0, 1.0], [-0.000007154783816076815, -0.3441331386566162, 1.7720025777816772], [1.4019975662231445, -0.7141380310058594, 0.00001542569043522235] ]) rgb = np.dot(yuv, m) rgb[:, :, 0] -= 179.45477266423404 rgb[:, :, 1] += 135.45870971679688 rgb[:, :, 2] -= 226.8183044444304 rgb = rgb.astype(np.uint8) elif fmt == libcam.formats.RGB888: rgb = data.reshape((h, w, 3)) rgb[:, :, [0, 1, 2]] = rgb[:, :, [2, 1, 0]] elif fmt == libcam.formats.BGR888: rgb = data.reshape((h, w, 3)) elif fmt in [libcam.formats.ARGB8888, libcam.formats.XRGB8888]: rgb = data.reshape((h, w, 4)) rgb = np.flip(rgb, axis=2) # drop alpha component rgb = np.delete(rgb, np.s_[0::4], axis=2) elif str(fmt).startswith('S'): fmt = str(fmt) bayer_pattern = fmt[1:5] bitspp = int(fmt[5:]) # \todo shifting leaves the lowest bits 0 if bitspp == 8: data = data.reshape((h, w)) data = data.astype(np.uint16) << 8 elif bitspp in [10, 12]: data = data.view(np.uint16) data = data.reshape((h, w)) data = data << (16 - bitspp) else: raise Exception('Bad bitspp:' + str(bitspp)) idx = bayer_pattern.find('R') assert(idx != -1) r0 = (idx % 2, idx // 2) idx = bayer_pattern.find('G') assert(idx != -1) g0 = (idx % 2, idx // 2) idx = bayer_pattern.find('G', idx + 1) assert(idx != -1) g1 = (idx % 2, idx // 2) idx = bayer_pattern.find('B') assert(idx != -1) b0 = (idx % 2, idx // 2) rgb = demosaic(data, r0, g0, g1, b0) rgb = (rgb >> 8).astype(np.uint8)


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