mirror of
https://git.libcamera.org/libcamera/libcamera.git
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py: cam: Move conversion funcs to helpers.py
Move conversion functions from cam_qt.py to helpers.py to clean up the code and so that they can be used from other cam renderers. Signed-off-by: Tomi Valkeinen <tomi.valkeinen@ideasonboard.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
This commit is contained in:
Tomi Valkeinen
Laurent Pinchart
parent
679b73640a
commit
0971ea7c8b
2 changed files with 161 additions and 155 deletions
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@ -1,16 +1,13 @@
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# SPDX-License-Identifier: GPL-2.0-or-later
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# SPDX-License-Identifier: GPL-2.0-or-later
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# Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com>
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# Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com>
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#
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# Debayering code from PiCamera documentation
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from helpers import mfb_to_rgb
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from io import BytesIO
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from io import BytesIO
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from numpy.lib.stride_tricks import as_strided
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from PIL import Image
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from PIL import Image
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from PIL.ImageQt import ImageQt
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from PIL.ImageQt import ImageQt
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from PyQt5 import QtCore, QtGui, QtWidgets
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from PyQt5 import QtCore, QtGui, QtWidgets
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import libcamera as libcam
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import libcamera as libcam
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import libcamera.utils
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import libcamera.utils
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import numpy as np
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import sys
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import sys
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@ -21,157 +18,6 @@ def rgb_to_pix(rgb):
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return pix
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return pix
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def demosaic(data, r0, g0, g1, b0):
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# Separate the components from the Bayer data to RGB planes
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rgb = np.zeros(data.shape + (3,), dtype=data.dtype)
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rgb[r0[1]::2, r0[0]::2, 0] = data[r0[1]::2, r0[0]::2] # Red
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rgb[g0[1]::2, g0[0]::2, 1] = data[g0[1]::2, g0[0]::2] # Green
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rgb[g1[1]::2, g1[0]::2, 1] = data[g1[1]::2, g1[0]::2] # Green
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rgb[b0[1]::2, b0[0]::2, 2] = data[b0[1]::2, b0[0]::2] # Blue
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# Below we present a fairly naive de-mosaic method that simply
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# calculates the weighted average of a pixel based on the pixels
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# surrounding it. The weighting is provided by a byte representation of
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# the Bayer filter which we construct first:
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bayer = np.zeros(rgb.shape, dtype=np.uint8)
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bayer[r0[1]::2, r0[0]::2, 0] = 1 # Red
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bayer[g0[1]::2, g0[0]::2, 1] = 1 # Green
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bayer[g1[1]::2, g1[0]::2, 1] = 1 # Green
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bayer[b0[1]::2, b0[0]::2, 2] = 1 # Blue
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# Allocate an array to hold our output with the same shape as the input
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# data. After this we define the size of window that will be used to
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# calculate each weighted average (3x3). Then we pad out the rgb and
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# bayer arrays, adding blank pixels at their edges to compensate for the
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# size of the window when calculating averages for edge pixels.
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output = np.empty(rgb.shape, dtype=rgb.dtype)
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window = (3, 3)
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borders = (window[0] - 1, window[1] - 1)
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border = (borders[0] // 2, borders[1] // 2)
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rgb = np.pad(rgb, [
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(border[0], border[0]),
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(border[1], border[1]),
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(0, 0),
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], 'constant')
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bayer = np.pad(bayer, [
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(border[0], border[0]),
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(border[1], border[1]),
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(0, 0),
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], 'constant')
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# For each plane in the RGB data, we use a nifty numpy trick
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# (as_strided) to construct a view over the plane of 3x3 matrices. We do
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# the same for the bayer array, then use Einstein summation on each
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# (np.sum is simpler, but copies the data so it's slower), and divide
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# the results to get our weighted average:
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for plane in range(3):
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p = rgb[..., plane]
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b = bayer[..., plane]
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pview = as_strided(p, shape=(
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p.shape[0] - borders[0],
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p.shape[1] - borders[1]) + window, strides=p.strides * 2)
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bview = as_strided(b, shape=(
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b.shape[0] - borders[0],
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b.shape[1] - borders[1]) + window, strides=b.strides * 2)
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psum = np.einsum('ijkl->ij', pview)
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bsum = np.einsum('ijkl->ij', bview)
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output[..., plane] = psum // bsum
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return output
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def to_rgb(fmt, size, data):
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w = size.width
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h = size.height
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if fmt == libcam.formats.YUYV:
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# YUV422
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yuyv = data.reshape((h, w // 2 * 4))
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# YUV444
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yuv = np.empty((h, w, 3), dtype=np.uint8)
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yuv[:, :, 0] = yuyv[:, 0::2] # Y
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yuv[:, :, 1] = yuyv[:, 1::4].repeat(2, axis=1) # U
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yuv[:, :, 2] = yuyv[:, 3::4].repeat(2, axis=1) # V
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m = np.array([
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[1.0, 1.0, 1.0],
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[-0.000007154783816076815, -0.3441331386566162, 1.7720025777816772],
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[1.4019975662231445, -0.7141380310058594, 0.00001542569043522235]
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])
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rgb = np.dot(yuv, m)
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rgb[:, :, 0] -= 179.45477266423404
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rgb[:, :, 1] += 135.45870971679688
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rgb[:, :, 2] -= 226.8183044444304
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rgb = rgb.astype(np.uint8)
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elif fmt == libcam.formats.RGB888:
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rgb = data.reshape((h, w, 3))
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rgb[:, :, [0, 1, 2]] = rgb[:, :, [2, 1, 0]]
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elif fmt == libcam.formats.BGR888:
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rgb = data.reshape((h, w, 3))
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elif fmt in [libcam.formats.ARGB8888, libcam.formats.XRGB8888]:
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rgb = data.reshape((h, w, 4))
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rgb = np.flip(rgb, axis=2)
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# drop alpha component
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rgb = np.delete(rgb, np.s_[0::4], axis=2)
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elif str(fmt).startswith('S'):
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fmt = str(fmt)
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bayer_pattern = fmt[1:5]
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bitspp = int(fmt[5:])
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# \todo shifting leaves the lowest bits 0
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if bitspp == 8:
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data = data.reshape((h, w))
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data = data.astype(np.uint16) << 8
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elif bitspp in [10, 12]:
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data = data.view(np.uint16)
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data = data.reshape((h, w))
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data = data << (16 - bitspp)
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else:
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raise Exception('Bad bitspp:' + str(bitspp))
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idx = bayer_pattern.find('R')
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assert(idx != -1)
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r0 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('G')
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assert(idx != -1)
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g0 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('G', idx + 1)
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assert(idx != -1)
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g1 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('B')
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assert(idx != -1)
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b0 = (idx % 2, idx // 2)
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rgb = demosaic(data, r0, g0, g1, b0)
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rgb = (rgb >> 8).astype(np.uint8)
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else:
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rgb = None
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return rgb
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# A naive format conversion to 24-bit RGB
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def mfb_to_rgb(mfb, cfg):
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data = np.array(mfb.planes[0], dtype=np.uint8)
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rgb = to_rgb(cfg.pixel_format, cfg.size, data)
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return rgb
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class QtRenderer:
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class QtRenderer:
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def __init__(self, state):
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def __init__(self, state):
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self.state = state
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self.state = state
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160
src/py/cam/helpers.py
Normal file
160
src/py/cam/helpers.py
Normal file
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@ -0,0 +1,160 @@
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# SPDX-License-Identifier: GPL-2.0-or-later
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# Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com>
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#
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# Debayering code from PiCamera documentation
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from numpy.lib.stride_tricks import as_strided
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import libcamera as libcam
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import libcamera.utils
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import numpy as np
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def demosaic(data, r0, g0, g1, b0):
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# Separate the components from the Bayer data to RGB planes
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rgb = np.zeros(data.shape + (3,), dtype=data.dtype)
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rgb[r0[1]::2, r0[0]::2, 0] = data[r0[1]::2, r0[0]::2] # Red
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rgb[g0[1]::2, g0[0]::2, 1] = data[g0[1]::2, g0[0]::2] # Green
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rgb[g1[1]::2, g1[0]::2, 1] = data[g1[1]::2, g1[0]::2] # Green
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rgb[b0[1]::2, b0[0]::2, 2] = data[b0[1]::2, b0[0]::2] # Blue
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# Below we present a fairly naive de-mosaic method that simply
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# calculates the weighted average of a pixel based on the pixels
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# surrounding it. The weighting is provided by a byte representation of
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# the Bayer filter which we construct first:
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bayer = np.zeros(rgb.shape, dtype=np.uint8)
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bayer[r0[1]::2, r0[0]::2, 0] = 1 # Red
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bayer[g0[1]::2, g0[0]::2, 1] = 1 # Green
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bayer[g1[1]::2, g1[0]::2, 1] = 1 # Green
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bayer[b0[1]::2, b0[0]::2, 2] = 1 # Blue
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# Allocate an array to hold our output with the same shape as the input
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# data. After this we define the size of window that will be used to
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# calculate each weighted average (3x3). Then we pad out the rgb and
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# bayer arrays, adding blank pixels at their edges to compensate for the
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# size of the window when calculating averages for edge pixels.
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output = np.empty(rgb.shape, dtype=rgb.dtype)
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window = (3, 3)
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borders = (window[0] - 1, window[1] - 1)
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border = (borders[0] // 2, borders[1] // 2)
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rgb = np.pad(rgb, [
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(border[0], border[0]),
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(border[1], border[1]),
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(0, 0),
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], 'constant')
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bayer = np.pad(bayer, [
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(border[0], border[0]),
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(border[1], border[1]),
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(0, 0),
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], 'constant')
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# For each plane in the RGB data, we use a nifty numpy trick
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# (as_strided) to construct a view over the plane of 3x3 matrices. We do
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# the same for the bayer array, then use Einstein summation on each
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# (np.sum is simpler, but copies the data so it's slower), and divide
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# the results to get our weighted average:
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for plane in range(3):
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p = rgb[..., plane]
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b = bayer[..., plane]
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pview = as_strided(p, shape=(
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p.shape[0] - borders[0],
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p.shape[1] - borders[1]) + window, strides=p.strides * 2)
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bview = as_strided(b, shape=(
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b.shape[0] - borders[0],
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b.shape[1] - borders[1]) + window, strides=b.strides * 2)
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psum = np.einsum('ijkl->ij', pview)
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bsum = np.einsum('ijkl->ij', bview)
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output[..., plane] = psum // bsum
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return output
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def to_rgb(fmt, size, data):
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w = size.width
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h = size.height
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if fmt == libcam.formats.YUYV:
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# YUV422
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yuyv = data.reshape((h, w // 2 * 4))
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# YUV444
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yuv = np.empty((h, w, 3), dtype=np.uint8)
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yuv[:, :, 0] = yuyv[:, 0::2] # Y
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yuv[:, :, 1] = yuyv[:, 1::4].repeat(2, axis=1) # U
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yuv[:, :, 2] = yuyv[:, 3::4].repeat(2, axis=1) # V
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m = np.array([
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[1.0, 1.0, 1.0],
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[-0.000007154783816076815, -0.3441331386566162, 1.7720025777816772],
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[1.4019975662231445, -0.7141380310058594, 0.00001542569043522235]
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])
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rgb = np.dot(yuv, m)
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rgb[:, :, 0] -= 179.45477266423404
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rgb[:, :, 1] += 135.45870971679688
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rgb[:, :, 2] -= 226.8183044444304
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rgb = rgb.astype(np.uint8)
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elif fmt == libcam.formats.RGB888:
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rgb = data.reshape((h, w, 3))
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rgb[:, :, [0, 1, 2]] = rgb[:, :, [2, 1, 0]]
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elif fmt == libcam.formats.BGR888:
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rgb = data.reshape((h, w, 3))
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elif fmt in [libcam.formats.ARGB8888, libcam.formats.XRGB8888]:
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rgb = data.reshape((h, w, 4))
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rgb = np.flip(rgb, axis=2)
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# drop alpha component
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rgb = np.delete(rgb, np.s_[0::4], axis=2)
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elif str(fmt).startswith('S'):
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fmt = str(fmt)
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bayer_pattern = fmt[1:5]
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bitspp = int(fmt[5:])
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# \todo shifting leaves the lowest bits 0
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if bitspp == 8:
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data = data.reshape((h, w))
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data = data.astype(np.uint16) << 8
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elif bitspp in [10, 12]:
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data = data.view(np.uint16)
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data = data.reshape((h, w))
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data = data << (16 - bitspp)
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else:
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raise Exception('Bad bitspp:' + str(bitspp))
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idx = bayer_pattern.find('R')
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assert(idx != -1)
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r0 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('G')
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assert(idx != -1)
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g0 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('G', idx + 1)
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assert(idx != -1)
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g1 = (idx % 2, idx // 2)
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idx = bayer_pattern.find('B')
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assert(idx != -1)
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b0 = (idx % 2, idx // 2)
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rgb = demosaic(data, r0, g0, g1, b0)
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rgb = (rgb >> 8).astype(np.uint8)
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else:
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rgb = None
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return rgb
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# A naive format conversion to 24-bit RGB
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def mfb_to_rgb(mfb: libcamera.utils.MappedFrameBuffer, cfg: libcam.StreamConfiguration):
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|
data = np.array(mfb.planes[0], dtype=np.uint8)
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|
rgb = to_rgb(cfg.pixel_format, cfg.size, data)
|
||||||
|
return rgb
|
||||||
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