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authorTomi Valkeinen <tomi.valkeinen@ideasonboard.com>2022-05-30 17:27:09 +0300
committerLaurent Pinchart <laurent.pinchart@ideasonboard.com>2022-06-01 12:08:51 +0300
commit0971ea7c8b89e911cd25c1f756197b113c0020af (patch)
tree487362eb1db0938d4e82976654ad18031b5cfbed /src/py/cam/helpers.py
parent679b73640a390cf294f33e865a39ea8e8390a0fd (diff)
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>
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+# 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:
+ # 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)
+
+ else:
+ rgb = None
+
+ return rgb
+
+
+# A naive format conversion to 24-bit RGB
+def mfb_to_rgb(mfb: libcamera.utils.MappedFrameBuffer, cfg: libcam.StreamConfiguration):
+ data = np.array(mfb.planes[0], dtype=np.uint8)
+ rgb = to_rgb(cfg.pixel_format, cfg.size, data)
+ return rgb