diff options
| -rw-r--r-- | src/py/cam/cam_qt.py | 156 | ||||
| -rw-r--r-- | src/py/cam/helpers.py | 160 |
2 files changed, 161 insertions, 155 deletions
diff --git a/src/py/cam/cam_qt.py b/src/py/cam/cam_qt.py index c294c999..61a77f45 100644 --- a/src/py/cam/cam_qt.py +++ b/src/py/cam/cam_qt.py @@ -1,16 +1,13 @@ # SPDX-License-Identifier: GPL-2.0-or-later # Copyright (C) 2022, Tomi Valkeinen <tomi.valkeinen@ideasonboard.com> -# -# Debayering code from PiCamera documentation +from helpers import mfb_to_rgb 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 @@ -21,157 +18,6 @@ def rgb_to_pix(rgb): 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) - - else: - rgb = None - - return rgb - - -# A naive format conversion to 24-bit RGB -def mfb_to_rgb(mfb, cfg): - data = np.array(mfb.planes[0], dtype=np.uint8) - rgb = to_rgb(cfg.pixel_format, cfg.size, data) - return rgb - - class QtRenderer: def __init__(self, state): self.state = state diff --git a/src/py/cam/helpers.py b/src/py/cam/helpers.py new file mode 100644 index 00000000..6b32a134 --- /dev/null +++ b/src/py/cam/helpers.py @@ -0,0 +1,160 @@ +# 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 |