src/ipa/ipu3/algorithms/tone_mapping.h


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/* SPDX-License-Identifier: LGPL-2.1-or-later */
/*
 * Copyright (C) 2021, Google inc.
 *
 * IPU3 ToneMapping and Gamma control
 */

#pragma once

#include "algorithm.h"

namespace libcamera {

namespace ipa::ipu3::algorithms {

class ToneMapping : public Algorithm
{
public:
	ToneMapping();

	int configure(IPAContext &context, const IPAConfigInfo &configInfo) override;
	void prepare(IPAContext &context, const uint32_t frame,
		     IPAFrameContext &frameContext, ipu3_uapi_params *params) override;
	void process(IPAContext &context, const uint32_t frame,
		     IPAFrameContext &frameContext,
		     const ipu3_uapi_stats_3a *stats,
		     ControlList &metadata) override;

private:
	double gamma_;
};

} /* namespace ipa::ipu3::algorithms */

} /* namespace libcamera */
# 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)