# 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 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