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path: root/src/android/jpeg/thumbnailer.cpp
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/* SPDX-License-Identifier: LGPL-2.1-or-later */
/*
 * Copyright (C) 2020, Google Inc.
 *
 * thumbnailer.cpp - Simple image thumbnailer
 */

#include "thumbnailer.h"

#include <libcamera/base/log.h>

#include <libcamera/formats.h>

#include "libcamera/internal/mapped_framebuffer.h"

using namespace libcamera;

LOG_DEFINE_CATEGORY(Thumbnailer)

Thumbnailer::Thumbnailer()
	: valid_(false)
{
}

void Thumbnailer::configure(const Size &sourceSize, PixelFormat pixelFormat)
{
	sourceSize_ = sourceSize;
	pixelFormat_ = pixelFormat;

	if (pixelFormat_ != formats::NV12) {
		LOG(Thumbnailer, Error)
			<< "Failed to configure: Pixel Format "
			<< pixelFormat_ << " unsupported.";
		return;
	}

	valid_ = true;
}

void Thumbnailer::createThumbnail(const FrameBuffer &source,
				  const Size &targetSize,
				  std::vector<unsigned char> *destination)
{
	MappedFrameBuffer frame(&source, MappedFrameBuffer::MapFlag::Read);
	if (!frame.isValid()) {
		LOG(Thumbnailer, Error)
			<< "Failed to map FrameBuffer : "
			<< strerror(frame.error());
		return;
	}

	if (!valid_) {
		LOG(Thumbnailer, Error) << "Config is unconfigured or invalid.";
		return;
	}

	const unsigned int sw = sourceSize_.width;
	const unsigned int sh = sourceSize_.height;
	const unsigned int tw = targetSize.width;
	const unsigned int th = targetSize.height;

	ASSERT(frame.planes().size() == 2);
	ASSERT(tw % 2 == 0 && th % 2 == 0);

	/* Image scaling block implementing nearest-neighbour algorithm. */
	unsigned char *src = frame.planes()[0].data();
	unsigned char *srcC = frame.planes()[1].data();
	unsigned char *srcCb, *srcCr;
	unsigned char *dstY, *srcY;

	size_t dstSize = (th * tw) + ((th / 2) * tw);
	destination->resize(dstSize);
	unsigned char *dst = destination->data();
	unsigned char *dstC = dst + th * tw;

	for (unsigned int y = 0; y < th; y += 2) {
		unsigned int sourceY = (sh * y + th / 2) / th;

		dstY = dst + y * tw;
		srcY = src + sw * sourceY;
		srcCb = srcC + (sourceY / 2) * sw + 0;
		srcCr = srcC + (sourceY / 2) * sw + 1;

		for (unsigned int x = 0; x < tw; x += 2) {
			unsigned int sourceX = (sw * x + tw / 2) / tw;

			dstY[x] = srcY[sourceX];
			dstY[tw + x] = srcY[sw + sourceX];
			dstY[x + 1] = srcY[sourceX + 1];
			dstY[tw + x + 1] = srcY[sw + sourceX + 1];

			dstC[(y / 2) * tw + x + 0] = srcCb[(sourceX / 2) * 2];
			dstC[(y / 2) * tw + x + 1] = srcCr[(sourceX / 2) * 2];
		}
	}
}
um">202], # neutral 8 [161, 163, 163], # neutral 6.5 [121, 121, 122], # neutral 5 [82, 84, 86], # neutral 3.5 [49, 49, 51] # black 2 ]) """ convert reference colours from srgb to rgb """ m_srgb = degamma(m_rgb) """ reorder reference values to match how patches are ordered """ m_srgb = np.array([m_srgb[i::6] for i in range(6)]).reshape((24, 3)) """ reformat alsc correction tables or set colour_cals to None if alsc is deactivated """ if cal_cr_list is None: colour_cals = None else: colour_cals = {} for cr, cb in zip(cal_cr_list, cal_cb_list): cr_tab = cr['table'] cb_tab = cb['table'] """ normalise tables so min value is 1 """ cr_tab = cr_tab/np.min(cr_tab) cb_tab = cb_tab/np.min(cb_tab) colour_cals[cr['ct']] = [cr_tab, cb_tab] """ for each image, perform awb and alsc corrections. Then calculate the colour correction matrix for that image, recording the ccm and the colour tempertaure. """ ccm_tab = {} for Img in imgs: Cam.log += '\nProcessing image: ' + Img.name """ get macbeth patches with alsc applied if alsc enabled. Note: if alsc is disabled then colour_cals will be set to None and no the function will simply return the macbeth patches """ r, b, g = get_alsc_patches(Img, colour_cals, grey=False) """ do awb Note: awb is done by measuring the macbeth chart in the image, rather than from the awb calibration. This is done so the awb will be perfect and the ccm matrices will be more accurate. """ r_greys, b_greys, g_greys = r[3::4], b[3::4], g[3::4] r_g = np.mean(r_greys/g_greys) b_g = np.mean(b_greys/g_greys) r = r / r_g b = b / b_g """ normalise brightness wrt reference macbeth colours and then average each channel for each patch """ gain = np.mean(m_srgb)/np.mean((r, g, b)) Cam.log += '\nGain with respect to standard colours: {:.3f}'.format(gain) r = np.mean(gain*r, axis=1) b = np.mean(gain*b, axis=1) g = np.mean(gain*g, axis=1) """ calculate ccm matrix """ ccm = do_ccm(r, g, b, m_srgb) """ if a ccm has already been calculated for that temperature then don't overwrite but save both. They will then be averaged later on """ if Img.col in ccm_tab.keys(): ccm_tab[Img.col].append(ccm) else: ccm_tab[Img.col] = [ccm] Cam.log += '\n' Cam.log += '\nFinished processing images' """ average any ccms that share a colour temperature """ for k, v in ccm_tab.items(): tab = np.mean(v, axis=0) tab = np.where((10000*tab) % 1 <= 0.05, tab+0.00001, tab) tab = np.where((10000*tab) % 1 >= 0.95, tab-0.00001, tab) ccm_tab[k] = list(np.round(tab, 5)) Cam.log += '\nMatrix calculated for colour temperature of {} K'.format(k) """ return all ccms with respective colour temperature in the correct format, sorted by their colour temperature """ sorted_ccms = sorted(ccm_tab.items(), key=lambda kv: kv[0]) ccms = [] for i in sorted_ccms: ccms.append({ 'ct': i[0], 'ccm': i[1] }) return ccms """ calculates the ccm for an individual image. ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3 matrix, each row must add up to 1 in order to conserve greyness, simplifying calculation. Should you want to fit them in another space (e.g. LAB) we wish you the best of luck and send us the code when you are done! :-) """ def do_ccm(r, g, b, m_srgb): rb = r-b gb = g-b rb_2s = (rb*rb) rb_gbs = (rb*gb) gb_2s = (gb*gb) r_rbs = rb * (m_srgb[..., 0] - b) r_gbs = gb * (m_srgb[..., 0] - b) g_rbs = rb * (m_srgb[..., 1] - b) g_gbs = gb * (m_srgb[..., 1] - b) b_rbs = rb * (m_srgb[..., 2] - b) b_gbs = gb * (m_srgb[..., 2] - b) """ Obtain least squares fit """ rb_2 = np.sum(rb_2s) gb_2 = np.sum(gb_2s) rb_gb = np.sum(rb_gbs) r_rb = np.sum(r_rbs) r_gb = np.sum(r_gbs) g_rb = np.sum(g_rbs) g_gb = np.sum(g_gbs) b_rb = np.sum(b_rbs) b_gb = np.sum(b_gbs) det = rb_2*gb_2 - rb_gb*rb_gb """ Raise error if matrix is singular... This shouldn't really happen with real data but if it does just take new pictures and try again, not much else to be done unfortunately... """ if det < 0.001: raise ArithmeticError r_a = (gb_2*r_rb - rb_gb*r_gb)/det r_b = (rb_2*r_gb - rb_gb*r_rb)/det """ Last row can be calculated by knowing the sum must be 1 """ r_c = 1 - r_a - r_b g_a = (gb_2*g_rb - rb_gb*g_gb)/det g_b = (rb_2*g_gb - rb_gb*g_rb)/det g_c = 1 - g_a - g_b b_a = (gb_2*b_rb - rb_gb*b_gb)/det b_b = (rb_2*b_gb - rb_gb*b_rb)/det b_c = 1 - b_a - b_b """ format ccm """ ccm = [r_a, r_b, r_c, g_a, g_b, g_c, b_a, b_b, b_c] return ccm