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/* SPDX-License-Identifier: GPL-2.0-or-later */
/*
 * Copyright (C) 2021, Vedant Paranjape
 *
 * libcamera Gstreamer element API tests
 */

#include <libcamera/libcamera.h>

#include <libcamera/base/utils.h>

#if HAVE_ASAN
#include <sanitizer/asan_interface.h>
#endif

#include "gstreamer_test.h"

#include "test.h"

using namespace std;

#if HAVE_ASAN
extern "C" {
const char *__asan_default_options()
{
	/*
	 * Disable leak detection due to a known global variable initialization
	 * leak in glib's g_quark_init(). This should ideally be handled by
	 * using a suppression file instead of disabling leak detection.
	 */
	return "detect_leaks=false";
}
}
#endif

GstreamerTest::GstreamerTest(unsigned int numStreams)
	: pipeline_(nullptr), libcameraSrc_(nullptr)
{
	/*
	 * GStreamer by default spawns a process to run the gst-plugin-scanner
	 * helper. If libcamera is compiled with ASan enabled, and as GStreamer
	 * is most likely not, this causes the ASan link order check to fail
	 * when gst-plugin-scanner dlopen()s the plugin as many libraries will
	 * have already been loaded by then. Fix this issue by disabling
	 * spawning of a child helper process when scanning the build directory
	 * for plugins.
	 */
	gst_registry_fork_set_enabled(false);

	/* Initialize GStreamer */
	g_autoptr(GError) errInit = NULL;
	if (!gst_init_check(nullptr, nullptr, &errInit)) {
		g_printerr("Could not initialize GStreamer: %s\n",
			   errInit ? errInit->message : "unknown error");

		status_ = TestFail;
		return;
	}

	/*
	 * Atleast one camera should be available with numStreams streams,
	 * otherwise skip the test entirely.
	 */
	if (!checkMinCameraStreamsAndSetCameraName(numStreams)) {
		status_ = TestSkip;
		return;
	}

	status_ = TestPass;
}

bool GstreamerTest::checkMinCameraStreamsAndSetCameraName(unsigned int numStreams)
{
	libcamera::CameraManager cm;
	bool cameraFound = false;

	cm.start();

	for (auto &camera : cm.cameras()) {
		if (camera->streams().size() < numStreams)
			continue;

		cameraFound = true;
		cameraName_ = camera->id();
		break;
	}

	cm.stop();

	return cameraFound;
}

GstreamerTest::~GstreamerTest()
{
	g_clear_object(&pipeline_);
	g_clear_object(&libcameraSrc_);

	gst_deinit();
}

int GstreamerTest::createPipeline()
{
	libcameraSrc_ = gst_element_factory_make("libcamerasrc", "libcamera");
	pipeline_ = gst_pipeline_new("test-pipeline");

	if (!libcameraSrc_ || !pipeline_) {
		g_printerr("Unable to create pipeline %p.%p\n",
			   libcameraSrc_, pipeline_);

		return TestFail;
	}

	g_object_set(libcameraSrc_, "camera-name", cameraName_.c_str(), NULL);
	g_object_ref_sink(libcameraSrc_);

	return TestPass;
}

int GstreamerTest::startPipeline()
{
	GstStateChangeReturn ret;

	/* Start playing */
	ret = gst_element_set_state(pipeline_, GST_STATE_PLAYING);
	if (ret == GST_STATE_CHANGE_FAILURE) {
		g_printerr("Unable to set the pipeline to the playing state.\n");
		return TestFail;
	}

	return TestPass;
}

int GstreamerTest::processEvent()
{
	/* Wait until error or EOS or timeout after 2 seconds */
	constexpr GstMessageType msgType =
		static_cast<GstMessageType>(GST_MESSAGE_ERROR | GST_MESSAGE_EOS);
	constexpr GstClockTime timeout = 2 * GST_SECOND;

	g_autoptr(GstBus) bus = gst_element_get_bus(pipeline_);
	g_autoptr(GstMessage) msg = gst_bus_timed_pop_filtered(bus, timeout, msgType);

	gst_element_set_state(pipeline_, GST_STATE_NULL);

	/* Parse error message */
	if (msg == NULL)
		return TestPass;

	switch (GST_MESSAGE_TYPE(msg)) {
	case GST_MESSAGE_ERROR:
		printError(msg);
		break;
	case GST_MESSAGE_EOS:
		g_print("End-Of-Stream reached.\n");
		break;
	default:
		g_printerr("Unexpected message received.\n");
		break;
	}

	return TestFail;
}

void GstreamerTest::printError(GstMessage *msg)
{
	g_autoptr(GError) err = NULL;
	g_autofree gchar *debug_info = NULL;

	gst_message_parse_error(msg, &err, &debug_info);
	g_printerr("Error received from element %s: %s\n",
		   GST_OBJECT_NAME(msg->src), err->message);
	g_printerr("Debugging information: %s\n",
		   debug_info ? debug_info : "none");
}
s="hl str"> r_hat = r/(1+r+b) b_hat = b/(1+r+b) To convert from hatspace to dehatspace (dehat operation): r = r_hat/(1-r_hat-b_hat) b = b_hat/(1-r_hat-b_hat) Proof is left as an excercise to the reader... Throughout the code, r and b are sometimes referred to as r_g and b_g as a reminder that they are ratios """ r_g_hat = r_g/(1+r_g+b_g) b_g_hat = b_g/(1+r_g+b_g) Cam.log += '\n r_hat : {:.4f} b_hat : {:.4f}'.format(r_g_hat, b_g_hat) rbs_hat.append((r_g_hat, b_g_hat, Img.col)) rb_raw.append((r_g, b_g)) Cam.log += '\n' Cam.log += '\nFinished processing images' """ sort all lits simultaneously by r_hat """ rbs_zip = list(zip(rbs_hat, rb_raw)) rbs_zip.sort(key=lambda x: x[0][0]) rbs_hat, rb_raw = list(zip(*rbs_zip)) """ unzip tuples ready for processing """ rbs_hat = list(zip(*rbs_hat)) rb_raw = list(zip(*rb_raw)) """ fit quadratic fit to r_g hat and b_g_hat """ a, b, c = np.polyfit(rbs_hat[0], rbs_hat[1], 2) Cam.log += '\nFit quadratic curve in hatspace' """ the algorithm now approximates the shortest distance from each point to the curve in dehatspace. Since the fit is done in hatspace, it is easier to find the actual shortest distance in hatspace and use the projection back into dehatspace as an overestimate. The distance will be used for two things: 1) In the case that colour temperature does not strictly decrease with increasing r/g, the closest point to the line will be chosen out of an increasing pair of colours. 2) To calculate transverse negative an dpositive, the maximum positive and negative distance from the line are chosen. This benefits from the overestimate as the transverse pos/neg are upper bound values. """ """ define fit function """ def f(x): return a*x**2 + b*x + c """ iterate over points (R, B are x and y coordinates of points) and calculate distance to line in dehatspace """ dists = [] for i, (R, B) in enumerate(zip(rbs_hat[0], rbs_hat[1])): """ define function to minimise as square distance between datapoint and point on curve. Squaring is monotonic so minimising radius squared is equivalent to minimising radius """ def f_min(x): y = f(x) return((x-R)**2+(y-B)**2) """ perform optimisation with scipy.optmisie.fmin """ x_hat = fmin(f_min, R, disp=0)[0] y_hat = f(x_hat) """ dehat """ x = x_hat/(1-x_hat-y_hat) y = y_hat/(1-x_hat-y_hat) rr = R/(1-R-B) bb = B/(1-R-B) """ calculate euclidean distance in dehatspace """ dist = ((x-rr)**2+(y-bb)**2)**0.5 """ return negative if point is below the fit curve """ if (x+y) > (rr+bb): dist *= -1 dists.append(dist) Cam.log += '\nFound closest point on fit line to each point in dehatspace' """ calculate wiggle factors in awb. 10% added since this is an upper bound """ transverse_neg = - np.min(dists) * 1.1 transverse_pos = np.max(dists) * 1.1 Cam.log += '\nTransverse pos : {:.5f}'.format(transverse_pos) Cam.log += '\nTransverse neg : {:.5f}'.format(transverse_neg) """ set minimum transverse wiggles to 0.1 . Wiggle factors dictate how far off of the curve the algorithm searches. 0.1 is a suitable minimum that gives better results for lighting conditions not within calibration dataset. Anything less will generalise poorly. """ if transverse_pos < 0.01: transverse_pos = 0.01 Cam.log += '\nForced transverse pos to 0.01' if transverse_neg < 0.01: transverse_neg = 0.01 Cam.log += '\nForced transverse neg to 0.01' """ generate new b_hat values at each r_hat according to fit """ r_hat_fit = np.array(rbs_hat[0]) b_hat_fit = a*r_hat_fit**2 + b*r_hat_fit + c """ transform from hatspace to dehatspace """ r_fit = r_hat_fit/(1-r_hat_fit-b_hat_fit) b_fit = b_hat_fit/(1-r_hat_fit-b_hat_fit) c_fit = np.round(rbs_hat[2], 0) """ round to 4dp """ r_fit = np.where((1000*r_fit) % 1 <= 0.05, r_fit+0.0001, r_fit) r_fit = np.where((1000*r_fit) % 1 >= 0.95, r_fit-0.0001, r_fit) b_fit = np.where((1000*b_fit) % 1 <= 0.05, b_fit+0.0001, b_fit) b_fit = np.where((1000*b_fit) % 1 >= 0.95, b_fit-0.0001, b_fit) r_fit = np.round(r_fit, 4) b_fit = np.round(b_fit, 4) """ The following code ensures that colour temperature decreases with increasing r/g """ """ iterate backwards over list for easier indexing """ i = len(c_fit) - 1 while i > 0: if c_fit[i] > c_fit[i-1]: Cam.log += '\nColour temperature increase found\n' Cam.log += '{} K at r = {} to '.format(c_fit[i-1], r_fit[i-1]) Cam.log += '{} K at r = {}'.format(c_fit[i], r_fit[i]) """ if colour temperature increases then discard point furthest from the transformed fit (dehatspace) """ error_1 = abs(dists[i-1]) error_2 = abs(dists[i]) Cam.log += '\nDistances from fit:\n' Cam.log += '{} K : {:.5f} , '.format(c_fit[i], error_1) Cam.log += '{} K : {:.5f}'.format(c_fit[i-1], error_2) """ find bad index note that in python false = 0 and true = 1 """ bad = i - (error_1 < error_2) Cam.log += '\nPoint at {} K deleted as '.format(c_fit[bad]) Cam.log += 'it is furthest from fit' """ delete bad point """ r_fit = np.delete(r_fit, bad) b_fit = np.delete(b_fit, bad) c_fit = np.delete(c_fit, bad).astype(np.uint16) """ note that if a point has been discarded then the length has decreased by one, meaning that decreasing the index by one will reassess the kept point against the next point. It is therefore possible, in theory, for two adjacent points to be discarded, although probably rare """ i -= 1 """ return formatted ct curve, ordered by increasing colour temperature """ ct_curve = list(np.array(list(zip(b_fit, r_fit, c_fit))).flatten())[::-1] Cam.log += '\nFinal CT curve:' for i in range(len(ct_curve)//3): j = 3*i Cam.log += '\n ct: {} '.format(ct_curve[j]) Cam.log += ' r: {} '.format(ct_curve[j+1]) Cam.log += ' b: {} '.format(ct_curve[j+2]) """ plotting code for debug """ if plot: x = np.linspace(np.min(rbs_hat[0]), np.max(rbs_hat[0]), 100) y = a*x**2 + b*x + c plt.subplot(2, 1, 1) plt.title('hatspace') plt.plot(rbs_hat[0], rbs_hat[1], ls='--', color='blue') plt.plot(x, y, color='green', ls='-') plt.scatter(rbs_hat[0], rbs_hat[1], color='red') for i, ct in enumerate(rbs_hat[2]): plt.annotate(str(ct), (rbs_hat[0][i], rbs_hat[1][i])) plt.xlabel('$\\hat{r}$') plt.ylabel('$\\hat{b}$') """ optional set axes equal to shortest distance so line really does looks perpendicular and everybody is happy """ # ax = plt.gca() # ax.set_aspect('equal') plt.grid() plt.subplot(2, 1, 2) plt.title('dehatspace - indoors?') plt.plot(r_fit, b_fit, color='blue') plt.scatter(rb_raw[0], rb_raw[1], color='green') plt.scatter(r_fit, b_fit, color='red') for i, ct in enumerate(c_fit): plt.annotate(str(ct), (r_fit[i], b_fit[i])) plt.xlabel('$r$') plt.ylabel('$b$') """ optional set axes equal to shortest distance so line really does looks perpendicular and everybody is happy """ # ax = plt.gca() # ax.set_aspect('equal') plt.subplots_adjust(hspace=0.5) plt.grid() plt.show() """ end of plotting code """ return(ct_curve, np.round(transverse_pos, 5), np.round(transverse_neg, 5)) """ obtain greyscale patches and perform alsc colour correction """ def get_alsc_patches(Img, colour_cals, grey=True): """ get patch centre coordinates, image colour and the actual patches for each channel, remembering to subtract blacklevel If grey then only greyscale patches considered """ if grey: cen_coords = Img.cen_coords[3::4] col = Img.col patches = [np.array(Img.patches[i]) for i in Img.order] r_patchs = patches[0][3::4] - Img.blacklevel_16 b_patchs = patches[3][3::4] - Img.blacklevel_16 """ note two green channels are averages """ g_patchs = (patches[1][3::4]+patches[2][3::4])/2 - Img.blacklevel_16 else: cen_coords = Img.cen_coords col = Img.col patches = [np.array(Img.patches[i]) for i in Img.order] r_patchs = patches[0] - Img.blacklevel_16 b_patchs = patches[3] - Img.blacklevel_16 g_patchs = (patches[1]+patches[2])/2 - Img.blacklevel_16 if colour_cals is None: return r_patchs, b_patchs, g_patchs """ find where image colour fits in alsc colour calibration tables """ cts = list(colour_cals.keys()) pos = bisect_left(cts, col) """ if img colour is below minimum or above maximum alsc calibration colour, simply pick extreme closest to img colour """ if pos % len(cts) == 0: """ this works because -0 = 0 = first and -1 = last index """ col_tabs = np.array(colour_cals[cts[-pos//len(cts)]]) """ else, perform linear interpolation between existing alsc colour calibration tables """ else: bef = cts[pos-1] aft = cts[pos] da = col-bef db = aft-col bef_tabs = np.array(colour_cals[bef]) aft_tabs = np.array(colour_cals[aft]) col_tabs = (bef_tabs*db + aft_tabs*da)/(da+db) col_tabs = np.reshape(col_tabs, (2, 12, 16)) """ calculate dx, dy used to calculate alsc table """ w, h = Img.w/2, Img.h/2 dx, dy = int(-(-(w-1)//16)), int(-(-(h-1)//12)) """ make list of pairs of gains for each patch by selecting the correct value in alsc colour calibration table """ patch_gains = [] for cen in cen_coords: x, y = cen[0]//dx, cen[1]//dy # We could probably do with some better spatial interpolation here? col_gains = (col_tabs[0][y][x], col_tabs[1][y][x]) patch_gains.append(col_gains) """ multiply the r and b channels in each patch by the respective gain, finally performing the alsc colour correction """ for i, gains in enumerate(patch_gains): r_patchs[i] = r_patchs[i] * gains[0] b_patchs[i] = b_patchs[i] * gains[1] """ return greyscale patches, g channel and correct r, b channels """ return r_patchs, b_patchs, g_patchs