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/* SPDX-License-Identifier: BSD-2-Clause */
/*
 * Copyright (C) 2019, Raspberry Pi (Trading) Limited
 *
 * agc.hpp - AGC/AEC control algorithm
 */
#pragma once

#include <vector>
#include <mutex>

#include "../agc_algorithm.hpp"
#include "../agc_status.h"
#include "../pwl.hpp"

// This is our implementation of AGC.

// This is the number actually set up by the firmware, not the maximum possible
// number (which is 16).

#define AGC_STATS_SIZE 15

namespace RPiController {

struct AgcMeteringMode {
	double weights[AGC_STATS_SIZE];
	void Read(boost::property_tree::ptree const &params);
};

struct AgcExposureMode {
	std::vector<double> shutter;
	std::vector<double> gain;
	void Read(boost::property_tree::ptree const &params);
};

struct AgcConstraint {
	enum class Bound { LOWER = 0, UPPER = 1 };
	Bound bound;
	double q_lo;
	double q_hi;
	Pwl Y_target;
	void Read(boost::property_tree::ptree const &params);
};

typedef std::vector<AgcConstraint> AgcConstraintMode;

struct AgcConfig {
	void Read(boost::property_tree::ptree const &params);
	std::map<std::string, AgcMeteringMode> metering_modes;
	std::map<std::string, AgcExposureMode> exposure_modes;
	std::map<std::string, AgcConstraintMode> constraint_modes;
	Pwl Y_target;
	double speed;
	uint16_t startup_frames;
	double max_change;
	double min_change;
	double fast_reduce_threshold;
	double speed_up_threshold;
	std::string default_metering_mode;
	std::string default_exposure_mode;
	std::string default_constraint_mode;
	double base_ev;
};

class Agc : public AgcAlgorithm
{
public:
	Agc(Controller *controller);
	char const *Name() const override;
	void Read(boost::property_tree::ptree const &params) override;
	void SetEv(double ev) override;
	void SetFlickerPeriod(double flicker_period) override;
	void SetFixedShutter(double fixed_shutter) override; // microseconds
	void SetFixedAnalogueGain(double fixed_analogue_gain) override;
	void SetMeteringMode(std::string const &metering_mode_name) override;
	void SetExposureMode(std::string const &exposure_mode_name) override;
	void SetConstraintMode(std::string const &contraint_mode_name) override;
	void SwitchMode(CameraMode const &camera_mode, Metadata *metadata) override;
	void Prepare(Metadata *image_metadata) override;
	void Process(StatisticsPtr &stats, Metadata *image_metadata) override;

private:
	AgcConfig config_;
	void housekeepConfig();
	void fetchCurrentExposure(Metadata *image_metadata);
	void computeGain(bcm2835_isp_stats *statistics, Metadata *image_metadata,
			 double &gain, double &target_Y);
	void computeTargetExposure(double gain);
	bool applyDigitalGain(Metadata *image_metadata, double gain,
			      double target_Y);
	void filterExposure(bool desaturate);
	void divvyupExposure();
	void writeAndFinish(Metadata *image_metadata, bool desaturate);
	AgcMeteringMode *metering_mode_;
	AgcExposureMode *exposure_mode_;
	AgcConstraintMode *constraint_mode_;
	uint64_t frame_count_;
	struct ExposureValues {
		ExposureValues() : shutter(0), analogue_gain(0),
				   total_exposure(0), total_exposure_no_dg(0) {}
		double shutter;
		double analogue_gain;
		double total_exposure;
		double total_exposure_no_dg; // without digital gain
	};
	ExposureValues current_;  // values for the current frame
	ExposureValues target_;   // calculate the values we want here
	ExposureValues filtered_; // these values are filtered towards target
	AgcStatus status_;        // to "latch" settings so they can't change
	AgcStatus output_status_; // the status we will write out
	std::mutex output_mutex_;
	int lock_count_;
	// Below here the "settings" that applications can change.
	std::mutex settings_mutex_;
	std::string metering_mode_name_;
	std::string exposure_mode_name_;
	std::string constraint_mode_name_;
	double ev_;
	double flicker_period_;
	double fixed_shutter_;
	double fixed_analogue_gain_;
};

} // namespace RPiController
opt">[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