Kstars

guidealgorithms.cpp
1/* Algorithms used in the internal guider.
2 Copyright (C) 2012 Andrew Stepanenko
3
4 Modified by Jasem Mutlaq for KStars.
5 SPDX-FileCopyrightText: Jasem Mutlaq <mutlaqja@ikarustech.com>
6
7 SPDX-License-Identifier: GPL-2.0-or-later
8 */
9
10#include "guidealgorithms.h"
11
12#include <set>
13#include <QObject>
14
15#include "ekos_guide_debug.h"
16#include "ekos/auxiliary/stellarsolverprofileeditor.h"
17#include "Options.h"
18#include "fitsviewer/fitsdata.h"
19
20#define SMART_THRESHOLD 0
21#define SEP_THRESHOLD 1
22#define CENTROID_THRESHOLD 2
23#define AUTO_THRESHOLD 3
24#define NO_THRESHOLD 4
25#define SEP_MULTISTAR 5
26
27// smart threshold algorithm param
28// width of outer frame for background calculation
29#define SMART_FRAME_WIDTH 4
30// cut-factor above average threshold
31#define SMART_CUT_FACTOR 0.1
32
33struct Peak
34{
35 int x;
36 int y;
37 float val;
38
39 Peak() = default;
40 Peak(int x_, int y_, float val_) : x(x_), y(y_), val(val_) { }
41 bool operator<(const Peak &rhs) const
42 {
43 return val < rhs.val;
44 }
45};
46
47// JM: Why not use QPoint?
48typedef struct
49{
50 int x, y;
51} point_t;
52
53namespace
54{
55
56void psf_conv(float *dst, const float *src, int width, int height)
57{
58 //dst.Init(src.Size);
59
60 // A B1 B2 C1 C2 C3 D1 D2 D3
61 const double PSF[] = { 0.906, 0.584, 0.365, .117, .049, -0.05, -.064, -.074, -.094 };
62
63 //memset(dst.px, 0, src.NPixels * sizeof(float));
64
65 /* PSF Grid is:
66 D3 D3 D3 D3 D3 D3 D3 D3 D3
67 D3 D3 D3 D2 D1 D2 D3 D3 D3
68 D3 D3 C3 C2 C1 C2 C3 D3 D3
69 D3 D2 C2 B2 B1 B2 C2 D2 D3
70 D3 D1 C1 B1 A B1 C1 D1 D3
71 D3 D2 C2 B2 B1 B2 C2 D2 D3
72 D3 D3 C3 C2 C1 C2 C3 D3 D3
73 D3 D3 D3 D2 D1 D2 D3 D3 D3
74 D3 D3 D3 D3 D3 D3 D3 D3 D3
75
76 1@A
77 4@B1, B2, C1, C3, D1
78 8@C2, D2
79 44 * D3
80 */
81
82 int psf_size = 4;
83
84 for (int y = psf_size; y < height - psf_size; y++)
85 {
86 for (int x = psf_size; x < width - psf_size; x++)
87 {
88 float A, B1, B2, C1, C2, C3, D1, D2, D3;
89
90#define PX(dx, dy) *(src + width * (y + (dy)) + x + (dx))
91 A = PX(+0, +0);
92 B1 = PX(+0, -1) + PX(+0, +1) + PX(+1, +0) + PX(-1, +0);
93 B2 = PX(-1, -1) + PX(+1, -1) + PX(-1, +1) + PX(+1, +1);
94 C1 = PX(+0, -2) + PX(-2, +0) + PX(+2, +0) + PX(+0, +2);
95 C2 = PX(-1, -2) + PX(+1, -2) + PX(-2, -1) + PX(+2, -1) + PX(-2, +1) + PX(+2, +1) + PX(-1, +2) + PX(+1, +2);
96 C3 = PX(-2, -2) + PX(+2, -2) + PX(-2, +2) + PX(+2, +2);
97 D1 = PX(+0, -3) + PX(-3, +0) + PX(+3, +0) + PX(+0, +3);
98 D2 = PX(-1, -3) + PX(+1, -3) + PX(-3, -1) + PX(+3, -1) + PX(-3, +1) + PX(+3, +1) + PX(-1, +3) + PX(+1, +3);
99 D3 = PX(-4, -2) + PX(-3, -2) + PX(+3, -2) + PX(+4, -2) + PX(-4, -1) + PX(+4, -1) + PX(-4, +0) + PX(+4, +0) + PX(-4,
100 +1) + PX(+4, +1) + PX(-4, +2) + PX(-3, +2) + PX(+3, +2) + PX(+4, +2);
101#undef PX
102 int i;
103 const float *uptr;
104
105 uptr = src + width * (y - 4) + (x - 4);
106 for (i = 0; i < 9; i++)
107 D3 += *uptr++;
108
109 uptr = src + width * (y - 3) + (x - 4);
110 for (i = 0; i < 3; i++)
111 D3 += *uptr++;
112 uptr += 3;
113 for (i = 0; i < 3; i++)
114 D3 += *uptr++;
115
116 uptr = src + width * (y + 3) + (x - 4);
117 for (i = 0; i < 3; i++)
118 D3 += *uptr++;
119 uptr += 3;
120 for (i = 0; i < 3; i++)
121 D3 += *uptr++;
122
123 uptr = src + width * (y + 4) + (x - 4);
124 for (i = 0; i < 9; i++)
125 D3 += *uptr++;
126
127 double mean = (A + B1 + B2 + C1 + C2 + C3 + D1 + D2 + D3) / 81.0;
128 double PSF_fit = PSF[0] * (A - mean) + PSF[1] * (B1 - 4.0 * mean) + PSF[2] * (B2 - 4.0 * mean) +
129 PSF[3] * (C1 - 4.0 * mean) + PSF[4] * (C2 - 8.0 * mean) + PSF[5] * (C3 - 4.0 * mean) +
130 PSF[6] * (D1 - 4.0 * mean) + PSF[7] * (D2 - 8.0 * mean) + PSF[8] * (D3 - 44.0 * mean);
131
132 dst[width * y + x] = (float) PSF_fit;
133 }
134 }
135}
136
137void GetStats(double *mean, double *stdev, int width, const float *img, const QRect &win)
138{
139 // Determine the mean and standard deviation
140 double sum = 0.0;
141 double a = 0.0;
142 double q = 0.0;
143 double k = 1.0;
144 double km1 = 0.0;
145
146 const float *p0 = img + win.top() * width + win.left();
147 for (int y = 0; y < win.height(); y++)
148 {
149 const float *end = p0 + win.height();
150 for (const float *p = p0; p < end; p++)
151 {
152 double const x = (double) * p;
153 sum += x;
154 double const a0 = a;
155 a += (x - a) / k;
156 q += (x - a0) * (x - a);
157 km1 = k;
158 k += 1.0;
159 }
160 p0 += width;
161 }
162
163 *mean = sum / km1;
164 *stdev = sqrt(q / km1);
165}
166
167void RemoveItems(std::set<Peak> &stars, const std::set<int> &to_erase)
168{
169 int n = 0;
170 for (std::set<Peak>::iterator it = stars.begin(); it != stars.end(); n++)
171 {
172 if (to_erase.find(n) != to_erase.end())
173 {
174 std::set<Peak>::iterator next = it;
175 ++next;
176 stars.erase(it);
177 it = next;
178 }
179 else
180 ++it;
181 }
182}
183
184float *createFloatImage(const QSharedPointer<FITSData> &target)
185{
186 QSharedPointer<FITSData> imageData;
187
188 /*************
189 if (target.isNull())
190 imageData = m_ImageData;
191 else
192 *********/ // shouldn't be null
193 imageData = target;
194
195 // #1 Convert to float array
196 // We only process 1st plane if it is a color image
197 uint32_t imgSize = imageData->width() * imageData->height();
198 float *imgFloat = new float[imgSize];
199
200 if (imgFloat == nullptr)
201 {
202 qCritical() << "Not enough memory for float image array!";
203 return nullptr;
204 }
205
206 switch (imageData->getStatistics().dataType)
207 {
208 case TBYTE:
209 {
210 uint8_t const *buffer = imageData->getImageBuffer();
211 for (uint32_t i = 0; i < imgSize; i++)
212 imgFloat[i] = buffer[i];
213 }
214 break;
215
216 case TSHORT:
217 {
218 int16_t const *buffer = reinterpret_cast<int16_t const *>(imageData->getImageBuffer());
219 for (uint32_t i = 0; i < imgSize; i++)
220 imgFloat[i] = buffer[i];
221 }
222 break;
223
224 case TUSHORT:
225 {
226 uint16_t const *buffer = reinterpret_cast<uint16_t const*>(imageData->getImageBuffer());
227 for (uint32_t i = 0; i < imgSize; i++)
228 imgFloat[i] = buffer[i];
229 }
230 break;
231
232 case TLONG:
233 {
234 int32_t const *buffer = reinterpret_cast<int32_t const*>(imageData->getImageBuffer());
235 for (uint32_t i = 0; i < imgSize; i++)
236 imgFloat[i] = buffer[i];
237 }
238 break;
239
240 case TULONG:
241 {
242 uint32_t const *buffer = reinterpret_cast<uint32_t const*>(imageData->getImageBuffer());
243 for (uint32_t i = 0; i < imgSize; i++)
244 imgFloat[i] = buffer[i];
245 }
246 break;
247
248 case TFLOAT:
249 {
250 float const *buffer = reinterpret_cast<float const*>(imageData->getImageBuffer());
251 for (uint32_t i = 0; i < imgSize; i++)
252 imgFloat[i] = buffer[i];
253 }
254 break;
255
256 case TLONGLONG:
257 {
258 int64_t const *buffer = reinterpret_cast<int64_t const*>(imageData->getImageBuffer());
259 for (uint32_t i = 0; i < imgSize; i++)
260 imgFloat[i] = buffer[i];
261 }
262 break;
263
264 case TDOUBLE:
265 {
266 double const *buffer = reinterpret_cast<double const*>(imageData->getImageBuffer());
267 for (uint32_t i = 0; i < imgSize; i++)
268 imgFloat[i] = buffer[i];
269 }
270 break;
271
272 default:
273 delete[] imgFloat;
274 return nullptr;
275 }
276
277 return imgFloat;
278}
279
280} // namespace
281
282// Based on PHD2 algorithm
283QList<Edge*> GuideAlgorithms::detectStars(const QSharedPointer<FITSData> &imageData, const QRect &trackingBox)
284{
285 //Debug.Write(wxString::Format("Star::AutoFind called with edgeAllowance = %d searchRegion = %d\n", extraEdgeAllowance, searchRegion));
286
287 // run a 3x3 median first to eliminate hot pixels
288 //usImage smoothed;
289 //smoothed.CopyFrom(image);
290 //Median3(smoothed);
291 constexpr int extraEdgeAllowance = 0;
292 const QSharedPointer<FITSData> smoothed(new FITSData(imageData));
293 smoothed->applyFilter(FITS_MEDIAN);
294
295 int searchRegion = trackingBox.width();
296
297 int subW = smoothed->width();
298 int subH = smoothed->height();
299 int size = subW * subH;
300
301 // convert to floating point
302 float *conv = createFloatImage(smoothed);
303
304 // run the PSF convolution
305 {
306 float *tmp = new float[size] {};
307 psf_conv(tmp, conv, subW, subH);
308 delete [] conv;
309 // Swap
310 conv = tmp;
311 }
312
313 enum { CONV_RADIUS = 4 };
314 int dw = subW; // width of the downsampled image
315 int dh = subH; // height of the downsampled image
316 QRect convRect(CONV_RADIUS, CONV_RADIUS, dw - 2 * CONV_RADIUS, dh - 2 * CONV_RADIUS); // region containing valid data
317
318 enum { TOP_N = 100 }; // keep track of the brightest stars
319 std::set<Peak> stars; // sorted by ascending intensity
320
321 double global_mean, global_stdev;
322 GetStats(&global_mean, &global_stdev, subW, conv, convRect);
323
324 //Debug.Write(wxString::Format("AutoFind: global mean = %.1f, stdev %.1f\n", global_mean, global_stdev));
325
326 const double threshold = 0.1;
327 //Debug.Write(wxString::Format("AutoFind: using threshold = %.1f\n", threshold));
328
329 // find each local maximum
330 int srch = 4;
331 for (int y = convRect.top() + srch; y <= convRect.bottom() - srch; y++)
332 {
333 for (int x = convRect.left() + srch; x <= convRect.right() - srch; x++)
334 {
335 float val = conv[dw * y + x];
336 bool ismax = false;
337 if (val > 0.0)
338 {
339 ismax = true;
340 for (int j = -srch; j <= srch; j++)
341 {
342 for (int i = -srch; i <= srch; i++)
343 {
344 if (i == 0 && j == 0)
345 continue;
346 if (conv[dw * (y + j) + (x + i)] > val)
347 {
348 ismax = false;
349 break;
350 }
351 }
352 }
353 }
354 if (!ismax)
355 continue;
356
357 // compare local maximum to mean value of surrounding pixels
358 const int local = 7;
359 double local_mean, local_stdev;
360 QRect localRect(x - local, y - local, 2 * local + 1, 2 * local + 1);
361 localRect = localRect.intersected(convRect);
362 GetStats(&local_mean, &local_stdev, subW, conv, localRect);
363
364 // this is our measure of star intensity
365 double h = (val - local_mean) / global_stdev;
366
367 if (h < threshold)
368 {
369 // Debug.Write(wxString::Format("AG: local max REJECT [%d, %d] PSF %.1f SNR %.1f\n", imgx, imgy, val, SNR));
370 continue;
371 }
372
373 // coordinates on the original image
374 int downsample = 1;
375 int imgx = x * downsample + downsample / 2;
376 int imgy = y * downsample + downsample / 2;
377
378 stars.insert(Peak(imgx, imgy, h));
379 if (stars.size() > TOP_N)
380 stars.erase(stars.begin());
381 }
382 }
383
384 //for (std::set<Peak>::const_reverse_iterator it = stars.rbegin(); it != stars.rend(); ++it)
385 //qCDebug(KSTARS_EKOS_GUIDE) << "AutoFind: local max [" << it->x << "," << it->y << "]" << it->val;
386
387 // merge stars that are very close into a single star
388 {
389 const int minlimitsq = 5 * 5;
390repeat:
391 for (std::set<Peak>::const_iterator a = stars.begin(); a != stars.end(); ++a)
392 {
393 std::set<Peak>::const_iterator b = a;
394 ++b;
395 for (; b != stars.end(); ++b)
396 {
397 int dx = a->x - b->x;
398 int dy = a->y - b->y;
399 int d2 = dx * dx + dy * dy;
400 if (d2 < minlimitsq)
401 {
402 // very close, treat as single star
403 //Debug.Write(wxString::Format("AutoFind: merge [%d, %d] %.1f - [%d, %d] %.1f\n", a->x, a->y, a->val, b->x, b->y, b->val));
404 // erase the dimmer one
405 stars.erase(a);
406 goto repeat;
407 }
408 }
409 }
410 }
411
412 // exclude stars that would fit within a single searchRegion box
413 {
414 // build a list of stars to be excluded
415 std::set<int> to_erase;
416 const int extra = 5; // extra safety margin
417 const int fullw = searchRegion + extra;
418 for (std::set<Peak>::const_iterator a = stars.begin(); a != stars.end(); ++a)
419 {
420 std::set<Peak>::const_iterator b = a;
421 ++b;
422 for (; b != stars.end(); ++b)
423 {
424 int dx = abs(a->x - b->x);
425 int dy = abs(a->y - b->y);
426 if (dx <= fullw && dy <= fullw)
427 {
428 // stars closer than search region, exclude them both
429 // but do not let a very dim star eliminate a very bright star
430 if (b->val / a->val >= 5.0)
431 {
432 //Debug.Write(wxString::Format("AutoFind: close dim-bright [%d, %d] %.1f - [%d, %d] %.1f\n", a->x, a->y, a->val, b->x, b->y, b->val));
433 }
434 else
435 {
436 //Debug.Write(wxString::Format("AutoFind: too close [%d, %d] %.1f - [%d, %d] %.1f\n", a->x, a->y, a->val, b->x, b->y, b->val));
437 to_erase.insert(std::distance(stars.begin(), a));
438 to_erase.insert(std::distance(stars.begin(), b));
439 }
440 }
441 }
442 }
443 RemoveItems(stars, to_erase);
444 }
445
446 // exclude stars too close to the edge
447 {
448 enum { MIN_EDGE_DIST = 40 };
449 int edgeDist = MIN_EDGE_DIST;//pConfig->Profile.GetInt("/StarAutoFind/MinEdgeDist", MIN_EDGE_DIST);
450 if (edgeDist < searchRegion)
451 edgeDist = searchRegion;
452 edgeDist += extraEdgeAllowance;
453
454 std::set<Peak>::iterator it = stars.begin();
455 while (it != stars.end())
456 {
457 std::set<Peak>::iterator next = it;
458 ++next;
459 if (it->x <= edgeDist || it->x >= subW - edgeDist ||
460 it->y <= edgeDist || it->y >= subH - edgeDist)
461 {
462 //Debug.Write(wxString::Format("AutoFind: too close to edge [%d, %d] %.1f\n", it->x, it->y, it->val));
463 stars.erase(it);
464 }
465 it = next;
466 }
467 }
468
469 QList<Edge*> centers;
470 for (std::set<Peak>::reverse_iterator it = stars.rbegin(); it != stars.rend(); ++it)
471 {
472 Edge *center = new Edge;
473 center->x = it->x;
474 center->y = it->y;
475 center->val = it->val;
476 centers.append(center);
477 }
478
479 delete [] conv;
480
481 return centers;
482}
483
484
485template <typename T>
486GuiderUtils::Vector GuideAlgorithms::findLocalStarPosition(QSharedPointer<FITSData> &imageData,
487 const int algorithmIndex,
488 const int videoWidth,
489 const int videoHeight,
490 const QRect &trackingBox)
491{
492 static const double P0 = 0.906, P1 = 0.584, P2 = 0.365, P3 = 0.117, P4 = 0.049, P5 = -0.05, P6 = -0.064, P7 = -0.074,
493 P8 = -0.094;
494
495 GuiderUtils::Vector ret(-1, -1, -1);
496 int i, j;
497 double resx, resy, mass, threshold, pval;
498 T const *psrc = nullptr;
499 T const *porigin = nullptr;
500 T const *pptr;
501
502 if (trackingBox.isValid() == false)
503 return GuiderUtils::Vector(-1, -1, -1);
504
505 if (imageData.isNull())
506 {
507 qCWarning(KSTARS_EKOS_GUIDE) << "Cannot process a nullptr image.";
508 return GuiderUtils::Vector(-1, -1, -1);
509 }
510
511 if (algorithmIndex == SEP_THRESHOLD)
512 {
513 QVariantMap settings;
514 settings["optionsProfileIndex"] = Options::guideOptionsProfile();
515 settings["optionsProfileGroup"] = static_cast<int>(Ekos::GuideProfiles);
516 imageData->setSourceExtractorSettings(settings);
517 QFuture<bool> result = imageData->findStars(ALGORITHM_SEP, trackingBox);
518 result.waitForFinished();
519 if (result.result())
520 {
521 imageData->getHFR(HFR_MEDIAN);
522 const Edge star = imageData->getSelectedHFRStar();
523 ret = GuiderUtils::Vector(star.x, star.y, 0);
524 }
525 else
526 ret = GuiderUtils::Vector(-1, -1, -1);
527
528 return ret;
529 }
530
531 T const *pdata = reinterpret_cast<T const*>(imageData->getImageBuffer());
532
533 qCDebug(KSTARS_EKOS_GUIDE) << "Tracking Square " << trackingBox;
534
535 double square_square = trackingBox.width() * trackingBox.width();
536
537 psrc = porigin = pdata + trackingBox.y() * videoWidth + trackingBox.x();
538
539 resx = resy = 0;
540 threshold = mass = 0;
541
542 // several threshold adaptive smart algorithms
543 switch (algorithmIndex)
544 {
545 case CENTROID_THRESHOLD:
546 {
547 int width = trackingBox.width();
548 int height = trackingBox.width();
549 float i0, i1, i2, i3, i4, i5, i6, i7, i8;
550 int ix = 0, iy = 0;
551 int xM4;
552 T const *p;
553 double average, fit, bestFit = 0;
554 int minx = 0;
555 int maxx = width;
556 int miny = 0;
557 int maxy = height;
558 for (int x = minx; x < maxx; x++)
559 for (int y = miny; y < maxy; y++)
560 {
561 i0 = i1 = i2 = i3 = i4 = i5 = i6 = i7 = i8 = 0;
562 xM4 = x - 4;
563 p = psrc + (y - 4) * videoWidth + xM4;
564 i8 += *p++;
565 i8 += *p++;
566 i8 += *p++;
567 i8 += *p++;
568 i8 += *p++;
569 i8 += *p++;
570 i8 += *p++;
571 i8 += *p++;
572 i8 += *p++;
573 p = psrc + (y - 3) * videoWidth + xM4;
574 i8 += *p++;
575 i8 += *p++;
576 i8 += *p++;
577 i7 += *p++;
578 i6 += *p++;
579 i7 += *p++;
580 i8 += *p++;
581 i8 += *p++;
582 i8 += *p++;
583 p = psrc + (y - 2) * videoWidth + xM4;
584 i8 += *p++;
585 i8 += *p++;
586 i5 += *p++;
587 i4 += *p++;
588 i3 += *p++;
589 i4 += *p++;
590 i5 += *p++;
591 i8 += *p++;
592 i8 += *p++;
593 p = psrc + (y - 1) * videoWidth + xM4;
594 i8 += *p++;
595 i7 += *p++;
596 i4 += *p++;
597 i2 += *p++;
598 i1 += *p++;
599 i2 += *p++;
600 i4 += *p++;
601 i8 += *p++;
602 i8 += *p++;
603 p = psrc + (y + 0) * videoWidth + xM4;
604 i8 += *p++;
605 i6 += *p++;
606 i3 += *p++;
607 i1 += *p++;
608 i0 += *p++;
609 i1 += *p++;
610 i3 += *p++;
611 i6 += *p++;
612 i8 += *p++;
613 p = psrc + (y + 1) * videoWidth + xM4;
614 i8 += *p++;
615 i7 += *p++;
616 i4 += *p++;
617 i2 += *p++;
618 i1 += *p++;
619 i2 += *p++;
620 i4 += *p++;
621 i8 += *p++;
622 i8 += *p++;
623 p = psrc + (y + 2) * videoWidth + xM4;
624 i8 += *p++;
625 i8 += *p++;
626 i5 += *p++;
627 i4 += *p++;
628 i3 += *p++;
629 i4 += *p++;
630 i5 += *p++;
631 i8 += *p++;
632 i8 += *p++;
633 p = psrc + (y + 3) * videoWidth + xM4;
634 i8 += *p++;
635 i8 += *p++;
636 i8 += *p++;
637 i7 += *p++;
638 i6 += *p++;
639 i7 += *p++;
640 i8 += *p++;
641 i8 += *p++;
642 i8 += *p++;
643 p = psrc + (y + 4) * videoWidth + xM4;
644 i8 += *p++;
645 i8 += *p++;
646 i8 += *p++;
647 i8 += *p++;
648 i8 += *p++;
649 i8 += *p++;
650 i8 += *p++;
651 i8 += *p++;
652 i8 += *p++;
653 average = (i0 + i1 + i2 + i3 + i4 + i5 + i6 + i7 + i8) / 85.0;
654 fit = P0 * (i0 - average) + P1 * (i1 - 4 * average) + P2 * (i2 - 4 * average) +
655 P3 * (i3 - 4 * average) + P4 * (i4 - 8 * average) + P5 * (i5 - 4 * average) +
656 P6 * (i6 - 4 * average) + P7 * (i7 - 8 * average) + P8 * (i8 - 48 * average);
657 if (bestFit < fit)
658 {
659 bestFit = fit;
660 ix = x;
661 iy = y;
662 }
663 }
664
665 if (bestFit > 50)
666 {
667 double sumX = 0;
668 double sumY = 0;
669 double total = 0;
670 for (int y = iy - 4; y <= iy + 4; y++)
671 {
672 p = psrc + y * width + ix - 4;
673 for (int x = ix - 4; x <= ix + 4; x++)
674 {
675 double w = *p++;
676 sumX += x * w;
677 sumY += y * w;
678 total += w;
679 }
680 }
681 if (total > 0)
682 {
683 ret = (GuiderUtils::Vector(trackingBox.x(), trackingBox.y(), 0) + GuiderUtils::Vector(sumX / total, sumY / total, 0));
684 return ret;
685 }
686 }
687
688 return GuiderUtils::Vector(-1, -1, -1);
689 }
690 break;
691 // Alexander's Stepanenko smart threshold algorithm
692 case SMART_THRESHOLD:
693 {
694 point_t bbox_lt = { trackingBox.x() - SMART_FRAME_WIDTH, trackingBox.y() - SMART_FRAME_WIDTH };
695 point_t bbox_rb = { trackingBox.x() + trackingBox.width() + SMART_FRAME_WIDTH,
696 trackingBox.y() + trackingBox.width() + SMART_FRAME_WIDTH
697 };
698 int offset = 0;
699
700 // clip frame
701 if (bbox_lt.x < 0)
702 bbox_lt.x = 0;
703 if (bbox_lt.y < 0)
704 bbox_lt.y = 0;
705 if (bbox_rb.x > videoWidth)
706 bbox_rb.x = videoWidth;
707 if (bbox_rb.y > videoHeight)
708 bbox_rb.y = videoHeight;
709
710 // calc top bar
711 int box_wd = bbox_rb.x - bbox_lt.x;
712 int box_ht = trackingBox.y() - bbox_lt.y;
713 int pix_cnt = 0;
714 if (box_wd > 0 && box_ht > 0)
715 {
716 pix_cnt += box_wd * box_ht;
717 for (j = bbox_lt.y; j < trackingBox.y(); ++j)
718 {
719 offset = j * videoWidth;
720 for (i = bbox_lt.x; i < bbox_rb.x; ++i)
721 {
722 pptr = pdata + offset + i;
723 threshold += *pptr;
724 }
725 }
726 }
727 // calc left bar
728 box_wd = trackingBox.x() - bbox_lt.x;
729 box_ht = trackingBox.width();
730 if (box_wd > 0 && box_ht > 0)
731 {
732 pix_cnt += box_wd * box_ht;
733 for (j = trackingBox.y(); j < trackingBox.y() + box_ht; ++j)
734 {
735 offset = j * videoWidth;
736 for (i = bbox_lt.x; i < trackingBox.x(); ++i)
737 {
738 pptr = pdata + offset + i;
739 threshold += *pptr;
740 }
741 }
742 }
743 // calc right bar
744 box_wd = bbox_rb.x - trackingBox.x() - trackingBox.width();
745 box_ht = trackingBox.width();
746 if (box_wd > 0 && box_ht > 0)
747 {
748 pix_cnt += box_wd * box_ht;
749 for (j = trackingBox.y(); j < trackingBox.y() + box_ht; ++j)
750 {
751 offset = j * videoWidth;
752 for (i = trackingBox.x() + trackingBox.width(); i < bbox_rb.x; ++i)
753 {
754 pptr = pdata + offset + i;
755 threshold += *pptr;
756 }
757 }
758 }
759 // calc bottom bar
760 box_wd = bbox_rb.x - bbox_lt.x;
761 box_ht = bbox_rb.y - trackingBox.y() - trackingBox.width();
762 if (box_wd > 0 && box_ht > 0)
763 {
764 pix_cnt += box_wd * box_ht;
765 for (j = trackingBox.y() + trackingBox.width(); j < bbox_rb.y; ++j)
766 {
767 offset = j * videoWidth;
768 for (i = bbox_lt.x; i < bbox_rb.x; ++i)
769 {
770 pptr = pdata + offset + i;
771 threshold += *pptr;
772 }
773 }
774 }
775 // find maximum
776 double max_val = 0;
777 for (j = 0; j < trackingBox.width(); ++j)
778 {
779 for (i = 0; i < trackingBox.width(); ++i)
780 {
781 pptr = psrc + i;
782 if (*pptr > max_val)
783 max_val = *pptr;
784 }
785 psrc += videoWidth;
786 }
787 if (pix_cnt != 0)
788 threshold /= (double)pix_cnt;
789
790 // cut by 10% higher then average threshold
791 if (max_val > threshold)
792 threshold += (max_val - threshold) * SMART_CUT_FACTOR;
793
794 //log_i("smart thr. = %f cnt = %d", threshold, pix_cnt);
795 break;
796 }
797 // simple adaptive threshold
798 case AUTO_THRESHOLD:
799 {
800 for (j = 0; j < trackingBox.width(); ++j)
801 {
802 for (i = 0; i < trackingBox.width(); ++i)
803 {
804 pptr = psrc + i;
805 threshold += *pptr;
806 }
807 psrc += videoWidth;
808 }
809 threshold /= square_square;
810 break;
811 }
812 // no threshold subtracion
813 default:
814 {
815 }
816 }
817
818 psrc = porigin;
819 for (j = 0; j < trackingBox.width(); ++j)
820 {
821 for (i = 0; i < trackingBox.width(); ++i)
822 {
823 pptr = psrc + i;
824 pval = *pptr - threshold;
825 pval = pval < 0 ? 0 : pval;
826
827 resx += (double)i * pval;
828 resy += (double)j * pval;
829
830 mass += pval;
831 }
832 psrc += videoWidth;
833 }
834
835 if (mass == 0)
836 mass = 1;
837
838 resx /= mass;
839 resy /= mass;
840
841 ret = GuiderUtils::Vector(trackingBox.x(), trackingBox.y(), 0) + GuiderUtils::Vector(resx, resy, 0);
842
843 return ret;
844}
845
846GuiderUtils::Vector GuideAlgorithms::findLocalStarPosition(QSharedPointer<FITSData> &imageData,
847 const int algorithmIndex,
848 const int videoWidth,
849 const int videoHeight,
850 const QRect &trackingBox)
851{
852 switch (imageData->dataType())
853 {
854 case TBYTE:
855 return findLocalStarPosition<uint8_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
856
857 case TSHORT:
858 return findLocalStarPosition<int16_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
859
860 case TUSHORT:
861 return findLocalStarPosition<uint16_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
862
863 case TLONG:
864 return findLocalStarPosition<int32_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
865
866 case TULONG:
867 return findLocalStarPosition<uint32_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
868
869 case TFLOAT:
870 return findLocalStarPosition<float>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
871
872 case TLONGLONG:
873 return findLocalStarPosition<int64_t>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
874
875 case TDOUBLE:
876 return findLocalStarPosition<double>(imageData, algorithmIndex, videoWidth, videoHeight, trackingBox);
877
878 default:
879 break;
880 }
881
882 return GuiderUtils::Vector(-1, -1, -1);
883}
QAction * repeat(const QObject *recvr, const char *slot, QObject *parent)
QAction * end(const QObject *recvr, const char *slot, QObject *parent)
QAction * next(const QObject *recvr, const char *slot, QObject *parent)
T result() const const
void waitForFinished()
void append(QList< T > &&value)
int height() const const
bool isValid() const const
int left() const const
int top() const const
int width() const const
int x() const const
int y() const const
bool isNull() const const
QTextStream & center(QTextStream &stream)
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