cmsgamma.c revision 10444:f08705540498
1/* 2 * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. 3 * 4 * This code is free software; you can redistribute it and/or modify it 5 * under the terms of the GNU General Public License version 2 only, as 6 * published by the Free Software Foundation. Oracle designates this 7 * particular file as subject to the "Classpath" exception as provided 8 * by Oracle in the LICENSE file that accompanied this code. 9 * 10 * This code is distributed in the hope that it will be useful, but WITHOUT 11 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 12 * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License 13 * version 2 for more details (a copy is included in the LICENSE file that 14 * accompanied this code). 15 * 16 * You should have received a copy of the GNU General Public License version 17 * 2 along with this work; if not, write to the Free Software Foundation, 18 * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. 19 * 20 * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA 21 * or visit www.oracle.com if you need additional information or have any 22 * questions. 23 */ 24 25// This file is available under and governed by the GNU General Public 26// License version 2 only, as published by the Free Software Foundation. 27// However, the following notice accompanied the original version of this 28// file: 29// 30//--------------------------------------------------------------------------------- 31// 32// Little Color Management System 33// Copyright (c) 1998-2013 Marti Maria Saguer 34// 35// Permission is hereby granted, free of charge, to any person obtaining 36// a copy of this software and associated documentation files (the "Software"), 37// to deal in the Software without restriction, including without limitation 38// the rights to use, copy, modify, merge, publish, distribute, sublicense, 39// and/or sell copies of the Software, and to permit persons to whom the Software 40// is furnished to do so, subject to the following conditions: 41// 42// The above copyright notice and this permission notice shall be included in 43// all copies or substantial portions of the Software. 44// 45// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, 46// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO 47// THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND 48// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE 49// LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION 50// OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION 51// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 52// 53//--------------------------------------------------------------------------------- 54// 55#include "lcms2_internal.h" 56 57// Tone curves are powerful constructs that can contain curves specified in diverse ways. 58// The curve is stored in segments, where each segment can be sampled or specified by parameters. 59// a 16.bit simplification of the *whole* curve is kept for optimization purposes. For float operation, 60// each segment is evaluated separately. Plug-ins may be used to define new parametric schemes, 61// each plug-in may define up to MAX_TYPES_IN_LCMS_PLUGIN functions types. For defining a function, 62// the plug-in should provide the type id, how many parameters each type has, and a pointer to 63// a procedure that evaluates the function. In the case of reverse evaluation, the evaluator will 64// be called with the type id as a negative value, and a sampled version of the reversed curve 65// will be built. 66 67// ----------------------------------------------------------------- Implementation 68// Maxim number of nodes 69#define MAX_NODES_IN_CURVE 4097 70#define MINUS_INF (-1E22F) 71#define PLUS_INF (+1E22F) 72 73// The list of supported parametric curves 74typedef struct _cmsParametricCurvesCollection_st { 75 76 int nFunctions; // Number of supported functions in this chunk 77 int FunctionTypes[MAX_TYPES_IN_LCMS_PLUGIN]; // The identification types 78 int ParameterCount[MAX_TYPES_IN_LCMS_PLUGIN]; // Number of parameters for each function 79 cmsParametricCurveEvaluator Evaluator; // The evaluator 80 81 struct _cmsParametricCurvesCollection_st* Next; // Next in list 82 83} _cmsParametricCurvesCollection; 84 85 86// This is the default (built-in) evaluator 87static cmsFloat64Number DefaultEvalParametricFn(cmsInt32Number Type, const cmsFloat64Number Params[], cmsFloat64Number R); 88 89// The built-in list 90static _cmsParametricCurvesCollection DefaultCurves = { 91 9, // # of curve types 92 { 1, 2, 3, 4, 5, 6, 7, 8, 108 }, // Parametric curve ID 93 { 1, 3, 4, 5, 7, 4, 5, 5, 1 }, // Parameters by type 94 DefaultEvalParametricFn, // Evaluator 95 NULL // Next in chain 96}; 97 98// The linked list head 99static _cmsParametricCurvesCollection* ParametricCurves = &DefaultCurves; 100 101// As a way to install new parametric curves 102cmsBool _cmsRegisterParametricCurvesPlugin(cmsContext id, cmsPluginBase* Data) 103{ 104 cmsPluginParametricCurves* Plugin = (cmsPluginParametricCurves*) Data; 105 _cmsParametricCurvesCollection* fl; 106 107 if (Data == NULL) { 108 109 ParametricCurves = &DefaultCurves; 110 return TRUE; 111 } 112 113 fl = (_cmsParametricCurvesCollection*) _cmsPluginMalloc(id, sizeof(_cmsParametricCurvesCollection)); 114 if (fl == NULL) return FALSE; 115 116 // Copy the parameters 117 fl ->Evaluator = Plugin ->Evaluator; 118 fl ->nFunctions = Plugin ->nFunctions; 119 120 // Make sure no mem overwrites 121 if (fl ->nFunctions > MAX_TYPES_IN_LCMS_PLUGIN) 122 fl ->nFunctions = MAX_TYPES_IN_LCMS_PLUGIN; 123 124 // Copy the data 125 memmove(fl->FunctionTypes, Plugin ->FunctionTypes, fl->nFunctions * sizeof(cmsUInt32Number)); 126 memmove(fl->ParameterCount, Plugin ->ParameterCount, fl->nFunctions * sizeof(cmsUInt32Number)); 127 128 // Keep linked list 129 fl ->Next = ParametricCurves; 130 ParametricCurves = fl; 131 132 // All is ok 133 return TRUE; 134} 135 136 137// Search in type list, return position or -1 if not found 138static 139int IsInSet(int Type, _cmsParametricCurvesCollection* c) 140{ 141 int i; 142 143 for (i=0; i < c ->nFunctions; i++) 144 if (abs(Type) == c ->FunctionTypes[i]) return i; 145 146 return -1; 147} 148 149 150// Search for the collection which contains a specific type 151static 152_cmsParametricCurvesCollection *GetParametricCurveByType(int Type, int* index) 153{ 154 _cmsParametricCurvesCollection* c; 155 int Position; 156 157 for (c = ParametricCurves; c != NULL; c = c ->Next) { 158 159 Position = IsInSet(Type, c); 160 161 if (Position != -1) { 162 if (index != NULL) 163 *index = Position; 164 return c; 165 } 166 } 167 168 return NULL; 169} 170 171// Low level allocate, which takes care of memory details. nEntries may be zero, and in this case 172// no optimation curve is computed. nSegments may also be zero in the inverse case, where only the 173// optimization curve is given. Both features simultaneously is an error 174static 175cmsToneCurve* AllocateToneCurveStruct(cmsContext ContextID, cmsInt32Number nEntries, 176 cmsInt32Number nSegments, const cmsCurveSegment* Segments, 177 const cmsUInt16Number* Values) 178{ 179 cmsToneCurve* p; 180 int i; 181 182 // We allow huge tables, which are then restricted for smoothing operations 183 if (nEntries > 65530 || nEntries < 0) { 184 cmsSignalError(ContextID, cmsERROR_RANGE, "Couldn't create tone curve of more than 65530 entries"); 185 return NULL; 186 } 187 188 if (nEntries <= 0 && nSegments <= 0) { 189 cmsSignalError(ContextID, cmsERROR_RANGE, "Couldn't create tone curve with zero segments and no table"); 190 return NULL; 191 } 192 193 // Allocate all required pointers, etc. 194 p = (cmsToneCurve*) _cmsMallocZero(ContextID, sizeof(cmsToneCurve)); 195 if (!p) return NULL; 196 197 // In this case, there are no segments 198 if (nSegments <= 0) { 199 p ->Segments = NULL; 200 p ->Evals = NULL; 201 } 202 else { 203 p ->Segments = (cmsCurveSegment*) _cmsCalloc(ContextID, nSegments, sizeof(cmsCurveSegment)); 204 if (p ->Segments == NULL) goto Error; 205 206 p ->Evals = (cmsParametricCurveEvaluator*) _cmsCalloc(ContextID, nSegments, sizeof(cmsParametricCurveEvaluator)); 207 if (p ->Evals == NULL) goto Error; 208 } 209 210 p -> nSegments = nSegments; 211 212 // This 16-bit table contains a limited precision representation of the whole curve and is kept for 213 // increasing xput on certain operations. 214 if (nEntries <= 0) { 215 p ->Table16 = NULL; 216 } 217 else { 218 p ->Table16 = (cmsUInt16Number*) _cmsCalloc(ContextID, nEntries, sizeof(cmsUInt16Number)); 219 if (p ->Table16 == NULL) goto Error; 220 } 221 222 p -> nEntries = nEntries; 223 224 // Initialize members if requested 225 if (Values != NULL && (nEntries > 0)) { 226 227 for (i=0; i < nEntries; i++) 228 p ->Table16[i] = Values[i]; 229 } 230 231 // Initialize the segments stuff. The evaluator for each segment is located and a pointer to it 232 // is placed in advance to maximize performance. 233 if (Segments != NULL && (nSegments > 0)) { 234 235 _cmsParametricCurvesCollection *c; 236 237 p ->SegInterp = (cmsInterpParams**) _cmsCalloc(ContextID, nSegments, sizeof(cmsInterpParams*)); 238 if (p ->SegInterp == NULL) goto Error; 239 240 for (i=0; i< nSegments; i++) { 241 242 // Type 0 is a special marker for table-based curves 243 if (Segments[i].Type == 0) 244 p ->SegInterp[i] = _cmsComputeInterpParams(ContextID, Segments[i].nGridPoints, 1, 1, NULL, CMS_LERP_FLAGS_FLOAT); 245 246 memmove(&p ->Segments[i], &Segments[i], sizeof(cmsCurveSegment)); 247 248 if (Segments[i].Type == 0 && Segments[i].SampledPoints != NULL) 249 p ->Segments[i].SampledPoints = (cmsFloat32Number*) _cmsDupMem(ContextID, Segments[i].SampledPoints, sizeof(cmsFloat32Number) * Segments[i].nGridPoints); 250 else 251 p ->Segments[i].SampledPoints = NULL; 252 253 254 c = GetParametricCurveByType(Segments[i].Type, NULL); 255 if (c != NULL) 256 p ->Evals[i] = c ->Evaluator; 257 } 258 } 259 260 p ->InterpParams = _cmsComputeInterpParams(ContextID, p ->nEntries, 1, 1, p->Table16, CMS_LERP_FLAGS_16BITS); 261 if (p->InterpParams != NULL) 262 return p; 263 264Error: 265 if (p -> Segments) _cmsFree(ContextID, p ->Segments); 266 if (p -> Evals) _cmsFree(ContextID, p -> Evals); 267 if (p ->Table16) _cmsFree(ContextID, p ->Table16); 268 _cmsFree(ContextID, p); 269 return NULL; 270} 271 272 273// Parametric Fn using floating point 274static 275cmsFloat64Number DefaultEvalParametricFn(cmsInt32Number Type, const cmsFloat64Number Params[], cmsFloat64Number R) 276{ 277 cmsFloat64Number e, Val, disc; 278 279 switch (Type) { 280 281 // X = Y ^ Gamma 282 case 1: 283 if (R < 0) { 284 285 if (fabs(Params[0] - 1.0) < MATRIX_DET_TOLERANCE) 286 Val = R; 287 else 288 Val = 0; 289 } 290 else 291 Val = pow(R, Params[0]); 292 break; 293 294 // Type 1 Reversed: X = Y ^1/gamma 295 case -1: 296 if (R < 0) { 297 298 if (fabs(Params[0] - 1.0) < MATRIX_DET_TOLERANCE) 299 Val = R; 300 else 301 Val = 0; 302 } 303 else 304 Val = pow(R, 1/Params[0]); 305 break; 306 307 // CIE 122-1966 308 // Y = (aX + b)^Gamma | X >= -b/a 309 // Y = 0 | else 310 case 2: 311 disc = -Params[2] / Params[1]; 312 313 if (R >= disc ) { 314 315 e = Params[1]*R + Params[2]; 316 317 if (e > 0) 318 Val = pow(e, Params[0]); 319 else 320 Val = 0; 321 } 322 else 323 Val = 0; 324 break; 325 326 // Type 2 Reversed 327 // X = (Y ^1/g - b) / a 328 case -2: 329 if (R < 0) 330 Val = 0; 331 else 332 Val = (pow(R, 1.0/Params[0]) - Params[2]) / Params[1]; 333 334 if (Val < 0) 335 Val = 0; 336 break; 337 338 339 // IEC 61966-3 340 // Y = (aX + b)^Gamma | X <= -b/a 341 // Y = c | else 342 case 3: 343 disc = -Params[2] / Params[1]; 344 if (disc < 0) 345 disc = 0; 346 347 if (R >= disc) { 348 349 e = Params[1]*R + Params[2]; 350 351 if (e > 0) 352 Val = pow(e, Params[0]) + Params[3]; 353 else 354 Val = 0; 355 } 356 else 357 Val = Params[3]; 358 break; 359 360 361 // Type 3 reversed 362 // X=((Y-c)^1/g - b)/a | (Y>=c) 363 // X=-b/a | (Y<c) 364 case -3: 365 if (R >= Params[3]) { 366 367 e = R - Params[3]; 368 369 if (e > 0) 370 Val = (pow(e, 1/Params[0]) - Params[2]) / Params[1]; 371 else 372 Val = 0; 373 } 374 else { 375 Val = -Params[2] / Params[1]; 376 } 377 break; 378 379 380 // IEC 61966-2.1 (sRGB) 381 // Y = (aX + b)^Gamma | X >= d 382 // Y = cX | X < d 383 case 4: 384 if (R >= Params[4]) { 385 386 e = Params[1]*R + Params[2]; 387 388 if (e > 0) 389 Val = pow(e, Params[0]); 390 else 391 Val = 0; 392 } 393 else 394 Val = R * Params[3]; 395 break; 396 397 // Type 4 reversed 398 // X=((Y^1/g-b)/a) | Y >= (ad+b)^g 399 // X=Y/c | Y< (ad+b)^g 400 case -4: 401 e = Params[1] * Params[4] + Params[2]; 402 if (e < 0) 403 disc = 0; 404 else 405 disc = pow(e, Params[0]); 406 407 if (R >= disc) { 408 409 Val = (pow(R, 1.0/Params[0]) - Params[2]) / Params[1]; 410 } 411 else { 412 Val = R / Params[3]; 413 } 414 break; 415 416 417 // Y = (aX + b)^Gamma + e | X >= d 418 // Y = cX + f | X < d 419 case 5: 420 if (R >= Params[4]) { 421 422 e = Params[1]*R + Params[2]; 423 424 if (e > 0) 425 Val = pow(e, Params[0]) + Params[5]; 426 else 427 Val = Params[5]; 428 } 429 else 430 Val = R*Params[3] + Params[6]; 431 break; 432 433 434 // Reversed type 5 435 // X=((Y-e)1/g-b)/a | Y >=(ad+b)^g+e), cd+f 436 // X=(Y-f)/c | else 437 case -5: 438 439 disc = Params[3] * Params[4] + Params[6]; 440 if (R >= disc) { 441 442 e = R - Params[5]; 443 if (e < 0) 444 Val = 0; 445 else 446 Val = (pow(e, 1.0/Params[0]) - Params[2]) / Params[1]; 447 } 448 else { 449 Val = (R - Params[6]) / Params[3]; 450 } 451 break; 452 453 454 // Types 6,7,8 comes from segmented curves as described in ICCSpecRevision_02_11_06_Float.pdf 455 // Type 6 is basically identical to type 5 without d 456 457 // Y = (a * X + b) ^ Gamma + c 458 case 6: 459 e = Params[1]*R + Params[2]; 460 461 if (e < 0) 462 Val = Params[3]; 463 else 464 Val = pow(e, Params[0]) + Params[3]; 465 break; 466 467 // ((Y - c) ^1/Gamma - b) / a 468 case -6: 469 e = R - Params[3]; 470 if (e < 0) 471 Val = 0; 472 else 473 Val = (pow(e, 1.0/Params[0]) - Params[2]) / Params[1]; 474 break; 475 476 477 // Y = a * log (b * X^Gamma + c) + d 478 case 7: 479 480 e = Params[2] * pow(R, Params[0]) + Params[3]; 481 if (e <= 0) 482 Val = Params[4]; 483 else 484 Val = Params[1]*log10(e) + Params[4]; 485 break; 486 487 // (Y - d) / a = log(b * X ^Gamma + c) 488 // pow(10, (Y-d) / a) = b * X ^Gamma + c 489 // pow((pow(10, (Y-d) / a) - c) / b, 1/g) = X 490 case -7: 491 Val = pow((pow(10.0, (R-Params[4]) / Params[1]) - Params[3]) / Params[2], 1.0 / Params[0]); 492 break; 493 494 495 //Y = a * b^(c*X+d) + e 496 case 8: 497 Val = (Params[0] * pow(Params[1], Params[2] * R + Params[3]) + Params[4]); 498 break; 499 500 501 // Y = (log((y-e) / a) / log(b) - d ) / c 502 // a=0, b=1, c=2, d=3, e=4, 503 case -8: 504 505 disc = R - Params[4]; 506 if (disc < 0) Val = 0; 507 else 508 Val = (log(disc / Params[0]) / log(Params[1]) - Params[3]) / Params[2]; 509 break; 510 511 // S-Shaped: (1 - (1-x)^1/g)^1/g 512 case 108: 513 Val = pow(1.0 - pow(1 - R, 1/Params[0]), 1/Params[0]); 514 break; 515 516 // y = (1 - (1-x)^1/g)^1/g 517 // y^g = (1 - (1-x)^1/g) 518 // 1 - y^g = (1-x)^1/g 519 // (1 - y^g)^g = 1 - x 520 // 1 - (1 - y^g)^g 521 case -108: 522 Val = 1 - pow(1 - pow(R, Params[0]), Params[0]); 523 break; 524 525 default: 526 // Unsupported parametric curve. Should never reach here 527 return 0; 528 } 529 530 return Val; 531} 532 533// Evaluate a segmented funtion for a single value. Return -1 if no valid segment found . 534// If fn type is 0, perform an interpolation on the table 535static 536cmsFloat64Number EvalSegmentedFn(const cmsToneCurve *g, cmsFloat64Number R) 537{ 538 int i; 539 540 for (i = g ->nSegments-1; i >= 0 ; --i) { 541 542 // Check for domain 543 if ((R > g ->Segments[i].x0) && (R <= g ->Segments[i].x1)) { 544 545 // Type == 0 means segment is sampled 546 if (g ->Segments[i].Type == 0) { 547 548 cmsFloat32Number R1 = (cmsFloat32Number) (R - g ->Segments[i].x0) / (g ->Segments[i].x1 - g ->Segments[i].x0); 549 cmsFloat32Number Out; 550 551 // Setup the table (TODO: clean that) 552 g ->SegInterp[i]-> Table = g ->Segments[i].SampledPoints; 553 554 g ->SegInterp[i] -> Interpolation.LerpFloat(&R1, &Out, g ->SegInterp[i]); 555 556 return Out; 557 } 558 else 559 return g ->Evals[i](g->Segments[i].Type, g ->Segments[i].Params, R); 560 } 561 } 562 563 return MINUS_INF; 564} 565 566// Access to estimated low-res table 567cmsUInt32Number CMSEXPORT cmsGetToneCurveEstimatedTableEntries(const cmsToneCurve* t) 568{ 569 _cmsAssert(t != NULL); 570 return t ->nEntries; 571} 572 573const cmsUInt16Number* CMSEXPORT cmsGetToneCurveEstimatedTable(const cmsToneCurve* t) 574{ 575 _cmsAssert(t != NULL); 576 return t ->Table16; 577} 578 579 580// Create an empty gamma curve, by using tables. This specifies only the limited-precision part, and leaves the 581// floating point description empty. 582cmsToneCurve* CMSEXPORT cmsBuildTabulatedToneCurve16(cmsContext ContextID, cmsInt32Number nEntries, const cmsUInt16Number Values[]) 583{ 584 return AllocateToneCurveStruct(ContextID, nEntries, 0, NULL, Values); 585} 586 587static 588int EntriesByGamma(cmsFloat64Number Gamma) 589{ 590 if (fabs(Gamma - 1.0) < 0.001) return 2; 591 return 4096; 592} 593 594 595// Create a segmented gamma, fill the table 596cmsToneCurve* CMSEXPORT cmsBuildSegmentedToneCurve(cmsContext ContextID, 597 cmsInt32Number nSegments, const cmsCurveSegment Segments[]) 598{ 599 int i; 600 cmsFloat64Number R, Val; 601 cmsToneCurve* g; 602 int nGridPoints = 4096; 603 604 _cmsAssert(Segments != NULL); 605 606 // Optimizatin for identity curves. 607 if (nSegments == 1 && Segments[0].Type == 1) { 608 609 nGridPoints = EntriesByGamma(Segments[0].Params[0]); 610 } 611 612 g = AllocateToneCurveStruct(ContextID, nGridPoints, nSegments, Segments, NULL); 613 if (g == NULL) return NULL; 614 615 // Once we have the floating point version, we can approximate a 16 bit table of 4096 entries 616 // for performance reasons. This table would normally not be used except on 8/16 bits transforms. 617 for (i=0; i < nGridPoints; i++) { 618 619 R = (cmsFloat64Number) i / (nGridPoints-1); 620 621 Val = EvalSegmentedFn(g, R); 622 623 // Round and saturate 624 g ->Table16[i] = _cmsQuickSaturateWord(Val * 65535.0); 625 } 626 627 return g; 628} 629 630// Use a segmented curve to store the floating point table 631cmsToneCurve* CMSEXPORT cmsBuildTabulatedToneCurveFloat(cmsContext ContextID, cmsUInt32Number nEntries, const cmsFloat32Number values[]) 632{ 633 cmsCurveSegment Seg[3]; 634 635 // A segmented tone curve should have function segments in the first and last positions 636 // Initialize segmented curve part up to 0 to constant value = samples[0] 637 Seg[0].x0 = MINUS_INF; 638 Seg[0].x1 = 0; 639 Seg[0].Type = 6; 640 641 Seg[0].Params[0] = 1; 642 Seg[0].Params[1] = 0; 643 Seg[0].Params[2] = 0; 644 Seg[0].Params[3] = values[0]; 645 Seg[0].Params[4] = 0; 646 647 // From zero to 1 648 Seg[1].x0 = 0; 649 Seg[1].x1 = 1.0; 650 Seg[1].Type = 0; 651 652 Seg[1].nGridPoints = nEntries; 653 Seg[1].SampledPoints = (cmsFloat32Number*) values; 654 655 // Final segment is constant = lastsample 656 Seg[2].x0 = 1.0; 657 Seg[2].x1 = PLUS_INF; 658 Seg[2].Type = 6; 659 660 Seg[2].Params[0] = 1; 661 Seg[2].Params[1] = 0; 662 Seg[2].Params[2] = 0; 663 Seg[2].Params[3] = values[nEntries-1]; 664 Seg[2].Params[4] = 0; 665 666 667 return cmsBuildSegmentedToneCurve(ContextID, 3, Seg); 668} 669 670// Parametric curves 671// 672// Parameters goes as: Curve, a, b, c, d, e, f 673// Type is the ICC type +1 674// if type is negative, then the curve is analyticaly inverted 675cmsToneCurve* CMSEXPORT cmsBuildParametricToneCurve(cmsContext ContextID, cmsInt32Number Type, const cmsFloat64Number Params[]) 676{ 677 cmsCurveSegment Seg0; 678 int Pos = 0; 679 cmsUInt32Number size; 680 _cmsParametricCurvesCollection* c = GetParametricCurveByType(Type, &Pos); 681 682 _cmsAssert(Params != NULL); 683 684 if (c == NULL) { 685 cmsSignalError(ContextID, cmsERROR_UNKNOWN_EXTENSION, "Invalid parametric curve type %d", Type); 686 return NULL; 687 } 688 689 memset(&Seg0, 0, sizeof(Seg0)); 690 691 Seg0.x0 = MINUS_INF; 692 Seg0.x1 = PLUS_INF; 693 Seg0.Type = Type; 694 695 size = c->ParameterCount[Pos] * sizeof(cmsFloat64Number); 696 memmove(Seg0.Params, Params, size); 697 698 return cmsBuildSegmentedToneCurve(ContextID, 1, &Seg0); 699} 700 701 702 703// Build a gamma table based on gamma constant 704cmsToneCurve* CMSEXPORT cmsBuildGamma(cmsContext ContextID, cmsFloat64Number Gamma) 705{ 706 return cmsBuildParametricToneCurve(ContextID, 1, &Gamma); 707} 708 709 710// Free all memory taken by the gamma curve 711void CMSEXPORT cmsFreeToneCurve(cmsToneCurve* Curve) 712{ 713 cmsContext ContextID; 714 715 if (Curve == NULL) return; 716 717 ContextID = Curve ->InterpParams->ContextID; 718 719 _cmsFreeInterpParams(Curve ->InterpParams); 720 721 if (Curve -> Table16) 722 _cmsFree(ContextID, Curve ->Table16); 723 724 if (Curve ->Segments) { 725 726 cmsUInt32Number i; 727 728 for (i=0; i < Curve ->nSegments; i++) { 729 730 if (Curve ->Segments[i].SampledPoints) { 731 _cmsFree(ContextID, Curve ->Segments[i].SampledPoints); 732 } 733 734 if (Curve ->SegInterp[i] != 0) 735 _cmsFreeInterpParams(Curve->SegInterp[i]); 736 } 737 738 _cmsFree(ContextID, Curve ->Segments); 739 _cmsFree(ContextID, Curve ->SegInterp); 740 } 741 742 if (Curve -> Evals) 743 _cmsFree(ContextID, Curve -> Evals); 744 745 if (Curve) _cmsFree(ContextID, Curve); 746} 747 748// Utility function, free 3 gamma tables 749void CMSEXPORT cmsFreeToneCurveTriple(cmsToneCurve* Curve[3]) 750{ 751 752 _cmsAssert(Curve != NULL); 753 754 if (Curve[0] != NULL) cmsFreeToneCurve(Curve[0]); 755 if (Curve[1] != NULL) cmsFreeToneCurve(Curve[1]); 756 if (Curve[2] != NULL) cmsFreeToneCurve(Curve[2]); 757 758 Curve[0] = Curve[1] = Curve[2] = NULL; 759} 760 761 762// Duplicate a gamma table 763cmsToneCurve* CMSEXPORT cmsDupToneCurve(const cmsToneCurve* In) 764{ 765 if (In == NULL) return NULL; 766 767 return AllocateToneCurveStruct(In ->InterpParams ->ContextID, In ->nEntries, In ->nSegments, In ->Segments, In ->Table16); 768} 769 770// Joins two curves for X and Y. Curves should be monotonic. 771// We want to get 772// 773// y = Y^-1(X(t)) 774// 775cmsToneCurve* CMSEXPORT cmsJoinToneCurve(cmsContext ContextID, 776 const cmsToneCurve* X, 777 const cmsToneCurve* Y, cmsUInt32Number nResultingPoints) 778{ 779 cmsToneCurve* out = NULL; 780 cmsToneCurve* Yreversed = NULL; 781 cmsFloat32Number t, x; 782 cmsFloat32Number* Res = NULL; 783 cmsUInt32Number i; 784 785 786 _cmsAssert(X != NULL); 787 _cmsAssert(Y != NULL); 788 789 Yreversed = cmsReverseToneCurveEx(nResultingPoints, Y); 790 if (Yreversed == NULL) goto Error; 791 792 Res = (cmsFloat32Number*) _cmsCalloc(ContextID, nResultingPoints, sizeof(cmsFloat32Number)); 793 if (Res == NULL) goto Error; 794 795 //Iterate 796 for (i=0; i < nResultingPoints; i++) { 797 798 t = (cmsFloat32Number) i / (nResultingPoints-1); 799 x = cmsEvalToneCurveFloat(X, t); 800 Res[i] = cmsEvalToneCurveFloat(Yreversed, x); 801 } 802 803 // Allocate space for output 804 out = cmsBuildTabulatedToneCurveFloat(ContextID, nResultingPoints, Res); 805 806Error: 807 808 if (Res != NULL) _cmsFree(ContextID, Res); 809 if (Yreversed != NULL) cmsFreeToneCurve(Yreversed); 810 811 return out; 812} 813 814 815 816// Get the surrounding nodes. This is tricky on non-monotonic tables 817static 818int GetInterval(cmsFloat64Number In, const cmsUInt16Number LutTable[], const struct _cms_interp_struc* p) 819{ 820 int i; 821 int y0, y1; 822 823 // A 1 point table is not allowed 824 if (p -> Domain[0] < 1) return -1; 825 826 // Let's see if ascending or descending. 827 if (LutTable[0] < LutTable[p ->Domain[0]]) { 828 829 // Table is overall ascending 830 for (i=p->Domain[0]-1; i >=0; --i) { 831 832 y0 = LutTable[i]; 833 y1 = LutTable[i+1]; 834 835 if (y0 <= y1) { // Increasing 836 if (In >= y0 && In <= y1) return i; 837 } 838 else 839 if (y1 < y0) { // Decreasing 840 if (In >= y1 && In <= y0) return i; 841 } 842 } 843 } 844 else { 845 // Table is overall descending 846 for (i=0; i < (int) p -> Domain[0]; i++) { 847 848 y0 = LutTable[i]; 849 y1 = LutTable[i+1]; 850 851 if (y0 <= y1) { // Increasing 852 if (In >= y0 && In <= y1) return i; 853 } 854 else 855 if (y1 < y0) { // Decreasing 856 if (In >= y1 && In <= y0) return i; 857 } 858 } 859 } 860 861 return -1; 862} 863 864// Reverse a gamma table 865cmsToneCurve* CMSEXPORT cmsReverseToneCurveEx(cmsInt32Number nResultSamples, const cmsToneCurve* InCurve) 866{ 867 cmsToneCurve *out; 868 cmsFloat64Number a = 0, b = 0, y, x1, y1, x2, y2; 869 int i, j; 870 int Ascending; 871 872 _cmsAssert(InCurve != NULL); 873 874 // Try to reverse it analytically whatever possible 875 if (InCurve ->nSegments == 1 && InCurve ->Segments[0].Type > 0 && InCurve -> Segments[0].Type <= 5) { 876 877 return cmsBuildParametricToneCurve(InCurve ->InterpParams->ContextID, 878 -(InCurve -> Segments[0].Type), 879 InCurve -> Segments[0].Params); 880 } 881 882 // Nope, reverse the table. 883 out = cmsBuildTabulatedToneCurve16(InCurve ->InterpParams->ContextID, nResultSamples, NULL); 884 if (out == NULL) 885 return NULL; 886 887 // We want to know if this is an ascending or descending table 888 Ascending = !cmsIsToneCurveDescending(InCurve); 889 890 // Iterate across Y axis 891 for (i=0; i < nResultSamples; i++) { 892 893 y = (cmsFloat64Number) i * 65535.0 / (nResultSamples - 1); 894 895 // Find interval in which y is within. 896 j = GetInterval(y, InCurve->Table16, InCurve->InterpParams); 897 if (j >= 0) { 898 899 900 // Get limits of interval 901 x1 = InCurve ->Table16[j]; 902 x2 = InCurve ->Table16[j+1]; 903 904 y1 = (cmsFloat64Number) (j * 65535.0) / (InCurve ->nEntries - 1); 905 y2 = (cmsFloat64Number) ((j+1) * 65535.0 ) / (InCurve ->nEntries - 1); 906 907 // If collapsed, then use any 908 if (x1 == x2) { 909 910 out ->Table16[i] = _cmsQuickSaturateWord(Ascending ? y2 : y1); 911 continue; 912 913 } else { 914 915 // Interpolate 916 a = (y2 - y1) / (x2 - x1); 917 b = y2 - a * x2; 918 } 919 } 920 921 out ->Table16[i] = _cmsQuickSaturateWord(a* y + b); 922 } 923 924 925 return out; 926} 927 928// Reverse a gamma table 929cmsToneCurve* CMSEXPORT cmsReverseToneCurve(const cmsToneCurve* InGamma) 930{ 931 _cmsAssert(InGamma != NULL); 932 933 return cmsReverseToneCurveEx(4096, InGamma); 934} 935 936// From: Eilers, P.H.C. (1994) Smoothing and interpolation with finite 937// differences. in: Graphic Gems IV, Heckbert, P.S. (ed.), Academic press. 938// 939// Smoothing and interpolation with second differences. 940// 941// Input: weights (w), data (y): vector from 1 to m. 942// Input: smoothing parameter (lambda), length (m). 943// Output: smoothed vector (z): vector from 1 to m. 944 945static 946cmsBool smooth2(cmsContext ContextID, cmsFloat32Number w[], cmsFloat32Number y[], cmsFloat32Number z[], cmsFloat32Number lambda, int m) 947{ 948 int i, i1, i2; 949 cmsFloat32Number *c, *d, *e; 950 cmsBool st; 951 952 953 c = (cmsFloat32Number*) _cmsCalloc(ContextID, MAX_NODES_IN_CURVE, sizeof(cmsFloat32Number)); 954 d = (cmsFloat32Number*) _cmsCalloc(ContextID, MAX_NODES_IN_CURVE, sizeof(cmsFloat32Number)); 955 e = (cmsFloat32Number*) _cmsCalloc(ContextID, MAX_NODES_IN_CURVE, sizeof(cmsFloat32Number)); 956 957 if (c != NULL && d != NULL && e != NULL) { 958 959 960 d[1] = w[1] + lambda; 961 c[1] = -2 * lambda / d[1]; 962 e[1] = lambda /d[1]; 963 z[1] = w[1] * y[1]; 964 d[2] = w[2] + 5 * lambda - d[1] * c[1] * c[1]; 965 c[2] = (-4 * lambda - d[1] * c[1] * e[1]) / d[2]; 966 e[2] = lambda / d[2]; 967 z[2] = w[2] * y[2] - c[1] * z[1]; 968 969 for (i = 3; i < m - 1; i++) { 970 i1 = i - 1; i2 = i - 2; 971 d[i]= w[i] + 6 * lambda - c[i1] * c[i1] * d[i1] - e[i2] * e[i2] * d[i2]; 972 c[i] = (-4 * lambda -d[i1] * c[i1] * e[i1])/ d[i]; 973 e[i] = lambda / d[i]; 974 z[i] = w[i] * y[i] - c[i1] * z[i1] - e[i2] * z[i2]; 975 } 976 977 i1 = m - 2; i2 = m - 3; 978 979 d[m - 1] = w[m - 1] + 5 * lambda -c[i1] * c[i1] * d[i1] - e[i2] * e[i2] * d[i2]; 980 c[m - 1] = (-2 * lambda - d[i1] * c[i1] * e[i1]) / d[m - 1]; 981 z[m - 1] = w[m - 1] * y[m - 1] - c[i1] * z[i1] - e[i2] * z[i2]; 982 i1 = m - 1; i2 = m - 2; 983 984 d[m] = w[m] + lambda - c[i1] * c[i1] * d[i1] - e[i2] * e[i2] * d[i2]; 985 z[m] = (w[m] * y[m] - c[i1] * z[i1] - e[i2] * z[i2]) / d[m]; 986 z[m - 1] = z[m - 1] / d[m - 1] - c[m - 1] * z[m]; 987 988 for (i = m - 2; 1<= i; i--) 989 z[i] = z[i] / d[i] - c[i] * z[i + 1] - e[i] * z[i + 2]; 990 991 st = TRUE; 992 } 993 else st = FALSE; 994 995 if (c != NULL) _cmsFree(ContextID, c); 996 if (d != NULL) _cmsFree(ContextID, d); 997 if (e != NULL) _cmsFree(ContextID, e); 998 999 return st; 1000} 1001 1002// Smooths a curve sampled at regular intervals. 1003cmsBool CMSEXPORT cmsSmoothToneCurve(cmsToneCurve* Tab, cmsFloat64Number lambda) 1004{ 1005 cmsFloat32Number w[MAX_NODES_IN_CURVE], y[MAX_NODES_IN_CURVE], z[MAX_NODES_IN_CURVE]; 1006 int i, nItems, Zeros, Poles; 1007 1008 if (Tab == NULL) return FALSE; 1009 1010 if (cmsIsToneCurveLinear(Tab)) return TRUE; // Nothing to do 1011 1012 nItems = Tab -> nEntries; 1013 1014 if (nItems >= MAX_NODES_IN_CURVE) { 1015 cmsSignalError(Tab ->InterpParams->ContextID, cmsERROR_RANGE, "cmsSmoothToneCurve: too many points."); 1016 return FALSE; 1017 } 1018 1019 memset(w, 0, nItems * sizeof(cmsFloat32Number)); 1020 memset(y, 0, nItems * sizeof(cmsFloat32Number)); 1021 memset(z, 0, nItems * sizeof(cmsFloat32Number)); 1022 1023 for (i=0; i < nItems; i++) 1024 { 1025 y[i+1] = (cmsFloat32Number) Tab -> Table16[i]; 1026 w[i+1] = 1.0; 1027 } 1028 1029 if (!smooth2(Tab ->InterpParams->ContextID, w, y, z, (cmsFloat32Number) lambda, nItems)) return FALSE; 1030 1031 // Do some reality - checking... 1032 Zeros = Poles = 0; 1033 for (i=nItems; i > 1; --i) { 1034 1035 if (z[i] == 0.) Zeros++; 1036 if (z[i] >= 65535.) Poles++; 1037 if (z[i] < z[i-1]) { 1038 cmsSignalError(Tab ->InterpParams->ContextID, cmsERROR_RANGE, "cmsSmoothToneCurve: Non-Monotonic."); 1039 return FALSE; 1040 } 1041 } 1042 1043 if (Zeros > (nItems / 3)) { 1044 cmsSignalError(Tab ->InterpParams->ContextID, cmsERROR_RANGE, "cmsSmoothToneCurve: Degenerated, mostly zeros."); 1045 return FALSE; 1046 } 1047 if (Poles > (nItems / 3)) { 1048 cmsSignalError(Tab ->InterpParams->ContextID, cmsERROR_RANGE, "cmsSmoothToneCurve: Degenerated, mostly poles."); 1049 return FALSE; 1050 } 1051 1052 // Seems ok 1053 for (i=0; i < nItems; i++) { 1054 1055 // Clamp to cmsUInt16Number 1056 Tab -> Table16[i] = _cmsQuickSaturateWord(z[i+1]); 1057 } 1058 1059 return TRUE; 1060} 1061 1062// Is a table linear? Do not use parametric since we cannot guarantee some weird parameters resulting 1063// in a linear table. This way assures it is linear in 12 bits, which should be enought in most cases. 1064cmsBool CMSEXPORT cmsIsToneCurveLinear(const cmsToneCurve* Curve) 1065{ 1066 cmsUInt32Number i; 1067 int diff; 1068 1069 _cmsAssert(Curve != NULL); 1070 1071 for (i=0; i < Curve ->nEntries; i++) { 1072 1073 diff = abs((int) Curve->Table16[i] - (int) _cmsQuantizeVal(i, Curve ->nEntries)); 1074 if (diff > 0x0f) 1075 return FALSE; 1076 } 1077 1078 return TRUE; 1079} 1080 1081// Same, but for monotonicity 1082cmsBool CMSEXPORT cmsIsToneCurveMonotonic(const cmsToneCurve* t) 1083{ 1084 int n; 1085 int i, last; 1086 cmsBool lDescending; 1087 1088 _cmsAssert(t != NULL); 1089 1090 // Degenerated curves are monotonic? Ok, let's pass them 1091 n = t ->nEntries; 1092 if (n < 2) return TRUE; 1093 1094 // Curve direction 1095 lDescending = cmsIsToneCurveDescending(t); 1096 1097 if (lDescending) { 1098 1099 last = t ->Table16[0]; 1100 1101 for (i = 1; i < n; i++) { 1102 1103 if (t ->Table16[i] - last > 2) // We allow some ripple 1104 return FALSE; 1105 else 1106 last = t ->Table16[i]; 1107 1108 } 1109 } 1110 else { 1111 1112 last = t ->Table16[n-1]; 1113 1114 for (i = n-2; i >= 0; --i) { 1115 1116 if (t ->Table16[i] - last > 2) 1117 return FALSE; 1118 else 1119 last = t ->Table16[i]; 1120 1121 } 1122 } 1123 1124 return TRUE; 1125} 1126 1127// Same, but for descending tables 1128cmsBool CMSEXPORT cmsIsToneCurveDescending(const cmsToneCurve* t) 1129{ 1130 _cmsAssert(t != NULL); 1131 1132 return t ->Table16[0] > t ->Table16[t ->nEntries-1]; 1133} 1134 1135 1136// Another info fn: is out gamma table multisegment? 1137cmsBool CMSEXPORT cmsIsToneCurveMultisegment(const cmsToneCurve* t) 1138{ 1139 _cmsAssert(t != NULL); 1140 1141 return t -> nSegments > 1; 1142} 1143 1144cmsInt32Number CMSEXPORT cmsGetToneCurveParametricType(const cmsToneCurve* t) 1145{ 1146 _cmsAssert(t != NULL); 1147 1148 if (t -> nSegments != 1) return 0; 1149 return t ->Segments[0].Type; 1150} 1151 1152// We need accuracy this time 1153cmsFloat32Number CMSEXPORT cmsEvalToneCurveFloat(const cmsToneCurve* Curve, cmsFloat32Number v) 1154{ 1155 _cmsAssert(Curve != NULL); 1156 1157 // Check for 16 bits table. If so, this is a limited-precision tone curve 1158 if (Curve ->nSegments == 0) { 1159 1160 cmsUInt16Number In, Out; 1161 1162 In = (cmsUInt16Number) _cmsQuickSaturateWord(v * 65535.0); 1163 Out = cmsEvalToneCurve16(Curve, In); 1164 1165 return (cmsFloat32Number) (Out / 65535.0); 1166 } 1167 1168 return (cmsFloat32Number) EvalSegmentedFn(Curve, v); 1169} 1170 1171// We need xput over here 1172cmsUInt16Number CMSEXPORT cmsEvalToneCurve16(const cmsToneCurve* Curve, cmsUInt16Number v) 1173{ 1174 cmsUInt16Number out; 1175 1176 _cmsAssert(Curve != NULL); 1177 1178 Curve ->InterpParams ->Interpolation.Lerp16(&v, &out, Curve ->InterpParams); 1179 return out; 1180} 1181 1182 1183// Least squares fitting. 1184// A mathematical procedure for finding the best-fitting curve to a given set of points by 1185// minimizing the sum of the squares of the offsets ("the residuals") of the points from the curve. 1186// The sum of the squares of the offsets is used instead of the offset absolute values because 1187// this allows the residuals to be treated as a continuous differentiable quantity. 1188// 1189// y = f(x) = x ^ g 1190// 1191// R = (yi - (xi^g)) 1192// R2 = (yi - (xi^g))2 1193// SUM R2 = SUM (yi - (xi^g))2 1194// 1195// dR2/dg = -2 SUM x^g log(x)(y - x^g) 1196// solving for dR2/dg = 0 1197// 1198// g = 1/n * SUM(log(y) / log(x)) 1199 1200cmsFloat64Number CMSEXPORT cmsEstimateGamma(const cmsToneCurve* t, cmsFloat64Number Precision) 1201{ 1202 cmsFloat64Number gamma, sum, sum2; 1203 cmsFloat64Number n, x, y, Std; 1204 cmsUInt32Number i; 1205 1206 _cmsAssert(t != NULL); 1207 1208 sum = sum2 = n = 0; 1209 1210 // Excluding endpoints 1211 for (i=1; i < (MAX_NODES_IN_CURVE-1); i++) { 1212 1213 x = (cmsFloat64Number) i / (MAX_NODES_IN_CURVE-1); 1214 y = (cmsFloat64Number) cmsEvalToneCurveFloat(t, (cmsFloat32Number) x); 1215 1216 // Avoid 7% on lower part to prevent 1217 // artifacts due to linear ramps 1218 1219 if (y > 0. && y < 1. && x > 0.07) { 1220 1221 gamma = log(y) / log(x); 1222 sum += gamma; 1223 sum2 += gamma * gamma; 1224 n++; 1225 } 1226 } 1227 1228 // Take a look on SD to see if gamma isn't exponential at all 1229 Std = sqrt((n * sum2 - sum * sum) / (n*(n-1))); 1230 1231 if (Std > Precision) 1232 return -1.0; 1233 1234 return (sum / n); // The mean 1235} 1236