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GeezerVison is showing github.com — arrayfire-python/arrayfire/image.py at master · arrayfire/arrayfire-python Expand file tree/ Copy pathimage.pyMore file actions 1405 lines (1062 loc) · 39.6 KB / Copy pathimage.pyFile metadata and controls1405 lines (1062 loc) · 39.6 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 ####################################################### # Copyright (c) 2015, ArrayFire # All rights reserved. # # This file is distributed under 3-clause BSD license. # The complete license agreement can be obtained at: # http://arrayfire.com/licenses/BSD-3-Clause ######################################################## """ Image processing functions. """ from .library import * from .array import * from .data import constant from .signal import medfilt import os def gradient(image): """ Find the horizontal and vertical gradients. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. Returns --------- (dx, dy) : Tuple of af.Array. - `dx` containing the horizontal gradients of `image`. - `dy` containing the vertical gradients of `image`. """ dx = Array() dy = Array() safe_call(backend.get().af_gradient(c_pointer(dx.arr), c_pointer(dy.arr), image.arr)) return dx, dy def load_image(file_name, is_color=False): """ Load an image on the disk as an array. Parameters ---------- file_name: str - Full path of the file name on disk. is_color : optional: bool. default: False. - Specifies if the image is loaded as 1 channel (if False) or 3 channel image (if True). Returns ------- image - af.Array A 2 dimensional (1 channel) or 3 dimensional (3 channel) array containing the image. """ assert(os.path.isfile(file_name)) image = Array() safe_call(backend.get().af_load_image(c_pointer(image.arr), c_char_ptr_t(file_name.encode('ascii')), is_color)) return image def save_image(image, file_name): """ Save an array as an image on the disk. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image. file_name: str - Full path of the file name on the disk. """ assert(isinstance(file_name, str)) safe_call(backend.get().af_save_image(c_char_ptr_t(file_name.encode('ascii')), image.arr)) return image def load_image_native(file_name): """ Load an image on the disk as an array in native format. Parameters ---------- file_name: str - Full path of the file name on disk. Returns ------- image - af.Array A 2 dimensional (1 channel) or 3 dimensional (3 or 4 channel) array containing the image. """ assert(os.path.isfile(file_name)) image = Array() safe_call(backend.get().af_load_image_native(c_pointer(image.arr), c_char_ptr_t(file_name.encode('ascii')))) return image def save_image_native(image, file_name): """ Save an array as an image on the disk in native format. Parameters ---------- image : af.Array - A 2 or 3 dimensional arrayfire array representing an image. file_name: str - Full path of the file name on the disk. """ assert(isinstance(file_name, str)) safe_call(backend.get().af_save_image_native(c_char_ptr_t(file_name.encode('ascii')), image.arr)) return image def resize(image, scale=None, odim0=None, odim1=None, method=INTERP.NEAREST): """ Resize an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. scale : optional: scalar. default: None. - Scale factor for the image resizing. odim0 : optional: int. default: None. - Size of the first dimension of the output. odim1 : optional: int. default: None. - Size of the second dimension of the output. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for resizing. Returns --------- out : af.Array - Output image after resizing. Note ----- - If `scale` is None, `odim0` and `odim1` need to be specified. - If `scale` is not None, `odim0` and `odim1` are ignored. """ if (scale is None): assert(odim0 is not None) assert(odim1 is not None) else: idims = image.dims() odim0 = int(scale * idims[0]) odim1 = int(scale * idims[1]) output = Array() safe_call(backend.get().af_resize(c_pointer(output.arr), image.arr, c_dim_t(odim0), c_dim_t(odim1), method.value)) return output def transform(image, trans_mat, odim0 = 0, odim1 = 0, method=INTERP.NEAREST, is_inverse=True): """ Transform an image using a transformation matrix. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. trans_mat : af.Array - A 2 D floating point arrayfire array of size [3, 2]. odim0 : optional: int. default: 0. - Size of the first dimension of the output. odim1 : optional: int. default: 0. - Size of the second dimension of the output. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for transformation. is_inverse : optional: bool. default: True. - Specifies if the inverse transform is applied. Returns --------- out : af.Array - Output image after transformation. Note ----- - If `odim0` and `odim` are 0, the output dimensions are automatically calculated by the function. """ output = Array() safe_call(backend.get().af_transform(c_pointer(output.arr), image.arr, trans_mat.arr, c_dim_t(odim0), c_dim_t(odim1), method.value, is_inverse)) return output def rotate(image, theta, is_crop = True, method = INTERP.NEAREST): """ Rotate an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. theta : scalar - The angle to rotate in radians. is_crop : optional: bool. default: True. - Specifies if the output should be cropped to the input size. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for rotating. Returns --------- out : af.Array - Output image after rotating. """ output = Array() safe_call(backend.get().af_rotate(c_pointer(output.arr), image.arr, c_float_t(theta), is_crop, method.value)) return output def translate(image, trans0, trans1, odim0 = 0, odim1 = 0, method = INTERP.NEAREST): """ Translate an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. trans0: int. - Translation along first dimension in pixels. trans1: int. - Translation along second dimension in pixels. odim0 : optional: int. default: 0. - Size of the first dimension of the output. odim1 : optional: int. default: 0. - Size of the second dimension of the output. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for translation. Returns --------- out : af.Array - Output image after translation. Note ----- - If `odim0` and `odim` are 0, the output dimensions are automatically calculated by the function. """ output = Array() safe_call(backend.get().af_translate(c_pointer(output.arr), image.arr, trans0, trans1, c_dim_t(odim0), c_dim_t(odim1), method.value)) return output def scale(image, scale0, scale1, odim0 = 0, odim1 = 0, method = INTERP.NEAREST): """ Scale an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. scale0 : scalar. - Scale factor for the first dimension. scale1 : scalar. - Scale factor for the second dimension. odim0 : optional: int. default: None. - Size of the first dimension of the output. odim1 : optional: int. default: None. - Size of the second dimension of the output. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for resizing. Returns --------- out : af.Array - Output image after scaling. Note ----- - If `odim0` and `odim` are 0, the output dimensions are automatically calculated by the function. """ output = Array() safe_call(backend.get().af_scale(c_pointer(output.arr), image.arr, c_float_t(scale0), c_float_t(scale1), c_dim_t(odim0), c_dim_t(odim1), method.value)) return output def skew(image, skew0, skew1, odim0 = 0, odim1 = 0, method = INTERP.NEAREST, is_inverse=True): """ Skew an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. skew0 : scalar. - Skew factor for the first dimension. skew1 : scalar. - Skew factor for the second dimension. odim0 : optional: int. default: None. - Size of the first dimension of the output. odim1 : optional: int. default: None. - Size of the second dimension of the output. method : optional: af.INTERP. default: af.INTERP.NEAREST. - Interpolation method used for resizing. is_inverse : optional: bool. default: True. - Specifies if the inverse skew is applied. Returns --------- out : af.Array - Output image after skewing. Note ----- - If `odim0` and `odim` are 0, the output dimensions are automatically calculated by the function. """ output = Array() safe_call(backend.get().af_skew(c_pointer(output.arr), image.arr, c_float_t(skew0), c_float_t(skew1), c_dim_t(odim0), c_dim_t(odim1), method.value, is_inverse)) return output def histogram(image, nbins, min_val = None, max_val = None): """ Find the histogram of an image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. nbins : int. - Number of bins in the histogram. min_val : optional: scalar. default: None. - The lower bound for the bin values. - If None, `af.min(image)` is used. max_val : optional: scalar. default: None. - The upper bound for the bin values. - If None, `af.max(image)` is used. Returns --------- hist : af.Array - Containing the histogram of the image. """ from .algorithm import min as af_min from .algorithm import max as af_max if min_val is None: min_val = af_min(image) if max_val is None: max_val = af_max(image) output = Array() safe_call(backend.get().af_histogram(c_pointer(output.arr), image.arr, c_uint_t(nbins), c_double_t(min_val), c_double_t(max_val))) return output def hist_equal(image, hist): """ Equalize an image based on a histogram. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. hist : af.Array - Containing the histogram of an image. Returns --------- output : af.Array - The equalized image. """ output = Array() safe_call(backend.get().af_hist_equal(c_pointer(output.arr), image.arr, hist.arr)) return output def dilate(image, mask = None): """ Run image dilate on the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. mask : optional: af.Array. default: None. - Specifies the neighborhood of a pixel. - When None, a [3, 3] array of all ones is used. Returns --------- output : af.Array - The dilated image. """ if mask is None: mask = constant(1, 3, 3, dtype=Dtype.f32) output = Array() safe_call(backend.get().af_dilate(c_pointer(output.arr), image.arr, mask.arr)) return output def dilate3(volume, mask = None): """ Run volume dilate on a volume. Parameters ---------- volume : af.Array - A 3 D arrayfire array representing a volume, or - A multi dimensional array representing batch of volumes. mask : optional: af.Array. default: None. - Specifies the neighborhood of a pixel. - When None, a [3, 3, 3] array of all ones is used. Returns --------- output : af.Array - The dilated volume. """ if mask is None: mask = constant(1, 3, 3, 3, dtype=Dtype.f32) output = Array() safe_call(backend.get().af_dilate3(c_pointer(output.arr), volume.arr, mask.arr)) return output def erode(image, mask = None): """ Run image erode on the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. mask : optional: af.Array. default: None. - Specifies the neighborhood of a pixel. - When None, a [3, 3] array of all ones is used. Returns --------- output : af.Array - The eroded image. """ if mask is None: mask = constant(1, 3, 3, dtype=Dtype.f32) output = Array() safe_call(backend.get().af_erode(c_pointer(output.arr), image.arr, mask.arr)) return output def erode3(volume, mask = None): """ Run volume erode on the volume. Parameters ---------- volume : af.Array - A 3 D arrayfire array representing an volume, or - A multi dimensional array representing batch of volumes. mask : optional: af.Array. default: None. - Specifies the neighborhood of a pixel. - When None, a [3, 3, 3] array of all ones is used. Returns --------- output : af.Array - The eroded volume. """ if mask is None: mask = constant(1, 3, 3, 3, dtype=Dtype.f32) output = Array() safe_call(backend.get().af_erode3(c_pointer(output.arr), volume.arr, mask.arr)) return output def bilateral(image, s_sigma, c_sigma, is_color = False): """ Apply bilateral filter to the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. s_sigma : scalar. - Sigma value for the co-ordinate space. c_sigma : scalar. - Sigma value for the color space. is_color : optional: bool. default: False. - Specifies if the third dimension is 3rd channel (if True) or a batch (if False). Returns --------- output : af.Array - The image after the application of the bilateral filter. """ output = Array() safe_call(backend.get().af_bilateral(c_pointer(output.arr), image.arr, c_float_t(s_sigma), c_float_t(c_sigma), is_color)) return output def mean_shift(image, s_sigma, c_sigma, n_iter, is_color = False): """ Apply mean shift to the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. s_sigma : scalar. - Sigma value for the co-ordinate space. c_sigma : scalar. - Sigma value for the color space. n_iter : int. - Number of mean shift iterations. is_color : optional: bool. default: False. - Specifies if the third dimension is 3rd channel (if True) or a batch (if False). Returns --------- output : af.Array - The image after the application of the meanshift. """ output = Array() safe_call(backend.get().af_mean_shift(c_pointer(output.arr), image.arr, c_float_t(s_sigma), c_float_t(c_sigma), c_uint_t(n_iter), is_color)) return output def minfilt(image, w_len = 3, w_wid = 3, edge_pad = PAD.ZERO): """ Apply min filter for the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. w0 : optional: int. default: 3. - The length of the filter along the first dimension. w1 : optional: int. default: 3. - The length of the filter along the second dimension. edge_pad : optional: af.PAD. default: af.PAD.ZERO - Flag specifying how the min at the edge should be treated. Returns --------- output : af.Array - The image after min filter is applied. """ output = Array() safe_call(backend.get().af_minfilt(c_pointer(output.arr), image.arr, c_dim_t(w_len), c_dim_t(w_wid), edge_pad.value)) return output def maxfilt(image, w_len = 3, w_wid = 3, edge_pad = PAD.ZERO): """ Apply max filter for the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. w0 : optional: int. default: 3. - The length of the filter along the first dimension. w1 : optional: int. default: 3. - The length of the filter along the second dimension. edge_pad : optional: af.PAD. default: af.PAD.ZERO - Flag specifying how the max at the edge should be treated. Returns --------- output : af.Array - The image after max filter is applied. """ output = Array() safe_call(backend.get().af_maxfilt(c_pointer(output.arr), image.arr, c_dim_t(w_len), c_dim_t(w_wid), edge_pad.value)) return output def regions(image, conn = CONNECTIVITY.FOUR, out_type = Dtype.f32): """ Find the connected components in the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image. conn : optional: af.CONNECTIVITY. default: af.CONNECTIVITY.FOUR. - Specifies the connectivity of the pixels. out_type : optional: af.Dtype. default: af.Dtype.f32. - Specifies the type for the output. Returns --------- output : af.Array - An array where each pixel is labeled with its component number. """ output = Array() safe_call(backend.get().af_regions(c_pointer(output.arr), image.arr, conn.value, out_type.value)) return output def confidenceCC(image, seedx, seedy, radius, multiplier, iters, segmented_value): """ Find the confidence connected components in the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image. Expects non-integral type seedx : af.Array - An array with x-coordinates of seed points seedy : af.Array - An array with y-coordinates of seed points radius : scalar - The neighborhood region to be considered around each seed point multiplier : scalar - Controls the threshold range computed from the mean and variance of seed point neighborhoods iters : scalar - is number of iterations segmented_value : scalar - the value to which output array valid pixels are set to. Returns --------- output : af.Array - Output array with resulting connected components """ output = Array() safe_call(backend.get().af_confidence_cc(c_pointer(output.arr), image.arr, seedx.arr, seedy.arr, c_uint_t(radius), c_uint_t(multiplier), c_int_t(iters), c_double_t(segmented_value))) return output def sobel_derivatives(image, w_len=3): """ Find the sobel derivatives of the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. w_len : optional: int. default: 3. - The size of the sobel operator. Returns --------- (dx, dy) : tuple of af.Arrays. - `dx` is the sobel derivative along the horizontal direction. - `dy` is the sobel derivative along the vertical direction. """ dx = Array() dy = Array() safe_call(backend.get().af_sobel_operator(c_pointer(dx.arr), c_pointer(dy.arr), image.arr, c_uint_t(w_len))) return dx,dy def gaussian_kernel(rows, cols, sigma_r = None, sigma_c = None): """ Create a gaussian kernel with the given parameters. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. rows : int - The number of rows in the gaussian kernel. cols : int - The number of columns in the gaussian kernel. sigma_r : optional: number. default: None. - The sigma value along rows - If None, calculated as (0.25 * rows + 0.75) sigma_c : optional: number. default: None. - The sigma value along columns - If None, calculated as (0.25 * cols + 0.75) Returns ------- out : af.Array - A gaussian kernel of size (rows, cols) """ out = Array() if (sigma_r is None): sigma_r = 0.25 * rows + 0.75 if (sigma_c is None): sigma_c = 0.25 * cols + 0.75 safe_call(backend.get().af_gaussian_kernel(c_pointer(out.arr), c_int_t(rows), c_int_t(cols), c_double_t(sigma_r), c_double_t(sigma_c))) return out def sobel_filter(image, w_len = 3, is_fast = False): """ Apply sobel filter to the image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. w_len : optional: int. default: 3. - The size of the sobel operator. is_fast : optional: bool. default: False. - Specifies if the magnitude is generated using SAD (if True) or SSD (if False). Returns --------- output : af.Array - Image containing the magnitude of the sobel derivatives. """ from .arith import abs as af_abs from .arith import hypot as af_hypot dx,dy = sobel_derivatives(image, w_len) if (is_fast): return af_abs(dx) + af_abs(dy) else: return af_hypot(dx, dy) def rgb2gray(image, r_factor = 0.2126, g_factor = 0.7152, b_factor = 0.0722): """ Convert RGB image to Grayscale. Parameters ---------- image : af.Array - A 3 D arrayfire array representing an 3 channel image, or - A multi dimensional array representing batch of images. r_factor : optional: scalar. default: 0.2126. - Weight for the red channel. g_factor : optional: scalar. default: 0.7152. - Weight for the green channel. b_factor : optional: scalar. default: 0.0722. - Weight for the blue channel. Returns -------- output : af.Array - A grayscale image. """ output=Array() safe_call(backend.get().af_rgb2gray(c_pointer(output.arr), image.arr, c_float_t(r_factor), c_float_t(g_factor), c_float_t(b_factor))) return output def gray2rgb(image, r_factor = 1.0, g_factor = 1.0, b_factor = 1.0): """ Convert Grayscale image to an RGB image. Parameters ---------- image : af.Array - A 2 D arrayfire array representing an image, or - A multi dimensional array representing batch of images. r_factor : optional: scalar. default: 1.0. - Scale factor for the red channel. g_factor : optional: scalar. default: 1.0. - Scale factor for the green channel. b_factor : optional: scalar. default: 1.0 - Scale factor for the blue channel. Returns -------- output : af.Array - An RGB image. - The channels are not coalesced, i.e. they appear along the third dimension. """ output=Array() safe_call(backend.get().af_gray2rgb(c_pointer(output.arr), image.arr, c_float_t(r_factor), c_float_t(g_factor), c_float_t(b_factor))) return output def hsv2rgb(image): """ Convert HSV image to RGB. Parameters ---------- image : af.Array - A 3 D arrayfire array representing an 3 channel image, or - A multi dimensional array representing batch of images. Returns -------- output : af.Array - A HSV image. """ output = Array() safe_call(backend.get().af_hsv2rgb(c_pointer(output.arr), image.arr)) return output def rgb2hsv(image): """ Convert RGB image to HSV. Parameters ---------- image : af.Array - A 3 D arrayfire array representing an 3 channel image, or - A multi dimensional array representing batch of images. Returns -------- output : af.Array - A RGB image. """ output = Array() safe_call(backend.get().af_rgb2hsv(c_pointer(output.arr), image.arr)) return output def color_space(image, to_type, from_type): """ Convert an image from one color space to another. Parameters ---------- image : af.Array - A multi dimensional array representing batch of images in `from_type` color space. to_type : af.CSPACE - An enum for the destination color space. from_type : af.CSPACE - An enum for the source color space. Returns -------- output : af.Array - An image in the `to_type` color space. """ output = Array() safe_call(backend.get().af_color_space(c_pointer(output.arr), image.arr, to_type.value, from_type.value)) return output def unwrap(image, wx, wy, sx, sy, px=0, py=0, is_column=True): """ Unrwap an image into an array. Parameters ---------- image : af.Array A multi dimensional array specifying an image or batch of images. wx : Integer. |
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