├── README.md ├── matlab教材示例图片 ├── dipum_images_ch01 │ ├── Fig0101(MATLAB_desktop_with_text).tif │ └── Thumbs.db ├── dipum_images_ch02 │ ├── Fig0203(a)(chest-xray).tif │ ├── Fig0204(a)(bubbles-q-100jpg).tif │ ├── Fig0205(a)(cktboard_200dpi).tif │ ├── Fig0206(a)(rose-original).tif │ ├── Fig0207(2Dsinewave).tif │ └── Thumbs.db ├── dipum_images_ch03 │ ├── Fig0303(a)(breast).tif │ ├── Fig0305(a)(spectrum).tif │ ├── Fig0306(a)(bone-scan-GE).tif │ ├── Fig0308(a)(pollen).tif │ ├── Fig0310(a)(Moon Phobos).tif │ ├── Fig0315(a)(original_test_pattern).tif │ ├── Fig0316(a)(moon).tif │ ├── Fig0318(a)(ckt-board-orig).tif │ ├── Fig0318(b)(ckt-board-slt-pep-both-0pt2).tif │ ├── Thumbs.db │ └── _vti_cnf │ │ ├── Fig0303(a)(breast).tif │ │ ├── Fig0305(a)(spectrum).tif │ │ ├── Fig0306(a)(bone-scan-GE).tif │ │ ├── Fig0308(a)(pollen).tif │ │ ├── Fig0310(a)(Moon Phobos).tif │ │ ├── Fig0315(a)(original_test_pattern).tif │ │ ├── Fig0316(a)(moon).tif │ │ ├── Fig0318(a)(ckt-board-orig).tif │ │ └── Fig0318(b)(ckt-board-slt-pep-both-0pt2).tif ├── dipum_images_ch04 │ ├── Fig0403(a)(image).tif │ ├── Fig0405(a)(square_original).tif │ ├── Fig0409(a)(bld).tif │ ├── Fig0413(a)(original_test_pattern).tif │ ├── Fig0419(a)(chestXray_original).tif │ └── _vti_cnf │ │ ├── Fig0403(a)(image).tif │ │ ├── Fig0405(a)(square_original).tif │ │ ├── Fig0409(a)(bld).tif │ │ ├── Fig0413(a)(original_test_pattern).tif │ │ └── Fig0419(a)(chestXray_original).tif ├── dipum_images_ch05 │ ├── Fig0503(a)(spectrum1).tif │ ├── Fig0503(c)(spectrum2).tif │ ├── Fig0503(e)(sine_pattern3).tif │ ├── Fig0503(f)(sine_pattern4).tif │ ├── Fig0504(a)(noisy_image).tif │ ├── Fig0504(b)(binary_mask).tif │ ├── Fig0505(a)(ckt_pepper_only).tif │ ├── Fig0505(b)(ckt_salt_only).tif │ ├── Fig0506(a)(ckt_salt_pep_pt25).tif │ ├── Fig0507(a)(checkeboard8_pixeldup_8).tif │ ├── Fig0511(a)(PSF_original).tif │ ├── Fig0515(a)(base-with-control-points).tif │ └── _vti_cnf │ │ ├── Fig0503(a)(spectrum1).tif │ │ ├── Fig0503(c)(spectrum2).tif │ │ ├── Fig0503(e)(sine_pattern3).tif │ │ ├── Fig0503(f)(sine_pattern4).tif │ │ ├── Fig0504(a)(noisy_image).tif │ │ ├── Fig0504(b)(binary_mask).tif │ │ ├── Fig0505(a)(ckt_pepper_only).tif │ │ ├── Fig0505(b)(ckt_salt_only).tif │ │ ├── Fig0506(a)(ckt_salt_pep_pt25).tif │ │ ├── Fig0507(a)(checkeboard8_pixeldup_8).tif │ │ ├── Fig0511(a)(PSF_original).tif │ │ └── Fig0515(a)(base-with-control-points).tif ├── dipum_images_ch06 │ ├── Fig0602(b)(RGB_color_cube).tif │ ├── Fig0604(a)(iris).tif │ ├── Fig0605(a)(HSV_color_hexagon).tif │ ├── Fig0608(a) Big Color Triangle.tif │ ├── Fig0608(b) Big Color Circle.tif │ ├── Fig0614(a)(Chalk Original).tif.tif │ ├── Fig0615(a)(Aerial Original).tif.tif │ ├── Fig0615(d)(Iris Original).tif.tif │ ├── Fig0616(a)(Weld Original).tif.tif │ ├── Fig0617(a)(JLK Magenta).tif.tif │ ├── Fig0618(a)(Caster Original).tif.tif │ ├── Fig0619(a)(RGB_iris).tif │ ├── Fig0624(a)RGB2-red.tif │ ├── Fig0624(b)RGB2-green.tif │ ├── Fig0624(c)(RGB2-blue).tif │ ├── Fig0624(d)(RGB2-fullcolor).tif │ ├── Fig0627(a)(jupitermoon_original).tif │ ├── Fig0627(b)(jupitermoon_ROI).tif │ └── _vti_cnf │ │ ├── Fig0602(b)(RGB_color_cube).tif │ │ ├── Fig0604(a)(iris).tif │ │ ├── Fig0605(a)(HSV_color_hexagon).tif │ │ ├── Fig0608(a) Big Color Triangle.tif │ │ ├── Fig0608(b) Big Color Circle.tif │ │ ├── Fig0614(a)(Chalk Original).tif.tif │ │ ├── Fig0615(a)(Aerial Original).tif.tif │ │ ├── Fig0615(d)(Iris Original).tif.tif │ │ ├── Fig0616(a)(Weld Original).tif.tif │ │ ├── Fig0617(a)(JLK Magenta).tif.tif │ │ ├── Fig0618(a)(Caster Original).tif.tif │ │ ├── Fig0619(a)(RGB_iris).tif │ │ ├── Fig0624(a)RGB2-red.tif │ │ ├── Fig0624(b)RGB2-green.tif │ │ ├── Fig0624(c)(RGB2-blue).tif │ │ ├── Fig0624(d)(RGB2-fullcolor).tif │ │ ├── Fig0627(a)(jupitermoon_original).tif │ │ └── Fig0627(b)(jupitermoon_ROI).tif ├── dipum_images_ch07 │ ├── Fig0704(Vase).tif.tif │ ├── Fig0707(a)(Original).tif.tif │ ├── Fig0709(original_strawberries).tif │ └── _vti_cnf │ │ ├── Fig0704(Vase).tif.tif │ │ ├── Fig0707(a)(Original).tif.tif │ │ └── Fig0709(original_strawberries).tif ├── dipum_images_ch08 │ ├── Fig0804(a)(Tracy).tif.tif │ ├── Fig0807(a)(Random).tif.tif │ ├── Fig0807(c)(Aligned).tif.tif │ ├── Fig0810(a)(Original).tif.tif │ └── _vti_cnf │ │ ├── Fig0804(a)(Tracy).tif.tif │ │ ├── Fig0807(a)(Random).tif.tif │ │ ├── Fig0807(c)(Aligned).tif.tif │ │ └── Fig0810(a)(Original).tif.tif ├── dipum_images_ch09 │ ├── Fig0903(a)(utk).tif │ ├── Fig0903(b)(gt).tif │ ├── Fig0906(a)(broken-text).tif │ ├── Fig0908(a)(wirebond-mask).tif │ ├── Fig0910(a)(shapes).tif │ ├── Fig0911(a)(noisy-fingerprint).tif │ ├── Fig0913(a)(small-squares).tif │ ├── Fig0914(a)(bone-skel).tif │ ├── Fig0916(a)(bone).tif │ ├── Fig0917(a)(ten-objects).tif │ ├── Fig0921(a)(recon-mask).tif │ ├── Fig0921(b)(recon-marker).tif │ ├── Fig0922(a)(book-text).tif │ ├── Fig0923(a)(aerial).tif │ ├── Fig0925(a)(dowels).tif │ ├── Fig0926(a)(rice).tif │ ├── Fig0930(a)(calculator).tif │ └── _vti_cnf │ │ ├── Fig0903(a)(utk).tif │ │ ├── Fig0903(b)(gt).tif │ │ ├── Fig0906(a)(broken-text).tif │ │ ├── Fig0908(a)(wirebond-mask).tif │ │ ├── Fig0910(a)(shapes).tif │ │ ├── Fig0911(a)(noisy-fingerprint).tif │ │ ├── Fig0913(a)(small-squares).tif │ │ ├── Fig0914(a)(bone-skel).tif │ │ ├── Fig0916(a)(bone).tif │ │ ├── Fig0917(a)(ten-objects).tif │ │ ├── Fig0921(a)(recon-mask).tif │ │ ├── Fig0921(b)(recon-marker).tif │ │ ├── Fig0922(a)(book-text).tif │ │ ├── Fig0923(a)(aerial).tif │ │ ├── Fig0925(a)(dowels).tif │ │ ├── Fig0926(a)(rice).tif │ │ └── Fig0930(a)(calculator).tif ├── dipum_images_ch10 │ ├── Fig1002(a)(test_pattern_with_single_pixel).tif │ ├── Fig1004(a)(wirebond_mask).tif │ ├── Fig1006(a)(building).tif │ ├── Fig1010(a)(five-dots).tif │ ├── Fig1013(a)(scanned-text-grayscale).tif │ ├── Fig1014(a)(defective_weld).tif │ ├── Fig1017(a)(cygnusloop_Xray_original).tif │ ├── Fig1020(a)(binary-dowel-image).tif │ ├── Fig1021(a)(small-blobs).tif │ ├── Fig1022(a)(gel-image).tif │ └── _vti_cnf │ │ ├── Fig1002(a)(test_pattern_with_single_pixel).tif │ │ ├── Fig1004(a)(wirebond_mask).tif │ │ ├── Fig1006(a)(building).tif │ │ ├── Fig1010(a)(five-dots).tif │ │ ├── Fig1013(a)(scanned-text-grayscale).tif │ │ ├── Fig1014(a)(defective_weld).tif │ │ ├── Fig1017(a)(cygnusloop_Xray_original).tif │ │ ├── Fig1020(a)(binary-dowel-image).tif │ │ ├── Fig1021(a)(small-blobs).tif │ │ └── Fig1022(a)(gel-image).tif ├── dipum_images_ch11 │ ├── Fig1102(a)(noisy_circular_stroke).tif │ ├── Fig1107(a)(mapleleaf).tif │ ├── Fig1111(a)(boundary_sq.tif │ ├── Fig1111(b)(boundary_triangle).tif │ ├── Fig1113(a)(chromo_original).tif │ ├── Fig1116(a)(chromo_binary).tif │ ├── Fig1119(a)(superconductor-with-window).tif │ ├── Fig1119(b)(cholesterol-with-window).tif │ ├── Fig1119(c)(microporcessor-with-window).tif │ ├── Fig1121(a)(random_matches).tif │ ├── Fig1121(b)(ordered_matches).tif │ ├── Fig1123(a)(Original_Padded_to_568_by_568).tif │ ├── Fig1125(a)(WashingtonDC_Band1_512).tif │ ├── Fig1125(b)(WashingtonDC_Band2_512).tif │ ├── Fig1125(c)(WashingtonDC_Band3_512).tif │ ├── Fig1125(d)(WashingtonDC_Band4_512).tif │ ├── Fig1125(e)(WashingtonDC_Band5_512).tif │ ├── Fig1125(f)(WashingtonDC_Band6_512).tif │ └── _vti_cnf │ │ ├── Fig1102(a)(noisy_circular_stroke).tif │ │ ├── Fig1107(a)(mapleleaf).tif │ │ ├── Fig1111(a)(boundary_sq.tif │ │ ├── Fig1111(b)(boundary_triangle).tif │ │ ├── Fig1113(a)(chromo_original).tif │ │ ├── Fig1116(a)(chromo_binary).tif │ │ ├── Fig1119(a)(superconductor-with-window).tif │ │ ├── Fig1119(b)(cholesterol-with-window).tif │ │ ├── Fig1119(c)(microporcessor-with-window).tif │ │ ├── Fig1121(a)(random_matches).tif │ │ ├── Fig1121(b)(ordered_matches).tif │ │ ├── Fig1123(a)(Original_Padded_to_568_by_568).tif │ │ ├── Fig1125(a)(WashingtonDC_Band1_512).tif │ │ ├── Fig1125(b)(WashingtonDC_Band2_512).tif │ │ ├── Fig1125(c)(WashingtonDC_Band3_512).tif │ │ ├── Fig1125(d)(WashingtonDC_Band4_512).tif │ │ ├── Fig1125(e)(WashingtonDC_Band5_512).tif │ │ └── Fig1125(f)(WashingtonDC_Band6_512).tif └── dipum_images_ch12 │ ├── Fig1201(a)(Hurricane Andrew).tif │ ├── Fig1201(b)(hurricane_mask).tif │ ├── Fig1202(a)(WashingtonDC_Band1_512).tif │ ├── Fig1202(b)(WashingtonDC_Band2_512).tif │ ├── Fig1202(c)(WashingtonDC_Band3_512).tif │ ├── Fig1202(d)(WashingtonDC_Band4_512).tif │ ├── Fig1202(e)(Mask_Composite).tif │ ├── Fig1202(f)(Misclassified).tif │ ├── Fig1203(a)(bottle_1).tif │ ├── Fig1203(d)(bottle_2).tif │ └── _vti_cnf │ ├── Fig1201(a)(Hurricane Andrew).tif │ ├── Fig1201(b)(hurricane_mask).tif │ ├── Fig1202(a)(WashingtonDC_Band1_512).tif │ ├── Fig1202(b)(WashingtonDC_Band2_512).tif │ ├── Fig1202(c)(WashingtonDC_Band3_512).tif │ ├── Fig1202(d)(WashingtonDC_Band4_512).tif │ ├── Fig1202(e)(Mask_Composite).tif │ ├── Fig1202(f)(Misclassified).tif │ ├── Fig1203(a)(bottle_1).tif │ └── Fig1203(d)(bottle_2).tif ├── 冈萨雷斯数字图像处理源代码 ├── 0_Read_Me_First.txt ├── CH10.m ├── CH11.m ├── CH12.m ├── CH2.m ├── CH3.m ├── CH4.m ├── CH5.m ├── CH6.m ├── CH7.m ├── CH8.m ├── CH9.m ├── Contents.m ├── Readme.m ├── adpmedian.m ├── average.m ├── bayesgauss.m ├── bound2eight.m ├── bound2four.m ├── bound2im.m ├── boundaries.m ├── bsubsamp.m ├── changeclass.m ├── colorgrad.m ├── colorseg.m ├── compare.m ├── connectpoly.m ├── conwaylaws.m ├── covmatrix.m ├── de2bi.m ├── dftcorr.m ├── dftfilt.m ├── dftuv.m ├── diameter.m ├── endpoints.m ├── entropy.m ├── fchcode.m ├── frdescp.m ├── fwtcompare.m ├── gmean.m ├── gscale.m ├── histroi.m ├── hough.m ├── houghlines.m ├── houghpeaks.m ├── houghpixels.m ├── hpfilter.m ├── hsi2rgb.m ├── huff2mat.m ├── huffman.m ├── ice.fig ├── ice.m ├── ice_stand_alone.m ├── ifrdescp.m ├── ifwtcompare.m ├── im2jpeg.m ├── im2jpeg2k.m ├── imnoise2.m ├── imnoise3.m ├── improd.m ├── imratio.m ├── imstack2vectors.m ├── intline.m ├── intrans.m ├── invmoments.m ├── jpeg2im.m ├── jpeg2k2im.m ├── lpc2mat.m ├── lpfilter.m ├── mahalanobis.m ├── manualhist.m ├── mat2huff.m ├── mat2lpc.m ├── minperpoly.m ├── paddedsize.m ├── pixeldup.m ├── polyangles.m ├── princomp.m ├── quantize.m ├── randvertex.m ├── regiongrow.m ├── rgb2hsi.m ├── rgbcube.m ├── signature.m ├── specxture.m ├── spfilt.m ├── splitmerge.m ├── statmoments.m ├── statxture.m ├── strsimilarity.m ├── subim.m ├── twodsin.m ├── twomodegauss.m ├── unravel.dll ├── unravel.m ├── unravel.mexaxp ├── unravel.mexglx ├── unravel.mexhpux ├── unravel.mexmac ├── unravel.mexrs6 ├── unravel.mexsg ├── unravel.mexsol ├── vistformfwd.m ├── wave2gray.m ├── waveback.m ├── wavecopy.m ├── wavecut.m ├── wavefast.m ├── wavefilter.m ├── wavepaste.m ├── wavework.m ├── wavezero.m └── x2majoraxis.m ├── 数字图像处理第三版 教材图片 ├── DIP3E_CH01_Original_Images.zip ├── DIP3E_CH02_Original_Images.zip ├── DIP3E_CH03_Original_Images.zip ├── DIP3E_CH04_Original_Images.zip ├── DIP3E_CH05_Original_Images.zip ├── DIP3E_CH06_Original_Images.zip ├── DIP3E_CH07_Original_Images.zip ├── DIP3E_CH08_Original_Images.zip ├── DIP3E_CH09_Original_Images.zip ├── DIP3E_CH10_Original_Images.zip ├── DIP3E_CH11_Original_Images.zip ├── DIP3E_CH12_Original_Images.zip └── DIP3E_Problem_Figures.zip ├── 数字图像处理第三版课后习题答案.pdf └── 数字图像处理第四版课后习题答案（影印版）.pdf /README.md: -------------------------------------------------------------------------------- 1 | # Digital Image Processing 学习资料 2 | 冈萨雷斯《数字图像处理》第三版-课后习题答案 3 | 4 | 冈萨雷斯《数字图像处理》第四版-课后习题答案（影印版） 5 | 6 | 《数字图像处理（matlab版）》-源代码及图片 7 | 8 | 《数字图像处理》第三版-教材上的图片 9 | 10 | 14 | -------------------------------------------------------------------------------- /matlab教材示例图片/dipum_images_ch01/Fig0101(MATLAB_desktop_with_text).tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/matlab教材示例图片/dipum_images_ch01/Fig0101(MATLAB_desktop_with_text).tif -------------------------------------------------------------------------------- /matlab教材示例图片/dipum_images_ch01/Thumbs.db: 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The median filter starts at size 3-by-3 and iterates up 5 | % to size SMAX-by-SMAX. SMAX must be an odd integer greater than 1. 6 | 7 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 8 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 9 | % $Revision: 1.5 $ $Date: 2003/11/21 14:19:05 $ 10 | 11 | % SMAX must be an odd, positive integer greater than 1. 12 | if (Smax <= 1) | (Smax/2 == round(Smax/2)) | (Smax ~= round(Smax)) 13 | error('SMAX must be an odd integer > 1.') 14 | end 15 | [M, N] = size(g); 16 | 17 | % Initial setup. 18 | f = g; 19 | f(:) = 0; 20 | alreadyProcessed = false(size(g)); 21 | 22 | % Begin filtering. 23 | for k = 3:2:Smax 24 | zmin = ordfilt2(g, 1, ones(k, k), 'symmetric'); 25 | zmax = ordfilt2(g, k * k, ones(k, k), 'symmetric'); 26 | zmed = medfilt2(g, [k k], 'symmetric'); 27 | 28 | processUsingLevelB = (zmed > zmin) & (zmax > zmed) & ... 29 | ~alreadyProcessed; 30 | zB = (g > zmin) & (zmax > g); 31 | outputZxy = processUsingLevelB & zB; 32 | outputZmed = processUsingLevelB & ~zB; 33 | f(outputZxy) = g(outputZxy); 34 | f(outputZmed) = zmed(outputZmed); 35 | 36 | alreadyProcessed = alreadyProcessed | processUsingLevelB; 37 | if all(alreadyProcessed(:)) 38 | break; 39 | end 40 | end 41 | 42 | % Output zmed for any remaining unprocessed pixels. Note that this 43 | % zmed was computed using a window of size Smax-by-Smax, which is 44 | % the final value of k in the loop. 45 | f(~alreadyProcessed) = zmed(~alreadyProcessed); 46 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/average.m: -------------------------------------------------------------------------------- 1 | function av = average(A) 2 | %AVERAGE Computes the average value of an array. 3 | % AV = AVERAGE(A) computes the average value of input array, A, 4 | % which must be a 1-D or 2-D array. 5 | 6 | % Sample M-file used in Chapter 2. 7 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 8 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 9 | % $Revision: 1.2 $ $Date: 2003/03/27 21:23:21 $ 10 | 11 | % Check the validity of the input. (Keep in mind that 12 | % a 1-D array is a special case of a 2-D array.) 13 | if ndims(A) > 2 14 | error('The dimensions of the input cannot exceed 2.') 15 | end 16 | 17 | % Compute the average 18 | av = sum(A(:))/length(A(:)); 19 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/bound2four.m: -------------------------------------------------------------------------------- 1 | function rc_new = bound2four(rc) 2 | %BOUND2FOUR Convert 8-connected boundary to 4-connected boundary. 3 | % RC_NEW = BOUND2FOUR(RC) converts an eight-connected boundary to a 4 | % four-connected boundary. RC is a P-by-2 matrix, each row of 5 | % which contains the row and column coordinates of a boundary 6 | % pixel. BOUND2FOUR inserts new boundary pixels wherever there is 7 | % a diagonal connection. 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.4 $ $Date: 2003/11/21 14:20:21 $ 12 | 13 | if size(rc, 1) > 1 14 | % Phase 1: remove diagonal turns, one at a time until they are all gone. 15 | done = 0; 16 | rc1 = [rc(end - 1, :); rc]; 17 | while ~done 18 | d = diff(rc1, 1); 19 | diagonal_locations = all(d, 2); 20 | double_diagonals = diagonal_locations(1:end - 1) & ... 21 | (diff(diagonal_locations, 1) == 0); 22 | double_diagonal_idx = find(double_diagonals); 23 | turns = any(d(double_diagonal_idx, :) ~= ... 24 | d(double_diagonal_idx + 1, :), 2); 25 | turns_idx = double_diagonal_idx(turns); 26 | if isempty(turns_idx) 27 | done = 1; 28 | else 29 | first_turn = turns_idx(1); 30 | rc1(first_turn + 1, :) = (rc1(first_turn, :) + ... 31 | rc1(first_turn + 2, :)) / 2; 32 | if first_turn == 1 33 | rc1(end, :) = rc1(2, :); 34 | end 35 | end 36 | end 37 | rc1 = rc1(2:end, :); 38 | end 39 | 40 | % Phase 2: insert extra pixels where there are diagonal connections. 41 | 42 | rowdiff = diff(rc1(:, 1)); 43 | coldiff = diff(rc1(:, 2)); 44 | 45 | diagonal_locations = rowdiff & coldiff; 46 | num_old_pixels = size(rc1, 1); 47 | num_new_pixels = num_old_pixels + sum(diagonal_locations); 48 | rc_new = zeros(num_new_pixels, 2); 49 | 50 | % Insert the original values into the proper locations in the new RC 51 | % matrix. 52 | idx = (1:num_old_pixels)' + [0; cumsum(diagonal_locations)]; 53 | rc_new(idx, :) = rc1; 54 | 55 | % Compute the new pixels to be inserted. 56 | new_pixel_offsets = [0 1; -1 0; 1 0; 0 -1]; 57 | offset_codes = 2 * (1 - (coldiff(diagonal_locations) + 1)/2) + ... 58 | (2 - (rowdiff(diagonal_locations) + 1)/2); 59 | new_pixels = rc1(diagonal_locations, :) + ... 60 | new_pixel_offsets(offset_codes, :); 61 | 62 | % Where do the new pixels go? 63 | insertion_locations = zeros(num_new_pixels, 1); 64 | insertion_locations(idx) = 1; 65 | insertion_locations = ~insertion_locations; 66 | 67 | % Insert the new pixels. 68 | rc_new(insertion_locations, :) = new_pixels; 69 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/bound2im.m: -------------------------------------------------------------------------------- 1 | function B = bound2im(b, M, N, x0, y0) 2 | %BOUND2IM Converts a boundary to an image. 3 | % B = BOUND2IM(b) converts b, an np-by-2 or 2-by-np array 4 | % representing the integer coordinates of a boundary, into a binary 5 | % image with 1s in the locations defined by the coordinates in b 6 | % and 0s elsewhere. 7 | % 8 | % B = BOUND2IM(b, M, N) places the boundary approximately centered 9 | % in an M-by-N image. If any part of the boundary is outside the 10 | % M-by-N rectangle, an error is issued. 11 | % 12 | % B = BOUND2IM(b, M, N, X0, Y0) places the boundary in an image of 13 | % size M-by-N, with the topmost boundary point located at X0 and 14 | % the leftmost point located at Y0. If the shifted boundary is 15 | % outside the M-by-N rectangle, an error is issued. XO and X0 must 16 | % be positive integers. 17 | 18 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 19 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 20 | % $Revision: 1.6 $ $Date: 2003/06/14 16:21:28 $ 21 | 22 | [np, nc] = size(b); 23 | if np < nc 24 | b = b'; % To convert to size np-by-2. 25 | [np, nc] = size(b); 26 | end 27 | 28 | % Make sure the coordinates are integers. 29 | x = round(b(:, 1)); 30 | y = round(b(:, 2)); 31 | 32 | % Set up the default size parameters. 33 | x = x - min(x) + 1; 34 | y = y - min(y) + 1; 35 | B = false(max(x), max(y)); 36 | C = max(x) - min(x) + 1; 37 | D = max(y) - min(y) + 1; 38 | 39 | if nargin == 1 40 | % Use the preceding default values. 41 | elseif nargin == 3 42 | if C > M | D > N 43 | error('The boundary is outside the M-by-N region.') 44 | end 45 | % The image size will be M-by-N. Set up the parameters for this. 46 | B = false(M, N); 47 | % Distribute extra rows approx. even between top and bottom. 48 | NR = round((M - C)/2); 49 | NC = round((N - D)/2); % The same for columns. 50 | x = x + NR; % Offset the boundary to new position. 51 | y = y + NC; 52 | elseif nargin == 5 53 | if x0 < 0 | y0 < 0 54 | error('x0 and y0 must be positive integers.') 55 | end 56 | x = x + round(x0) - 1; 57 | y = y + round(y0) - 1; 58 | C = C + x0 - 1; 59 | D = D + y0 - 1; 60 | if C > M | D > N 61 | error('The shifted boundary is outside the M-by-N region.') 62 | end 63 | B = false(M, N); 64 | else 65 | error('Incorrect number of inputs.') 66 | end 67 | 68 | B(sub2ind(size(B), x, y)) = true; 69 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/changeclass.m: -------------------------------------------------------------------------------- 1 | function image = changeclass(class, varargin) 2 | %CHANGECLASS changes the storage class of an image. 3 | % I2 = CHANGECLASS(CLASS, I); 4 | % RGB2 = CHANGECLASS(CLASS, RGB); 5 | % BW2 = CHANGECLASS(CLASS, BW); 6 | % X2 = CHANGECLASS(CLASS, X, 'indexed'); 7 | 8 | % Copyright 1993-2002 The MathWorks, Inc. Used with permission. 9 | % $Revision: 1.2 $ $Date: 2003/02/19 22:09:58 $ 10 | 11 | switch class 12 | case 'uint8' 13 | image = im2uint8(varargin{:}); 14 | case 'uint16' 15 | image = im2uint16(varargin{:}); 16 | case 'double' 17 | image = im2double(varargin{:}); 18 | otherwise 19 | error('Unsupported IPT data class.'); 20 | end 21 | 22 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/colorgrad.m: -------------------------------------------------------------------------------- 1 | function [VG, A, PPG]= colorgrad(f, T) 2 | %COLORGRAD Computes the vector gradient of an RGB image. 3 | % [VG, VA, PPG] = COLORGRAD(F, T) computes the vector gradient, VG, 4 | % and corresponding angle array, VA, (in radians) of RGB image 5 | % F. It also computes PPG, the per-plane composite gradient 6 | % obtained by summing the 2-D gradients of the individual color 7 | % planes. Input T is a threshold in the range [0, 1]. If it is 8 | % included in the argument list, the values of VG and PPG are 9 | % thresholded by letting VG(x,y) = 0 for values <= T and VG(x,y) = 10 | % VG(x,y) otherwise. Similar comments apply to PPG. If T is not 11 | % included in the argument list then T is set to 0. Both output 12 | % gradients are scaled to the range [0, 1]. 13 | 14 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 15 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 16 | % $Revision: 1.6 $ $Date: 2003/11/21 14:27:21 $ 17 | 18 | if (ndims(f) ~= 3) | (size(f, 3) ~= 3) 19 | error('Input image must be RGB.'); 20 | end 21 | 22 | % Compute the x and y derivatives of the three component images 23 | % using Sobel operators. 24 | sh = fspecial('sobel'); 25 | sv = sh'; 26 | Rx = imfilter(double(f(:, :, 1)), sh, 'replicate'); 27 | Ry = imfilter(double(f(:, :, 1)), sv, 'replicate'); 28 | Gx = imfilter(double(f(:, :, 2)), sh, 'replicate'); 29 | Gy = imfilter(double(f(:, :, 2)), sv, 'replicate'); 30 | Bx = imfilter(double(f(:, :, 3)), sh, 'replicate'); 31 | By = imfilter(double(f(:, :, 3)), sv, 'replicate'); 32 | 33 | % Compute the parameters of the vector gradient. 34 | gxx = Rx.^2 + Gx.^2 + Bx.^2; 35 | gyy = Ry.^2 + Gy.^2 + By.^2; 36 | gxy = Rx.*Ry + Gx.*Gy + Bx.*By; 37 | A = 0.5*(atan(2*gxy./(gxx - gyy + eps))); 38 | G1 = 0.5*((gxx + gyy) + (gxx - gyy).*cos(2*A) + 2*gxy.*sin(2*A)); 39 | 40 | % Now repeat for angle + pi/2. Then select the maximum at each point. 41 | A = A + pi/2; 42 | G2 = 0.5*((gxx + gyy) + (gxx - gyy).*cos(2*A) + 2*gxy.*sin(2*A)); 43 | G1 = G1.^0.5; 44 | G2 = G2.^0.5; 45 | % Form VG by picking the maximum at each (x,y) and then scale 46 | % to the range [0, 1]. 47 | VG = mat2gray(max(G1, G2)); 48 | 49 | % Compute the per-plane gradients. 50 | RG = sqrt(Rx.^2 + Ry.^2); 51 | GG = sqrt(Gx.^2 + Gy.^2); 52 | BG = sqrt(Bx.^2 + By.^2); 53 | % Form the composite by adding the individual results and 54 | % scale to [0, 1]. 55 | PPG = mat2gray(RG + GG + BG); 56 | 57 | % Threshold the result. 58 | if nargin == 2 59 | VG = (VG > T).*VG; 60 | PPG = (PPG > T).*PPG; 61 | end 62 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/colorseg.m: -------------------------------------------------------------------------------- 1 | function I = colorseg(varargin) 2 | %COLORSEG Performs segmentation of a color image. 3 | % S = COLORSEG('EUCLIDEAN', F, T, M) performs segmentation of color 4 | % image F using a Euclidean measure of similarity. M is a 1-by-3 5 | % vector representing the average color used for segmentation (this 6 | % is the center of the sphere in Fig. 6.26 of DIPUM). T is the 7 | % threshold against which the distances are compared. 8 | % 9 | % S = COLORSEG('MAHALANOBIS', F, T, M, C) performs segmentation of 10 | % color image F using the Mahalanobis distance as a measure of 11 | % similarity. C is the 3-by-3 covariance matrix of the sample color 12 | % vectors of the class of interest. See function covmatrix for the 13 | % computation of C and M. 14 | % 15 | % S is the segmented image (a binary matrix) in which 0s denote the 16 | % background. 17 | 18 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 19 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 20 | % $Revision: 1.5 $ $Date: 2003/11/21 14:28:34 $ 21 | 22 | % Preliminaries. 23 | % Recall that varargin is a cell array. 24 | f = varargin{2}; 25 | if (ndims(f) ~= 3) | (size(f, 3) ~= 3) 26 | error('Input image must be RGB.'); 27 | end 28 | M = size(f, 1); N = size(f, 2); 29 | % Convert f to vector format using function imstack2vectors. 30 | [f, L] = imstack2vectors(f); 31 | f = double(f); 32 | % Initialize I as a column vector. It will be reshaped later 33 | % into an image. 34 | I = zeros(M*N, 1); 35 | T = varargin{3}; 36 | m = varargin{4}; 37 | m = m(:)'; % Make sure that m is a row vector. 38 | 39 | if length(varargin) == 4 40 | method = 'euclidean'; 41 | elseif length(varargin) == 5 42 | method = 'mahalanobis'; 43 | else 44 | error('Wrong number of inputs.'); 45 | end 46 | 47 | switch method 48 | case 'euclidean' 49 | % Compute the Euclidean distance between all rows of X and m. See 50 | % Section 12.2 of DIPUM for an explanation of the following 51 | % expression. D(i) is the Euclidean distance between vector X(i,:) 52 | % and vector m. 53 | p = length(f); 54 | D = sqrt(sum(abs(f - repmat(m, p, 1)).^2, 2)); 55 | case 'mahalanobis' 56 | C = varargin{5}; 57 | D = mahalanobis(f, C, m); 58 | otherwise 59 | error('Unknown segmentation method.') 60 | end 61 | 62 | % D is a vector of size MN-by-1 containing the distance computations 63 | % from all the color pixels to vector m. Find the distances <= T. 64 | J = find(D <= T); 65 | 66 | % Set the values of I(J) to 1. These are the segmented 67 | % color pixels. 68 | I(J) = 1; 69 | 70 | % Reshape I into an M-by-N image. 71 | I = reshape(I, M, N); 72 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/compare.m: -------------------------------------------------------------------------------- 1 | function rmse = compare(f1, f2, scale) 2 | %COMPARE Computes and displays the error between two matrices. 3 | % RMSE = COMPARE(F1, F2, SCALE) returns the root-mean-square error 4 | % between inputs F1 and F2, displays a histogram of the difference, 5 | % and displays a scaled difference image. When SCALE is omitted, a 6 | % scale factor of 1 is used. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004. 10 | % $Revision: 1.3 $ $Date: 2003/04/18 05:07:33 $ 11 | 12 | % Check input arguments and set defaults. 13 | error(nargchk(2, 3, nargin)); 14 | if nargin < 3 15 | scale = 1; 16 | end 17 | 18 | % Compute the root-mean-square error. 19 | e = double(f1) - double(f2); 20 | [m, n] = size(e); 21 | rmse = sqrt(sum(e(:) .^ 2) / (m * n)); 22 | 23 | % Output error image & histogram if an error (i.e., rmse ~= 0). 24 | if rmse 25 | % Form error histogram. 26 | emax = max(abs(e(:))); 27 | [h, x] = hist(e(:), emax); 28 | if length(h) >= 1 29 | figure; bar(x, h, 'k'); 30 | 31 | % Scale the error image symmetrically and display 32 | emax = emax / scale; 33 | e = mat2gray(e, [-emax, emax]); 34 | figure; imshow(e); 35 | end 36 | end 37 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/connectpoly.m: -------------------------------------------------------------------------------- 1 | function c = connectpoly(x, y) 2 | %CONNECTPOLY Connects vertices of a polygon. 3 | % C = CONNECTPOLY(X, Y) connects the points with coordinates given 4 | % in X and Y with straight lines. These points are assumed to be a 5 | % sequence of polygon vertices organized in the clockwise or 6 | % counterclockwise direction. The output, C, is the set of points 7 | % along the boundary of the polygon in the form of an nr-by-2 8 | % coordinate sequence in the same direction as the input. The last 9 | % point in the sequence is equal to the first. 10 | 11 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 12 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 13 | % $Revision: 1.4 $ $Date: 2003/11/21 14:29:16 $ 14 | 15 | v = [x(:), y(:)]; 16 | 17 | % Close polygon. 18 | if ~isequal(v(end, :), v(1, :)) 19 | v(end + 1, :) = v(1, :); 20 | end 21 | 22 | % Connect vertices. 23 | segments = cell(1, length(v) - 1); 24 | for I = 2:length(v) 25 | [x, y] = intline(v(I - 1, 1), v(I, 1), v(I - 1, 2), v(I, 2)); 26 | segments{I - 1} = [x, y]; 27 | end 28 | 29 | c = cat(1, segments{:}); 30 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/conwaylaws.m: -------------------------------------------------------------------------------- 1 | function out = conwaylaws(nhood) 2 | %CONWAYLAWS Applies Conway's genetic laws to a single pixel. 3 | % OUT = CONWAYLAWS(NHOOD) applies Conway's genetic laws to a single 4 | % pixel and its 3-by-3 neighborhood, NHOOD. 5 | 6 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 7 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 8 | % $Revision: 1.4 $ $Date: 2003/04/23 01:36:41 $ 9 | 10 | num_neighbors = sum(nhood(:)) - nhood(2, 2); 11 | if nhood(2, 2) == 1 12 | if num_neighbors <= 1 13 | out = 0; % Pixel dies from isolation. 14 | elseif num_neighbors >= 4 15 | out = 0; % Pixel dies from overpopulation. 16 | else 17 | out = 1; % Pixel survives. 18 | end 19 | else 20 | if num_neighbors == 3 21 | out = 1; % Birth pixel. 22 | else 23 | out = 0; % Pixel remains empty. 24 | end 25 | end 26 | 27 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/covmatrix.m: -------------------------------------------------------------------------------- 1 | function [C, m] = covmatrix(X) 2 | %COVMATRIX Computes the covariance matrix of a vector population. 3 | % [C, M] = COVMATRIX(X) computes the covariance matrix C and the 4 | % mean vector M of a vector population organized as the rows of 5 | % matrix X. C is of size N-by-N and M is of size N-by-1, where N is 6 | % the dimension of the vectors (the number of columns of X). 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.4 $ $Date: 2003/05/19 12:09:06 $ 11 | 12 | [K, n] = size(X); 13 | X = double(X); 14 | if n == 1 % Handle special case. 15 | C = 0; 16 | m = X; 17 | else 18 | % Compute an unbiased estimate of m. 19 | m = sum(X, 1)/K; 20 | % Subtract the mean from each row of X. 21 | X = X - m(ones(K, 1), :); 22 | % Compute an unbiased estimate of C. Note that the product is 23 | % X'*X because the vectors are rows of X. 24 | C = (X'*X)/(K - 1); 25 | m = m'; % Convert to a column vector. 26 | end 27 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/de2bi.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/de2bi.m -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/dftcorr.m: -------------------------------------------------------------------------------- 1 | function g = dftcorr(f, w) 2 | %DFTCORR 2-D correlation in the frequency domain. 3 | % G = DFTCORR(F, W) performs the correlation of a mask, W, with 4 | % image F. The output, G, is the correlation image, of class 5 | % double. The output is of the same size as F. When, as is 6 | % generally true in practice, the mask image is much smaller than 7 | % G, wraparound error is negligible if W is padded to size(F). 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.4 $ $Date: 2003/10/26 23:20:44 $ 12 | 13 | [M, N] = size(f); 14 | f = fft2(f); 15 | w = conj(fft2(w, M, N)); 16 | g = real(ifft2(w.*f)); 17 | 18 | 19 | 20 | 21 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/dftfilt.m: -------------------------------------------------------------------------------- 1 | function g = dftfilt(f, H) 2 | %DFTFILT Performs frequency domain filtering. 3 | % G = DFTFILT(F, H) filters F in the frequency domain using the 4 | % filter transfer function H. The output, G, is the filtered 5 | % image, which has the same size as F. DFTFILT automatically pads 6 | % F to be the same size as H. Function PADDEDSIZE can be used to 7 | % determine an appropriate size for H. 8 | % 9 | % DFTFILT assumes that F is real and that H is a real, uncentered 10 | % circularly-symmetric filter function. 11 | 12 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 13 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 14 | % $Revision: 1.5 $ $Date: 2003/08/25 14:28:22 $ 15 | 16 | % Obtain the FFT of the padded input. 17 | F = fft2(f, size(H, 1), size(H, 2)); 18 | 19 | % Perform filtering. 20 | g = real(ifft2(H.*F)); 21 | 22 | % Crop to original size. 23 | g = g(1:size(f, 1), 1:size(f, 2)); 24 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/dftuv.m: -------------------------------------------------------------------------------- 1 | function [U, V] = dftuv(M, N) 2 | %DFTUV Computes meshgrid frequency matrices. 3 | % [U, V] = DFTUV(M, N) computes meshgrid frequency matrices U and 4 | % V. U and V are useful for computing frequency-domain filter 5 | % functions that can be used with DFTFILT. U and V are both 6 | % M-by-N. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.3 $ $Date: 2003/04/16 22:30:34 $ 11 | 12 | % Set up range of variables. 13 | u = 0:(M - 1); 14 | v = 0:(N - 1); 15 | 16 | % Compute the indices for use in meshgrid. 17 | idx = find(u > M/2); 18 | u(idx) = u(idx) - M; 19 | idy = find(v > N/2); 20 | v(idy) = v(idy) - N; 21 | 22 | % Compute the meshgrid arrays. 23 | [V, U] = meshgrid(v, u); 24 | 25 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/endpoints.m: -------------------------------------------------------------------------------- 1 | function g = endpoints(f) 2 | %ENDPOINTS Computes end points of a binary image. 3 | % G = ENDPOINTS(F) computes the end points of the binary image F 4 | % and returns them in the binary image G. 5 | 6 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 7 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 8 | % $Revision: 1.3 $ $Date: 2003/04/22 01:18:18 $ 9 | 10 | persistent lut 11 | 12 | if isempty(lut) 13 | lut = makelut(@endpoint_fcn, 3); 14 | end 15 | 16 | g = applylut(f,lut); 17 | 18 | %-------------------------------------------------------------------% 19 | function is_end_point = endpoint_fcn(nhood) 20 | % Determines if a pixel is an end point. 21 | % IS_END_POINT = ENDPOINT_FCN(NHOOD) accepts a 3-by-3 binary 22 | % neighborhood, NHOOD, and returns a 1 if the center element is an 23 | % end point; otherwise it returns a 0. 24 | 25 | is_end_point = nhood(2,2) & (sum(nhood(:)) == 2); 26 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/entropy.m: -------------------------------------------------------------------------------- 1 | function h = entropy(x, n) 2 | %ENTROPY Computes a first-order estimate of the entropy of a matrix. 3 | % H = ENTROPY(X, N) returns the first-order estimate of matrix X 4 | % with N symbols (N = 256 if omitted) in bits/symbol. The estimate 5 | % assumes a statistically independent source characterized by the 6 | % relative frequency of occurrence of the elements in X. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.4 $ $Date: 2003/10/26 18:35:35 $ 11 | 12 | error(nargchk(1, 2, nargin)); % Check input arguments 13 | if nargin < 2 14 | n = 256; % Default for n. 15 | end 16 | 17 | x = double(x); % Make input double 18 | xh = hist(x(:), n); % Compute N-bin histogram 19 | xh = xh / sum(xh(:)); % Compute probabilities 20 | 21 | % Make mask to eliminate 0's since log2(0) = -inf. 22 | i = find(xh); 23 | 24 | h = -sum(xh(i) .* log2(xh(i))); % Compute entropy 25 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/frdescp.m: -------------------------------------------------------------------------------- 1 | function z = frdescp(s) 2 | %FRDESCP Computes Fourier descriptors. 3 | % Z = FRDESCP(S) computes the Fourier descriptors of S, which is an 4 | % np-by-2 sequence of image coordinates describing a boundary. 5 | % 6 | % Due to symmetry considerations when working with inverse Fourier 7 | % descriptors based on fewer than np terms, the number of points in 8 | % S when computing the descriptors must be even. If the number of 9 | % points is odd, FRDESCP duplicates the end point and adds it at 10 | % the end of the sequence. If a different treatment is desired, the 11 | % sequence must be processed externally so that it has an even 12 | % number of points. 13 | % 14 | % See function IFRDESCP for computing the inverse descriptors. 15 | 16 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 17 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 18 | % $Revision: 1.4 $ $Date: 2003/10/26 23:13:28 $ 19 | 20 | % Preliminaries 21 | [np, nc] = size(s); 22 | if nc ~= 2 23 | error('S must be of size np-by-2.'); 24 | end 25 | if np/2 ~= round(np/2); 26 | s(end + 1, :) = s(end, :); 27 | np = np + 1; 28 | end 29 | 30 | % Create an alternating sequence of 1s and -1s for use in centering 31 | % the transform. 32 | x = 0:(np - 1); 33 | m = ((-1) .^ x)'; 34 | 35 | % Multiply the input sequence by alternating 1s and -1s to 36 | % center the transform. 37 | s(:, 1) = m .* s(:, 1); 38 | s(:, 2) = m .* s(:, 2); 39 | % Convert coordinates to complex numbers. 40 | s = s(:, 1) + i*s(:, 2); 41 | % Compute the descriptors. 42 | z = fft(s); 43 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/fwtcompare.m: -------------------------------------------------------------------------------- 1 | function [ratio, maxdiff] = fwtcompare(f, n, wname) 2 | %FWTCOMPARE Compare wavedec2 and wavefast. 3 | % [RATIO, MAXDIFF] = FWTCOMPARE(F, N, WNAME) compares the operation 4 | % of toolbox function WAVEDEC2 and custom function WAVEFAST. 5 | % 6 | % INPUTS: 7 | % F Image to be transformed. 8 | % N Number of scales to compute. 9 | % WNAME Wavelet to use. 10 | % 11 | % OUTPUTS: 12 | % RATIO Execution time ratio (custom/toolbox) 13 | % MAXDIFF Maximum coefficient difference. 14 | 15 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 16 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 17 | % $Revision: 1.3 $ $Date: 2003/04/18 05:07:33 $ 18 | 19 | % Get transform and computation time for wavedec2. 20 | tic; 21 | [c1, s1] = wavedec2(f, n, wname); 22 | reftime = toc; 23 | 24 | % Get transform and computation time for wavefast. 25 | tic; 26 | [c2, s2] = wavefast(f, n, wname); 27 | t2 = toc; 28 | 29 | % Compare the results. 30 | ratio = t2 / (reftime + eps); 31 | maxdiff = abs(max(c1 - c2)); 32 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/gmean.m: -------------------------------------------------------------------------------- 1 | function v = gmean(A) 2 | %GMEAN Geometric mean of columns. 3 | % V = GMEAN(A) computes the geometric mean of the columns of A. V 4 | % is a row vector with size(A,2) elements. 5 | % 6 | % Sample M-file used in Chapter 3. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.2 $ $Date: 2003/04/16 22:31:47 $ 11 | 12 | m = size(A, 1); 13 | v = prod(A, 1) .^ (1/m); 14 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/gscale.m: -------------------------------------------------------------------------------- 1 | function g = gscale(f, varargin) 2 | %GSCALE Scales the intensity of the input image. 3 | % G = GSCALE(F, 'full8') scales the intensities of F to the full 4 | % 8-bit intensity range [0, 255]. This is the default if there is 5 | % only one input argument. 6 | % 7 | % G = GSCALE(F, 'full16') scales the intensities of F to the full 8 | % 16-bit intensity range [0, 65535]. 9 | % 10 | % G = GSCALE(F, 'minmax', LOW, HIGH) scales the intensities of F to 11 | % the range [LOW, HIGH]. These values must be provided, and they 12 | % must be in the range [0, 1], independently of the class of the 13 | % input. GSCALE performs any necessary scaling. If the input is of 14 | % class double, and its values are not in the range [0, 1], then 15 | % GSCALE scales it to this range before processing. 16 | % 17 | % The class of the output is the same as the class of the input. 18 | 19 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 20 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 21 | % $Revision: 1.5 $ $Date: 2003/11/21 14:36:09 $ 22 | 23 | if length(varargin) == 0 % If only one argument it must be f. 24 | method = 'full8'; 25 | else 26 | method = varargin{1}; 27 | end 28 | 29 | if strcmp(class(f), 'double') & (max(f(:)) > 1 | min(f(:)) < 0) 30 | f = mat2gray(f); 31 | end 32 | 33 | % Perform the specified scaling. 34 | switch method 35 | case 'full8' 36 | g = im2uint8(mat2gray(double(f))); 37 | case 'full16' 38 | g = im2uint16(mat2gray(double(f))); 39 | case 'minmax' 40 | low = varargin{2}; high = varargin{3}; 41 | if low > 1 | low < 0 | high > 1 | high < 0 42 | error('Parameters low and high must be in the range [0, 1].') 43 | end 44 | if strcmp(class(f), 'double') 45 | low_in = min(f(:)); 46 | high_in = max(f(:)); 47 | elseif strcmp(class(f), 'uint8') 48 | low_in = double(min(f(:)))./255; 49 | high_in = double(max(f(:)))./255; 50 | elseif strcmp(class(f), 'uint16') 51 | low_in = double(min(f(:)))./65535; 52 | high_in = double(max(f(:)))./65535; 53 | end 54 | % imadjust automatically matches the class of the input. 55 | g = imadjust(f, [low_in high_in], [low high]); 56 | otherwise 57 | error('Unknown method.') 58 | end 59 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/histroi.m: -------------------------------------------------------------------------------- 1 | function [p, npix] = histroi(f, c, r) 2 | %HISTROI Computes the histogram of an ROI in an image. 3 | % [P, NPIX] = HISTROI(F, C, R) computes the histogram, P, of a 4 | % polygonal region of interest (ROI) in image F. The polygonal 5 | % region is defined by the column and row coordinates of its 6 | % vertices, which are specified (sequentially) in vectors C and R, 7 | % respectively. All pixels of F must be >= 0. Parameter NPIX is the 8 | % number of pixels in the polygonal region. 9 | 10 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 11 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 12 | % $Revision: 1.5 $ $Date: 2003/09/05 16:14:35 $ 13 | 14 | % Generate the binary mask image. 15 | B = roipoly(f, c, r); 16 | 17 | % Compute the histogram of the pixels in the ROI. 18 | p = imhist(f(B)); 19 | 20 | % Obtain the number of pixels in the ROI if requested in the output. 21 | if nargout > 1 22 | npix = sum(B(:)); 23 | end 24 | 25 | 26 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/hough.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/hough.m -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/houghlines.m: -------------------------------------------------------------------------------- 1 | function lines = houghlines(f,theta,rho,rr,cc,fillgap,minlength) 2 | %HOUGHLINES Extract line segments based on the Hough transform. 3 | % LINES = HOUGHLINES(F, THETA, RHO, RR, CC, FILLGAP, MINLENGTH) 4 | % extracts line segments in the image F associated with particular 5 | % bins in a Hough transform. THETA and RHO are vectors returned by 6 | % function HOUGH. Vectors RR and CC specify the rows and columns 7 | % of the Hough transform bins to use in searching for line 8 | % segments. If HOUGHLINES finds two line segments associated with 9 | % the same Hough transform bin that are separated by less than 10 | % FILLGAP pixels, HOUGHLINES merges them into a single line 11 | % segment. FILLGAP defaults to 20 if omitted. Merged line 12 | % segments less than MINLENGTH pixels long are discarded. 13 | % MINLENGTH defaults to 40 if omitted. 14 | % 15 | % LINES is a structure array whose length equals the number of 16 | % merged line segments found. Each element of the structure array 17 | % has these fields: 18 | % 19 | % point1 End-point of the line segment; two-element vector 20 | % point2 End-point of the line segment; two-element vector 21 | % length Distance between point1 and point2 22 | % theta Angle (in degrees) of the Hough transform bin 23 | % rho Rho-axis position of the Hough transform bin 24 | 25 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 26 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 27 | % $Revision: 1.4 $ $Date: 2003/10/26 22:34:10 $ 28 | 29 | if nargin < 6 30 | fillgap = 20; 31 | end 32 | if nargin < 7 33 | minlength = 40; 34 | end 35 | 36 | numlines = 0; lines = struct; 37 | for k = 1:length(rr) 38 | rbin = rr(k); cbin = cc(k); 39 | 40 | % Get all pixels associated with Hough transform cell. 41 | [r, c] = houghpixels(f, theta, rho, rbin, cbin); 42 | if isempty(r) 43 | continue 44 | end 45 | 46 | % Rotate the pixel locations about (1,1) so that they lie 47 | % approximately along a vertical line. 48 | omega = (90 - theta(cbin)) * pi / 180; 49 | T = [cos(omega) sin(omega); -sin(omega) cos(omega)]; 50 | xy = [r - 1 c - 1] * T; 51 | x = sort(xy(:,1)); 52 | 53 | % Find the gaps larger than the threshold. 54 | diff_x = [diff(x); Inf]; 55 | idx = [0; find(diff_x > fillgap)]; 56 | for p = 1:length(idx) - 1 57 | x1 = x(idx(p) + 1); x2 = x(idx(p + 1)); 58 | linelength = x2 - x1; 59 | if linelength >= minlength 60 | point1 = [x1 rho(rbin)]; point2 = [x2 rho(rbin)]; 61 | % Rotate the end-point locations back to the original 62 | % angle. 63 | Tinv = inv(T); 64 | point1 = point1 * Tinv; point2 = point2 * Tinv; 65 | 66 | numlines = numlines + 1; 67 | lines(numlines).point1 = point1 + 1; 68 | lines(numlines).point2 = point2 + 1; 69 | lines(numlines).length = linelength; 70 | lines(numlines).theta = theta(cbin); 71 | lines(numlines).rho = rho(rbin); 72 | end 73 | end 74 | end 75 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/houghpeaks.m: -------------------------------------------------------------------------------- 1 | function [r, c, hnew] = houghpeaks(h, numpeaks, threshold, nhood) 2 | %HOUGHPEAKS Detect peaks in Hough transform. 3 | % [R, C, HNEW] = HOUGHPEAKS(H, NUMPEAKS, THRESHOLD, NHOOD) detects 4 | % peaks in the Hough transform matrix H. NUMPEAKS specifies the 5 | % maximum number of peak locations to look for. Values of H below 6 | % THRESHOLD will not be considered to be peaks. NHOOD is a 7 | % two-element vector specifying the size of the suppression 8 | % neighborhood. This is the neighborhood around each peak that is 9 | % set to zero after the peak is identified. The elements of NHOOD 10 | % must be positive, odd integers. R and C are the row and column 11 | % coordinates of the identified peaks. HNEW is the Hough transform 12 | % with peak neighborhood suppressed. 13 | % 14 | % If NHOOD is omitted, it defaults to the smallest odd values >= 15 | % size(H)/50. If THRESHOLD is omitted, it defaults to 16 | % 0.5*max(H(:)). If NUMPEAKS is omitted, it defaults to 1. 17 | 18 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 19 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 20 | % $Revision: 1.5 $ $Date: 2003/11/21 13:34:50 $ 21 | 22 | if nargin < 4 23 | nhood = size(h)/50; 24 | % Make sure the neighborhood size is odd. 25 | nhood = max(2*ceil(nhood/2) + 1, 1); 26 | end 27 | if nargin < 3 28 | threshold = 0.5 * max(h(:)); 29 | end 30 | if nargin < 2 31 | numpeaks = 1; 32 | end 33 | 34 | done = false; 35 | hnew = h; r = []; c = []; 36 | while ~done 37 | [p, q] = find(hnew == max(hnew(:))); 38 | p = p(1); q = q(1); 39 | if hnew(p, q) >= threshold 40 | r(end + 1) = p; c(end + 1) = q; 41 | 42 | % Suppress this maximum and its close neighbors. 43 | p1 = p - (nhood(1) - 1)/2; p2 = p + (nhood(1) - 1)/2; 44 | q1 = q - (nhood(2) - 1)/2; q2 = q + (nhood(2) - 1)/2; 45 | [pp, qq] = ndgrid(p1:p2,q1:q2); 46 | pp = pp(:); qq = qq(:); 47 | 48 | % Throw away neighbor coordinates that are out of bounds in 49 | % the rho direction. 50 | badrho = find((pp < 1) | (pp > size(h, 1))); 51 | pp(badrho) = []; qq(badrho) = []; 52 | 53 | % For coordinates that are out of bounds in the theta 54 | % direction, we want to consider that H is antisymmetric 55 | % along the rho axis for theta = +/- 90 degrees. 56 | theta_too_low = find(qq < 1); 57 | qq(theta_too_low) = size(h, 2) + qq(theta_too_low); 58 | pp(theta_too_low) = size(h, 1) - pp(theta_too_low) + 1; 59 | theta_too_high = find(qq > size(h, 2)); 60 | qq(theta_too_high) = qq(theta_too_high) - size(h, 2); 61 | pp(theta_too_high) = size(h, 1) - pp(theta_too_high) + 1; 62 | 63 | % Convert to linear indices to zero out all the values. 64 | hnew(sub2ind(size(hnew), pp, qq)) = 0; 65 | 66 | done = length(r) == numpeaks; 67 | else 68 | done = true; 69 | end 70 | end 71 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/houghpixels.m: -------------------------------------------------------------------------------- 1 | function [r, c] = houghpixels(f, theta, rho, rbin, cbin) 2 | %HOUGHPIXELS Compute image pixels belonging to Hough transform bin. 3 | % [R, C] = HOUGHPIXELS(F, THETA, RHO, RBIN, CBIN) computes the 4 | % row-column indices (R, C) for nonzero pixels in image F that map 5 | % to a particular Hough transform bin, (RBIN, CBIN). RBIN and CBIN 6 | % are scalars indicating the row-column bin location in the Hough 7 | % transform matrix returned by function HOUGH. THETA and RHO are 8 | % the second and third output arguments from the HOUGH function. 9 | 10 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 11 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 12 | % $Revision: 1.4 $ $Date: 2003/10/26 22:35:03 $ 13 | 14 | [x, y, val] = find(f); 15 | x = x - 1; y = y - 1; 16 | 17 | theta_c = theta(cbin) * pi / 180; 18 | rho_xy = x*cos(theta_c) + y*sin(theta_c); 19 | nrho = length(rho); 20 | slope = (nrho - 1)/(rho(end) - rho(1)); 21 | rho_bin_index = round(slope*(rho_xy - rho(1)) + 1); 22 | 23 | idx = find(rho_bin_index == rbin); 24 | 25 | r = x(idx) + 1; c = y(idx) + 1; 26 | 27 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/hpfilter.m: -------------------------------------------------------------------------------- 1 | function H = hpfilter(type, M, N, D0, n) 2 | %HPFILTER Computes frequency domain highpass filters. 3 | % H = HPFILTER(TYPE, M, N, D0, n) creates the transfer function of 4 | % a highpass filter, H, of the specified TYPE and size (M-by-N). 5 | % Valid values for TYPE, D0, and n are: 6 | % 7 | % 'ideal' Ideal highpass filter with cutoff frequency D0. n 8 | % need not be supplied. D0 must be positive. 9 | % 10 | % 'btw' Butterworth highpass filter of order n, and cutoff 11 | % D0. The default value for n is 1.0. D0 must be 12 | % positive. 13 | % 14 | % 'gaussian' Gaussian highpass filter with cutoff (standard 15 | % deviation) D0. n need not be supplied. D0 must be 16 | % positive. 17 | 18 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 19 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 20 | % $Revision: 1.4 $ $Date: 2003/08/25 14:28:22 $ 21 | 22 | % The transfer function Hhp of a highpass filter is 1 - Hlp, 23 | % where Hlp is the transfer function of the corresponding lowpass 24 | % filter. Thus, we can use function lpfilter to generate highpass 25 | % filters. 26 | 27 | if nargin == 4 28 | n = 1; % Default value of n. 29 | end 30 | 31 | % Generate highpass filter. 32 | Hlp = lpfilter(type, M, N, D0, n); 33 | H = 1 - Hlp; 34 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/hsi2rgb.m: -------------------------------------------------------------------------------- 1 | function rgb = hsi2rgb(hsi) 2 | %HSI2RGB Converts an HSI image to RGB. 3 | % RGB = HSI2RGB(HSI) converts an HSI image to RGB, where HSI is 4 | % assumed to be of class double with: 5 | % hsi(:, :, 1) = hue image, assumed to be in the range 6 | % [0, 1] by having been divided by 2*pi. 7 | % hsi(:, :, 2) = saturation image, in the range [0, 1]. 8 | % hsi(:, :, 3) = intensity image, in the range [0, 1]. 9 | % 10 | % The components of the output image are: 11 | % rgb(:, :, 1) = red. 12 | % rgb(:, :, 2) = green. 13 | % rgb(:, :, 3) = blue. 14 | 15 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 16 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 17 | % $Revision: 1.5 $ $Date: 2003/10/13 01:01:06 $ 18 | 19 | % Extract the individual HSI component images. 20 | H = hsi(:, :, 1) * 2 * pi; 21 | S = hsi(:, :, 2); 22 | I = hsi(:, :, 3); 23 | 24 | % Implement the conversion equations. 25 | R = zeros(size(hsi, 1), size(hsi, 2)); 26 | G = zeros(size(hsi, 1), size(hsi, 2)); 27 | B = zeros(size(hsi, 1), size(hsi, 2)); 28 | 29 | % RG sector (0 <= H < 2*pi/3). 30 | idx = find( (0 <= H) & (H < 2*pi/3)); 31 | B(idx) = I(idx) .* (1 - S(idx)); 32 | R(idx) = I(idx) .* (1 + S(idx) .* cos(H(idx)) ./ ... 33 | cos(pi/3 - H(idx))); 34 | G(idx) = 3*I(idx) - (R(idx) + B(idx)); 35 | 36 | % BG sector (2*pi/3 <= H < 4*pi/3). 37 | idx = find( (2*pi/3 <= H) & (H < 4*pi/3) ); 38 | R(idx) = I(idx) .* (1 - S(idx)); 39 | G(idx) = I(idx) .* (1 + S(idx) .* cos(H(idx) - 2*pi/3) ./ ... 40 | cos(pi - H(idx))); 41 | B(idx) = 3*I(idx) - (R(idx) + G(idx)); 42 | 43 | % BR sector. 44 | idx = find( (4*pi/3 <= H) & (H <= 2*pi)); 45 | G(idx) = I(idx) .* (1 - S(idx)); 46 | B(idx) = I(idx) .* (1 + S(idx) .* cos(H(idx) - 4*pi/3) ./ ... 47 | cos(5*pi/3 - H(idx))); 48 | R(idx) = 3*I(idx) - (G(idx) + B(idx)); 49 | 50 | % Combine all three results into an RGB image. Clip to [0, 1] to 51 | % compensate for floating-point arithmetic rounding effects. 52 | rgb = cat(3, R, G, B); 53 | rgb = max(min(rgb, 1), 0); 54 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/huff2mat.m: -------------------------------------------------------------------------------- 1 | function x = huff2mat(y) 2 | %HUFF2MAT Decodes a Huffman encoded matrix. 3 | % X = HUFF2MAT(Y) decodes a Huffman encoded structure Y with uint16 4 | % fields: 5 | % Y.min Minimum value of X plus 32768 6 | % Y.size Size of X 7 | % Y.hist Histogram of X 8 | % Y.code Huffman code 9 | % 10 | % The output X is of class double. 11 | % 12 | % See also MAT2HUFF. 13 | 14 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 15 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 16 | % $Revision: 1.5 $ $Date: 2003/11/21 13:17:50 $ 17 | 18 | if ~isstruct(y) | ~isfield(y, 'min') | ~isfield(y, 'size') | ... 19 | ~isfield(y, 'hist') | ~isfield(y, 'code') 20 | error('The input must be a structure as returned by MAT2HUFF.'); 21 | end 22 | 23 | sz = double(y.size); m = sz(1); n = sz(2); 24 | xmin = double(y.min) - 32768; % Get X minimum 25 | map = huffman(double(y.hist)); % Get Huffman code (cell) 26 | 27 | % Create a binary search table for the Huffman decoding process. 28 | % 'code' contains source symbol strings corresponding to 'link' 29 | % nodes, while 'link' contains the addresses (+) to node pairs for 30 | % node symbol strings plus '0' and '1' or addresses (-) to decoded 31 | % Huffman codewords in 'map'. Array 'left' is a list of nodes yet to 32 | % be processed for 'link' entries. 33 | 34 | code = cellstr(char('', '0', '1')); % Set starting conditions as 35 | link = [2; 0; 0]; left = [2 3]; % 3 nodes w/2 unprocessed 36 | found = 0; tofind = length(map); % Tracking variables 37 | 38 | while length(left) & (found < tofind) 39 | look = find(strcmp(map, code{left(1)})); % Is string in map? 40 | if look % Yes 41 | link(left(1)) = -look; % Point to Huffman map 42 | left = left(2:end); % Delete current node 43 | found = found + 1; % Increment codes found 44 | 45 | else % No, add 2 nodes & pointers 46 | len = length(code); % Put pointers in node 47 | link(left(1)) = len + 1; 48 | 49 | link = [link; 0; 0]; % Add unprocessed nodes 50 | code{end + 1} = strcat(code{left(1)}, '0'); 51 | code{end + 1} = strcat(code{left(1)}, '1'); 52 | 53 | left = left(2:end); % Remove processed node 54 | left = [left len + 1 len + 2]; % Add 2 unprocessed nodes 55 | end 56 | end 57 | 58 | x = unravel(y.code', link, m * n); % Decode using C 'unravel' 59 | x = x + xmin - 1; % X minimum offset adjust 60 | x = reshape(x, m, n); % Make vector an array 61 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/huffman.m: -------------------------------------------------------------------------------- 1 | function CODE = huffman(p) 2 | %HUFFMAN Builds a variable-length Huffman code for a symbol source. 3 | % CODE = HUFFMAN(P) returns a Huffman code as binary strings in 4 | % cell array CODE for input symbol probability vector P. Each word 5 | % in CODE corresponds to a symbol whose probability is at the 6 | % corresponding index of P. 7 | % 8 | % Based on huffman5 by Sean Danaher, University of Northumbria, 9 | % Newcastle UK. Available at the MATLAB Central File Exchange: 10 | % Category General DSP in Signal Processing and Communications. 11 | 12 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 13 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 14 | % $Revision: 1.5 $ $Date: 2003/10/26 18:37:16 $ 15 | 16 | % Check the input arguments for reasonableness. 17 | error(nargchk(1, 1, nargin)); 18 | if (ndims(p) ~= 2) | (min(size(p)) > 1) | ~isreal(p) | ~isnumeric(p) 19 | error('P must be a real numeric vector.'); 20 | end 21 | 22 | % Global variable surviving all recursions of function 'makecode' 23 | global CODE 24 | CODE = cell(length(p), 1); % Init the global cell array 25 | 26 | if length(p) > 1 % When more than one symbol ... 27 | p = p / sum(p); % Normalize the input probabilities 28 | s = reduce(p); % Do Huffman source symbol reductions 29 | makecode(s, []); % Recursively generate the code 30 | else 31 | CODE = {'1'}; % Else, trivial one symbol case! 32 | end; 33 | 34 | %-------------------------------------------------------------------% 35 | function s = reduce(p); 36 | % Create a Huffman source reduction tree in a MATLAB cell structure 37 | % by performing source symbol reductions until there are only two 38 | % reduced symbols remaining 39 | 40 | s = cell(length(p), 1); 41 | 42 | % Generate a starting tree with symbol nodes 1, 2, 3, ... to 43 | % reference the symbol probabilities. 44 | for i = 1:length(p) 45 | s{i} = i; 46 | end 47 | 48 | while numel(s) > 2 49 | [p, i] = sort(p); % Sort the symbol probabilities 50 | p(2) = p(1) + p(2); % Merge the 2 lowest probabilities 51 | p(1) = []; % and prune the lowest one 52 | 53 | s = s(i); % Reorder tree for new probabilities 54 | s{2} = {s{1}, s{2}}; % and merge & prune its nodes 55 | s(1) = []; % to match the probabilities 56 | end 57 | 58 | %-------------------------------------------------------------------% 59 | function makecode(sc, codeword) 60 | % Scan the nodes of a Huffman source reduction tree recursively to 61 | % generate the indicated variable length code words. 62 | 63 | % Global variable surviving all recursive calls 64 | global CODE 65 | 66 | if isa(sc, 'cell') % For cell array nodes, 67 | makecode(sc{1}, [codeword 0]); % add a 0 if the 1st element 68 | makecode(sc{2}, [codeword 1]); % or a 1 if the 2nd 69 | else % For leaf (numeric) nodes, 70 | CODE{sc} = char('0' + codeword); % create a char code string 71 | end -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/ice.fig: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/ice.fig -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/ice.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/ice.m -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/ice_stand_alone.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/ice_stand_alone.m -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/ifrdescp.m: -------------------------------------------------------------------------------- 1 | function s = ifrdescp(z, nd) 2 | %IFRDESCP Computes inverse Fourier descriptors. 3 | % S = IFRDESCP(Z, ND) computes the inverse Fourier descriptors of 4 | % of Z, which is a sequence of Fourier descriptor obtained, for 5 | % example, by using function FRDESCP. ND is the number of 6 | % descriptors used in computing the inverse; ND must be an even 7 | % integer no greater than length(Z). If ND is omitted, it defaults 8 | % to length(Z). The output, S, is a length(Z)-by-2 matrix containing 9 | % the coordinates of a closed boundary. 10 | 11 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 12 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 13 | % $Revision: 1.7 $ $Date: 2005/01/18 13:44:34 $ 14 | 15 | % Preliminaries. 16 | np = length(z); 17 | % Check inputs. 18 | if nargin == 1 | nd > np 19 | nd = np; 20 | end 21 | 22 | % Create an alternating sequence of 1s and -1s for use in centering 23 | % the transform. 24 | x = 0:(np - 1); 25 | m = ((-1) .^ x)'; 26 | 27 | % Use only nd descriptors in the inverse. Since the 28 | % descriptors are centered, (np - nd)/2 terms from each end of 29 | % the sequence are set to 0. 30 | d = round((np - nd)/2); % Round in case nd is odd. 31 | z(1:d) = 0; 32 | z(np - d + 1:np) = 0; 33 | % Compute the inverse and convert back to coordinates. 34 | zz = ifft(z); 35 | s(:, 1) = real(zz); 36 | s(:, 2) = imag(zz); 37 | % Multiply by alternating 1 and -1s to undo the earlier 38 | % centering. 39 | s(:, 1) = m.*s(:, 1); 40 | s(:, 2) = m.*s(:, 2); 41 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/ifwtcompare.m: -------------------------------------------------------------------------------- 1 | function [ratio, maxdiff] = ifwtcompare(f, n, wname) 2 | %IFWTCOMPARE Compare waverec2 and waveback. 3 | % [RATIO, MAXDIFF] = IFWTCOMPARE(F, N, WNAME) compares the 4 | % operation of Wavelet Toolbox function WAVEREC2 and custom 5 | % function WAVEBACK. 6 | % 7 | % INPUTS: 8 | % F Image to transform and inverse transform. 9 | % N Number of scales to compute. 10 | % WNAME Wavelet to use. 11 | % 12 | % OUTPUTS: 13 | % RATIO Execution time ratio (custom/toolbox). 14 | % MAXDIFF Maximum generated image difference. 15 | 16 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 17 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 18 | % $Revision: 1.5 $ $Date: 2003/10/13 01:30:46 $ 19 | 20 | % Compute the transform and get output and computation time for 21 | % waverec2. 22 | [c1, s1] = wavedec2(f, n, wname); 23 | tic; 24 | g1 = waverec2(c1, s1, wname); 25 | reftime = toc; 26 | 27 | % Compute the transform and get output and computation time for 28 | % waveback. 29 | [c2, s2] = wavefast(f, n, wname); 30 | tic; 31 | g2 = waveback(c2, s2, wname); 32 | t2 = toc; 33 | 34 | % Compare the results. 35 | ratio = t2 / (reftime + eps); 36 | maxdiff = abs(max(max(g1 - g2))); 37 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/imnoise3.m: -------------------------------------------------------------------------------- 1 | function [r, R, S] = imnoise3(M, N, C, A, B) 2 | %IMNOISE3 Generates periodic noise. 3 | % [r, R, S] = IMNOISE3(M, N, C, A, B), generates a spatial 4 | % sinusoidal noise pattern, r, of size M-by-N, its Fourier 5 | % transform, R, and spectrum, S. The remaining parameters are: 6 | % 7 | % C is a K-by-2 matrix with K pairs of freq. domain coordinates (u, 8 | % v) that define the locations of impulses in the freq. domain. The 9 | % locations are with respect to the frequency rectangle center at 10 | % (floor(M/2) + 1, floor(N/2) + 1). The impulse locations are spe- 11 | % cified as increments with respect to the center. For ex, if M = 12 | % N = 512, then the center is at (257, 257). To specify an impulse 13 | % at (280, 300) we specify the pair (23, 43); i.e., 257 + 23 = 280, 14 | % and 257 + 43 = 300. Only one pair of coordinates is required for 15 | % each impulse. The conjugate pairs are generated automatically. 16 | % 17 | % A is a 1-by-K vector that contains the amplitude of each of the 18 | % K impulse pairs. If A is not included in the argument, the 19 | % default used is A = ONES(1, K). B is then automatically set to 20 | % its default values (see next paragraph). The value specified 21 | % for A(j) is associated with the coordinates in C(j, 1:2). 22 | % 23 | % B is a K-by-2 matrix containing the Bx and By phase components 24 | % for each impulse pair. The default values for B are B(1:K, 1:2) 25 | % = 0. 26 | 27 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 28 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 29 | % $Revision: 1.5 $ $Date: 2004/11/04 22:32:42 $ 30 | 31 | % Process input parameters. 32 | [K, n] = size(C); 33 | if nargin == 3 34 | A(1:K) = 1.0; 35 | B(1:K, 1:2) = 0; 36 | elseif nargin == 4 37 | B(1:K, 1:2) = 0; 38 | end 39 | 40 | % Generate R. 41 | R = zeros(M, N); 42 | for j = 1:K 43 | u1 = M/2 + 1 + C(j, 1); v1 = N/2 + 1 + C(j, 2); 44 | R(u1, v1) = i * (A(j)/2) * exp(i*2*pi*C(j, 1) * B(j, 1)/M); 45 | % Complex conjugate. 46 | u2 = M/2 + 1 - C(j, 1); v2 = N/2 + 1 - C(j, 2); 47 | R(u2, v2) = -i * (A(j)/2) * exp(i*2*pi*C(j, 2) * B(j, 2)/N); 48 | end 49 | 50 | % Compute spectrum and spatial sinusoidal pattern. 51 | S = abs(R); 52 | r = real(ifft2(ifftshift(R))); 53 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/improd.m: -------------------------------------------------------------------------------- 1 | function [p, pmax, pmin, pn] = improd(f, g) 2 | %IMPROD Compute the product of two images. 3 | % [P, PMAX, PMIN, PN] = IMPROD(F, G) outputs the element-by-element 4 | % product of two input images, F and G, the product maximum and 5 | % minimum values, and a normalized product array with values in the 6 | % range [0, 1]. The input images must be of the same size. They 7 | % can be of class uint8, unit16, or double. The outputs are of 8 | % class double. 9 | % 10 | % Sample M-file used in Chapter 2. 11 | 12 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 13 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 14 | % $Revision: 1.1 $ $Date: 2003/02/22 17:00:54 $ 15 | 16 | fd = double(f); 17 | gd = double(g); 18 | p = fd.*gd; 19 | pmax = max(p(:)); 20 | pmin = min(p(:)); 21 | pn = mat2gray(p); 22 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/imratio.m: -------------------------------------------------------------------------------- 1 | function cr = imratio(f1, f2) 2 | %IMRATIO Computes the ratio of the bytes in two images/variables. 3 | % CR = IMRATIO(F1, F2) returns the ratio of the number of bytes in 4 | % variables/files F1 and F2. If F1 and F2 are an original and 5 | % compressed image, respectively, CR is the compression ratio. 6 | 7 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 8 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 9 | % $Revision: 1.4 $ $Date: 2003/11/21 13:11:15 $ 10 | 11 | error(nargchk(2, 2, nargin)); % Check input arguments 12 | cr = bytes(f1) / bytes(f2); % Compute the ratio 13 | 14 | %-------------------------------------------------------------------% 15 | function b = bytes(f) 16 | % Return the number of bytes in input f. If f is a string, assume 17 | % that it is an image filename; if not, it is an image variable. 18 | 19 | if ischar(f) 20 | info = dir(f); b = info.bytes; 21 | elseif isstruct(f) 22 | % MATLAB's whos function reports an extra 124 bytes of memory 23 | % per structure field because of the way MATLAB stores 24 | % structures in memory. Don't count this extra memory; instead, 25 | % add up the memory associated with each field. 26 | b = 0; 27 | fields = fieldnames(f); 28 | for k = 1:length(fields) 29 | b = b + bytes(f.(fields{k})); 30 | end 31 | else 32 | info = whos('f'); b = info.bytes; 33 | end 34 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/imstack2vectors.m: -------------------------------------------------------------------------------- 1 | function [X, R] = imstack2vectors(S, MASK) 2 | %IMSTACK2VECTORS Extracts vectors from an image stack. 3 | % [X, R] = imstack2vectors(S, MASK) extracts vectors from S, which 4 | % is an M-by-N-by-n stack array of n registered images of size 5 | % M-by-N each (see Fig. 11.24). The extracted vectors are arranged 6 | % as the rows of array X. Input MASK is an M-by-N logical or 7 | % numeric image with nonzero values (1s if it is a logical array) 8 | % in the locations where elements of S are to be used in forming X 9 | % and 0s in locations to be ignored. The number of row vectors in X 10 | % is equal to the number of nonzero elements of MASK. If MASK is 11 | % omitted, all M*N locations are used in forming X. A simple way 12 | % to obtain MASK interactively is to use function roipoly. Finally, 13 | % R is an array whose rows are the 2-D coordinates containing the 14 | % region locations in MASK from which the vectors in S were 15 | % extracted to form X. 16 | 17 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 18 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 19 | % $Revision: 1.6 $ $Date: 2003/11/21 14:37:21 $ 20 | 21 | % Preliminaries. 22 | [M, N, n] = size(S); 23 | if nargin == 1 24 | MASK = true(M, N); 25 | else 26 | MASK = MASK ~= 0; 27 | end 28 | 29 | % Find the set of locations where the vectors will be kept before 30 | % MASK is changed later in the program. 31 | [I, J] = find(MASK); 32 | R = [I, J]; 33 | 34 | % Now find X. 35 | 36 | % First reshape S into X by turning each set of n values along the third 37 | % dimension of S so that it becomes a row of X. The order is from top to 38 | % bottom along the first column, the second column, and so on. 39 | Q = M*N; 40 | X = reshape(S, Q, n); 41 | 42 | % Now reshape MASK so that it corresponds to the right locations 43 | % vertically along the elements of X. 44 | MASK = reshape(MASK, Q, 1); 45 | 46 | % Keep the rows of X at locations where MASK is not 0. 47 | X = X(MASK, :); 48 | 49 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/intline.m: -------------------------------------------------------------------------------- 1 | function [x, y] = intline(x1, x2, y1, y2) 2 | %INTLINE Integer-coordinate line drawing algorithm. 3 | % [X, Y] = INTLINE(X1, X2, Y1, Y2) computes an 4 | % approximation to the line segment joining (X1, Y1) and 5 | % (X2, Y2) with integer coordinates. X1, X2, Y1, and Y2 6 | % should be integers. INTLINE is reversible; that is, 7 | % INTLINE(X1, X2, Y1, Y2) produces the same results as 8 | % FLIPUD(INTLINE(X2, X1, Y2, Y1)). 9 | 10 | % Copyright 1993-2002 The MathWorks, Inc. Used with permission. 11 | % $Revision: 1.4 $ $Date: 2003/11/21 14:38:20 $ 12 | 13 | dx = abs(x2 - x1); 14 | dy = abs(y2 - y1); 15 | 16 | % Check for degenerate case. 17 | if ((dx == 0) & (dy == 0)) 18 | x = x1; 19 | y = y1; 20 | return; 21 | end 22 | 23 | flip = 0; 24 | if (dx >= dy) 25 | if (x1 > x2) 26 | % Always "draw" from left to right. 27 | t = x1; x1 = x2; x2 = t; 28 | t = y1; y1 = y2; y2 = t; 29 | flip = 1; 30 | end 31 | m = (y2 - y1)/(x2 - x1); 32 | x = (x1:x2).'; 33 | y = round(y1 + m*(x - x1)); 34 | else 35 | if (y1 > y2) 36 | % Always "draw" from bottom to top. 37 | t = x1; x1 = x2; x2 = t; 38 | t = y1; y1 = y2; y2 = t; 39 | flip = 1; 40 | end 41 | m = (x2 - x1)/(y2 - y1); 42 | y = (y1:y2).'; 43 | x = round(x1 + m*(y - y1)); 44 | end 45 | 46 | if (flip) 47 | x = flipud(x); 48 | y = flipud(y); 49 | end 50 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/jpeg2im.m: -------------------------------------------------------------------------------- 1 | function x = jpeg2im(y) 2 | %JPEG2IM Decodes an IM2JPEG compressed image. 3 | % X = JPEG2IM(Y) decodes compressed image Y, generating 4 | % reconstructed approximation X. Y is a structure generated by 5 | % IM2JPEG. 6 | % 7 | % See also IM2JPEG. 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.4 $ $Date: 2003/10/26 18:39:08 $ 12 | 13 | error(nargchk(1, 1, nargin)); % Check input arguments 14 | 15 | m = [16 11 10 16 24 40 51 61 % JPEG normalizing array 16 | 12 12 14 19 26 58 60 55 % and zig-zag reordering 17 | 14 13 16 24 40 57 69 56 % pattern. 18 | 14 17 22 29 51 87 80 62 19 | 18 22 37 56 68 109 103 77 20 | 24 35 55 64 81 104 113 92 21 | 49 64 78 87 103 121 120 101 22 | 72 92 95 98 112 100 103 99]; 23 | 24 | order = [1 9 2 3 10 17 25 18 11 4 5 12 19 26 33 ... 25 | 41 34 27 20 13 6 7 14 21 28 35 42 49 57 50 ... 26 | 43 36 29 22 15 8 16 23 30 37 44 51 58 59 52 ... 27 | 45 38 31 24 32 39 46 53 60 61 54 47 40 48 55 ... 28 | 62 63 56 64]; 29 | rev = order; % Compute inverse ordering 30 | for k = 1:length(order) 31 | rev(k) = find(order == k); 32 | end 33 | 34 | m = double(y.quality) / 100 * m; % Get encoding quality. 35 | xb = double(y.numblocks); % Get x blocks. 36 | sz = double(y.size); 37 | xn = sz(2); % Get x columns. 38 | xm = sz(1); % Get x rows. 39 | x = huff2mat(y.huffman); % Huffman decode. 40 | eob = max(x(:)); % Get end-of-block symbol 41 | 42 | z = zeros(64, xb); k = 1; % Form block columns by copying 43 | for j = 1:xb % successive values from x into 44 | for i = 1:64 % columns of z, while changing 45 | if x(k) == eob % to the next column whenever 46 | k = k + 1; break; % an EOB symbol is found. 47 | else 48 | z(i, j) = x(k); 49 | k = k + 1; 50 | end 51 | end 52 | end 53 | 54 | z = z(rev, :); % Restore order 55 | x = col2im(z, [8 8], [xm xn], 'distinct'); % Form matrix blocks 56 | x = blkproc(x, [8 8], 'x .* P1', m); % Denormalize DCT 57 | t = dctmtx(8); % Get 8 x 8 DCT matrix 58 | x = blkproc(x, [8 8], 'P1 * x * P2', t', t); % Compute block DCT-1 59 | x = uint8(x + 128); % Level shift 60 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/jpeg2k2im.m: -------------------------------------------------------------------------------- 1 | function x = jpeg2k2im(y) 2 | %JPEG2K2IM Decodes an IM2JPEG2K compressed image. 3 | % X = JPEG2K2IM(Y) decodes compressed image Y, reconstructing an 4 | % approximation of the original image X. Y is an encoding 5 | % structure returned by IM2JPEG2K. 6 | % 7 | % See also IM2JPEG2K. 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.4 $ $Date: 2003/10/26 18:39:40 $ 12 | 13 | error(nargchk(1, 1, nargin)); % Check input arguments 14 | 15 | % Get decoding parameters: scale, quantization vector, run-length 16 | % table size, zero run code, end-of-data code, wavelet bookkeeping 17 | % array, and run-length table. 18 | n = double(y.n); 19 | q = double(y.q) / 100; 20 | runs = double(y.runs); 21 | rlen = length(runs); 22 | zrc = -double(y.zrc); 23 | eoc = zrc - 1; 24 | s = double(y.s); 25 | s = reshape(s, n + 2, 2); 26 | 27 | % Compute the size of the wavelet transform. 28 | cl = prod(s(1, :)); 29 | for i = 2:n + 1 30 | cl = cl + 3 * prod(s(i, :)); 31 | end 32 | 33 | % Perform Huffman decoding followed by zero run decoding. 34 | r = huff2mat(y.huffman); 35 | 36 | c = []; zi = find(r == zrc); i = 1; 37 | for j = 1:length(zi) 38 | c = [c r(i:zi(j) - 1) zeros(1, runs(r(zi(j) + 1)))]; 39 | i = zi(j) + 2; 40 | end 41 | 42 | zi = find(r == eoc); % Undo terminating zero run 43 | if length(zi) == 1 % or last non-zero run. 44 | c = [c r(i:zi - 1)]; 45 | c = [c zeros(1, cl - length(c))]; 46 | else 47 | c = [c r(i:end)]; 48 | end 49 | 50 | % Denormalize the coefficients. 51 | c = c + (c > 0) - (c < 0); 52 | for k = 1:n 53 | qi = 3 * k - 2; 54 | c = wavepaste('h', c, s, k, wavecopy('h', c, s, k) * q(qi)); 55 | c = wavepaste('v', c, s, k, wavecopy('v', c, s, k) * q(qi + 1)); 56 | c = wavepaste('d', c, s, k, wavecopy('d', c, s, k) * q(qi + 2)); 57 | end 58 | c = wavepaste('a', c, s, k, wavecopy('a', c, s, k) * q(qi + 3)); 59 | 60 | % Compute the inverse wavelet transform and level shift. 61 | x = waveback(c, s, 'jpeg9.7', n); 62 | x = uint8(x + 128); -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/lpc2mat.m: -------------------------------------------------------------------------------- 1 | function x = lpc2mat(y, f) 2 | %LPC2MAT Decompresses a 1-D lossless predictive encoded matrix. 3 | % X = LPC2MAT(Y, F) decodes input matrix Y based on linear 4 | % prediction coefficients in F and the assumption of 1-D lossless 5 | % predictive coding. If F is omitted, filter F = 1 (for previous 6 | % pixel coding) is assumed. 7 | % 8 | % See also MAT2LPC. 9 | 10 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 11 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 12 | % $Revision: 1.5 $ $Date: 2003/11/21 13:21:09 $ 13 | 14 | error(nargchk(1, 2, nargin)); % Check input arguments 15 | if nargin < 2 % Set default filter if omitted 16 | f = 1; 17 | end 18 | 19 | f = f(end:-1:1); % Reverse the filter coefficients 20 | [m, n] = size(y); % Get dimensions of output matrix 21 | order = length(f); % Get order of linear predictor 22 | f = repmat(f, m, 1); % Duplicate filter for vectorizing 23 | x = zeros(m, n + order); % Pad for 1st 'order' column decodes 24 | 25 | % Decode the output one column at a time. Compute a prediction based 26 | % on the 'order' previous elements and add it to the prediction 27 | % error. The result is appended to the output matrix being built. 28 | for j = 1:n 29 | jj = j + order; 30 | x(:, jj) = y(:, j) + round(sum(f(:, order:-1:1) .* ... 31 | x(:, (jj - 1):-1:(jj - order)), 2)); 32 | end 33 | 34 | x = x(:, order + 1:end); % Remove left padding -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/lpfilter.m: -------------------------------------------------------------------------------- 1 | function H = lpfilter(type, M, N, D0, n) 2 | %LPFILTER Computes frequency domain lowpass filters. 3 | % H = LPFILTER(TYPE, M, N, D0, n) creates the transfer function of 4 | % a lowpass filter, H, of the specified TYPE and size (M-by-N). To 5 | % view the filter as an image or mesh plot, it should be centered 6 | % using H = fftshift(H). 7 | % 8 | % Valid values for TYPE, D0, and n are: 9 | % 10 | % 'ideal' Ideal lowpass filter with cutoff frequency D0. n need 11 | % not be supplied. D0 must be positive. 12 | % 13 | % 'btw' Butterworth lowpass filter of order n, and cutoff 14 | % D0. The default value for n is 1.0. D0 must be 15 | % positive. 16 | % 17 | % 'gaussian' Gaussian lowpass filter with cutoff (standard 18 | % deviation) D0. n need not be supplied. D0 must be 19 | % positive. 20 | 21 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 22 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 23 | % $Revision: 1.8 $ $Date: 2004/11/04 22:33:16 $ 24 | 25 | % Use function dftuv to set up the meshgrid arrays needed for 26 | % computing the required distances. 27 | [U, V] = dftuv(M, N); 28 | 29 | % Compute the distances D(U, V). 30 | D = sqrt(U.^2 + V.^2); 31 | 32 | % Begin filter computations. 33 | switch type 34 | case 'ideal' 35 | H = double(D <= D0); 36 | case 'btw' 37 | if nargin == 4 38 | n = 1; 39 | end 40 | H = 1./(1 + (D./D0).^(2*n)); 41 | case 'gaussian' 42 | H = exp(-(D.^2)./(2*(D0^2))); 43 | otherwise 44 | error('Unknown filter type.') 45 | end 46 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/mahalanobis.m: -------------------------------------------------------------------------------- 1 | function d = mahalanobis(varargin) 2 | %MAHALANOBIS Computes the Mahalanobis distance. 3 | % D = MAHALANOBIS(Y, X) computes the Mahalanobis distance between 4 | % each vector in Y to the mean (centroid) of the vectors in X, and 5 | % outputs the result in vector D, whose length is size(Y, 1). The 6 | % vectors in X and Y are assumed to be organized as rows. The 7 | % input data can be real or complex. The outputs are real 8 | % quantities. 9 | % 10 | % D = MAHALANOBIS(Y, CX, MX) computes the Mahalanobis distance 11 | % between each vector in Y and the given mean vector, MX. The 12 | % results are output in vector D, whose length is size(Y, 1). The 13 | % vectors in Y are assumed to be organized as the rows of this 14 | % array. The input data can be real or complex. The outputs are 15 | % real quantities. In addition to the mean vector MX, the 16 | % covariance matrix CX of a population of vectors X also must be 17 | % provided. Use function COVMATRIX (Section 11.5) to compute MX and 18 | % CX. 19 | 20 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 21 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 22 | % $Revision: 1.6 $ $Date: 2005/01/18 13:44:47 $ 23 | 24 | % Reference: Acklam, P. J. [2002]. "MATLAB Array Manipulation Tips 25 | % and Tricks." Available at 26 | % home.online.no/~pjacklam/matlab/doc/mtt/index.html 27 | % or at 28 | % www.prenhall.com/gonzalezwoodseddins 29 | 30 | param = varargin; % Keep in mind that param is a cell array. 31 | Y = param{1}; 32 | ny = size(Y, 1); % Number of vectors in Y. 33 | 34 | if length(param) == 2 35 | X = param{2}; 36 | % Compute the mean vector and covariance matrix of the vectors 37 | % in X. 38 | [Cx, mx] = covmatrix(X); 39 | elseif length(param) == 3 % Cov. matrix and mean vector provided. 40 | Cx = param{2}; 41 | mx = param{3}; 42 | else 43 | error('Wrong number of inputs.') 44 | end 45 | mx = mx(:)'; % Make sure that mx is a row vector. 46 | 47 | % Subtract the mean vector from each vector in Y. 48 | Yc = Y - mx(ones(ny, 1), :); 49 | 50 | % Compute the Mahalanobis distances. 51 | d = real(sum(Yc/Cx.*conj(Yc), 2)); 52 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/manualhist.m: -------------------------------------------------------------------------------- 1 | function p = manualhist 2 | %MANUALHIST Generates a two-mode histogram interactively. 3 | % P = MANUALHIST generates a two-mode histogram using 4 | % TWOMODEGAUSS(m1, sig1, m2, sig2, A1, A2, k). m1 and m2 are the 5 | % means of the two modes and must be in the range [0,1]. sig1 and 6 | % sig2 are the standard deviations of the two modes. A1 and A2 are 7 | % amplitude values, and k is an offset value that raised the 8 | % "floor" of histogram. The number of elements in the histogram 9 | % vector P is 256 and sum(P) is normalized to 1. MANUALHIST 10 | % repeatedly prompts for the parameters and plots the resulting 11 | % histogram until the user types an 'x' to quit, and then it returns 12 | % the last histogram computed. 13 | % 14 | % A good set of starting values is: (0.15, 0.05, 0.75, 0.05, 1, 15 | % 0.07, 0.002). 16 | 17 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 18 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 19 | % $Revision: 1.7 $ $Date: 2003/10/13 00:49:57 $ 20 | 21 | % Initialize. 22 | repeats = true; 23 | quitnow = 'x'; 24 | 25 | % Compute a default histogram in case the user quits before 26 | % estimating at least one histogram. 27 | p = twomodegauss(0.15, 0.05, 0.75, 0.05, 1, 0.07, 0.002); 28 | 29 | % Cycle until an x is input. 30 | while repeats 31 | s = input('Enter m1, sig1, m2, sig2, A1, A2, k OR x to quit:','s'); 32 | if s == quitnow 33 | break 34 | end 35 | 36 | % Convert the input string to a vector of numerical values and 37 | % verify the number of inputs. 38 | v = str2num(s); 39 | if numel(v) ~= 7 40 | disp('Incorrect number of inputs') 41 | continue 42 | end 43 | 44 | p = twomodegauss(v(1), v(2), v(3), v(4), v(5), v(6), v(7)); 45 | % Start a new figure and scale the axes. Specifying only xlim 46 | % leaves ylim on auto. 47 | figure, plot(p) 48 | xlim([0 255]) 49 | end 50 | 51 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/mat2huff.m: -------------------------------------------------------------------------------- 1 | function y = mat2huff(x) 2 | %MAT2HUFF Huffman encodes a matrix. 3 | % Y = MAT2HUFF(X) Huffman encodes matrix X using symbol 4 | % probabilities in unit-width histogram bins between X's minimum 5 | % and maximum values. The encoded data is returned as a structure 6 | % Y: 7 | % Y.code The Huffman-encoded values of X, stored in 8 | % a uint16 vector. The other fields of Y contain 9 | % additional decoding information, including: 10 | % Y.min The minimum value of X plus 32768 11 | % Y.size The size of X 12 | % Y.hist The histogram of X 13 | % 14 | % If X is logical, uint8, uint16, uint32, int8, int16, or double, 15 | % with integer values, it can be input directly to MAT2HUFF. The 16 | % minimum value of X must be representable as an int16. 17 | % 18 | % If X is double with non-integer values---for example, an image 19 | % with values between 0 and 1---first scale X to an appropriate 20 | % integer range before the call. For example, use Y = 21 | % MAT2HUFF(255*X) for 256 gray level encoding. 22 | % 23 | % NOTE: The number of Huffman code words is round(max(X(:))) - 24 | % round(min(X(:))) + 1. You may need to scale input X to generate 25 | % codes of reasonable length. The maximum row or column dimension 26 | % of X is 65535. 27 | % 28 | % See also HUFF2MAT. 29 | 30 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 31 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 32 | % $Revision: 1.5 $ $Date: 2003/11/21 15:21:12 $ 33 | 34 | if ndims(x) ~= 2 | ~isreal(x) | (~isnumeric(x) & ~islogical(x)) 35 | error('X must be a 2-D real numeric or logical matrix.'); 36 | end 37 | 38 | % Store the size of input x. 39 | y.size = uint32(size(x)); 40 | 41 | % Find the range of x values and store its minimum value biased 42 | % by +32768 as a UINT16. 43 | x = round(double(x)); 44 | xmin = min(x(:)); 45 | xmax = max(x(:)); 46 | pmin = double(int16(xmin)); 47 | pmin = uint16(pmin + 32768); y.min = pmin; 48 | 49 | % Compute the input histogram between xmin and xmax with unit 50 | % width bins, scale to UINT16, and store. 51 | x = x(:)'; 52 | h = histc(x, xmin:xmax); 53 | if max(h) > 65535 54 | h = 65535 * h / max(h); 55 | end 56 | h = uint16(h); y.hist = h; 57 | 58 | % Code the input matrix and store the result. 59 | map = huffman(double(h)); % Make Huffman code map 60 | hx = map(x(:) - xmin + 1); % Map image 61 | hx = char(hx)'; % Convert to char array 62 | hx = hx(:)'; 63 | hx(hx == ' ') = []; % Remove blanks 64 | ysize = ceil(length(hx) / 16); % Compute encoded size 65 | hx16 = repmat('0', 1, ysize * 16); % Pre-allocate modulo-16 vector 66 | hx16(1:length(hx)) = hx; % Make hx modulo-16 in length 67 | hx16 = reshape(hx16, 16, ysize); % Reshape to 16-character words 68 | hx16 = hx16' - '0'; % Convert binary string to decimal 69 | twos = pow2(15:-1:0); 70 | y.code = uint16(sum(hx16 .* twos(ones(ysize, 1), :), 2))'; 71 | 72 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/mat2lpc.m: -------------------------------------------------------------------------------- 1 | function y = mat2lpc(x, f) 2 | %MAT2LPC Compresses a matrix using 1-D lossles predictive coding. 3 | % Y = MAT2LPC(X, F) encodes matrix X using 1-D lossless predictive 4 | % coding. A linear prediction of X is made based on the 5 | % coefficients in F. If F is omitted, F = 1 (for previous pixel 6 | % coding) is assumed. The prediction error is then computed and 7 | % output as encoded matrix Y. 8 | % 9 | % See also LPC2MAT. 10 | 11 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 12 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 13 | % $Revision: 1.4 $ $Date: 2003/06/18 12:38:39 $ 14 | 15 | error(nargchk(1, 2, nargin)); % Check input arguments 16 | if nargin < 2 % Set default filter if omitted 17 | f = 1; 18 | end 19 | 20 | x = double(x); % Ensure double for computations 21 | [m, n] = size(x); % Get dimensions of input matrix 22 | p = zeros(m, n); % Init linear prediction to 0 23 | xs = x; zc = zeros(m, 1); % Prepare for input shift and pad 24 | 25 | for j = 1:length(f) % For each filter coefficient ... 26 | xs = [zc xs(:, 1:end - 1)]; % Shift and zero pad x 27 | p = p + f(j) * xs; % Form partial prediction sums 28 | end 29 | 30 | y = x - round(p); % Compute the prediction error -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/paddedsize.m: -------------------------------------------------------------------------------- 1 | function PQ = paddedsize(AB, CD, PARAM) 2 | %PADDEDSIZE Computes padded sizes useful for FFT-based filtering. 3 | % PQ = PADDEDSIZE(AB), where AB is a two-element size vector, 4 | % computes the two-element size vector PQ = 2*AB. 5 | % 6 | % PQ = PADDEDSIZE(AB, 'PWR2') computes the vector PQ such that 7 | % PQ(1) = PQ(2) = 2^nextpow2(2*m), where m is MAX(AB). 8 | % 9 | % PQ = PADDEDSIZE(AB, CD), where AB and CD are two-element size 10 | % vectors, computes the two-element size vector PQ. The elements 11 | % of PQ are the smallest even integers greater than or equal to 12 | % AB + CD - 1. 13 | % 14 | % PQ = PADDEDSIZE(AB, CD, 'PWR2') computes the vector PQ such that 15 | % PQ(1) = PQ(2) = 2^nextpow2(2*m), where m is MAX([AB CD]). 16 | 17 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 18 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 19 | % $Revision: 1.5 $ $Date: 2003/08/25 14:28:22 $ 20 | 21 | if nargin == 1 22 | PQ = 2*AB; 23 | elseif nargin == 2 & ~ischar(CD) 24 | PQ = AB + CD - 1; 25 | PQ = 2 * ceil(PQ / 2); 26 | elseif nargin == 2 27 | m = max(AB); % Maximum dimension. 28 | 29 | % Find power-of-2 at least twice m. 30 | P = 2^nextpow2(2*m); 31 | PQ = [P, P]; 32 | elseif nargin == 3 33 | m = max([AB CD]); % Maximum dimension. 34 | P = 2^nextpow2(2*m); 35 | PQ = [P, P]; 36 | else 37 | error('Wrong number of inputs.') 38 | end 39 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/pixeldup.m: -------------------------------------------------------------------------------- 1 | function B = pixeldup(A, m, n) 2 | %PIXELDUP Duplicates pixels of an image in both directions. 3 | % B = PIXELDUP(A, M, N) duplicates each pixel of A M times in the 4 | % vertical direction and N times in the horizontal direction. 5 | % Parameters M and N must be integers. If N is not included, it 6 | % defaults to M. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.5 $ $Date: 2005/01/03 00:01:28 $ 11 | 12 | % Check inputs. 13 | if nargin < 2 14 | error('At least two inputs are required.'); 15 | end 16 | if nargin == 2 17 | n = m; 18 | end 19 | 20 | % Generate a vector with elements 1:size(A, 1). 21 | u = 1:size(A, 1); 22 | 23 | % Duplicate each element of the vector m times. 24 | m = round(m); % Protect against nonintegers. 25 | u = u(ones(1, m), :); 26 | u = u(:); 27 | 28 | % Now repeat for the other direction. 29 | v = 1:size(A, 2); 30 | n = round(n); 31 | v = v(ones(1, n), :); 32 | v = v(:); 33 | B = A(u, v); 34 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/quantize.m: -------------------------------------------------------------------------------- 1 | function y = quantize(x, b, type) 2 | %QUANTIZE Quantizes the elements of a UINT8 matrix. 3 | % Y = QUANTIZE(X, B, TYPE) quantizes X to B bits. Truncation is 4 | % used unless TYPE is 'igs' for Improved Gray Scale quantization. 5 | 6 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 7 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 8 | % $Revision: 1.4 $ $Date: 2003/10/26 18:49:23 $ 9 | 10 | error(nargchk(2, 3, nargin)); % Check input arguments 11 | if ndims(x) ~= 2 | ~isreal(x) | ... 12 | ~isnumeric(x) | ~isa(x, 'uint8') 13 | error('The input must be a UINT8 numeric matrix.'); 14 | end 15 | 16 | % Create bit masks for the quantization 17 | lo = uint8(2 ^ (8 - b) - 1); 18 | hi = uint8(2 ^ 8 - double(lo) - 1); 19 | 20 | % Perform standard quantization unless IGS is specified 21 | if nargin < 3 | ~strcmpi(type, 'igs') 22 | y = bitand(x, hi); 23 | 24 | % Else IGS quantization. Process column-wise. If the MSB's of the 25 | % pixel are all 1's, the sum is set to the pixel value. Else, add 26 | % the pixel value to the LSB's of the previous sum. Then take the 27 | % MSB's of the sum as the quantized value. 28 | else 29 | [m, n] = size(x); s = zeros(m, 1); 30 | hitest = double(bitand(x, hi) ~= hi); x = double(x); 31 | for j = 1:n 32 | s = x(:, j) + hitest(:, j) .* double(bitand(uint8(s), lo)); 33 | y(:, j) = bitand(uint8(s), hi); 34 | end 35 | end 36 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/randvertex.m: -------------------------------------------------------------------------------- 1 | function [xn, yn] = randvertex(x, y, npix) 2 | %RANDVERTEX Adds random noise to the vertices of a polygon. 3 | % [XN, YN] = RANDVERTEX[X, Y, NPIX] adds uniformly distributed 4 | % noise to the coordinates of vertices of a polygon. The 5 | % coordinates of the vertices are input in X and Y, and NPIX is the 6 | % maximum number of pixel locations by which any pair (X(i), Y(i)) 7 | % is allowed to deviate. For example, if NPIX = 1, the location of 8 | % any X(i) will not deviate by more than one pixel location in the 9 | % x-direction, and similarly for Y(i). Noise is added independently 10 | % to the two coordinates. 11 | 12 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 13 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 14 | % $Revision: 1.4 $ $Date: 2003/06/14 16:31:17 $ 15 | 16 | % Convert to columns. 17 | x = x(:); 18 | y = y(:); 19 | 20 | % Preliminary calculations. 21 | L = length(x); 22 | xnoise = rand(L, 1); 23 | ynoise = rand(L, 1); 24 | xdev = npix*xnoise.*sign(xnoise - 0.5); 25 | ydev = npix*ynoise.*sign(ynoise - 0.5); 26 | 27 | % Add noise and round. 28 | xn = round(x + xdev); 29 | yn = round(y + ydev); 30 | 31 | % All pixel locations must be no less than 1. 32 | xn = max(xn, 1); 33 | yn = max(yn, 1); 34 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/regiongrow.m: -------------------------------------------------------------------------------- 1 | function [g, NR, SI, TI] = regiongrow(f, S, T) 2 | %REGIONGROW Perform segmentation by region growing. 3 | % [G, NR, SI, TI] = REGIONGROW(F, SR, T). S can be an array (the 4 | % same size as F) with a 1 at the coordinates of every seed point 5 | % and 0s elsewhere. S can also be a single seed value. Similarly, 6 | % T can be an array (the same size as F) containing a threshold 7 | % value for each pixel in F. T can also be a scalar, in which 8 | % case it becomes a global threshold. 9 | % 10 | % On the output, G is the result of region growing, with each 11 | % region labeled by a different integer, NR is the number of 12 | % regions, SI is the final seed image used by the algorithm, and TI 13 | % is the image consisting of the pixels in F that satisfied the 14 | % threshold test. 15 | 16 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 17 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 18 | % $Revision: 1.4 $ $Date: 2003/10/26 22:35:37 $ 19 | 20 | f = double(f); 21 | % If S is a scalar, obtain the seed image. 22 | if numel(S) == 1 23 | SI = f == S; 24 | S1 = S; 25 | else 26 | % S is an array. Eliminate duplicate, connected seed locations 27 | % to reduce the number of loop executions in the following 28 | % sections of code. 29 | SI = bwmorph(S, 'shrink', Inf); 30 | J = find(SI); 31 | S1 = f(J); % Array of seed values. 32 | end 33 | 34 | TI = false(size(f)); 35 | for K = 1:length(S1) 36 | seedvalue = S1(K); 37 | S = abs(f - seedvalue) <= T; 38 | TI = TI | S; 39 | end 40 | 41 | % Use function imreconstruct with SI as the marker image to 42 | % obtain the regions corresponding to each seed in S. Function 43 | % bwlabel assigns a different integer to each connected region. 44 | [g, NR] = bwlabel(imreconstruct(SI, TI)); 45 | 46 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/rgb2hsi.m: -------------------------------------------------------------------------------- 1 | function hsi = rgb2hsi(rgb) 2 | %RGB2HSI Converts an RGB image to HSI. 3 | % HSI = RGB2HSI(RGB) converts an RGB image to HSI. The input image 4 | % is assumed to be of size M-by-N-by-3, where the third dimension 5 | % accounts for three image planes: red, green, and blue, in that 6 | % order. If all RGB component images are equal, the HSI conversion 7 | % is undefined. The input image can be of class double (with values 8 | % in the range [0, 1]), uint8, or uint16. 9 | % 10 | % The output image, HSI, is of class double, where: 11 | % hsi(:, :, 1) = hue image normalized to the range [0, 1] by 12 | % dividing all angle values by 2*pi. 13 | % hsi(:, :, 2) = saturation image, in the range [0, 1]. 14 | % hsi(:, :, 3) = intensity image, in the range [0, 1]. 15 | 16 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 17 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 18 | % $Revision: 1.5 $ $Date: 2005/01/18 13:44:59 $ 19 | 20 | % Extract the individual component images. 21 | rgb = im2double(rgb); 22 | r = rgb(:, :, 1); 23 | g = rgb(:, :, 2); 24 | b = rgb(:, :, 3); 25 | 26 | % Implement the conversion equations. 27 | num = 0.5*((r - g) + (r - b)); 28 | den = sqrt((r - g).^2 + (r - b).*(g - b)); 29 | theta = acos(num./(den + eps)); 30 | 31 | H = theta; 32 | H(b > g) = 2*pi - H(b > g); 33 | H = H/(2*pi); 34 | 35 | num = min(min(r, g), b); 36 | den = r + g + b; 37 | den(den == 0) = eps; 38 | S = 1 - 3.* num./den; 39 | 40 | H(S == 0) = 0; 41 | 42 | I = (r + g + b)/3; 43 | 44 | % Combine all three results into an hsi image. 45 | hsi = cat(3, H, S, I); 46 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/rgbcube.m: -------------------------------------------------------------------------------- 1 | function rgbcube(vx, vy, vz) 2 | %RGBCUBE Displays an RGB cube on the MATLAB desktop. 3 | % RGBCUBE(VX, VY, VZ) displays an RGB color cube, viewed from point 4 | % (VX, VY, VZ). With no input arguments, RGBCUBE uses (10, 10, 4) 5 | % as the default viewing coordinates. To view individual color 6 | % planes, use the following viewing coordinates, where the first 7 | % color in the sequence is the closest to the viewing axis, and the 8 | % other colors are as seen from that axis, proceeding to the right 9 | % right (or above), and then moving clockwise. 10 | % 11 | % ------------------------------------------------- 12 | % COLOR PLANE ( vx, vy, vz) 13 | % ------------------------------------------------- 14 | % Blue-Magenta-White-Cyan ( 0, 0, 10) 15 | % Red-Yellow-White-Magenta ( 10, 0, 0) 16 | % Green-Cyan-White-Yellow ( 0, 10, 0) 17 | % Black-Red-Magenta-Blue ( 0, -10, 0) 18 | % Black-Blue-Cyan-Green (-10, 0, 0) 19 | % Black-Red-Yellow-Green ( 0, 0, -10) 20 | % 21 | 22 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 23 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 24 | % $Revision: 1.5 $ $Date: 2003/10/13 00:52:14 $ 25 | 26 | % Set up parameters for function patch. 27 | vertices_matrix = [0 0 0;0 0 1;0 1 0;0 1 1;1 0 0;1 0 1;1 1 0;1 1 1]; 28 | faces_matrix = [1 5 6 2;1 3 7 5;1 2 4 3;2 4 8 6;3 7 8 4;5 6 8 7]; 29 | colors = vertices_matrix; 30 | % The order of the cube vertices was selected to be the same as 31 | % the order of the (R,G,B) colors (e.g., (0,0,0) corresponds to 32 | % black, (1,1,1) corresponds to white, and so on.) 33 | 34 | % Generate RGB cube using function patch. 35 | patch('Vertices', vertices_matrix, 'Faces', faces_matrix, ... 36 | 'FaceVertexCData', colors, 'FaceColor', 'interp', ... 37 | 'EdgeAlpha', 0) 38 | 39 | % Set up viewing point. 40 | if nargin == 0 41 | vx = 10; vy = 10; vz = 4; 42 | elseif nargin ~= 3 43 | error('Wrong number of inputs.') 44 | end 45 | axis off 46 | view([vx, vy, vz]) 47 | axis square 48 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/specxture.m: -------------------------------------------------------------------------------- 1 | function [srad, sang, S] = specxture(f); 2 | %SPECXTURE Computes spectral texture of an image. 3 | % [SRAD, SANG, S] = SPECXTURE(F) computes SRAD, the spectral energy 4 | % distribution as a function of radius from the center of the 5 | % spectrum, SANG, the spectral energy distribution as a function of 6 | % angle for 0 to 180 degrees in increments of 1 degree, and S = 7 | % log(1 + spectrum of f), normalized to the range [0, 1]. The 8 | % maximum value of radius is min(M,N), where M and N are the number 9 | % of rows and columns of image (region) f. Thus, SRAD is a row 10 | % vector of length = (min(M, N)/2) - 1; and SANG is a row vector of 11 | % length 180. 12 | 13 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 14 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 15 | % $Revision: 1.7 $ $Date: 2003/11/21 14:48:47 $ 16 | 17 | % Obtain the centered spectrum, S, of f. The variables of S are 18 | % (u, v), running from 1:M and 1:N, with the center (zero frequency) 19 | % at [M/2 + 1, N/2 + 1] (see Chapter 4). 20 | S = fftshift(fft2(f)); 21 | S = abs(S); 22 | [M, N] = size(S); 23 | x0 = M/2 + 1; 24 | y0 = N/2 + 1; 25 | 26 | % Maximum radius that guarantees a circle centered at (x0, y0) that 27 | % does not exceed the boundaries of S. 28 | rmax = min(M, N)/2 - 1; 29 | 30 | % Compute srad. 31 | srad = zeros(1, rmax); 32 | srad(1) = S(x0, y0); 33 | for r = 2:rmax 34 | [xc, yc] = halfcircle(r, x0, y0); 35 | srad(r) = sum(S(sub2ind(size(S), xc, yc))); 36 | end 37 | 38 | % Compute sang. 39 | [xc, yc] = halfcircle(rmax, x0, y0); 40 | sang = zeros(1, length(xc)); 41 | for a = 1:length(xc) 42 | [xr, yr] = radial(x0, y0, xc(a), yc(a)); 43 | sang(a) = sum(S(sub2ind(size(S), xr, yr))); 44 | end 45 | 46 | % Output the log of the spectrum for easier viewing, scaled to the 47 | % range [0, 1]. 48 | S = mat2gray(log(1 + S)); 49 | 50 | %-------------------------------------------------------------------% 51 | function [xc, yc] = halfcircle(r, x0, y0) 52 | % Computes the integer coordinates of a half circle of radius r and 53 | % center at (x0,y0) using one degree increments. 54 | % 55 | % Goes from 91 to 270 because we want the half circle to be in the 56 | % region defined by top right and top left quadrants, in the 57 | % standard image coordinates. 58 | 59 | theta=91:270; 60 | theta = theta*pi/180; 61 | [xc, yc] = pol2cart(theta, r); 62 | xc = round(xc)' + x0; % Column vector. 63 | yc = round(yc)' + y0; 64 | 65 | %-------------------------------------------------------------------% 66 | function [xr, yr] = radial(x0, y0, x, y); 67 | % Computes the coordinates of a straight line segment extending 68 | % from (x0, y0) to (x, y). 69 | % 70 | % Based on function intline.m. xr and yr are returned as column 71 | % vectors. 72 | 73 | [xr, yr] = intline(x0, x, y0, y); 74 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/statmoments.m: -------------------------------------------------------------------------------- 1 | function [v, unv] = statmoments(p, n) 2 | %STATMOMENTS Computes statistical central moments of image histogram. 3 | % [W, UNV] = STATMOMENTS(P, N) computes up to the Nth statistical 4 | % central moment of a histogram whose components are in vector 5 | % P. The length of P must equal 256 or 65536. 6 | % 7 | % The program outputs a vector V with V(1) = mean, V(2) = variance, 8 | % V(3) = 3rd moment, . . . V(N) = Nth central moment. The random 9 | % variable values are normalized to the range [0, 1], so all 10 | % moments also are in this range. 11 | % 12 | % The program also outputs a vector UNV containing the same moments 13 | % as V, but using un-normalized random variable values (e.g., 0 to 14 | % 255 if length(P) = 2^8). For example, if length(P) = 256 and V(1) 15 | % = 0.5, then UNV(1) would have the value UNV(1) = 127.5 (half of 16 | % the [0 255] range). 17 | 18 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 19 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 20 | % $Revision: 1.3 $ $Date: 2003/05/24 22:43:02 $ 21 | 22 | Lp = length(p); 23 | if (Lp ~= 256) & (Lp ~= 65536) 24 | error('P must be a 256- or 65536-element vector.'); 25 | end 26 | G = Lp - 1; 27 | 28 | % Make sure the histogram has unit area, and convert it to a 29 | % column vector. 30 | p = p/sum(p); p = p(:); 31 | 32 | % Form a vector of all the possible values of the 33 | % random variable. 34 | z = 0:G; 35 | 36 | % Now normalize the z's to the range [0, 1]. 37 | z = z./G; 38 | 39 | % The mean. 40 | m = z*p; 41 | 42 | % Center random variables about the mean. 43 | z = z - m; 44 | 45 | % Compute the central moments. 46 | v = zeros(1, n); 47 | v(1) = m; 48 | for j = 2:n 49 | v(j) = (z.^j)*p; 50 | end 51 | 52 | if nargout > 1 53 | % Compute the uncentralized moments. 54 | unv = zeros(1, n); 55 | unv(1)=m.*G; 56 | for j = 2:n 57 | unv(j) = ((z*G).^j)*p; 58 | end 59 | end 60 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/statxture.m: -------------------------------------------------------------------------------- 1 | function t = statxture(f, scale) 2 | %STATXTURE Computes statistical measures of texture in an image. 3 | % T = STATXURE(F, SCALE) computes six measures of texture from an 4 | % image (region) F. Parameter SCALE is a 6-dim row vector whose 5 | % elements multiply the 6 corresponding elements of T for scaling 6 | % purposes. If SCALE is not provided it defaults to all 1s. The 7 | % output T is 6-by-1 vector with the following elements: 8 | % T(1) = Average gray level 9 | % T(2) = Average contrast 10 | % T(3) = Measure of smoothness 11 | % T(4) = Third moment 12 | % T(5) = Measure of uniformity 13 | % T(6) = Entropy 14 | 15 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 16 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 17 | % $Revision: 1.5 $ $Date: 2004/11/04 22:33:43 $ 18 | 19 | if nargin == 1 20 | scale(1:6) = 1; 21 | else % Make sure it's a row vector. 22 | scale = scale(:)'; 23 | end 24 | 25 | % Obtain histogram and normalize it. 26 | p = imhist(f); 27 | p = p./numel(f); 28 | L = length(p); 29 | 30 | % Compute the three moments. We need the unnormalized ones 31 | % from function statmoments. These are in vector mu. 32 | [v, mu] = statmoments(p, 3); 33 | 34 | % Compute the six texture measures: 35 | % Average gray level. 36 | t(1) = mu(1); 37 | % Standard deviation. 38 | t(2) = mu(2).^0.5; 39 | % Smoothness. 40 | % First normalize the variance to [0 1] by 41 | % dividing it by (L-1)^2. 42 | varn = mu(2)/(L - 1)^2; 43 | t(3) = 1 - 1/(1 + varn); 44 | % Third moment (normalized by (L - 1)^2 also). 45 | t(4) = mu(3)/(L - 1)^2; 46 | % Uniformity. 47 | t(5) = sum(p.^2); 48 | % Entropy. 49 | t(6) = -sum(p.*(log2(p + eps))); 50 | 51 | % Scale the values. 52 | t = t.*scale; 53 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/strsimilarity.m: -------------------------------------------------------------------------------- 1 | function R = strsimilarity(a, b) 2 | %STRSIMILARITY Computes a similarity measure between two strings. 3 | % R = STRSIMILARITY(A, B) computes the similarity measure, R, 4 | % defined in Section 12.4.2 for strings A and B. The strings do not 5 | % have to be of the same length, but if one is shorter than other, 6 | % then it is assumed that the shorter string has been padded with 7 | % leading blanks so that it is brought into the necessary 8 | % registration prior to using this function. Only one of the 9 | % strings can have blanks, and these must be leading and/or 10 | % trailing blanks. Blanks are not counted when computing the length 11 | % of the strings for use in the similarity measure. 12 | 13 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 14 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 15 | % $Revision: 1.4 $ $Date: 2003/05/18 18:07:48 $ 16 | 17 | % Verify that a and b are character strings. 18 | if ~ischar(a) | ~ischar(b) 19 | error('Inputs must be character strings.') 20 | end 21 | 22 | % Find any blank spaces. 23 | I = find(a == ' '); 24 | J = find(b == ' '); 25 | LI = length(I); LJ = length(J); 26 | if LI ~= 0 & LJ ~= 0 27 | error('Only one of the strings can contain blanks.') 28 | end 29 | 30 | % Pad the end of the appropriate string. It is assumed 31 | % that they are registered in terms of their beginning 32 | % positions. 33 | a = a(:); b = b(:); 34 | La = length(a); Lb = length(b); 35 | if LI == 0 & LJ == 0 36 | if La > Lb 37 | b = [b; blanks(La - Lb)']; 38 | else 39 | a = [a; blanks(Lb - La)']; 40 | end 41 | elseif isempty(I) 42 | Lb = length(b) - length(J); 43 | b = [b; blanks(La - Lb - LJ)']; 44 | else 45 | La = length(a) - length(I); 46 | a = [a; blanks(Lb - La - LI)']; 47 | end 48 | 49 | % Compute the similarity measure. 50 | I = find(a == b); 51 | alpha = length(I); 52 | den = max(La, Lb) - alpha; 53 | if den == 0 54 | R = Inf; 55 | else 56 | R = alpha/den; 57 | end 58 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/subim.m: -------------------------------------------------------------------------------- 1 | function s = subim(f, m, n, rx, cy) 2 | %SUBIM Extract subimage. 3 | % S = SUBIM(F, M, N, RX, CY) extracts a subimage, S, from the input 4 | % image, F. The subimage is of size M-by-N, and the coordinates of 5 | % its top, left corner are (RX, CY). 6 | % 7 | % Sample M-file used in Chapter 2. 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.1 $ $Date: 2003/02/22 17:00:54 $ 12 | 13 | s = zeros(m, n); 14 | rowhigh = rx + m - 1; 15 | colhigh = cy + n - 1; 16 | xcount = 0; 17 | for r = rx:rowhigh 18 | xcount = xcount + 1; 19 | ycount = 0; 20 | for c = cy:colhigh 21 | ycount = ycount + 1; 22 | s(xcount, ycount) = f(r, c); 23 | end 24 | end 25 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/twodsin.m: -------------------------------------------------------------------------------- 1 | function [rt, f, g] = twodsin(A, u0, v0, M, N) 2 | %TWODSIN Compare for-loops vs. vectorization. 3 | % The comparison is based on implementing the function f(x, y) = 4 | % Asin(u0x + v0y) for x = 0, 1, 2,..., M - 1 and y = 0, 1, 2,..., 5 | % N - 1. The inputs to the function are M and N and the constants 6 | % in the function. 7 | % 8 | % Sample M-file used in Chapter 2. 9 | 10 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 11 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 12 | % $Revision: 1.3 $ $Date: 2003/10/13 00:53:47 $ 13 | 14 | % First implement using for loops. 15 | 16 | tic % Start timing. 17 | 18 | for r = 1:M 19 | u0x = u0*(r - 1); 20 | for c = 1:N 21 | v0y = v0*(c - 1); 22 | f(r, c) = A*sin(u0x + v0y); 23 | end 24 | end 25 | 26 | t1 = toc; % End timing. 27 | 28 | % Now implement using vectorization. Call the image g. 29 | 30 | tic % Start timing. 31 | 32 | r = 0:M - 1; 33 | c = 0:N - 1; 34 | [C, R] = meshgrid(c, r); 35 | g = A*sin(u0*R + v0*C); 36 | 37 | t2 = toc; % End timing. 38 | 39 | % Compute the ratio of the two times. 40 | 41 | rt = t1/(t2 + eps); % Use eps in case t2 is close to 0 42 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/twomodegauss.m: -------------------------------------------------------------------------------- 1 | function p = twomodegauss(m1, sig1, m2, sig2, A1, A2, k) 2 | %TWOMODEGAUSS Generates a two-mode Gaussian function. 3 | % P = TWOMODEGAUSS(M1, SIG1, M2, SIG2, A1, A2, K) generates a 4 | % two-mode, Gaussian-like function in the interval [0,1]. P is a 5 | % 256-element vector normalized so that SUM(P) equals 1. The mean 6 | % and standard deviation of the modes are (M1, SIG1) and (M2, 7 | % SIG2), respectively. A1 and A2 are the amplitude values of the 8 | % two modes. Since the output is normalized, only the relative 9 | % values of A1 and A2 are important. K is an offset value that 10 | % raises the "floor" of the function. A good set of values to try 11 | % is M1=0.15, S1=0.05, M2=0.75, S2=0.05, A1=1, A2=0.07, and 12 | % K=0.002. 13 | 14 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 15 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 16 | % $Revision: 1.6 $ $Date: 2003/10/13 00:54:47 $ 17 | 18 | c1 = A1 * (1 / ((2 * pi) ^ 0.5) * sig1); 19 | k1 = 2 * (sig1 ^ 2); 20 | c2 = A2 * (1 / ((2 * pi) ^ 0.5) * sig2); 21 | k2 = 2 * (sig2 ^ 2); 22 | z = linspace(0, 1, 256); 23 | 24 | p = k + c1 * exp(-((z - m1) .^ 2) ./ k1) + ... 25 | c2 * exp(-((z - m2) .^ 2) ./ k2); 26 | p = p ./ sum(p(:)); 27 | 28 | 29 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.dll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.dll -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.m: -------------------------------------------------------------------------------- 1 | %UNRAVEL Decodes a variable-length bit stream. 2 | % X = UNRAVEL(Y, LINK, XLEN) decodes UINT16 input vector Y based on 3 | % transition and output table LINK. The elements of Y are 4 | % considered to be a contiguous stream of encoded bits--i.e., the 5 | % MSB of one element follows the LSB of the previous element. Input 6 | % XLEN is the number code words in Y, and thus the size of output 7 | % vector X (class DOUBLE). Input LINK is a transition and output 8 | % table (that drives a series of binary searches): 9 | % 10 | % 1. LINK(0) is the entry point for decoding, i.e., state n = 0. 11 | % 2. If LINK(n) < 0, the decoded output is |LINK(n)|; set n = 0. 12 | % 3. If LINK(n) > 0, get the next encoded bit and transition to 13 | % state [LINK(n) - 1] if the bit is 0, else LINK(n). 14 | 15 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 16 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 17 | % $Revision: 1.4 $ $Date: 2003/10/26 18:41:41 $ 18 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexaxp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexaxp -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexglx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexglx -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexhpux: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexhpux -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexmac: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexmac -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexrs6: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexrs6 -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexsg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexsg -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/unravel.mexsol: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/冈萨雷斯数字图像处理源代码/unravel.mexsol -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/vistformfwd.m: -------------------------------------------------------------------------------- 1 | function vistformfwd(tform, wdata, zdata, N) 2 | %VISTFORMFWD Visualize forward geometric transform. 3 | % VISTFORMFWD(TFORM, WRANGE, ZRANGE, N) shows two plots: an N-by-N 4 | % grid in the W-Z coordinate system, and the spatially transformed 5 | % grid in the X-Y coordinate system. WRANGE and ZRANGE are 6 | % two-element vectors specifying the desired range for the grid. N 7 | % can be omitted, in which case the default value is 10. 8 | 9 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 10 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 11 | % $Revision: 1.4 $ $Date: 2003/04/18 05:07:34 $ 12 | 13 | if nargin < 4 14 | N = 10; 15 | end 16 | 17 | % Create the w-z grid and transform it. 18 | [w, z] = meshgrid(linspace(wdata(1), zdata(2), N), ... 19 | linspace(wdata(1), zdata(2), N)); 20 | wz = [w(:) z(:)]; 21 | xy = tformfwd([w(:) z(:)], tform); 22 | 23 | % Calculate the minimum and maximum values of w and x, 24 | % as well as z and y. These are used so the two plots can be 25 | % displayed using the same scale. 26 | x = reshape(xy(:, 1), size(w)); % reshape is discussed in Sec. 8.2.2. 27 | y = reshape(xy(:, 2), size(z)); 28 | wx = [w(:); x(:)]; 29 | wxlimits = [min(wx) max(wx)]; 30 | zy = [z(:); y(:)]; 31 | zylimits = [min(zy) max(zy)]; 32 | 33 | % Create the w-z plot. 34 | subplot(1,2,1) % See Section 7.2.1 for a discussion of this function. 35 | plot(w, z, 'b'), axis equal, axis ij 36 | hold on 37 | plot(w', z', 'b') 38 | hold off 39 | xlim(wxlimits) 40 | ylim(zylimits) 41 | set(gca, 'XAxisLocation', 'top') 42 | xlabel('w'), ylabel('z') 43 | 44 | % Create the x-y plot. 45 | subplot(1, 2, 2) 46 | plot(x, y, 'b'), axis equal, axis ij 47 | hold on 48 | plot(x', y', 'b') 49 | hold off 50 | xlim(wxlimits) 51 | ylim(zylimits) 52 | set(gca, 'XAxisLocation', 'top') 53 | xlabel('x'), ylabel('y') 54 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/wavecopy.m: -------------------------------------------------------------------------------- 1 | function y = wavecopy(type, c, s, n) 2 | %WAVECOPY Fetches coefficients of a wavelet decomposition structure. 3 | % Y = WAVECOPY(TYPE, C, S, N) returns a coefficient array based on 4 | % TYPE and N. 5 | % 6 | % INPUTS: 7 | % TYPE Coefficient category 8 | % ------------------------------------- 9 | % 'a' Approximation coefficients 10 | % 'h' Horizontal details 11 | % 'v' Vertical details 12 | % 'd' Diagonal details 13 | % 14 | % [C, S] is a wavelet data structure. 15 | % N specifies a decomposition level (ignored if TYPE = 'a'). 16 | % 17 | % See also WAVEWORK, WAVECUT, and WAVEPASTE. 18 | 19 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 20 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 21 | % $Revision: 1.4 $ $Date: 2003/10/13 01:20:41 $ 22 | 23 | error(nargchk(3, 4, nargin)); 24 | if nargin == 4 25 | y = wavework('copy', type, c, s, n); 26 | else 27 | y = wavework('copy', type, c, s); 28 | end 29 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/wavecut.m: -------------------------------------------------------------------------------- 1 | function [nc, y] = wavecut(type, c, s, n) 2 | %WAVECUT Zeroes coefficients in a wavelet decomposition structure. 3 | % [NC, Y] = WAVECUT(TYPE, C, S, N) returns a new decomposition 4 | % vector whose detail or approximation coefficients (based on TYPE 5 | % and N) have been zeroed. The coefficients that were zeroed are 6 | % returned in Y. 7 | % 8 | % INPUTS: 9 | % TYPE Coefficient category 10 | % ------------------------------------- 11 | % 'a' Approximation coefficients 12 | % 'h' Horizontal details 13 | % 'v' Vertical details 14 | % 'd' Diagonal details 15 | % 16 | % [C, S] is a wavelet data structure. 17 | % N specifies a decomposition level (ignored if TYPE = 'a'). 18 | % 19 | % See also WAVEWORK, WAVECOPY, and WAVEPASTE. 20 | 21 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 22 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 23 | % $Revision: 1.4 $ $Date: 2003/10/13 01:20:09 $ 24 | 25 | error(nargchk(3, 4, nargin)); 26 | if nargin == 4 27 | [nc, y] = wavework('cut', type, c, s, n); 28 | else 29 | [nc, y] = wavework('cut', type, c, s); 30 | end 31 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/wavepaste.m: -------------------------------------------------------------------------------- 1 | function nc = wavepaste(type, c, s, n, x) 2 | %WAVEPASTE Puts coefficients in a wavelet decomposition structure. 3 | % NC = WAVEPASTE(TYPE, C, S, N, X) returns the new decomposition 4 | % structure after pasting X into it based on TYPE and N. 5 | % 6 | % INPUTS: 7 | % TYPE Coefficient category 8 | % ------------------------------------- 9 | % 'a' Approximation coefficients 10 | % 'h' Horizontal details 11 | % 'v' Vertical details 12 | % 'd' Diagonal details 13 | % 14 | % [C, S] is a wavelet data structure. 15 | % N specifies a decomposition level (Ignored if TYPE = 'a'). 16 | % X is a two-dimensional approximation or detail coefficient 17 | % matrix whose dimensions are appropriate for decomposition 18 | % level N. 19 | % 20 | % See also WAVEWORK, WAVECUT, and WAVECOPY. 21 | 22 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 23 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 24 | % $Revision: 1.4 $ $Date: 2003/10/13 01:21:13 $ 25 | 26 | error(nargchk(5, 5, nargin)) 27 | nc = wavework('paste', type, c, s, n, x); 28 | -------------------------------------------------------------------------------- /冈萨雷斯数字图像处理源代码/wavezero.m: -------------------------------------------------------------------------------- 1 | function [nc, g8] = wavezero(c, s, l, wname) 2 | %WAVEZERO Zeroes wavelet transform detail coefficients. 3 | % [NC, G8] = WAVEZERO(C, S, L, WNAME) zeroes the level L detail 4 | % coefficients in wavelet decomposition structure [C, S] and 5 | % computes the resulting inverse transform with respect to WNAME 6 | % wavelets. 7 | 8 | % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins 9 | % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 10 | % $Revision: 1.4 $ $Date: 2003/10/13 01:31:35 $ 11 | 12 | [nc, foo] = wavecut('h', c, s, l); 13 | [nc, foo] = wavecut('v', nc, s, l); 14 | [nc, foo] = wavecut('d', nc, s, l); 15 | i = waveback(nc, s, wname); 16 | g8 = im2uint8(mat2gray(i)); 17 | figure; imshow(g8); 18 | -------------------------------------------------------------------------------- /数字图像处理第三版 教材图片/DIP3E_CH01_Original_Images.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ultrasty/Digital-Image-Processing/eec3a106d8c6d6a77ccee354fcb07591bd382c6f/数字图像处理第三版 教材图片/DIP3E_CH01_Original_Images.zip -------------------------------------------------------------------------------- /数字图像处理第三版 教材图片/DIP3E_CH02_Original_Images.zip: -------------------------------------------------------------------------------- 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