├── 3rdparty
├── colorspace
│ ├── colorcalc.c
│ ├── colorspace.c
│ ├── colorspace.h
│ ├── colorspace.html
│ ├── colorspace.m
│ ├── colorspace_01.jpg
│ ├── colorspace_02.jpg
│ ├── colorspace_03.jpg
│ ├── colorspace_ciedemo.m
│ ├── colorspace_demo.m
│ └── colorspace_test.m
├── guided_filter
│ ├── boxfilter.m
│ ├── example_enhancement.m
│ ├── example_feathering.m
│ ├── example_flash.m
│ ├── example_smoothing.m
│ ├── guidedfilter.m
│ ├── guidedfilter_color.m
│ ├── img_enhancement
│ │ └── tulips.bmp
│ ├── img_feathering
│ │ ├── toy-mask.bmp
│ │ └── toy.bmp
│ ├── img_flash
│ │ ├── cave-flash.bmp
│ │ └── cave-noflash.bmp
│ ├── img_smoothing
│ │ └── cat.bmp
│ └── readme.txt
└── patchmatch-2.0
│ ├── allegro_emu.cpp
│ ├── allegro_emu.h
│ ├── build_mac.sh
│ ├── build_unix.sh
│ ├── build_windows.bat
│ ├── console_test_descriptor_mex.m
│ ├── console_test_knn.m
│ ├── console_test_mex.m
│ ├── console_test_rot_scale.m
│ ├── gen_all.m
│ ├── knn.cpp
│ ├── knn.h
│ ├── load_vgg_features.m
│ ├── log.txt
│ ├── mex.sln
│ ├── mex.v12.suo
│ ├── mexutil.cpp
│ ├── mexutil.h
│ ├── neural_patchmatch.m
│ ├── nn.cpp
│ ├── nn.h
│ ├── nnmex.cpp
│ ├── nnmex.m
│ ├── nnmex.mexa64
│ ├── nnmex.mexw64
│ ├── patch.cpp
│ ├── patch.h
│ ├── pm_minimal.cpp
│ ├── readme.txt
│ ├── simnn.cpp
│ ├── simnn.h
│ ├── simpatch.h
│ ├── test_descriptor_mex.m
│ ├── test_knn.m
│ ├── test_mex.m
│ ├── test_rot_scale.m
│ ├── vecnn.cpp
│ ├── vecnn.h
│ ├── vecpatch.h
│ ├── votemex.cpp
│ ├── votemex.m
│ ├── votemex.mexa64
│ ├── votemex.mexw64
│ └── writeim.m
├── README.md
├── README2.md
├── cuda_utils.cu
├── data
├── 0_c_mask.jpg
├── 0_c_mask_dilated.jpg
├── 0_naive.jpg
├── 0_target.jpg
├── 10_c_mask.jpg
├── 10_c_mask_dilated.jpg
├── 10_naive.jpg
├── 10_target.jpg
├── 11_c_mask.jpg
├── 11_c_mask_dilated.jpg
├── 11_naive.jpg
├── 11_target.jpg
├── 12_c_mask.jpg
├── 12_c_mask_dilated.jpg
├── 12_naive.jpg
├── 12_target.jpg
├── 13_c_mask.jpg
├── 13_c_mask_dilated.jpg
├── 13_naive.jpg
├── 13_target.jpg
├── 14_c_mask.jpg
├── 14_c_mask_dilated.jpg
├── 14_naive.jpg
├── 14_target.jpg
├── 15_c_mask.jpg
├── 15_c_mask_dilated.jpg
├── 15_naive.jpg
├── 15_target.jpg
├── 16_c_mask.jpg
├── 16_c_mask_dilated.jpg
├── 16_naive.jpg
├── 16_target.jpg
├── 17_c_mask.jpg
├── 17_c_mask_dilated.jpg
├── 17_naive.jpg
├── 17_target.jpg
├── 18_c_mask.jpg
├── 18_c_mask_dilated.jpg
├── 18_naive.jpg
├── 18_target.jpg
├── 19_c_mask.jpg
├── 19_c_mask_dilated.jpg
├── 19_naive.jpg
├── 19_target.jpg
├── 1_c_mask.jpg
├── 1_c_mask_dilated.jpg
├── 1_naive.jpg
├── 1_target.jpg
├── 20_c_mask.jpg
├── 20_c_mask_dilated.jpg
├── 20_naive.jpg
├── 20_target.jpg
├── 21_c_mask.jpg
├── 21_c_mask_dilated.jpg
├── 21_naive.jpg
├── 21_target.jpg
├── 22_c_mask.jpg
├── 22_c_mask_dilated.jpg
├── 22_naive.jpg
├── 22_target.jpg
├── 23_c_mask.jpg
├── 23_c_mask_dilated.jpg
├── 23_naive.jpg
├── 23_target.jpg
├── 24_c_mask.jpg
├── 24_c_mask_dilated.jpg
├── 24_naive.jpg
├── 24_target.jpg
├── 25_c_mask.jpg
├── 25_c_mask_dilated.jpg
├── 25_naive.jpg
├── 25_target.jpg
├── 26_c_mask.jpg
├── 26_c_mask_dilated.jpg
├── 26_naive.jpg
├── 26_target.jpg
├── 27_c_mask.jpg
├── 27_c_mask_dilated.jpg
├── 27_naive.jpg
├── 27_target.jpg
├── 28_c_mask.jpg
├── 28_c_mask_dilated.jpg
├── 28_naive.jpg
├── 28_target.jpg
├── 29_c_mask.jpg
├── 29_c_mask_dilated.jpg
├── 29_naive.jpg
├── 29_target.jpg
├── 2_c_mask.jpg
├── 2_c_mask_dilated.jpg
├── 2_naive.jpg
├── 2_target.jpg
├── 30_c_mask.jpg
├── 30_c_mask_dilated.jpg
├── 30_naive.jpg
├── 30_target.jpg
├── 31_c_mask.jpg
├── 31_c_mask_dilated.jpg
├── 31_naive.jpg
├── 31_target.jpg
├── 32_c_mask.jpg
├── 32_c_mask_dilated.jpg
├── 32_naive.jpg
├── 32_target.jpg
├── 33_c_mask.jpg
├── 33_c_mask_dilated.jpg
├── 33_naive.jpg
├── 33_target.jpg
├── 34_c_mask.jpg
├── 34_c_mask_dilated.jpg
├── 34_naive.jpg
├── 34_target.jpg
├── 35_target.jpg
├── 3_c_mask.jpg
├── 3_c_mask_dilated.jpg
├── 3_naive.jpg
├── 3_target.jpg
├── 4_c_mask.jpg
├── 4_c_mask_dilated.jpg
├── 4_naive.jpg
├── 4_target.jpg
├── 5_c_mask.jpg
├── 5_c_mask_dilated.jpg
├── 5_naive.jpg
├── 5_target.jpg
├── 6_c_mask.jpg
├── 6_c_mask_dilated.jpg
├── 6_naive.jpg
├── 6_target.jpg
├── 7_c_mask.jpg
├── 7_c_mask_dilated.jpg
├── 7_naive.jpg
├── 7_target.jpg
├── 8_c_mask.jpg
├── 8_c_mask_dilated.jpg
├── 8_naive.jpg
├── 8_target.jpg
├── 9_c_mask.jpg
├── 9_c_mask_dilated.jpg
├── 9_naive.jpg
├── 9_target.jpg
└── wikiart_output.txt
├── dilate_mask.m
├── filt_cnn_artifact.m
├── gen_all.py
├── makefile
├── models
└── download_models.sh
├── neural_gram.lua
├── neural_paint.lua
└── results
├── 0_final_res.png
├── 0_final_res2.png
├── 0_inter_res.jpg
├── 10_final_res.png
├── 10_final_res2.png
├── 10_inter_res.jpg
├── 11_final_res.png
├── 11_final_res2.png
├── 11_inter_res.jpg
├── 12_final_res.png
├── 12_final_res2.png
├── 12_inter_res.jpg
├── 13_final_res.png
├── 13_final_res2.png
├── 13_inter_res.jpg
├── 14_final_res.png
├── 14_final_res2.png
├── 14_inter_res.jpg
├── 15_final_res.png
├── 15_final_res2.png
├── 15_inter_res.jpg
├── 16_final_res.png
├── 16_final_res2.png
├── 16_inter_res.jpg
├── 17_final_res.png
├── 17_final_res2.png
├── 17_inter_res.jpg
├── 17_naive_sherlock.jpg
├── 17_result_sherlock.jpg
├── 18_final_res.png
├── 18_final_res2.png
├── 18_inter_res.jpg
├── 19_final_res.png
├── 19_final_res2.png
├── 19_inter_res.jpg
├── 1_final_res.png
├── 1_final_res2.png
├── 1_inter_res.jpg
├── 20_final_res.png
├── 20_final_res2.png
├── 20_inter_res.jpg
├── 21_final_res.png
├── 21_final_res2.png
├── 21_inter_res.jpg
├── 22_final_res.png
├── 22_final_res2.png
├── 22_inter_res.jpg
├── 23_final_res.png
├── 23_final_res2.png
├── 23_inter_res.jpg
├── 24_final_res.png
├── 24_final_res2.png
├── 24_inter_res.jpg
├── 25_final_res.png
├── 25_final_res2.png
├── 25_inter_res.jpg
├── 26_final_res.png
├── 26_final_res2.png
├── 26_inter_res.jpg
├── 27_final_res.png
├── 27_final_res2.png
├── 27_inter_res.jpg
├── 28_final_res.png
├── 28_final_res2.png
├── 28_inter_res.jpg
├── 28_naive_cow.png
├── 28_result_cow.jpg
├── 29_final_res.png
├── 29_final_res2.png
├── 29_inter_res.jpg
├── 2_final_res.png
├── 2_final_res2.png
├── 2_inter_res.jpg
├── 30_final_res.png
├── 30_final_res2.png
├── 30_inter_res.jpg
├── 31_final_res.png
├── 31_final_res2.png
├── 31_inter_res.jpg
├── 32_final_res.png
├── 32_final_res2.png
├── 32_inter_res.jpg
├── 33_final_res.png
├── 33_final_res2.png
├── 33_inter_res.jpg
├── 33_naive_buddha.png
├── 33_result_buddha.jpg
├── 34_final_res.png
├── 34_final_res2.png
├── 34_inter_res.jpg
├── 35_naive.jpg
├── 35_result.png
├── 3_final_res.png
├── 3_final_res2.png
├── 3_inter_res.jpg
├── 4_final_res.png
├── 4_final_res2.png
├── 4_inter_res.jpg
├── 5_final_res.png
├── 5_final_res2.png
├── 5_inter_res.jpg
├── 6_final_res.png
├── 6_final_res2.png
├── 6_inter_res.jpg
├── 7_final_res.png
├── 7_final_res2.png
├── 7_inter_res.jpg
├── 7_naive_popeye.png
├── 7_result_popeye.jpg
├── 7_target.jpg
├── 8_final_res.png
├── 8_final_res2.png
├── 8_inter_res.jpg
├── 8_naive_aircraft.png
├── 8_naive_balloon.jpg
├── 8_result_aircraft.png
├── 8_result_balloon.jpg
├── 9_final_res.png
├── 9_final_res2.png
├── 9_inter_res.jpg
├── 9_naive_dragon.png
└── 9_result_dragon.jpg
/3rdparty/colorspace/colorcalc.c:
--------------------------------------------------------------------------------
1 | /**
2 | * @file colorcalc.c
3 | * @author Pascal Getreuer 2010 <getreuer@gmail.com>
4 | *
5 | * This is a small command line program to demonstrate colorspace.c. The
6 | * program accepts an sRGB color as input and transforms it to all the spaces
7 | * supported by colorspace.c.
8 | *
9 | * ==Usage==
10 | * The syntax for the program is
11 | * colorcalc R G B
12 | * where R, G, B are values between 0 and 1 specifying a color in the gamma-
13 | * corrected sRGB color space. For example,
14 | * colorcalc 0.5 0.85 0.61
15 | *
16 | * ==Compiling Instructions==
17 | * Compile the files colorcalc.c and colorspace.c with an ANSI C compiler. The
18 | * program is compiled with GCC by
19 | * gcc colorcalc.c colorspace.c -lm -o colorcalc
20 | */
21 | #include <stdio.h>
22 | #include <stdlib.h>
23 | #include "colorspace.h"
24 |
25 |
26 | static void HelpMessage();
27 |
28 |
29 | int main(int argc, char *argv[])
30 | {
31 | num R, G, B;
32 | num D[3];
33 |
34 |
35 | if(argc != 4)
36 | {
37 | HelpMessage();
38 | return 1;
39 | }
40 |
41 | /* Read the input sRGB values */
42 | R = atof(argv[1]);
43 | G = atof(argv[2]);
44 | B = atof(argv[3]);
45 |
46 | if(!(0 <= R && R <= 1 && 0 <= G && G <= 1 && 0 <= B && B <= 1))
47 | printf("\nWarning: Input sRGB values should be between 0 and 1.\n\n");
48 |
49 | printf("sRGB R':%8.3f G':%8.3f B':%8.3f\n", R, G, B);
50 |
51 | /* Transform sRGB to Y'CbCr */
52 | Rgb2Ycbcr(&D[0], &D[1], &D[2], R, G, B);
53 | printf("Y'CbCr Y':%8.3f Cb:%8.3f Cr:%8.3f\n", D[0], D[1], D[2]);
54 |
55 | /* Transform sRGB to JPEG-Y'CbCr */
56 | Rgb2Jpegycbcr(&D[0], &D[1], &D[2], R, G, B);
57 | printf("JPEG-Y'CbCr Y':%8.3f Cb:%8.3f Cr:%8.3f\n", D[0], D[1], D[2]);
58 |
59 | /* Transform sRGB to Y'PbPr */
60 | Rgb2Ypbpr(&D[0], &D[1], &D[2], R, G, B);
61 | printf("Y'PbPr Y':%8.3f Pb:%8.3f Pr:%8.3f\n", D[0], D[1], D[2]);
62 |
63 | /* Transform sRGB to Y'DbDr */
64 | Rgb2Ydbdr(&D[0], &D[1], &D[2], R, G, B);
65 | printf("Y'DbDr Y':%8.3f Db:%8.3f Dr:%8.3f\n", D[0], D[1], D[2]);
66 |
67 | /* Transform sRGB to Y'UV */
68 | Rgb2Yuv(&D[0], &D[1], &D[2], R, G, B);
69 | printf("Y'UV Y':%8.3f U:%8.3f V:%8.3f\n", D[0], D[1], D[2]);
70 |
71 | /* Transform sRGB to Y'IQ */
72 | Rgb2Yiq(&D[0], &D[1], &D[2], R, G, B);
73 | printf("Y'IQ Y':%8.3f I:%8.3f Q:%8.3f\n", D[0], D[1], D[2]);
74 |
75 | /* Transform sRGB to HSV */
76 | Rgb2Hsv(&D[0], &D[1], &D[2], R, G, B);
77 | printf("HSV H:%8.3f S:%8.3f V:%8.3f\n", D[0], D[1], D[2]);
78 |
79 | /* Transform sRGB to HSL */
80 | Rgb2Hsl(&D[0], &D[1], &D[2], R, G, B);
81 | printf("HSL H:%8.3f S:%8.3f L:%8.3f\n", D[0], D[1], D[2]);
82 |
83 | /* Transform sRGB to HSI */
84 | Rgb2Hsi(&D[0], &D[1], &D[2], R, G, B);
85 | printf("HSI H:%8.3f S:%8.3f I:%8.3f\n", D[0], D[1], D[2]);
86 |
87 | /* Transform sRGB to CIE XYZ */
88 | Rgb2Xyz(&D[0], &D[1], &D[2], R, G, B);
89 | printf("XYZ X:%8.3f Y:%8.3f Z:%8.3f\n", D[0], D[1], D[2]);
90 |
91 | /* Transform sRGB to CIE L*a*b */
92 | Rgb2Lab(&D[0], &D[1], &D[2], R, G, B);
93 | printf("L*a*b* L*:%8.3f a*:%8.3f b*:%8.3f\n", D[0], D[1], D[2]);
94 |
95 | /* Transform sRGB to CIE L*u*v* */
96 | Rgb2Luv(&D[0], &D[1], &D[2], R, G, B);
97 | printf("L*u*v* L*:%8.3f u*:%8.3f v*:%8.3f\n", D[0], D[1], D[2]);
98 |
99 | /* Transform sRGB to CIE L*C*H* */
100 | Rgb2Lch(&D[0], &D[1], &D[2], R, G, B);
101 | printf("L*C*H* L*:%8.3f C*:%8.3f H*:%8.3f\n", D[0], D[1], D[2]);
102 |
103 | /* Transform sRGB to CIE CAT02 LMS */
104 | Rgb2Cat02lms(&D[0], &D[1], &D[2], R, G, B);
105 | printf("CAT02 LMS L:%8.3f M:%8.3f S:%8.3f\n", D[0], D[1], D[2]);
106 |
107 | return 0;
108 | }
109 |
110 |
111 | /** @brief Print program help message */
112 | static void HelpMessage()
113 | {
114 | printf("Color calculator, P. Getreuer 2010\n");
115 | printf("\nSyntax: colorcalc R G B\n\n");
116 | printf("where R, G, B are values between 0 and 1 specifying a color in\n");
117 | printf("the gamma-corrected sRGB color space. The color is transformed\n");
118 | printf("to all spaces supported by colorspace.c.\n\n");
119 | printf("Example: colorcalc 0.5 0.85 0.61\n");
120 | }
121 |
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace.h:
--------------------------------------------------------------------------------
1 | /**
2 | * @file colorspace.h
3 | * @author Pascal Getreuer 2005-2010 <getreuer@gmail.com>
4 | */
5 | #ifndef _COLORSPACE_H_
6 | #define _COLORSPACE_H_
7 |
8 | /** @brief Datatype to use for representing real numbers
9 | * Set this typedef to either double or float depending on the application.
10 | */
11 | typedef double num;
12 |
13 |
14 | /** @brief XYZ color of the D65 white point */
15 | #define WHITEPOINT_X 0.950456
16 | #define WHITEPOINT_Y 1.0
17 | #define WHITEPOINT_Z 1.088754
18 |
19 |
20 |
21 | /** @brief struct for representing a color transform */
22 | typedef struct
23 | {
24 | int NumStages;
25 | void (*Fun[2])(num*, num*, num*, num, num, num);
26 | } colortransform;
27 |
28 | int GetColorTransform(colortransform *Trans, const char *TransformString);
29 | void ApplyColorTransform(colortransform Trans,
30 | num *D0, num *D1, num *D2, num S0, num S1, num S2);
31 |
32 | void Rgb2Yuv(num *Y, num *U, num *V, num R, num G, num B);
33 | void Yuv2Rgb(num *R, num *G, num *B, num Y, num U, num V);
34 | void Rgb2Ycbcr(num *Y, num *Cb, num *Cr, num R, num G, num B);
35 | void Ycbcr2Rgb(num *R, num *G, num *B, num Y, num Cb, num Cr);
36 | void Rgb2Jpegycbcr(num *R, num *G, num *B, num Y, num Cb, num Cr);
37 | void Jpegycbcr2Rgb(num *R, num *G, num *B, num Y, num Cb, num Cr);
38 | void Rgb2Ypbpr(num *Y, num *Pb, num *Pr, num R, num G, num B);
39 | void Ypbpr2Rgb(num *R, num *G, num *B, num Y, num Pb, num Pr);
40 | void Rgb2Ydbdr(num *Y, num *Db, num *Dr, num R, num G, num B);
41 | void Ydbdr2Rgb(num *R, num *G, num *B, num Y, num Db, num Dr);
42 | void Rgb2Yiq(num *Y, num *I, num *Q, num R, num G, num B);
43 | void Yiq2Rgb(num *R, num *G, num *B, num Y, num I, num Q);
44 |
45 | void Rgb2Hsv(num *H, num *S, num *V, num R, num G, num B);
46 | void Hsv2Rgb(num *R, num *G, num *B, num H, num S, num V);
47 | void Rgb2Hsl(num *H, num *S, num *L, num R, num G, num B);
48 | void Hsl2Rgb(num *R, num *G, num *B, num H, num S, num L);
49 | void Rgb2Hsi(num *H, num *S, num *I, num R, num G, num B);
50 | void Hsi2Rgb(num *R, num *G, num *B, num H, num S, num I);
51 |
52 | void Rgb2Xyz(num *X, num *Y, num *Z, num R, num G, num B);
53 | void Xyz2Rgb(num *R, num *G, num *B, num X, num Y, num Z);
54 | void Xyz2Lab(num *L, num *a, num *b, num X, num Y, num Z);
55 | void Lab2Xyz(num *X, num *Y, num *Z, num L, num a, num b);
56 | void Xyz2Luv(num *L, num *u, num *v, num X, num Y, num Z);
57 | void Luv2Xyz(num *X, num *Y, num *Z, num L, num u, num v);
58 | void Xyz2Lch(num *L, num *C, num *H, num X, num Y, num Z);
59 | void Lch2Xyz(num *X, num *Y, num *Z, num L, num C, num H);
60 | void Xyz2Cat02lms(num *L, num *M, num *S, num X, num Y, num Z);
61 | void Cat02lms2Xyz(num *X, num *Y, num *Z, num L, num M, num S);
62 |
63 | void Rgb2Lab(num *L, num *a, num *b, num R, num G, num B);
64 | void Lab2Rgb(num *R, num *G, num *B, num L, num a, num b);
65 | void Rgb2Luv(num *L, num *u, num *v, num R, num G, num B);
66 | void Luv2Rgb(num *R, num *G, num *B, num L, num u, num v);
67 | void Rgb2Lch(num *L, num *C, num *H, num R, num G, num B);
68 | void Lch2Rgb(num *R, num *G, num *B, num L, num C, num H);
69 | void Rgb2Cat02lms(num *L, num *M, num *S, num R, num G, num B);
70 | void Cat02lms2Rgb(num *R, num *G, num *B, num L, num M, num S);
71 |
72 | #endif /* _COLORSPACE_H_ */
73 |
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_01.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/colorspace/colorspace_01.jpg
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_02.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/colorspace/colorspace_02.jpg
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_03.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/colorspace/colorspace_03.jpg
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_ciedemo.m:
--------------------------------------------------------------------------------
1 | % Demo for colorspace.m - the CIE xyY "tongue"
2 |
3 | % Pascal Getreuer 2006
4 |
5 | N = 800;
6 | Nx = round(N*0.736);
7 | Ny = round(N*0.84);
8 | % Generate colors in the xyY color space
9 | x = linspace(0,0.736,Nx);
10 | y = linspace(0,0.84,Ny);
11 | [xx,yy] = meshgrid(x,y);
12 | % i = (xx + yy <= 1.01 & (xx - 0.2)/2 - yy <= 0.02);
13 |
14 | % Convert from xyY to XYZ
15 | a = 0.6491;
16 | X = a*xx;
17 | Y = a*yy;
18 | Z = a*(1 - xx - yy);
19 |
20 | Corner = colorspace('xyz<-rgb', [1,0,0;0,1,0;0,0,1]);
21 | Corner = Corner(:,1:2)./repmat(sum(Corner,2),1,2);
22 |
23 | v0x = Corner(3,1) - Corner(1,1);
24 | v0y = Corner(3,2) - Corner(1,2);
25 | v1x = Corner(2,1) - Corner(1,1);
26 | v1y = Corner(2,2) - Corner(1,2);
27 | v2x = xx - Corner(1,1);
28 | v2y = yy - Corner(1,2);
29 |
30 | dot00 = v0x*v0x + v0y*v0y;
31 | dot01 = v0x*v1x + v0y*v1y;
32 | dot02 = v0x*v2x + v0y*v2y;
33 | dot11 = v1x*v1x + v1y*v1y;
34 | dot12 = v1x*v2x + v1y*v2y;
35 | Denom = dot00*dot11 - dot01*dot01;
36 | bu = (dot11*dot02 - dot01*dot12) / Denom;
37 | bv = (dot00*dot12 - dot01*dot02) / Denom;
38 |
39 |
40 | i = (bu > 0 & bv > 0 & bu + bv < 1);
41 | ie = conv2(double(i),[0,1,0;1,1,1;0,1,0],'same');
42 |
43 | a = 0.3 + ie*0.7/5;
44 | X = a.*X;
45 | Y = a.*Y;
46 | Z = a.*Z;
47 |
48 | % Convert from XYZ to R'G'B'
49 | Color = colorspace('rgb<-xyz',cat(3,X,Y,Z));
50 | Color = min(max(Color,0),1);
51 |
52 | % Render the colors on the tongue
53 | clf;
54 | plot(xx,yy,'b.');
55 | image(x,y,Color)
56 | colormap(gray);
57 | set(gca,'YDir','normal');
58 | axis image
59 |
60 | % The boundary of the tongue
61 | xy = [[0.1740, 0.0050];[0.1736, 0.0049];[0.1703, 0.0058];
62 | [0.1566, 0.0177];[0.1440, 0.0297];[0.1241, 0.0578];
63 | [0.1096, 0.0868];[0.0913, 0.1327];[0.0687, 0.2007];
64 | [0.0454, 0.2950];[0.0235, 0.4127];[0.0082, 0.5384];
65 | [0.0039, 0.6548];[0.0139, 0.7502];[0.0389, 0.8120];
66 | [0.0743, 0.8338];[0.1142, 0.8262];[0.1547, 0.8059];
67 | [0.1929, 0.7816];[0.2296, 0.7543];[0.2658, 0.7243];
68 | [0.3016, 0.6923];[0.3373, 0.6589];[0.3731, 0.6245];
69 | [0.4087, 0.5896];[0.4441, 0.5547];[0.4788, 0.5202];
70 | [0.5125, 0.4866];[0.5448, 0.4544];[0.5752, 0.4242];
71 | [0.6029, 0.3965];[0.6270, 0.3725];[0.6482, 0.3514];
72 | [0.6658, 0.3340];[0.6801, 0.3197];[0.7006, 0.2993];
73 | [0.7140, 0.2859];[0.7260, 0.2740];[0.7340, 0.2660]];
74 | % Make a smooth boundary with spline interpolation
75 | xi = [interp1(xy(:,1),1:0.25:size(xy,1),'spline'),xy(1,1)];
76 | yi = [interp1(xy(:,2),1:0.25:size(xy,1),'spline'),xy(1,2)];
77 |
78 | % Draw the boundary of the tongue
79 | hold on;
80 | % set(patch([0.8;Corner([1,2,3,1],1);0.8;0.8;0;0;0.8],...
81 | % [0.9;Corner([1,2,3,1],2);0.9;0;0;0.9;0.9],...
82 | % [0,0,0] + 0.3),'EdgeColor','none');
83 | set(patch([0.8,-1e2,-1e2,0.8,xi(:).',0.8],[0.9,0.9,-1e2,-1e2,yi(:).',-1e2],...
84 | [0,0,0]),'EdgeColor','none');
85 | plot(xi,yi,'w-');
86 |
87 | for k = 0:8
88 | set(text(xy(4 + k*4,1), xy(4 + k*4,2), sprintf('%d', 460 + k*20)),'Color',[1,1,1]);
89 | end
90 |
91 | axis([0,0.8,0,0.9]);
92 | xlabel('x');
93 | ylabel('y');
94 | title('The CIE "tongue": the region of all colors over x and y');
95 | set(gca,'Color',[0,0,0]);
96 | shg;
97 |
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_demo.m:
--------------------------------------------------------------------------------
1 | function colorspace_demo(Cmd)
2 | % Demo for colorspace.m - 3D visualizations of various color spaces
3 |
4 | % Pascal Getreuer 2006
5 |
6 | if nargin == 0
7 | % Create a figure with a drop-down menu
8 | figure('Color',[1,1,1]);
9 | h = uicontrol('Style','popup','Position',[15,10,90,21],...
10 | 'BackgroundColor',[1,1,1],'Value',2,...
11 | 'string',{'sRGB','Y''PbPr','HSV','HSL','L*a*b*','L*u*v*','L*ch'});
12 | set(h,'Callback',sprintf('%s(get(%.20g,''Value''))',mfilename,h));
13 | Cmd = 2;
14 | end
15 |
16 | % Plot a figure
17 | switch Cmd
18 | case 1
19 | [x,y,z,Tri] = makeshape('Cube');
20 | CData = [x,y,z];
21 | myplot((x-0.5)*0.8,(y-0.5)*0.8,(z)*0.8,Tri,CData);
22 | coloraxis('R''',5,0.5*0.8);
23 | coloraxis('G''',6,0.5*0.8);
24 | coloraxis('B''',3);
25 | case 2
26 | [x,y,z,Tri] = makeshape('Cylinder');
27 | CData = colorspace('rgb<-ypbpr',[z,x/2,y/2]);
28 | myplot(x,y,0.8*z,Tri,CData);
29 | coloraxis('Y''',3);
30 | coloraxis('P_b',5,0.8);
31 | coloraxis('P_r',6,0.8);
32 | case 3
33 | [x,y,z,Tri] = makeshape('Hexacone');
34 | CData = colorspace('rgb<-hsv',[(pi+atan2(-y,-x))*180/pi,sqrt(x.^2+y.^2),z]);
35 | myplot(x,y,z,Tri,CData);
36 | coloraxis('H',1);
37 | coloraxis('S',2);
38 | coloraxis('V',3);
39 | case 4
40 | [x,y,z,Tri] = makeshape('Double Hexacone');
41 | CData = colorspace('rgb<-hsl',[(pi+atan2(-y,-x))*180/pi,sqrt(x.^2+y.^2),z]);
42 | myplot(x,y,2*z,Tri,CData);
43 | coloraxis('H',1);
44 | coloraxis('S',2);
45 | coloraxis('L',4);
46 | case 5
47 | [x,y,z,Tri] = makeshape('Blobs');
48 | CData = colorspace('rgb<-lab',[z*100,x*100,y*100]);
49 | myplot(x,y,2*z,Tri,CData);
50 | coloraxis('L*',4);
51 | coloraxis('a*',5,2);
52 | coloraxis('b*',6,2);
53 | case 6
54 | [x,y,z,Tri] = makeshape('Blobs');
55 | CData = colorspace('rgb<-luv',[z*100,x*125,y*125]);
56 | myplot(x,y,2*z,Tri,CData);
57 | coloraxis('L*',4);
58 | coloraxis('u*',5,2);
59 | coloraxis('v*',6,2);
60 | case 7
61 | [x,y,z,Tri] = makeshape('Blobs');
62 | CData = colorspace('rgb<-lab',[z*100,x*100,y*100]);
63 | myplot(x,y,2*z,Tri,CData);
64 | coloraxis('L*',4);
65 | coloraxis('c',2);
66 | coloraxis('h',1);
67 | end
68 |
69 | axis equal;
70 | axis off;
71 | pbaspect([1,1,1]);
72 | view(70,27);
73 | rotate3d on;
74 |
75 | return;
76 |
77 |
78 | function myplot(x,y,z,Tri,CData)
79 | % Plot a triangular mesh with color data
80 | cla;
81 | CData = min(max(CData,0),1);
82 | patch('Faces',Tri,'Vertices',[x,y,z],'FaceVertexCData',CData,...
83 | 'FaceColor','interp','EdgeColor','none');
84 | hold on;
85 | return;
86 |
87 |
88 | function coloraxis(Name,Type,h)
89 | % Draw color axes as 3D objects
90 | FontSize = 14;
91 |
92 | switch Type
93 | case 1
94 | set(text(-0.25,-1.3,1.1,Name),'FontWeight','bold',...
95 | 'FontSize',FontSize,'Color',[0,0,0]);
96 | t = linspace(0,pi*3/2,60);
97 | x = cos(t)*1.1;
98 | y = sin(t)*1.1;
99 | set(plot3(x,y,zeros(size(x)),'k-'),'LineWidth',2.5,'Color',[1,1,1]*0.8);
100 | x = [x,-0.1,0,-0.1];
101 | y = [y,-1.05,-1.1,-1.15];
102 | set(plot3(x,y,ones(size(x)),'k-'),'LineWidth',2.5);
103 | case 2
104 | set(text(0,-0.6,0.15,Name),'FontWeight','bold','FontSize',FontSize,'Color',[0,0,0]);
105 | set(plot3([-0.05,0,0.05,0,0],[-0.9,-1,-0.9,-1,0],[0,0,0,0,0],'k-'),'LineWidth',2.5);
106 | case 3
107 | set(text(0,0.15,1.3,Name),'FontWeight','bold','FontSize',FontSize,'Color',[0,0,0]);
108 | set(plot3([0,0,0,0,0],[-0.05,0,0.05,0,0],[1.6,1.7,1.6,1.7,0],'k-'),'LineWidth',2.5);
109 | case 4
110 | set(text(0,0.15,2.4,Name),'FontWeight','bold','FontSize',FontSize,'Color',[0,0,0]);
111 | set(plot3([0,0,0,0,0],[-0.05,0,0.05,0,0],[2.4,2.5,2.4,2.5,0],'k-'),'LineWidth',2.5);
112 | case 5
113 | set(text(0.6,0,h+0.15,Name),'FontWeight','bold','FontSize',FontSize,'Color',[0,0,0]);
114 | set(plot3([0.9,1,0.9,1,-1,-0.9,-1,-0.9],[-0.05,0,0.05,0,0,-0.05,0,0.05],...
115 | zeros(1,8)+h,'k-'),'LineWidth',2.5);
116 | case 6
117 | set(text(0,0.6,h+0.15,Name),'FontWeight','bold','FontSize',FontSize,'Color',[0,0,0]);
118 | set(plot3([-0.05,0,0.05,0,0,-0.05,0,0.05],[0.9,1,0.9,1,-1,-0.9,-1,-0.9],...
119 | zeros(1,8)+h,'k-'),'LineWidth',2.5);
120 | end
121 | return;
122 |
123 |
124 | function [x,y,z,Tri] = makeshape(Shape)
125 | % Make a 3D shape: Shape = 'Cube', 'Cylinder', 'Cone', 'Double Cone', 'Blobs'
126 |
127 | % Cube
128 | N = 12; % Vertices per edge
129 | % Cylinder, Cone, Double Cone
130 | Nth = 25; % Vertices over angles, (Nth - 1) should be a multiple of 12
131 | Nr = 4; % Vertices over radius
132 | Nz = 8; % Vertices over z-dimension
133 |
134 | switch lower(Shape)
135 | case 'cube'
136 | [u,v] = meshgrid(linspace(0,1,N),linspace(0,1,N));
137 | u = u(:);
138 | v = v(:);
139 | x = [u;u;u;u;zeros(N^2,1);ones(N^2,1)];
140 | y = [v;v;zeros(N^2,1);ones(N^2,1);v;v];
141 | z = [zeros(N^2,1);ones(N^2,1);v;v;u;u];
142 | Tri = trigrid(N,N);
143 | Tri = [Tri;N^2+Tri;2*N^2+Tri;3*N^2+Tri;4*N^2+Tri;5*N^2+Tri];
144 | case 'cylinder'
145 | Nth = ceil(Nth*0.75);
146 | [u,v] = meshgrid(linspace(0,pi*3/2,Nth),linspace(0,1,Nz));
147 | Tri = trigrid(Nth,Nz);
148 | x = cos(u(:));
149 | y = sin(u(:));
150 | z = v(:);
151 | [u,v] = meshgrid(linspace(0,pi*3/2,Nth),linspace(0,1,Nr));
152 | Tri = [Tri;Nth*Nz+trigrid(Nth,Nr);Nth*Nz+Nth*Nr+trigrid(Nth,Nr)];
153 | x = [x;v(:).*cos(u(:));v(:).*cos(u(:))];
154 | y = [y;v(:).*sin(u(:));v(:).*sin(u(:))];
155 | z = [z;zeros(Nth*Nr,1);ones(Nth*Nr,1)];
156 | [u,v] = meshgrid(linspace(0,1,Nr),linspace(0,1,Nz));
157 | Tri = [Tri;Nth*Nz+2*Nth*Nr+trigrid(Nr,Nz);Nth*Nz+2*Nth*Nr+Nr*Nz+trigrid(Nr,Nz)];
158 | x = [x;u(:);zeros(Nr*Nz,1)];
159 | y = [y;zeros(Nr*Nz,1);-u(:)];
160 | z = [z;v(:);v(:)];
161 | case 'cone'
162 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nz));
163 | Tri = trigrid(Nth,Nz);
164 | x = v(:).*cos(u(:));
165 | y = v(:).*sin(u(:));
166 | z = v(:);
167 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nr));
168 | Tri = [Tri;Nth*Nz+trigrid(Nth,Nr);];
169 | x = [x;v(:).*cos(u(:));];
170 | y = [y;v(:).*sin(u(:));];
171 | z = [z;ones(Nth*Nr,1)];
172 | case 'double cone'
173 | Nz = floor(Nz/2)*2+1;
174 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nz));
175 | Tri = trigrid(Nth,Nz);
176 | r = 1 - abs(2*v(:) - 1);
177 | x = r.*cos(u(:));
178 | y = r.*sin(u(:));
179 | z = v(:);
180 | case 'hexacone'
181 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nz));
182 | Tri = trigrid(Nth,Nz);
183 | r = 0.92./max(max(abs(cos(u)),abs(cos(u - pi/3))),abs(cos(u + pi/3)));
184 | x = v(:).*cos(u(:)).*r(:);
185 | y = v(:).*sin(u(:)).*r(:);
186 | z = v(:);
187 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nr));
188 | Tri = [Tri;Nth*Nz+trigrid(Nth,Nr);];
189 | v = 0.92*v./max(max(abs(cos(u)),abs(cos(u - pi/3))),abs(cos(u + pi/3)));
190 | x = [x;v(:).*cos(u(:));];
191 | y = [y;v(:).*sin(u(:));];
192 | z = [z;ones(Nth*Nr,1)];
193 | case 'double hexacone'
194 | Nz = floor(Nz/2)*2+1;
195 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nz));
196 | Tri = trigrid(Nth,Nz);
197 | r = 1 - abs(2*v - 1);
198 | r = 0.92*r./max(max(abs(cos(u)),abs(cos(u - pi/3))),abs(cos(u + pi/3)));
199 | x = r(:).*cos(u(:));
200 | y = r(:).*sin(u(:));
201 | z = v(:);
202 | case 'blobs'
203 | Nz = 47;
204 | [u,v] = meshgrid(linspace(0,2*pi,Nth),linspace(0,1,Nz));
205 | Tri = trigrid(Nth,Nz);
206 | r = sin(v(:)*pi*3).^2.*(1 - 0.6*abs(2*v(:) - 1));
207 | x = r.*cos(u(:));
208 | y = r.*sin(u(:));
209 | z = v(:);
210 | end
211 | return;
212 |
213 | function Tri = trigrid(Nu,Nv)
214 | % Construct the connectivity data for a grid of triangular patches
215 | i = (1:(Nu-1)*Nv-1).';
216 | i(Nv:Nv:end) = [];
217 | Tri = [i,i+1,i+Nv;i+1,i+Nv+1,i+Nv];
218 | return;
219 |
220 |
--------------------------------------------------------------------------------
/3rdparty/colorspace/colorspace_test.m:
--------------------------------------------------------------------------------
1 | % Transform accuracy test
2 | % Test script for colorspace
3 |
4 | % Pascal Getreuer 2006-2010
5 |
6 |
7 | fprintf(['\nTransform accuracy test\n\n',...
8 | 'To verify the invertibility of the color transfomations, this test\n',...
9 | 'transforms sRGB data to a space, inverts, and compares with the\n',...
10 | 'original data.\n']);
11 | N = 1e5; % Number of points to test
12 | A = rand(N,3); % Generate points uniformly in the sRGB colorspace
13 |
14 | % Include pure black and pure white
15 | A(1,:) = 0;
16 | A(2,:) = 1;
17 |
18 | Space = {'YPbPr', 'YCbCr', 'JPEG-YCbCr', 'YDbDr', 'YIQ','YUV', 'HSV', ...
19 | 'HSL', 'HSI', 'XYZ', 'Lab', 'Luv', 'LCH', 'CAT02 LMS'};
20 | fprintf('\n Transform RMSE Error Max Error\n\n');
21 |
22 | for k = 1:length(Space)
23 | B = colorspace([Space{k},'<-RGB'],A); % Convert to Space{k}
24 | R = colorspace(['RGB<-',Space{k}],B); % Convert back to sRGB
25 | RMSE = sqrt(mean((A(:) - R(:)).^2));
26 | MaxError = max(abs(A(:) - R(:)));
27 | fprintf(' RGB<->%-10s %9.2e %9.2e\n', Space{k}, RMSE, MaxError);
28 | end
29 |
30 | fprintf('\n\n');
31 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/boxfilter.m:
--------------------------------------------------------------------------------
1 | function imDst = boxfilter(imSrc, r)
2 |
3 | % BOXFILTER O(1) time box filtering using cumulative sum
4 | %
5 | % - Definition imDst(x, y)=sum(sum(imSrc(x-r:x+r,y-r:y+r)));
6 | % - Running time independent of r;
7 | % - Equivalent to the function: colfilt(imSrc, [2*r+1, 2*r+1], 'sliding', @sum);
8 | % - But much faster.
9 |
10 | [hei, wid] = size(imSrc);
11 | imDst = zeros(size(imSrc));
12 |
13 | %cumulative sum over Y axis
14 | imCum = cumsum(imSrc, 1);
15 | %difference over Y axis
16 | imDst(1:r+1, :) = imCum(1+r:2*r+1, :);
17 | imDst(r+2:hei-r, :) = imCum(2*r+2:hei, :) - imCum(1:hei-2*r-1, :);
18 | imDst(hei-r+1:hei, :) = repmat(imCum(hei, :), [r, 1]) - imCum(hei-2*r:hei-r-1, :);
19 |
20 | %cumulative sum over X axis
21 | imCum = cumsum(imDst, 2);
22 | %difference over Y axis
23 | imDst(:, 1:r+1) = imCum(:, 1+r:2*r+1);
24 | imDst(:, r+2:wid-r) = imCum(:, 2*r+2:wid) - imCum(:, 1:wid-2*r-1);
25 | imDst(:, wid-r+1:wid) = repmat(imCum(:, wid), [1, r]) - imCum(:, wid-2*r:wid-r-1);
26 | end
27 |
28 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/example_enhancement.m:
--------------------------------------------------------------------------------
1 | % example: detail enhancement
2 | % figure 6 in our paper
3 |
4 | close all;
5 |
6 | I = double(imread('.\img_enhancement\tulips.bmp')) / 255;
7 | p = I;
8 |
9 | r = 16;
10 | eps = 0.1^2;
11 |
12 | q = zeros(size(I));
13 |
14 | q(:, :, 1) = guidedfilter(I(:, :, 1), p(:, :, 1), r, eps);
15 | q(:, :, 2) = guidedfilter(I(:, :, 2), p(:, :, 2), r, eps);
16 | q(:, :, 3) = guidedfilter(I(:, :, 3), p(:, :, 3), r, eps);
17 |
18 | I_enhanced = (I - q) * 5 + q;
19 |
20 | figure();
21 | imshow([I, q, I_enhanced], [0, 1]);
22 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/example_feathering.m:
--------------------------------------------------------------------------------
1 | % example: guided feathering
2 | % figure 9 in our paper
3 |
4 | close all;
5 |
6 | I = double(imread('.\img_feathering\toy.bmp')) / 255;
7 | p = double(rgb2gray(imread('.\img_feathering\toy-mask.bmp'))) / 255;
8 |
9 | r = 60;
10 | eps = 10^-6;
11 |
12 | q = guidedfilter_color(I, p, r, eps);
13 |
14 | figure();
15 | imshow([I, repmat(p, [1, 1, 3]), repmat(q, [1, 1, 3])], [0, 1]);
16 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/example_flash.m:
--------------------------------------------------------------------------------
1 | % example: flash/noflash denoising
2 | % figure 8 in our paper
3 | % *** Errata ***: there is a typo in the caption of figure 8, the eps should be 0.02^2 instead of 0.2^2; sig_r should be 0.02 instead of 0.2.
4 |
5 | close all;
6 |
7 | I = double(imread('.\img_flash\cave-flash.bmp')) / 255;
8 | p = double(imread('.\img_flash\cave-noflash.bmp')) / 255;
9 |
10 | r = 8;
11 | eps = 0.02^2;
12 |
13 | q = zeros(size(I));
14 |
15 | q(:, :, 1) = guidedfilter(I(:, :, 1), p(:, :, 1), r, eps);
16 | q(:, :, 2) = guidedfilter(I(:, :, 2), p(:, :, 2), r, eps);
17 | q(:, :, 3) = guidedfilter(I(:, :, 3), p(:, :, 3), r, eps);
18 |
19 | figure();
20 | imshow([I, p, q], [0, 1]);
21 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/example_smoothing.m:
--------------------------------------------------------------------------------
1 | % example: edge-preserving smoothing
2 | % figure 1 in our paper
3 |
4 | close all;
5 |
6 | I = double(imread('.\img_smoothing\cat.bmp')) / 255;
7 | p = I;
8 | r = 4; % try r=2, 4, or 8
9 | eps = 0.2^2; % try eps=0.1^2, 0.2^2, 0.4^2
10 |
11 | q = guidedfilter(I, p, r, eps);
12 |
13 | figure();
14 | imshow([I, q], [0, 1]);
15 |
--------------------------------------------------------------------------------
/3rdparty/guided_filter/guidedfilter.m:
--------------------------------------------------------------------------------
1 | function q = guidedfilter(I, p, r, eps)
2 | % GUIDEDFILTER O(1) time implementation of guided filter.
3 | %
4 | % - guidance image: I (should be a gray-scale/single channel image)
5 | % - filtering input image: p (should be a gray-scale/single channel image)
6 | % - local window radius: r
7 | % - regularization parameter: eps
8 |
9 | [hei, wid] = size(I);
10 | N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
11 |
12 | mean_I = boxfilter(I, r) ./ N;
13 | mean_p = boxfilter(p, r) ./ N;
14 | mean_Ip = boxfilter(I.*p, r) ./ N;
15 | cov_Ip = mean_Ip - mean_I .* mean_p; % this is the covariance of (I, p) in each local patch.
16 |
17 | mean_II = boxfilter(I.*I, r) ./ N;
18 | var_I = mean_II - mean_I .* mean_I;
19 |
20 | a = cov_Ip ./ (var_I + eps); % Eqn. (5) in the paper;
21 | b = mean_p - a .* mean_I; % Eqn. (6) in the paper;
22 |
23 | mean_a = boxfilter(a, r) ./ N;
24 | mean_b = boxfilter(b, r) ./ N;
25 |
26 | q = mean_a .* I + mean_b; % Eqn. (8) in the paper;
27 | end
--------------------------------------------------------------------------------
/3rdparty/guided_filter/guidedfilter_color.m:
--------------------------------------------------------------------------------
1 | function q = guidedfilter_color(I, p, r, eps)
2 | % GUIDEDFILTER_COLOR O(1) time implementation of guided filter using a color image as the guidance.
3 | %
4 | % - guidance image: I (should be a color (RGB) image)
5 | % - filtering input image: p (should be a gray-scale/single channel image)
6 | % - local window radius: r
7 | % - regularization parameter: eps
8 |
9 | [hei, wid] = size(p);
10 | N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
11 |
12 | mean_I_r = boxfilter(I(:, :, 1), r) ./ N;
13 | mean_I_g = boxfilter(I(:, :, 2), r) ./ N;
14 | mean_I_b = boxfilter(I(:, :, 3), r) ./ N;
15 |
16 | mean_p = boxfilter(p, r) ./ N;
17 |
18 | mean_Ip_r = boxfilter(I(:, :, 1).*p, r) ./ N;
19 | mean_Ip_g = boxfilter(I(:, :, 2).*p, r) ./ N;
20 | mean_Ip_b = boxfilter(I(:, :, 3).*p, r) ./ N;
21 |
22 | % covariance of (I, p) in each local patch.
23 | cov_Ip_r = mean_Ip_r - mean_I_r .* mean_p;
24 | cov_Ip_g = mean_Ip_g - mean_I_g .* mean_p;
25 | cov_Ip_b = mean_Ip_b - mean_I_b .* mean_p;
26 |
27 | % variance of I in each local patch: the matrix Sigma in Eqn (14).
28 | % Note the variance in each local patch is a 3x3 symmetric matrix:
29 | % rr, rg, rb
30 | % Sigma = rg, gg, gb
31 | % rb, gb, bb
32 | var_I_rr = boxfilter(I(:, :, 1).*I(:, :, 1), r) ./ N - mean_I_r .* mean_I_r;
33 | var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ N - mean_I_r .* mean_I_g;
34 | var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ N - mean_I_r .* mean_I_b;
35 | var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ N - mean_I_g .* mean_I_g;
36 | var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ N - mean_I_g .* mean_I_b;
37 | var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ N - mean_I_b .* mean_I_b;
38 |
39 | a = zeros(hei, wid, 3);
40 | for y=1:hei
41 | for x=1:wid
42 | Sigma = [var_I_rr(y, x), var_I_rg(y, x), var_I_rb(y, x);
43 | var_I_rg(y, x), var_I_gg(y, x), var_I_gb(y, x);
44 | var_I_rb(y, x), var_I_gb(y, x), var_I_bb(y, x)];
45 | %Sigma = Sigma + eps * eye(3);
46 |
47 | cov_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x)];
48 |
49 | a(y, x, :) = cov_Ip * inv(Sigma + eps * eye(3)); % Eqn. (14) in the paper;
50 | end
51 | end
52 |
53 | b = mean_p - a(:, :, 1) .* mean_I_r - a(:, :, 2) .* mean_I_g - a(:, :, 3) .* mean_I_b; % Eqn. (15) in the paper;
54 |
55 | q = (boxfilter(a(:, :, 1), r).* I(:, :, 1)...
56 | + boxfilter(a(:, :, 2), r).* I(:, :, 2)...
57 | + boxfilter(a(:, :, 3), r).* I(:, :, 3)...
58 | + boxfilter(b, r)) ./ N; % Eqn. (16) in the paper;
59 | end
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_enhancement/tulips.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_enhancement/tulips.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_feathering/toy-mask.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_feathering/toy-mask.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_feathering/toy.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_feathering/toy.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_flash/cave-flash.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_flash/cave-flash.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_flash/cave-noflash.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_flash/cave-noflash.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/img_smoothing/cat.bmp:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/guided_filter/img_smoothing/cat.bmp
--------------------------------------------------------------------------------
/3rdparty/guided_filter/readme.txt:
--------------------------------------------------------------------------------
1 |
2 | ***************************************************************************************
3 | ***************************************************************************************
4 |
5 | Matlab demo code for "Guided Image Filtering" (ECCV 2010)
6 |
7 | by Kaiming He (kahe@microsoft.com)
8 |
9 | If you use/adapt our code in your work (either as a stand-alone tool or as a component
10 | of any algorithm), you need to appropriately cite our ECCV 2010 paper.
11 |
12 | This code is for academic purpose only. Not for commercial/industrial activities.
13 |
14 |
15 | The running time reported in the paper is from C++ implementation. This matlab code is
16 | a reference for those who would like to reimplement our method.
17 |
18 | ***************************************************************************************
19 | ***************************************************************************************
20 |
21 | Usage:
22 |
23 | guidedfilter.m - guided filter implementation (Eqn(5), (6), (8) in the paper)
24 | guidedfilter_color.m - guided filter for color guidance (Eqn(14), (15), (16) in the paper)
25 |
26 | Run the four examples to see the results shown in the paper.
27 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/allegro_emu.cpp:
--------------------------------------------------------------------------------
1 |
2 | #include "allegro_emu.h"
3 | #include <stdlib.h>
4 | #include <stdio.h>
5 |
6 | BITMAP *create_bitmap(int w, int h) {
7 | BITMAP *ans = new BITMAP(w, h);
8 | ans->data = new unsigned char[4*w*h]; // always 32bit
9 | ans->line = new unsigned char*[h];
10 | for (int y = 0; y < h; y++)
11 | ans->line[y] = &ans->data[y*4*w];
12 | return ans;
13 | }
14 |
15 | void blit(BITMAP *a, BITMAP *b, int ax, int ay, int bx, int by, int w, int h) {
16 | if (ax != 0 || ay != 0 || bx != 0 || by != 0) { fprintf(stderr, "blit not fully implemented, nonzero coords not supported\n"); exit(1); }
17 | for (int y = 0; y < h; y++) {
18 | int *arow = (int *) a->line[y];
19 | int *brow = (int *) b->line[y];
20 | for (int x = 0; x < w; x++) {
21 | brow[x] = arow[x];
22 | }
23 | }
24 | }
25 |
26 | void destroy_bitmap(BITMAP *bmp) {
27 | if (bmp) {
28 | delete[] bmp->line;
29 | delete[] bmp->data;
30 | delete bmp;
31 | }
32 | }
33 |
34 | fixed fixmul(fixed a0, fixed b0) {
35 | long long a = a0;
36 | long long b = b0;
37 | return (int) ((a*b)>>16);
38 | }
39 |
40 | void clear(BITMAP *bmp) {
41 | clear_to_color(bmp, 0);
42 | }
43 |
44 | void clear_to_color(BITMAP *bmp, int c) {
45 | for (int y = 0; y < bmp->h; y++) {
46 | int *row = (int *) bmp->line[y];
47 | for (int x = 0; x < bmp->w; x++) {
48 | row[x] = c;
49 | }
50 | }
51 | }
52 |
53 | int bitmap_color_depth(BITMAP *bmp) {
54 | return 32;
55 | }
56 |
57 | BITMAP *create_bitmap_ex(int depth, int w, int h) {
58 | if (depth != 32) { fprintf(stderr, "depth not supported for create_bitmap_ex: %d\n", depth); exit(1); }
59 | return create_bitmap(w, h);
60 | }
61 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/allegro_emu.h:
--------------------------------------------------------------------------------
1 |
2 | #ifndef _allegro_emu_h
3 | #define _allegro_emu_h
4 |
5 | struct BITMAP {
6 | int w, h;
7 | unsigned char **line;
8 | unsigned char *data;
9 | BITMAP(int ww = 1, int hh = 1): w(ww), h(hh) {}
10 | };
11 |
12 | inline int _getpixel32(BITMAP *a, int x, int y) { return ((int *) a->line[y])[x]; }
13 | inline void _putpixel32(BITMAP *a, int x, int y, int c) { ((int *) a->line[y])[x] = c; }
14 |
15 | inline int getr32(int c) { return c&255; }
16 | inline int getg32(int c) { return (c>>8)&255; }
17 | inline int getb32(int c) { return (c>>16)&255; }
18 |
19 | BITMAP *create_bitmap(int w, int h);
20 | void blit(BITMAP *a, BITMAP *b, int ax, int ay, int bx, int by, int w, int h);
21 | void destroy_bitmap(BITMAP *bmp);
22 | typedef int fixed;
23 | fixed fixmul(fixed a, fixed b);
24 | void clear(BITMAP *bmp);
25 | void clear_to_color(BITMAP *bmp, int c);
26 |
27 | /*
28 | unsigned makecol(int r, int g, int b);
29 | int bitmap_mask_color(BITMAP *bmp);
30 | */
31 |
32 | int bitmap_color_depth(BITMAP *bmp);
33 | BITMAP *create_bitmap_ex(int depth, int w, int h);
34 |
35 | #endif
36 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/build_mac.sh:
--------------------------------------------------------------------------------
1 | mex CXX='/usr/local/bin/g++' CC='/usr/local/bin/gcc' LD='/usr/local/bin/g++' CXXOPTIMFLAGS='-O6 -w -s -ffast-math -fomit-frame-pointer -fstrength-reduce -fopenmp -msse2 -funroll-loops -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,-e,mexFunction -shared' LDFLAGS='-Wl,-twolevel_namespace -arch i386 -Wl,-syslibroot,/Developer/SDKs/MacOSX10.5.sdk -Wl,-exported_symbols_list,/Applications/MATLAB_R2009a.app/extern/lib/maci/mexFunction.map -fopenmp' mexutil.cpp nn.cpp nnmex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -cxx -output nnmex
2 | mex CXX='/usr/local/bin/g++' CC='/usr/local/bin/gcc' LD='/usr/local/bin/g++' CXXOPTIMFLAGS='-O6 -w -s -ffast-math -fomit-frame-pointer -fstrength-reduce -fopenmp -msse2 -funroll-loops -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,-e,mexFunction -shared' LDFLAGS='-Wl,-twolevel_namespace -arch i386 -Wl,-syslibroot,/Developer/SDKs/MacOSX10.5.sdk -Wl,-exported_symbols_list,/Applications/MATLAB_R2009a.app/extern/lib/maci/mexFunction.map -fopenmp' mexutil.cpp nn.cpp votemex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -cxx -output votemex
3 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/build_unix.sh:
--------------------------------------------------------------------------------
1 | # Optimized (slow to build)
2 | mex CXXOPTIMFLAGS='-O6 -w -s -ffast-math -fomit-frame-pointer -fstrength-reduce -fopenmp -msse2 -funroll-loops -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,--export-dynamic -Wl,-e,mexFunction -shared -lgomp' -inline knn.cpp mexutil.cpp nn.cpp nnmex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex -output nnmex
3 | mex CXXOPTIMFLAGS='-O6 -w -s -ffast-math -fomit-frame-pointer -fstrength-reduce -fopenmp -msse2 -funroll-loops -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,--export-dynamic -Wl,-e,mexFunction -shared -lgomp' -inline knn.cpp mexutil.cpp nn.cpp votemex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex -output votemex
4 |
5 | # Unoptimized (fast to build)
6 | #mex CXXOPTIMFLAGS='-w -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,--export-dynamic -Wl,-e,mexFunction -shared -lgomp' -inline knn.cpp mexutil.cpp nn.cpp nnmex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex -output nnmex
7 | #mex CXXOPTIMFLAGS='-w -fPIC' CXXFLAGS='-DNDEBUG -DUNIX_MODE -DMEXMODE -fopenmp' CXXLIBS='${CXXLIBS} -Wl,--export-dynamic -Wl,-e,mexFunction -shared -lgomp' -inline knn.cpp mexutil.cpp nn.cpp votemex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex -output votemex
8 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/build_windows.bat:
--------------------------------------------------------------------------------
1 | @rem It works without /nodefaultlib:libcmt.lib
2 |
3 | @rem Optimized (slow to build)
4 | @rem call mex OPTIMFLAGS="/Ox /Oi /Oy /DNDEBUG /fp:fast /arch:SSE2 /DMEX_MODE /openmp" mexutil.cpp nn.cpp nnmex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex
5 | @rem call mex OPTIMFLAGS="/Ox /Oi /Oy /DNDEBUG /fp:fast /arch:SSE2 /DMEX_MODE /openmp" mexutil.cpp nn.cpp votemex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output votemex
6 |
7 | @rem Unoptimized (fast to build)
8 | call mex OPTIMFLAGS="/DNDEBUG /DMEX_MODE /openmp" knn.cpp mexutil.cpp nn.cpp nnmex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output nnmex
9 | call mex OPTIMFLAGS="/DNDEBUG /DMEX_MODE /openmp" knn.cpp mexutil.cpp nn.cpp votemex.cpp patch.cpp vecnn.cpp simnn.cpp allegro_emu.cpp -output votemex
10 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/console_test_descriptor_mex.m:
--------------------------------------------------------------------------------
1 | %------------------------------------------------------------------------%
2 | % Copyright 2008-2009 Adobe Systems Inc., for noncommercial use only.
3 | % Citation:
4 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
5 | % PatchMatch: A Randomized Correspondence Algorithm for Structural Image
6 | % Editing. ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
7 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
8 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
9 | % Version: 1.0, 21-June-2008
10 | %------------------------------------------------------------------------%
11 |
12 | % Test 'image mode' nnmex, and votemex (see test_square.m and test_daisy.m for tests of 'descriptor mode' nnmex).
13 |
14 | cores = 4; % Use more cores for more speed
15 |
16 | disp('start testing descriptor mode');
17 |
18 | if cores==1
19 | algo = 'cpu';
20 | else
21 | algo = 'cputiled';
22 | end
23 |
24 |
25 | A=imread('b.png');
26 | B=imread('c.jpg');
27 | B=B(1:end-1,1:end-1,:);
28 |
29 | SA = A; SA(:,:,4:6) = A; SA(:,:,7:9) = A; SA = double(SA);
30 | SB = B; SB(:,:,4:6) = B; SB(:,:,7:9) = B; SB = double(SB);
31 | disp('data generated');
32 |
33 | patch_w = 7;
34 |
35 | % Display field
36 | ann = nnmex(SA, SB, algo, [], [], [], [], [], [], cores);
37 | bnn = nnmex(SB, SA, algo, [], [], [], [], [], [], cores);
38 |
39 | % Display reconstruction
40 | writeim(votemex(B, ann), 'test13.png') % Coherence
41 | writeim(votemex(B, ann, bnn), 'test14.png') % BDS
42 |
43 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/console_test_knn.m:
--------------------------------------------------------------------------------
1 |
2 | cores = 2;
3 |
4 | % kNN without enrichment
5 | b=imread('b.png');
6 | c=imread('c.jpgg');
7 | tic;
8 | cnn=nnmex(b, b, 'cputiled', 7, 16, [], [], [], [], cores, [], [], [], [], [], 4);
9 | toc
10 | writeim(cnn(:,:,1,1),'test17.png')
11 | writeim(cnn(:,:,1,2),'test18.png')
12 | writeim(cnn(:,:,1,3),'test19.png')
13 | writeim(cnn(:,:,1,4),'test20.png')
14 | D = sqrt(double(cnn(:,:,3,:)));
15 | format long;
16 | disp(['Average dist (no enrichment):', num2str(mean(D(:)))]);
17 |
18 | % kNN with enrichment -- both images must be the same. Enrichment requires the number of NN iterations to be even -- if not it will round down to the next even number.
19 | tic;
20 | cnn=nnmex(b, b, 'enrich', 7, 6, [], [], [], [], cores, [], [], [], [], [], 4);
21 | toc
22 | writeim(cnn(:,:,1,1),'test21.png')
23 | writeim(cnn(:,:,1,2),'test22.png')
24 | writeim(cnn(:,:,1,3),'test23.png')
25 | writeim(cnn(:,:,1,4),'test24.png')
26 | D = sqrt(double(cnn(:,:,3,:)));
27 | disp(['Average dist (enrichment):', num2str(mean(D(:)))]);
28 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/console_test_mex.m:
--------------------------------------------------------------------------------
1 | %------------------------------------------------------------------------%
2 | % Copyright 2008-2009 Adobe Systems Inc., for noncommercial use only.
3 | % Citation:
4 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
5 | % PatchMatch: A Randomized Correspondence Algorithm for Structural Image
6 | % Editing. ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
7 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
8 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
9 | % Version: 1.0, 21-June-2008
10 | %------------------------------------------------------------------------%
11 |
12 | % Test 'image mode' nnmex, and votemex (see test_square.m and test_daisy.m for tests of 'descriptor mode' nnmex).
13 |
14 | cores = 4; % Use more cores for more speed
15 |
16 | if cores==1
17 | algo = 'cpu';
18 | else
19 | algo = 'cputiled';
20 | end
21 |
22 | A=imread('in.png');
23 | B=imread('tar.png');
24 | A = imhistmatch(A, B);
25 | figure; imshow(A)
26 | figure; imshow(B)
27 | B=B(1:end-1,1:end-1,:);
28 |
29 | patch_w = 7;
30 | ann0 = nnmex(A, B, algo, [], [], [], [], [], [], cores); % Warm up
31 |
32 | % Benchmark
33 | tic;
34 | nnmex(A, B, algo, [], [], [], [], [], [], cores);
35 | nnmex(B, A, algo, [], [], [], [], [], [], cores);
36 | disp(['NN A <-> B time: ', num2str(toc), ' sec']);
37 |
38 | % Display field
39 | ann = nnmex(A, B, algo, [], [], [], [], [], [], cores);
40 | bnn = nnmex(B, A, algo, [], [], [], [], [], [], cores);
41 | writeim(ann(:,:,1), 'test1.png');
42 | writeim(sqrt(double(ann(1:end-patch_w,1:end-patch_w,3))), 'test2.png');
43 |
44 | % Display reconstruction
45 | writeim(votemex(B, ann), 'test3.png') % Coherence
46 | writeim(votemex(B, ann, bnn), 'test4.png') % BDS
47 |
48 | % Test 3x3 patches, and clipping
49 | patch_w = 3;
50 | ann = nnmex(A, B, algo, patch_w, [], [], [], [], [], cores);
51 | bnn = nnmex(B, A, algo, patch_w, [], [], [], [], [], cores);
52 | % figure
53 | ann(1,1,1) = -2000;
54 | ann(2,1,1) = 2000;
55 | ann(3,1,2) = 2000;
56 | ann(4,1,2) = -2000;
57 | bnn(1,1,1) = -2000;
58 | bnn(2,1,1) = 2000;
59 | bnn(3,1,2) = 2000;
60 | bnn(4,1,2) = -2000;
61 | % imshow(votemex(B, ann, bnn, algo, patch_w));
62 |
63 | % Test initial guess
64 | tic;
65 | annp = nnmex(A, B, algo, [], 0, [], [], [], [], cores, [], [], ann0);
66 | disp(['Initial guess mode run for 0 iterations: ', num2str(toc), ' sec']);
67 | writeim(votemex(B, annp), 'test5.png') % Coherence
68 |
69 | ann0(1:200,1:200) = 0;
70 | tic;
71 | annp = nnmex(A, B, algo, [], [], [], [], [], [], cores, [], [], ann0);
72 | disp(['Initial guess mode run for 5 iterations: ', num2str(toc), ' sec']);
73 | writeim(votemex(B, annp), 'test6.png') % Coherence
74 |
75 |
76 | %close all;
77 | %% Test for memory leaks
78 | %A=imresize(A,0.25);
79 | %B=imresize(B,0.25);
80 | %B=B(1:end-1,1:end-1,:);
81 | %ann = nnmex(A, B); bnn = nnmex(B, A);
82 | %
83 | %user = memory;
84 | %disp(['before memory leak test: ', num2str(user.MemUsedMATLAB/1e6), ' MB']);
85 | %for i=1:100
86 | % ann = nnmex(A, B); bnn = nnmex(B, A);
87 | %end
88 | %user = memory;
89 | %disp(['after memory leak test: ', num2str(user.MemUsedMATLAB/1e6), ' MB']);
90 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/console_test_rot_scale.m:
--------------------------------------------------------------------------------
1 |
2 | b=imread('b.png');
3 | c=imread('c.png');
4 | tic; cnn=nnmex(c,b,'rotscale',7,20); toc
5 | writeim(cnn(:,:,1),'test7.png')
6 | writeim(cnn(:,:,4),'test8.png')
7 |
8 | % Limiting to only unity scales on this input still works, as the object hasn't changed size too much
9 | tic; cnn=nnmex(c,b,'rotscale',7,20, [], [], [], [], [], [], [], [], [], [], [], 1); toc
10 | writeim(cnn(:,:,1),'test9.png')
11 | writeim(cnn(:,:,4),'test10.png')
12 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/gen_all.m:
--------------------------------------------------------------------------------
1 |
2 |
3 | N = 24;
4 | patch = 7;
5 |
6 | for i = 1 : N
7 | i
8 | exp_folder = ['D:\Dropbox\Public\StyleTransfer\GainMapResults\results_9_9\segs'];
9 | A = imread([exp_folder '\in' int2str(i) '.png']);
10 | B = imread([exp_folder '\tar' int2str(i) '.png']);
11 |
12 | layers = {'relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1'};
13 | num_features = [64, 128, 256, 512, 512];
14 |
15 | [A_f, B_f] = load_vgg_features([exp_folder '\segmentations_' int2str(i)], i, A, B, layers, num_features);
16 |
17 | [C, field] = neural_patchmatch(A, B, A_f, B_f, patch);
18 |
19 | imwrite(C, [exp_folder '\match' int2str(i) '.png']);
20 | end
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/knn.h:
--------------------------------------------------------------------------------
1 |
2 | /* PatchMatch finding k-NN, with similarity transform of patches (rotation+scale). */
3 |
4 | #ifndef _knn_h
5 | #define _knn_h
6 |
7 | #include "nn.h"
8 | #include "simnn.h"
9 | #include "vecnn.h"
10 |
11 | #define SAVE_DIST 0
12 |
13 | #define VBMP VECBITMAP<int>
14 |
15 | BITMAP *greyscale(BITMAP *a);
16 | BITMAP *greyscale16(BITMAP *a);
17 | BITMAP *greyscale_to_color(BITMAP *a);
18 | BITMAP *gaussian_blur16(BITMAP *a, double sigma);
19 | BITMAP *greyscale16_to_color(BITMAP *a);
20 | BITMAP *gaussian_deriv_angle(BITMAP *a, double sigma, BITMAP **dx_out=NULL, BITMAP **dy_out=NULL);
21 |
22 | BITMAP *color_gaussian_blur(BITMAP *a, double sigma, int aconstraint_alpha);
23 |
24 | BITMAP *extract_vbmp(VBMP *bmp, int i);
25 | void insert_vbmp(VBMP *bmp, int i, BITMAP *a);
26 | //void sort_knn(Params *p, BITMAP *a, VBMP *ann, VBMP *ann_sim, VBMP *annd);
27 | VBMP *copy_vbmp(VBMP *a);
28 |
29 | #define N_PRINCIPAL_ANGLE_SHIFT 8
30 | #define N_PRINCIPAL_ANGLE (1<<N_PRINCIPAL_ANGLE_SHIFT)
31 |
32 | class PRINCIPAL_ANGLE { public:
33 | BITMAP *angle[N_PRINCIPAL_ANGLE];
34 | };
35 |
36 | PRINCIPAL_ANGLE *create_principal_angle(Params *p, BITMAP *bmp);
37 | void destroy_principal_angle(PRINCIPAL_ANGLE *b);
38 | int get_principal_angle(Params *p, PRINCIPAL_ANGLE *b, int x0, int y0, int scale);
39 |
40 | VBMP *knn_init_nn(Params *p, BITMAP *a, BITMAP *b, VBMP *&ann_sim, PRINCIPAL_ANGLE *pa=NULL);
41 | VBMP *knn_init_dist(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim);
42 |
43 | void knn(Params *p, BITMAP *a, BITMAP *b,
44 | VBMP *&ann, VBMP *&ann_sim, VBMP *&annd,
45 | RegionMasks *amask=NULL, BITMAP *bmask=NULL,
46 | int level=0, int em_iter=0, RecomposeParams *rp=NULL, int offset_iter=0, int update_type=0, int cache_b=0,
47 | RegionMasks *region_masks=NULL, int tiles=-1, PRINCIPAL_ANGLE *pa=NULL, int save_first=0);
48 |
49 | class KNNWeightFunc { public:
50 | virtual double weight(double d, int is_center) = 0;
51 | };
52 |
53 | class KNNSolverWeightFunc: public KNNWeightFunc { public:
54 | double param[3];
55 | KNNSolverWeightFunc(double x[3]);
56 | virtual double weight(double d, int is_center);
57 | };
58 |
59 | class ObjectiveFunc { public:
60 | virtual double f(double x[]) = 0;
61 | };
62 |
63 | double patsearch(ObjectiveFunc *f, double *x, double *ap, int n, int iters);
64 |
65 | BITMAP *knn_vote(Params *p, BITMAP *b,
66 | VBMP *ann, VBMP *ann_sim, VBMP *annd, VBMP *bnn=NULL, VBMP *bnn_sim=NULL,
67 | BITMAP *bmask=NULL, BITMAP *bweight=NULL,
68 | double coherence_weight=COHERENCE_WEIGHT, double complete_weight=COMPLETE_WEIGHT,
69 | RegionMasks *amask=NULL, BITMAP *aweight=NULL, BITMAP *ainit=NULL, RegionMasks *region_masks=NULL, BITMAP *aconstraint=NULL, int mask_self_only=0, KNNWeightFunc *weight_func=NULL, double **accum_out=NULL);
70 |
71 | BITMAP *knn_vote_solve(Params *p, BITMAP *b,
72 | VBMP *ann, VBMP *ann_sim, VBMP *annd, int n, BITMAP *aorig, double weight_out[3]);
73 |
74 | void knn_vis(Params *p, BITMAP *a, VBMP *ann, VBMP *ann_sim, VBMP *annd, int is_bitmap=0, BITMAP *vote=NULL, BITMAP *orig=NULL, BITMAP *vote_uniform=NULL);
75 |
76 | void knn_dual_vis(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, int is_bitmap=0, BITMAP *vote=NULL, BITMAP *orig=NULL);
77 |
78 | double knn_avg_dist(Params *p, VBMP *annd);
79 |
80 | void knn_enrich(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, PRINCIPAL_ANGLE *pa=NULL);
81 |
82 | void knn_enrich3(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, PRINCIPAL_ANGLE *pa=NULL);
83 |
84 | void knn_enrich4(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, PRINCIPAL_ANGLE *pa=NULL);
85 |
86 | void knn_inverse_enrich(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, PRINCIPAL_ANGLE *pa=NULL);
87 |
88 | void knn_inverse_enrich2(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, PRINCIPAL_ANGLE *pa=NULL);
89 |
90 | void knn_check(Params *p, BITMAP *a, BITMAP *b, VBMP *ann, VBMP *ann_sim, VBMP *annd, int check_duplicates=1);
91 |
92 | void save_dist(Params *p, VBMP *annd, const char *suffix);
93 |
94 | /* Also changes p->knn to kp. */
95 | void change_knn(Params *p, BITMAP *a, BITMAP *b, VBMP *&ann, VBMP *&ann_sim, VBMP *&annd, int kp, PRINCIPAL_ANGLE *pa=NULL);
96 |
97 | void combine_knn(Params *p1, Params *p2, BITMAP *a, BITMAP *b, VBMP *ann1, VBMP *ann_sim1, VBMP *annd1, VBMP *ann2, VBMP *ann_sim2, VBMP *annd2, VBMP *&ann, VBMP *&ann_sim, VBMP *&annd);
98 |
99 | void check_change_knn(Params *p, BITMAP *a, BITMAP *b);
100 |
101 | void sort_knn(Params *p, VBMP *ann, VBMP *ann_sim, VBMP *annd);
102 |
103 | #endif
104 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/load_vgg_features.m:
--------------------------------------------------------------------------------
1 | function [A_f, B_f] = load_vgg_features(exp_folder, id, A, B, layers, num_features)
2 | [hA, wA, cA] = size(A);
3 | [hB, wB, cB] = size(B);
4 |
5 | total_num_features = sum(num_features);
6 | A_f = zeros(hA, wA, total_num_features);
7 | B_f = zeros(hB, wB, total_num_features);
8 |
9 | index = 0;
10 |
11 | for i = 1 : length(layers)
12 | layer = layers{i}
13 | num_f = num_features(i)
14 |
15 |
16 | for j = 1 : num_f
17 | prefix = [exp_folder '\' layer '\content_' int2str(j)];
18 | content_f = load_img(prefix);
19 | prefix = [exp_folder '\' layer '\style_' int2str(j)];
20 | style_f = load_img(prefix);
21 |
22 | content_f = imresize(content_f, [hA, wA]);
23 | style_f = imresize(style_f , [hB, wB]);
24 |
25 | index = index + 1;
26 | A_f(:,:,index) = content_f;
27 | B_f(:,:,index) = style_f;
28 | end
29 | end
30 |
31 | end
32 |
33 | function img = load_img(prefix)
34 | img = im2double(imread([prefix '.png']));
35 | txt = load([prefix '.txt']);
36 | img = img * (txt(2) - txt(1)) + txt(1);
37 | end
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/log.txt:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/log.txt
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/mex.sln:
--------------------------------------------------------------------------------
1 |
2 | Microsoft Visual Studio Solution File, Format Version 12.00
3 | # Visual Studio 2013
4 | VisualStudioVersion = 12.0.31101.0
5 | MinimumVisualStudioVersion = 10.0.40219.1
6 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "nnmex", "nnmex\nnmex.vcproj", "{E6C3A339-5722-4328-81F5-B52D79CB2139}"
7 | EndProject
8 | Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "votemex", "votemex\votemex.vcproj", "{D08F59CD-D05E-4CA0-AA34-7EBB74044150}"
9 | EndProject
10 | Global
11 | GlobalSection(SolutionConfigurationPlatforms) = preSolution
12 | Debug|Win32 = Debug|Win32
13 | Release|Win32 = Release|Win32
14 | EndGlobalSection
15 | GlobalSection(ProjectConfigurationPlatforms) = postSolution
16 | {E6C3A339-5722-4328-81F5-B52D79CB2139}.Debug|Win32.ActiveCfg = Debug|Win32
17 | {E6C3A339-5722-4328-81F5-B52D79CB2139}.Debug|Win32.Build.0 = Debug|Win32
18 | {E6C3A339-5722-4328-81F5-B52D79CB2139}.Release|Win32.ActiveCfg = Release|Win32
19 | {E6C3A339-5722-4328-81F5-B52D79CB2139}.Release|Win32.Build.0 = Release|Win32
20 | {D08F59CD-D05E-4CA0-AA34-7EBB74044150}.Debug|Win32.ActiveCfg = Debug|Win32
21 | {D08F59CD-D05E-4CA0-AA34-7EBB74044150}.Debug|Win32.Build.0 = Debug|Win32
22 | {D08F59CD-D05E-4CA0-AA34-7EBB74044150}.Release|Win32.ActiveCfg = Release|Win32
23 | {D08F59CD-D05E-4CA0-AA34-7EBB74044150}.Release|Win32.Build.0 = Release|Win32
24 | EndGlobalSection
25 | GlobalSection(SolutionProperties) = preSolution
26 | HideSolutionNode = FALSE
27 | EndGlobalSection
28 | EndGlobal
29 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/mex.v12.suo:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/mex.v12.suo
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/mexutil.cpp:
--------------------------------------------------------------------------------
1 |
2 | #include "mexutil.h"
3 | #include "nn.h"
4 | #include <string.h>
5 |
6 | // stack RGB to a single 32bit integer
7 | BITMAP *convert_bitmap(const mxArray *A) {
8 | if (mxGetNumberOfDimensions(A) != 3) { mexErrMsgTxt("dims != 3"); }
9 | if (mxGetDimensions(A)[2] != 3) { mexErrMsgTxt("color channels != 3"); }
10 |
11 | // matlab, h * w * n: (y, x, i) -> y + x * h + i * w * h
12 | int h = mxGetDimensions(A)[0];
13 | int w = mxGetDimensions(A)[1];
14 | int offset = h * w;
15 | int offset2 = offset << 1; // h * w * 2
16 |
17 | if (mxIsUint8(A)) {
18 | unsigned char *data = (unsigned char *) mxGetData(A);
19 | BITMAP *ans = create_bitmap(w, h);
20 | for (int y = 0; y < h; y++) {
21 | int *row = (int *) ans->line[y];
22 | for (int x = 0; x < w; x++) {
23 | unsigned char *base = data + y + x * h;
24 | int r = base[0];
25 | int g = base[offset];
26 | int b = base[offset2];
27 | clip_value<int>(&r, 0, 255);
28 | clip_value<int>(&g, 0, 255);
29 | clip_value<int>(&b, 0, 255);
30 | row[x] = r|(g<<8)|(b<<16);
31 | }
32 | }
33 | return ans;
34 | }
35 | else if (mxIsSingle(A)) {
36 | float *data = (float *) mxGetData(A);
37 | BITMAP *ans = create_bitmap(w, h);
38 | for (int y = 0; y < h; y++) {
39 | int *row = (int *) ans->line[y];
40 | for (int x = 0; x < w; x++) {
41 | float *base = data + y + x * h;
42 | float r = base[0];
43 | float g = base[offset];
44 | float b = base[offset2];
45 | clip_value<float>(&r, 0, 1);
46 | clip_value<float>(&g, 0, 1);
47 | clip_value<float>(&b, 0, 1);
48 | row[x] = int(r*255)|(int(g*255)<<8)|(int(b*255)<<16);
49 | }
50 | }
51 | return ans;
52 | }
53 | else if (mxIsDouble(A)) {
54 | double *data = (double *) mxGetData(A);
55 | BITMAP *ans = create_bitmap(w, h);
56 | for (int y = 0; y < h; y++) {
57 | int *row = (int *) ans->line[y];
58 | for (int x = 0; x < w; x++) {
59 | double *base = data + y + x * h;
60 | double r = base[0];
61 | double g = base[offset];
62 | double b = base[offset2];
63 | clip_value<double>(&r, 0, 1);
64 | clip_value<double>(&g, 0, 1);
65 | clip_value<double>(&b, 0, 1);
66 | row[x] = int(r*255)|(int(g*255)<<8)|(int(b*255)<<16);
67 | }
68 | }
69 | return ans;
70 | }
71 | else {
72 | mexErrMsgTxt("bitmap not uint8, single, or double");
73 | return NULL;
74 | }
75 | }
76 |
77 | BITMAP *convert_bitmapf(const mxArray *A) {
78 | if (!(mxGetNumberOfDimensions(A) == 2 ||
79 | (mxGetNumberOfDimensions(A) == 3 && mxGetDimensions(A)[2] == 3))) { mexErrMsgTxt("float bitmap doesn't have 2 dims or 3 dims with 3 channels"); }
80 |
81 | int h = mxGetDimensions(A)[0];
82 | int w = mxGetDimensions(A)[1];
83 |
84 | if (mxIsUint8(A)) { // treated as 32bit float
85 | unsigned char *data = (unsigned char *) mxGetData(A);
86 | BITMAP *ans = create_bitmap(w, h);
87 | for (int y = 0; y < h; y++) {
88 | float *row = (float *) ans->line[y];
89 | for (int x = 0; x < w; x++) {
90 | row[x] = data[y+x*h];
91 | }
92 | }
93 | return ans;
94 | }
95 | else if (mxIsDouble(A)) {
96 | double *data = (double *) mxGetData(A);
97 | BITMAP *ans = create_bitmap(w, h);
98 | for (int y = 0; y < h; y++) {
99 | float *row = (float *) ans->line[y];
100 | for (int x = 0; x < w; x++) {
101 | row[x] = data[y+x*h];
102 | }
103 | }
104 | return ans;
105 | }
106 | else {
107 | mexErrMsgTxt("float bitmap not uint8 or double");
108 | return NULL;
109 | }
110 | }
111 |
112 | // stack (x, y) to an integer by concatenate "yx" (assume y and x has at most 12 bit, or 4096 values)
113 | BITMAP *convert_field(Params *p, const mxArray *A, int bw, int bh, int &nclip, int trim_patch) {
114 | nclip = 0;
115 | int h = mxGetDimensions(A)[0];
116 | int w = mxGetDimensions(A)[1];
117 | int offset = h * w;
118 | int ndims = mxGetNumberOfDimensions(A);
119 | if (ndims != 3) {
120 | char buf[256];
121 | if (ndims == 1) {
122 | sprintf(buf, "field dims != 3 (%d 1d array where nn field expected)", h);
123 | } else if (ndims == 2) {
124 | sprintf(buf, "field dims != 3 (%dx%d 2d array where nn field expected)", h, w);
125 | } else {
126 | sprintf(buf, "field dims != 3 (%d dimension array where nn field expected)", ndims);
127 | }
128 | mexErrMsgTxt(buf);
129 | }
130 | if (mxGetDimensions(A)[2] != 3) { mexErrMsgTxt("field channels != 3"); }
131 | if (!mxIsInt32(A)) { mexErrMsgTxt("field is not int32"); }
132 |
133 | int bew = trim_patch ? (bw - p->patch_w + 1): bw;
134 | int beh = trim_patch ? (bh - p->patch_w + 1): bh;
135 |
136 | unsigned int *data = (unsigned int *) mxGetData(A);
137 | BITMAP *ann = create_bitmap(w, h);
138 | //annd = create_bitmap(w, h);
139 | for (int y = 0; y < h; y++) {
140 | int *ann_row = (int *) ann->line[y];
141 | //int *annd_row = (int *) annd->line[y];
142 | for (int x = 0; x < w; x++) {
143 | unsigned int *base = data + y + x*h;
144 | int xp = base[0];
145 | int yp = base[offset];
146 | if ((unsigned) xp >= (unsigned) bew ||
147 | (unsigned) yp >= (unsigned) beh) {
148 | nclip++;
149 | clip_value<int>(&xp, 0, bew-1);
150 | clip_value<int>(&yp, 0, beh-1);
151 | }
152 |
153 | //int dp = data[(y+x*h)+2*w*h];
154 | ann_row[x] = XY_TO_INT(xp, yp);
155 | //annd_row[x] = dp;
156 | }
157 | }
158 | return ann;
159 | }
160 |
161 | BITMAP *convert_winsize_field(Params *p, const mxArray *A, int w, int h) {
162 | int ndims = mxGetNumberOfDimensions(A);
163 | if (!( (ndims == 2)||((ndims == 3)&&(mxGetDimensions(A)[2] == 2))&&(w==mxGetDimensions(A)[1])&&(h==mxGetDimensions(A)[0]) )) {
164 | char buf[256];
165 | //sprintf(buf, "ndims=%d, mxGetDim(A)=[%d %d %d], [2 w h]=[2 %d %d] \n", ndims,mxGetDimensions(A)[2],mxGetDimensions(A)[1],mxGetDimensions(A)[0],h,w);
166 | sprintf(buf, "winsize field dims=%d (a [%dx%d] or [%dx%dx2] field of window size is expected)", ndims, h, w, h, w);
167 | mexErrMsgTxt(buf);
168 | }
169 | if (!mxIsInt32(A)) { mexErrMsgTxt("winsize field is not int32"); }
170 |
171 | unsigned int *data = (unsigned int *) mxGetData(A);
172 | int offset = h * w;
173 | BITMAP *awsz = create_bitmap(w, h);
174 | char buf[256];
175 | if (ndims == 2) {
176 | for (int y = 0; y < h; y++) {
177 | int *awsz_row = (int *) awsz->line[y];
178 | for (int x = 0; x < w; x++) {
179 | int wp = data[y+x*h];
180 | awsz_row[x] = XY_TO_INT(wp, wp);
181 | }
182 | }
183 | }
184 | else if ((ndims == 3)&&(mxGetDimensions(A)[2] == 2)) {
185 | for (int y = 0; y < h; y++) {
186 | int *awsz_row = (int *) awsz->line[y];
187 | for (int x = 0; x < w; x++) {
188 | unsigned int *base = data + y + x * h;
189 | int wp = base[0];
190 | int hp = base[offset];
191 | awsz_row[x] = XY_TO_INT(wp, hp);
192 | }
193 | }
194 | }
195 | return awsz;
196 | }
197 |
198 | mxArray *bitmap_to_array(BITMAP *a) {
199 | mwSize dims[3] = { a->h, a->w, 3 };
200 | mxArray *ans = mxCreateNumericArray(3, dims, mxUINT8_CLASS, mxREAL);
201 | unsigned char *data = (unsigned char *) mxGetData(ans);
202 | int offset = a->w * a->h;
203 | int offset2 = offset << 1;
204 | unsigned char *rchan = &data[0];
205 | unsigned char *gchan = &data[offset];
206 | unsigned char *bchan = &data[offset2];
207 | for (int y = 0; y < a->h; y++) {
208 | int *row = (int *) a->line[y];
209 | for (int x = 0; x < a->w; x++) {
210 | int c = row[x];
211 | int pos = y + x * a->h;
212 | rchan[pos] = c&255;
213 | gchan[pos] = (c>>8)&255;
214 | bchan[pos] = (c>>16);
215 | }
216 | }
217 | return ans;
218 | }
219 |
220 | int mxStringEquals(const mxArray *A, const char *s) {
221 | char buf[256];
222 | if (!mxIsChar(A)) { return 0; }
223 | if (mxGetString(A, buf, 255)) { return 0; }
224 | return strcmp(s, buf) == 0;
225 | }
226 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/mexutil.h:
--------------------------------------------------------------------------------
1 |
2 | #ifndef _mexutil_h
3 | #define _mexutil_h
4 |
5 | #include "allegro_emu.h"
6 | #include "mex.h"
7 | #include "nn.h"
8 | #include "vecnn.h"
9 |
10 | BITMAP *convert_bitmap(const mxArray *A);
11 | BITMAP *convert_bitmapf(const mxArray *A);
12 | BITMAP *convert_field(Params *p, const mxArray *A, int bw, int bh, int &nclip, int trim_patch=1);
13 | BITMAP *convert_winsize_field(Params *p, const mxArray *A, int w, int h);
14 | mxArray *bitmap_to_array(BITMAP *a);
15 | int mxStringEquals(const mxArray *A, const char *s);
16 |
17 | template<class T>
18 | inline void clip_value(T *p, T low, T high) {
19 | if(*p < low) *p = low;
20 | else if(*p > high) *p = high;
21 | }
22 |
23 | // from MATLAB's column major to C++'s row major
24 | template<class T>
25 | VECBITMAP<T> *convert_vecbitmap(const mxArray *A) {
26 | if (mxGetNumberOfDimensions(A) != 3) { mexErrMsgTxt("dims != 3"); }
27 |
28 | int h = mxGetDimensions(A)[0];
29 | int w = mxGetDimensions(A)[1];
30 | int n = mxGetDimensions(A)[2];
31 |
32 | VECBITMAP<T> *ans = new VECBITMAP<T>(w, h, n);
33 |
34 | int offset = w*h;
35 | if (mxIsUint8(A)) {
36 | unsigned char *data = (unsigned char *) mxGetData(A);
37 | for (int y = 0; y < h; y++) {
38 | for (int x = 0; x < w; x++) {
39 | T *patch = ans->get(x, y);
40 | unsigned char *pos0 = &data[y+x*h];
41 | for (int i = 0; i < n; i++) {
42 | patch[i] = pos0[offset*i];
43 | }
44 | }
45 | }
46 | }
47 | else if (mxIsSingle(A)) {
48 | float *data = (float *) mxGetData(A);
49 | for (int y = 0; y < h; y++) {
50 | for (int x = 0; x < w; x++) {
51 | T *patch = ans->get(x, y);
52 | float *pos0 = &data[y+x*h];
53 | for (int i = 0; i < n; i++) {
54 | patch[i] = pos0[offset*i];
55 | }
56 | }
57 | }
58 | }
59 | else if (mxIsDouble(A)) {
60 | double *data = (double *) mxGetData(A);
61 | for (int y = 0; y < h; y++) {
62 | for (int x = 0; x < w; x++) {
63 | T *patch = ans->get(x, y);
64 | double *pos0 = &data[y+x*h];
65 | for (int i = 0; i < n; i++) {
66 | patch[i] = pos0[offset*i];
67 | }
68 | }
69 | }
70 | }
71 | else {
72 | mexErrMsgTxt("bitmap not uint8, single, or double");
73 | }
74 | return ans;
75 | }
76 |
77 | #endif
78 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/neural_patchmatch.m:
--------------------------------------------------------------------------------
1 | function [C, field] = neural_patchmatch(A, B, A_f, B_f, patch_w)
2 | cores = 2;
3 |
4 | if cores==1
5 | algo = 'cpu';
6 | else
7 | algo = 'cputiled';
8 | end
9 |
10 | field = nnmex(A_f, B_f, algo, patch_w, [], [], [], [], [], cores);
11 |
12 | C = votemex(B, field);
13 | writeim(C, 'C.png')
14 |
15 | disp('done!')
16 | end
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/nn.h:
--------------------------------------------------------------------------------
1 |
2 | /* Code for 3-channel input image. Warning: weight BITMAPs always encode 32-bit floats, not ints. */
3 |
4 | #ifndef _nn_h
5 | #define _nn_h
6 |
7 | #include "allegro_emu.h"
8 | #include <stdio.h>
9 | #include <vector>
10 | #include <limits.h>
11 | #include <string.h>
12 | #include <stdlib.h>
13 |
14 | /* Set this to 0 if your compiler does have OpenMP support. */
15 | #define USE_OPENMP 1
16 |
17 | #if USE_OPENMP
18 | #include <omp.h>
19 | #endif
20 |
21 | using namespace std;
22 |
23 | /* --------------------------------------------------------------------
24 | Defines
25 | -------------------------------------------------------------------- */
26 |
27 | #define IS_VERBOSE 0
28 | #define SYNC_WRITEBACK 1
29 | #define MAX_PATCH_W 32
30 |
31 | #define AEW (a->w - p->patch_w + 1)
32 | #define AEH (a->h - p->patch_w + 1)
33 | #define BEW (b->w - p->patch_w + 1)
34 | #define BEH (b->h - p->patch_w + 1)
35 |
36 | #ifndef MAX
37 | //template<class T> inline T MAX(T a, T b) { a > b ? a : b; }
38 | //template<class T> inline T MIN(T a, T b) { a < b ? a : b; }
39 | #define MAX(a, b) ((a)>(b)?(a):(b))
40 | #define MIN(a, b) ((a)<(b)?(a):(b))
41 | #endif
42 |
43 | /* --------------------------------------------------------------------
44 | Defaults
45 | -------------------------------------------------------------------- */
46 |
47 | #define COHERENCE_WEIGHT 0.5
48 | #define COMPLETE_WEIGHT 0.5
49 |
50 | #define XY_TO_INT(x, y) (((y)<<12)|(x))
51 | #define INT_TO_X(v) ((v)&((1<<12)-1))
52 | #define INT_TO_Y(v) ((v)>>12)
53 | #define XY_TO_INT_SHIFT 12
54 |
55 | /* --------------------------------------------------------------------
56 | Randomized NN algorithm
57 | -------------------------------------------------------------------- */
58 |
59 | #if !IS_VERBOSE
60 | #ifdef printf
61 | #undef printf
62 | #endif
63 | #define printf (void)
64 | #define fflush (void)
65 | #endif
66 |
67 | class RecomposeParams;
68 | class Params;
69 | class RegionMasks;
70 |
71 | #define ALGO_CPU 0
72 | #define ALGO_GPUCPU 6
73 | #define ALGO_FULLRAND 7
74 | #define ALGO_CPUTILED 8
75 |
76 | #define KNN_ALGO_HEAP 0
77 | #define KNN_ALGO_AVOID 1
78 | #define KNN_ALGO_PRINANGLE 2
79 | #define KNN_ALGO_TOP1NN 3
80 | #define KNN_ALGO_WINDOW 4
81 | #define KNN_ALGO_KDTREE 5
82 | #define KNN_ALGO_CHANGEK 6
83 | #define KNN_ALGO_FLANN 7
84 |
85 | #define VOTE_MEAN 0
86 | #define VOTE_SUM 1
87 |
88 | class Params { public:
89 | int algo; /* Algorithm to use, one of ALGO_*. */
90 |
91 | /* Randomized NN algorithm parameters. */
92 | int patch_w; /* Width and height of square patch. */
93 | int vec_len; /* Length of vector if using vectorized NN algorithms (vecnn.h), for non-square patches and feature descriptors. */
94 | int nn_iters; /* Iters of randomized NN algorithm. */
95 | int rs_max; /* Maximum width for RS. */
96 | int rs_min; /* Minimum width for RS. */
97 | double rs_ratio; /* Ratio (< 1) of successive RS sizes. */
98 | double rs_iters; /* RS iters per pixel. 1 => search all scales once. */
99 | int do_propagate; /* Do propagation. */
100 | int gpu_prop; /* Maximum propagation distance for GPU algo. */
101 | int xmin, ymin; /* Min update region coord, or -1 for no box. */
102 | int xmax, ymax; /* Max update region coord, or -1 for no box. */
103 | int resample_seamcarv; /* Resample via seam carving. */
104 | int vote_algo; /* Vote algorithm, one of VOTE_*. */
105 | int prefer_coherent; /* Prefer coherent regions, bool, default false. */
106 | int allow_coherent; /* This must be enabled for the previous flag to take effect. */
107 | int cores; /* If > 1, use OpenMP. */
108 | int window_w; /* Constraint search window width. */
109 | int window_h; /* Constraint search window height. */
110 | int weight_r; /* Multiplicative weights for R, G, B in distance computation. */
111 | int weight_g;
112 | int weight_b;
113 |
114 | /* Tree NN algorithm parameters. */
115 | int pca_dim; /* Tree PCA dim, INT_MAX for no PCA. */
116 | double pca_var; /* Fraction of total variance, e.g. 0.95 for 95%, negative to not use this param. */
117 | float eps; /* Tree epsilon. */
118 | int kcoherence_k; /* k for kcoherence. */
119 | int kcoherence_iters; /* Iters of kcoherence "propagation", 2 iters is Lefebre '95. */
120 | int kcoherence_neighbors; /* k-coherence neighbors, 4 or 8. */
121 |
122 | int knn;
123 | int knn_algo;
124 | int restarts;
125 | int enrich_iters;
126 | int enrich_times;
127 | int do_inverse_enrich;
128 | int do_enrich;
129 |
130 | /* Defaults. */
131 | Params()
132 | :algo(ALGO_CPU),
133 | patch_w(7),
134 | vec_len(0),
135 | nn_iters(5),
136 | rs_max(INT_MAX),
137 | rs_min(1),
138 | rs_ratio(0.5),
139 | rs_iters(1),
140 | do_propagate(1),
141 | gpu_prop(8),
142 | xmin(-1), ymin(-1),
143 | xmax(-1), ymax(-1),
144 | resample_seamcarv(0),
145 | vote_algo(VOTE_MEAN),
146 | pca_dim(25),
147 | pca_var(-1),
148 | eps(2),
149 | prefer_coherent(0),
150 | allow_coherent(0),
151 | cores(2),
152 | window_w(INT_MAX),
153 | window_h(INT_MAX),
154 | weight_r(1),
155 | weight_g(1),
156 | weight_b(1),
157 | kcoherence_k(2),
158 | kcoherence_iters(2),
159 | kcoherence_neighbors(8),
160 | knn(0),
161 | knn_algo(KNN_ALGO_HEAP),
162 | restarts(1),
163 | enrich_iters(0),
164 | enrich_times(1),
165 | do_inverse_enrich(1),
166 | do_enrich(1)
167 | { }
168 | };
169 |
170 | void init_params(Params *p);
171 | void init_openmp(Params *p);
172 | void srand2(unsigned seed);
173 |
174 | BITMAP *init_nn(Params *p, BITMAP *a, BITMAP *b, BITMAP *bmask=NULL, RegionMasks *region_masks=NULL, RegionMasks *amask=NULL, int trim_patch=1, BITMAP *ann_window=NULL, BITMAP *awinsize=NULL);
175 | BITMAP *init_dist(Params *p, BITMAP *a, BITMAP *b, BITMAP *ann, BITMAP *bmask=NULL, RegionMasks *region_masks=NULL, RegionMasks *amask=NULL);
176 |
177 | void nn(Params *p, BITMAP *a, BITMAP *b,
178 | BITMAP *ann, BITMAP *annd,
179 | RegionMasks *amask=NULL, BITMAP *bmask=NULL,
180 | int level=0, int em_iter=0, RecomposeParams *rp=NULL, int offset_iter=0, int update_type=0, int cache_b=0,
181 | RegionMasks *region_masks=NULL, int tiles=-1, BITMAP *ann_window=NULL, BITMAP *awinsize=NULL);
182 |
183 | class Box {
184 | public:
185 | int xmin, ymin, xmax, ymax;
186 | };
187 |
188 | class RegionMasks {
189 | public:
190 | BITMAP *bmp;
191 | Box box[256];
192 | RegionMasks(Params *p, BITMAP *region_masks, int full=0, BITMAP *bmask=NULL);
193 | };
194 |
195 | void destroy_region_masks(RegionMasks *m);
196 |
197 | /* --------------------------------------------------------------------
198 | Utility functions
199 | -------------------------------------------------------------------- */
200 |
201 | /* PRNG without global variable, pass nonzero seed as argument. */
202 | #define RANDI(u) (18000 * ((u) & 65535) + ((u) >> 16))
203 |
204 | BITMAP *norm_image(double *accum, int w, int h);
205 | BITMAP *norm_image(int *accum, int w, int h);
206 |
207 | int is_window(Params *p);
208 |
209 | int window_constraint(Params *p, BITMAP *a, BITMAP *b, int ax, int ay, int bx, int by, BITMAP *ann_window=NULL, BITMAP *awinsize=NULL);
210 |
211 | Box get_abox(Params *p, BITMAP *a, RegionMasks *amask, int trim_patch=1);
212 |
213 | BITMAP *copy_image(BITMAP *a);
214 |
215 | BITMAP *vote(Params *p, BITMAP *b,
216 | BITMAP *ann, BITMAP *bnn=NULL,
217 | BITMAP *bmask=NULL, BITMAP *bweight=NULL,
218 | double coherence_weight=COHERENCE_WEIGHT, double complete_weight=COMPLETE_WEIGHT,
219 | RegionMasks *amask=NULL, BITMAP *aweight=NULL, BITMAP *ainit=NULL, RegionMasks *region_masks=NULL, BITMAP *aconstraint=NULL, int mask_self_only=0);
220 |
221 | int patch_dist(Params *p, BITMAP *a, int ax, int ay,
222 | BITMAP *b, int bx, int by, int maxval=INT_MAX, RegionMasks *region_masks=NULL);
223 |
224 | /* Clip votes to valid rectangle, return number of votes clipped. */
225 | int clip_nn(Params *p, BITMAP *ann, BITMAP *b);
226 |
227 | void check_colors(BITMAP *bmp);
228 | void check_dists(Params *p, BITMAP *a, BITMAP *b, BITMAP *ann, BITMAP *annd, int max_dist=3, RegionMasks *amask=NULL);
229 | void check_nn(Params *p, BITMAP *ann, BITMAP *b, BITMAP *bmask=NULL, RegionMasks *amask=NULL, RegionMasks *region_masks=NULL);
230 |
231 | void getnn(BITMAP *ann, int x, int y, int &xp, int &yp);
232 |
233 | void minnn(Params *p, BITMAP *a, BITMAP *b, BITMAP *ann, BITMAP *annd, BITMAP *ann_prev, BITMAP *bmask, int level, int em_iter, RecomposeParams *rp=NULL, RegionMasks *region_masks=NULL, RegionMasks *amask=NULL, int ntiles=-1);
234 |
235 |
236 | class VECBITMAP_ARB {
237 | public:
238 | vector<unsigned> *data;
239 | int w, h;
240 | vector<unsigned> *get(int x, int y);
241 | };
242 |
243 | VECBITMAP_ARB *create_vecbitmap_arb(int w, int h);
244 | void destroy_vecbitmap_arb(VECBITMAP_ARB *bmp);
245 | void save_vecbitmap_arb(const char *filename, VECBITMAP_ARB *bmp);
246 | void sort_vecbitmap_arb(VECBITMAP_ARB *bmp);
247 |
248 | /* Parameters for high-level recomposition. */
249 | class RecomposeParams { public:
250 | int minnn_optp_nn_iters; /* Optimized params: NN iters for previous offsets. */
251 | int minnn_optp_rs_max; /* Optimized params: Max RS for previous offsets. */
252 |
253 | RecomposeParams()
254 | :minnn_optp_nn_iters(2),
255 | minnn_optp_rs_max(1) {}
256 | };
257 |
258 | #endif
259 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/nnmex.m:
--------------------------------------------------------------------------------
1 | %function ann = nnmex(A, B, [algo='cpu'], [patch_w=7], [nn_iters=5], [rs_max=100000], [rs_min=1], [rs_ratio=0.5], [rs_iters=1.0], [cores=2], [bmask=NULL]...
2 | % [win_size=[INT_MAX INT_MAX]], [nnfield_prev=[]], [nnfield_prior=[]], [prior_winsize=[]], [knn=1], [scalerange=4])
3 | % (Pass [] to leave a parameter at its default).
4 | %
5 | %MATLAB Interface for PatchMatch + Generalized PatchMatch
6 | %
7 | %Input signals A, B, are 3D arrays of size either hxwx3 (image mode) or hxwxn (descriptor mode).
8 | % In image mode, the input images are broken up into overlapping patches of size patch_w x patch_w
9 | % (allowed data types: uint8, or floats in [0, 1], which are quantized).
10 | % In descriptor mode, the inputs have an n dimensional descriptor at each (x, y) coordinate
11 | % (allowed data types: uint8, or floats of any range, which are not quantized).
12 | % Pass a patch width of 1 unless you want these descriptors to be stacked in a larger patch_w^2 x n descriptor prior to distance comparison.
13 | %
14 | %Returns 'ann' - NN field (hxwx3, int32) mapping A -> B.
15 | %Channel 1 is x coord, channel 2 is y coord, channel 3 is squared L2 distance.
16 | % (In descriptor mode with float input, the output NN field reports all zeros in channel 3 for the output.)
17 | % (When searching over all rotations+scales, the output NN field has channel 4 as theta 0...2pi, and channel 5 as scale. The patches in
18 | % image A are not rotated or scaled; the patches in image B are rotated and scaled around their center coordinate as if they had scale 1).
19 | %
20 | %The default distance metric to compare patches/descriptors is L2. To use L1 distance, set USE_L1 to 1 in patch.h.
21 | %
22 | %Options are:
23 | %algo x - One of 'cpu', 'gpucpu', 'cputiled', 'rotscale' (search over all rotations and scales), 'enrich' (an acceleration for kNN when both images are the same)
24 | %patch_w p - Width (and height) of patch, currently support sizes up to 32
25 | %nn_iters n - Iters of randomized NN algo
26 | %rs_max w - Maximum width for RS
27 | %rs_min w - Minimum width for RS
28 | %rs_ratio r - Ratio (< 1) of successive RS sizes
29 | %rs_iters n - Iters (double precision) of RS algorithm
30 | %cores n - Cores to run GPU-CPU algorithm on
31 | %bmask [hxwx3] - "1" indicates a hole
32 | %win_size [w h] - Size of search window [2*h+1 x 2*w+1] around the input pixel location
33 | % (interpolated linearly to the output coordinates in case of different
34 | % sizes). Slower but allows to limit the search space locally.
35 | %ann_prev - (hxwx3, double) initial mapping A -> B. The final result is
36 | % the minimum distance between initial mapping and random initialization + a
37 | % few final iterations. The squared distance channel in ann_prev is not used.
38 | %ann_prior - (hxwx2) field that constrains the search in a local window around the locations in B defined by the ann_prior field.
39 | %ann_winsize - (hxwx2) array matching ann_prior that defines locally the window size (per pixel) - first channel for window width, second channel for the height.
40 | %knn - Defines number of k-Nearest Neighbors to return. Returns a NNF of size h x w x 3 x k.
41 | %scalerange - When searching over rotations+scales, patches in image B can have size in [1/scalerange, scalerange].
42 | %
43 | %------------------------------------------------------------------------%
44 | % Copyright 2008-2010 Adobe Systems Inc. and Connelly Barnes
45 | %
46 | % For noncommercial use only.
47 | %
48 | % Please cite the appropriate paper(s) if used in research:
49 | %
50 | % - PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing.
51 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
52 | % ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
53 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
54 | %
55 | % - The Generalized PatchMatch Correspondence Algorithm
56 | % Connelly Barnes, Eli Shechtman, Dan B Goldman, and Adam Finkelstein.
57 | % Proc. European Conference on Computer Vision 2010
58 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2010_TGP/index.php
59 | % (k-Nearest Neighbors, Rotations+Scales, Descriptor Matching)
60 | %
61 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
62 | % Version: 2.0, 2010-11-05
63 | %------------------------------------------------------------------------%
64 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/nnmex.mexa64:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/nnmex.mexa64
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/nnmex.mexw64:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/nnmex.mexw64
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/patch.h:
--------------------------------------------------------------------------------
1 |
2 | /* Patch distance templates. */
3 |
4 | // Three groups of functions
5 | // fast_patch_dist: taking adata, b_bitmap, and do early termination according to maxval
6 | // fast_patch_nobranch: taking adata, b_bitmap, but NO early termination
7 | // patch_dist_ab: taking a_bitmap, b_bitmap, and do early termination according to maxval
8 | //
9 | // Use template specialization to speed up for smaller patch sizes
10 | //
11 |
12 | #ifndef _patch_h
13 | #define _patch_h
14 |
15 | #include "allegro_emu.h"
16 | #include "nn.h"
17 |
18 | #define USE_L1 0
19 |
20 | #define ALLOC_ADATA int adata[PATCH_W*PATCH_W];
21 |
22 | #if USE_L1
23 | #define DELTA_TERM_R(dr) (abs(dr))
24 | #define DELTA_TERM_RGB_ex(dr, dg, db) (abs(dr)+abs(dg)+abs(db))
25 | #else
26 | #define DELTA_TERM_R(dr) ((dr)*(dr))
27 | #define DELTA_TERM_RGB_ex(dr, dg, db) (dr*dr+dg*dg+db*db)
28 | #endif
29 | #define DELTA_TERM DELTA_TERM_RGB_ex(dr, dg, db)
30 | #define DELTA_TERM_RGB(dr, dg, db) DELTA_TERM_RGB_ex(dr, dg, db)
31 |
32 | template<int TPATCH_W, int IS_WINDOW>
33 | int fast_patch_dist(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p) {
34 | if (IS_WINDOW) {
35 | int ans = 0;
36 | for (int dy = 0; dy < TPATCH_W; dy++) {
37 | int *row2 = ((int *) b->line[by+dy])+bx;
38 | for (int dx = 0; dx < TPATCH_W; dx++) {
39 | unsigned int c1 = adata[dx];
40 | unsigned int c2 = row2[dx];
41 | int dr = (c1&255)-(c2&255);
42 | int dg = ((c1>>8)&255)-((c2>>8)&255);
43 | int db = (c1>>16)-(c2>>16);
44 | dr *= p->weight_r;
45 | dg *= p->weight_g;
46 | db *= p->weight_b;
47 | ans += dr*dr+dg*dg+db*db;
48 | if (ans > maxval) { return ans; } // early termination, the only difference from fast_patch_nobranch
49 | }
50 | adata += TPATCH_W; // trick here to advance pointer adata
51 | }
52 | return ans;
53 | } // end of (IS_WINDOW == true)
54 | else {
55 | int ans = 0;
56 | for (int dy = 0; dy < TPATCH_W; dy++) {
57 | int *row2 = ((int *) b->line[by+dy])+bx;
58 | for (int dx = 0; dx < TPATCH_W; dx++) {
59 | unsigned int c1 = adata[dx];
60 | unsigned int c2 = row2[dx];
61 | int dr = (c1&255)-(c2&255);
62 | int dg = ((c1>>8)&255)-((c2>>8)&255);
63 | int db = (c1>>16)-(c2>>16);
64 | ans += dr*dr+dg*dg+db*db;
65 | if (ans > maxval) { return ans; }
66 | }
67 | adata += TPATCH_W;
68 | }
69 | return ans;
70 | }
71 | }
72 |
73 |
74 | // !!!Note: this speeds up greatly
75 | template<>
76 | int fast_patch_dist<1, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
77 |
78 | template<>
79 | int fast_patch_dist<2, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
80 |
81 | template<>
82 | int fast_patch_dist<3, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
83 |
84 | template<>
85 | int fast_patch_dist<4, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
86 |
87 | template<>
88 | int fast_patch_dist<5, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
89 |
90 | template<>
91 | int fast_patch_dist<6, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
92 |
93 | template<>
94 | int fast_patch_dist<7, 0>(int *adata, BITMAP *b, int bx, int by, int maxval, Params *p);
95 |
96 | // !!!notice that "err", "xbest", "ybest" might be changed in this function
97 | template<int PATCH_W, int IS_MASK, int IS_WINDOW>
98 | void attempt_n(int &err, int &xbest, int &ybest, int *adata, BITMAP *b, int bx, int by, BITMAP *bmask, RegionMasks *region_masks, int src_mask, Params *p) {
99 | if ((bx != xbest || by != ybest) &&
100 | (unsigned) bx < (unsigned) (b->w-PATCH_W+1) &&
101 | (unsigned) by < (unsigned) (b->h-PATCH_W+1))
102 | {
103 | if (IS_MASK && region_masks && src_mask != ((int *) region_masks->bmp->line[by])[bx]) { return; }
104 | if (IS_MASK && bmask && ((int *) bmask->line[by])[bx]) { return; }
105 | int current = fast_patch_dist<PATCH_W, IS_WINDOW>(adata, b, bx, by, err, p);
106 | if (current < err) {
107 | err = current;
108 | xbest = bx;
109 | ybest = by;
110 | }
111 | }
112 | }
113 |
114 | template<int TPATCH_W, int IS_WINDOW>
115 | int fast_patch_nobranch(int *adata, BITMAP *b, int bx, int by, Params *p) {
116 | //if (IS_MASK && bmask && ((int *) bmask->line[by])[bx]) { return INT_MAX; }
117 | if (IS_WINDOW) {
118 | int ans = 0;
119 | for (int dy = 0; dy < TPATCH_W; dy++) {
120 | int *row2 = ((int *) b->line[by+dy])+bx;
121 | for (int dx = 0; dx < TPATCH_W; dx++) {
122 | unsigned int c1 = adata[dx];
123 | unsigned int c2 = row2[dx];
124 | int dr = (c1&255)-(c2&255);
125 | int dg = ((c1>>8)&255)-((c2>>8)&255);
126 | int db = (c1>>16)-(c2>>16);
127 | dr *= p->weight_r;
128 | dg *= p->weight_g;
129 | db *= p->weight_b;
130 | ans += dr*dr+dg*dg+db*db;
131 | }
132 | adata += TPATCH_W; // this is the trick!!!
133 | }
134 | return ans;
135 | } else {
136 | int ans = 0;
137 | for (int dy = 0; dy < TPATCH_W; dy++) {
138 | int *row2 = ((int *) b->line[by+dy])+bx;
139 | for (int dx = 0; dx < TPATCH_W; dx++) {
140 | unsigned int c1 = adata[dx];
141 | unsigned int c2 = row2[dx];
142 | int dr = (c1&255)-(c2&255);
143 | int dg = ((c1>>8)&255)-((c2>>8)&255);
144 | int db = (c1>>16)-(c2>>16);
145 | ans += dr*dr+dg*dg+db*db;
146 | }
147 | adata += TPATCH_W;
148 | }
149 | return ans;
150 | }
151 | }
152 |
153 | template<>
154 | int fast_patch_nobranch<1, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
155 |
156 | template<>
157 | int fast_patch_nobranch<2, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
158 |
159 | template<>
160 | int fast_patch_nobranch<3, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
161 |
162 | template<>
163 | int fast_patch_nobranch<4, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
164 |
165 | template<>
166 | int fast_patch_nobranch<5, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
167 |
168 | template<>
169 | int fast_patch_nobranch<6, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
170 |
171 | template<>
172 | int fast_patch_nobranch<7, 0>(int *adata, BITMAP *b, int bx, int by, Params *p);
173 |
174 | template<int TPATCH_W, int IS_WINDOW, int HAS_MASKS>
175 | int patch_dist_ab(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks) {
176 | if (region_masks && ((int *) region_masks->bmp->line[ay])[ax] != ((int *) region_masks->bmp->line[by])[bx]) { return INT_MAX; }
177 |
178 | if (IS_WINDOW) {
179 | int ans = 0;
180 | for (int dy = 0; dy < TPATCH_W; dy++) {
181 | int *row1 = ((int *) a->line[ay+dy])+ax;
182 | int *row2 = ((int *) b->line[by+dy])+bx;
183 | for (int dx = 0; dx < TPATCH_W; dx++) {
184 | int c1 = row1[dx];
185 | int c2 = row2[dx];
186 | int dr = (c1&255)-(c2&255);
187 | int dg = ((c1>>8)&255)-((c2>>8)&255);
188 | int db = (c1>>16)-(c2>>16);
189 | dr *= p->weight_r;
190 | dg *= p->weight_g;
191 | db *= p->weight_b;
192 | ans += dr*dr+dg*dg+db*db;
193 | if (ans > maxval) { return ans; }
194 | }
195 | }
196 | return ans;
197 | } else {
198 | int ans = 0;
199 | for (int dy = 0; dy < TPATCH_W; dy++) {
200 | int *row1 = ((int *) a->line[ay+dy])+ax;
201 | int *row2 = ((int *) b->line[by+dy])+bx;
202 | for (int dx = 0; dx < TPATCH_W; dx++) {
203 | int c1 = row1[dx];
204 | int c2 = row2[dx];
205 | int dr = (c1&255)-(c2&255);
206 | int dg = ((c1>>8)&255)-((c2>>8)&255);
207 | int db = (c1>>16)-(c2>>16);
208 | ans += dr*dr+dg*dg+db*db;
209 | if (ans > maxval) { return ans; }
210 | }
211 | }
212 | return ans;
213 | }
214 | }
215 |
216 | template<>
217 | int patch_dist_ab<1, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
218 |
219 | template<>
220 | int patch_dist_ab<2, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
221 |
222 | template<>
223 | int patch_dist_ab<3, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
224 |
225 | template<>
226 | int patch_dist_ab<4, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
227 |
228 | template<>
229 | int patch_dist_ab<5, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
230 |
231 | template<>
232 | int patch_dist_ab<6, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
233 |
234 | template<>
235 | int patch_dist_ab<7, 0, 0>(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int maxval, RegionMasks *region_masks);
236 |
237 | #endif
238 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/readme.txt:
--------------------------------------------------------------------------------
1 |
2 | --------------------------------------------------------------------------------------------------
3 |
4 | PatchMatch -- Core matching algorithm only
5 | MATLAB Mex Version 2.0 (2010-11-05)
6 |
7 | By Connelly Barnes
8 | Copyright 2008-2010 Adobe Systems Inc and Connelly Barnes
9 | Licensed by Adobe for noncommercial research use only.
10 |
11 | --------------------------------------------------------------------------------------------------
12 | Background
13 | --------------------------------------------------------------------------------------------------
14 |
15 | This code implements a fast randomized matching algorithm described in two publications as part of
16 | my Ph.D. thesis at Princeton.
17 |
18 | The algorithm solves the following problem: For each patch (a small fixed size rectangular region,
19 | e.g. 7x7) in image A, find the nearest patch in image B, where "nearest" is defined as say the L2
20 | distance between corresponding RGB tuples. Our algorithm converges quickly to an approximate
21 | solution. In our original publication we find only 1 nearest neighbor, for patches that translate
22 | only. In our later work (Generalized PatchMatch) we extend to k-Nearest Neighbors (k-NN), and allow
23 | patches to rotate+scale, and match arbitrary descriptors (tuples of an arbitrary dimension rather
24 | than just RGB, e.g. one can match densely computed SIFT descriptors).
25 |
26 | For more information on the problem we are solving and our solution see:
27 |
28 | - PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing
29 | SIGGRAPH 2009, Connelly Barnes, Eli Shechtman, Adam Finkelstein, Dan B Goldman
30 | http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/index.php
31 |
32 | - The Generalized PatchMatch Correspondence Algorithm
33 | ECCV 2010, Connelly Barnes, Eli Shechtman, Dan B Goldman, Adam Finkelstein
34 | http://www.cs.princeton.edu/gfx/pubs/Barnes_2010_TGP/index.php
35 |
36 | Please cite these paper(s) if you use this code in an academic publication.
37 |
38 | --------------------------------------------------------------------------------------------------
39 | Contents
40 | --------------------------------------------------------------------------------------------------
41 |
42 | - For a portable, unoptimized, but very easy to understand PatchMatch code (200 lines, only 100
43 | in the core algorithm), see: pm_minimal.cpp. This can be built without any dependencies, and
44 | runs only requiring that ImageMagick be installed on your Windows/Mac/Unix machine.
45 |
46 | - For an optimized and more powerful PatchMatch, build the MATLAB interface:
47 |
48 | Build: Use build_windows.bat / build_mac.sh / build_unix.sh for your system
49 | (Known to work on Windows XP, Vista, Mac OS X, and Linux, after varying amounts of fighting with the compiler).
50 | (Disable optimizations if you're just trying to get it to build, this will help speed up your build process).
51 | (OpenMP is used, but not required -- set USE_OPENMP in nn.h to 0 if your compiler does not support OpenMP)
52 |
53 | Usage: do 'help nnmex' or 'help votemex' in MATLAB, or run test_mex.m, test_rot_scale.m, test_descriptor_mex.m, or test_knn.m.
54 |
55 | For efficiency, use the 'cputiled' algorithm and set the cores argument to the number of processor cores on your system.
56 |
57 | --------------------------------------------------------------------------------------------------
58 | Version History
59 | --------------------------------------------------------------------------------------------------
60 |
61 | * Wish list (to be added soon):
62 | - Window constraints for kNN matching, or minimal distance between the kNN matches, to keep matches from being in nearby spatial locations.
63 | - Examples: Object detection
64 | - C commandline interface (not requiring MATLAB)
65 | - Further optimizations (SSE, precomputed random search samples)
66 |
67 | * Version 2.0 (2010-11-05)
68 | Connelly Barnes
69 | - Added features from "The Generalized PatchMatch Correspondence Algorithm"
70 | - Exposed k-Nearest Neighbor matching, matching across rotations+scales, and enrichment (a feature which accelerates convergence of kNN)
71 | - Fixed multicore tiled algorithm to not have contention issues.
72 | - Exposed min and max scale and rotation as parameters for rotation+scale matching.
73 | - Minimal 200 line implementation of PatchMatch, with no build dependencies, provided in pm_minimal.cpp.
74 |
75 | * Version 1.1 (2010-06-30)
76 | Xiaobai Chen
77 | - New solution file "mex.sln" with two projects "nnmex" and "votemex" (important to set /openmp, otherwise must disable those omp_* calls)
78 | - nnmex with bmask (changed the interface of nnmex)
79 | - "clip_value" function to replace range examination code in mexutil.cpp and mexutil.h
80 | - Added specialized templates for larger patches (up to 32)
81 | - Need to guarantee patch size is less than the image size (no guarantee in the C++ code)
82 | - New descriptor mode to support patches
83 | - Re-implemented functions start with "XC", places with minor changes are not always marked
84 | - Updated nnmex.m and votemex.m
85 |
86 | * Version 1.0 (2009-10-23)
87 | Connelly Barnes Initial Release
88 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/simnn.h:
--------------------------------------------------------------------------------
1 |
2 | /* PatchMatch with similarity transform of patches (rotation+scale). */
3 |
4 | #ifndef _simnn_h
5 | #define _simnn_h
6 |
7 | #include "nn.h"
8 |
9 | /*
10 | #define ANGLE_SHIFT XY_TO_INT_SHIFT
11 | #define NUM_ANGLES (1<<ANGLE_SHIFT)
12 | #define SCALE_SHIFT XY_TO_INT_SHIFT
13 | #define NUM_SCALES (1<<SCALE_SHIFT)
14 | #define SCALE_MIN 1.0
15 | #define SCALE_MAX 1.0
16 | */
17 |
18 | #define ANGLE_SHIFT XY_TO_INT_SHIFT
19 | #define NUM_ANGLES (1<<ANGLE_SHIFT)
20 | #define SCALE_SHIFT XY_TO_INT_SHIFT
21 | #define NUM_SCALES (1<<SCALE_SHIFT)
22 | //#define SCALE_MIN 0.5
23 | //#define SCALE_MAX 2.0
24 | #define SCALE_UNITY (NUM_SCALES/2) /* exp(log(SCALE_MIN)+(log(SCALE_MAX)-log(SCALE_MIN))*SCALE_UNITY*1.0/NUM_SCALES) should be exactly 1.0. */
25 |
26 | class XFORM {
27 | public:
28 | int x0, y0, dxdu, dydu, dxdv, dydv; /* Coords left shifted by 16. */
29 | };
30 |
31 | void init_xform_tables(double SCALE_MIN=0.5, double SCALE_MAX=2.0, int force_init=0);
32 | XFORM get_xform(Params *p, int x, int y, int scale, int theta); /* x and y not left shifted. */
33 |
34 | void getpixel_bilin(BITMAP *bimg, int bx, int by, int &r, int &g, int &b); /* Coords left shifted by 16. */
35 |
36 | BITMAP *sim_init_nn(Params *p, BITMAP *a, BITMAP *b, BITMAP *&ann_sim);
37 | BITMAP *sim_init_dist(Params *p, BITMAP *a, BITMAP *b, BITMAP *ann, BITMAP *ann_sim);
38 |
39 | void sim_nn(Params *p, BITMAP *a, BITMAP *b,
40 | BITMAP *ann, BITMAP *ann_sim, BITMAP *annd,
41 | RegionMasks *amask=NULL, BITMAP *bmask=NULL,
42 | int level=0, int em_iter=0, RecomposeParams *rp=NULL, int offset_iter=0, int update_type=0, int cache_b=0,
43 | RegionMasks *region_masks=NULL, int tiles=-1);
44 |
45 | BITMAP *sim_vote(Params *p, BITMAP *b,
46 | BITMAP *ann, BITMAP *ann_sim, BITMAP *bnn=NULL, BITMAP *bnn_sim=NULL,
47 | BITMAP *bmask=NULL, BITMAP *bweight=NULL,
48 | double coherence_weight=COHERENCE_WEIGHT, double complete_weight=COMPLETE_WEIGHT,
49 | RegionMasks *amask=NULL, BITMAP *aweight=NULL, BITMAP *ainit=NULL, RegionMasks *region_masks=NULL, BITMAP *aconstraint=NULL, int mask_self_only=0);
50 |
51 | #endif
52 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/simpatch.h:
--------------------------------------------------------------------------------
1 |
2 | /* Patch distance templates for similarity transform (rotation+scale). */
3 |
4 | #ifndef _simpatch_h
5 | #define _simpatch_h
6 |
7 | #include "patch.h"
8 | #include "simnn.h"
9 |
10 | // Turn one of these on. The fastest and most accurate way to sample is actually to nearest neighbor sample from a bilinearly upsampled image, but that's not implemented here.
11 | #define SAMPLE_NN 1
12 | #define SAMPLE_BILINEAR_EXACT 0
13 |
14 | template<int TPATCH_W, int DO_BRANCH>
15 | int sim_fast_patch_dist(int *adata, BITMAP *b, XFORM bpos, int maxval) {
16 | /* Do bounds checking outside the inner loop */
17 | int ul_x = bpos.x0;
18 | int ul_y = bpos.y0;
19 | int ur_x = bpos.x0+bpos.dxdu*(TPATCH_W-1);
20 | int ur_y = bpos.y0+bpos.dydu*(TPATCH_W-1);
21 | int ll_x = bpos.x0+bpos.dxdv*(TPATCH_W-1);
22 | int ll_y = bpos.y0+bpos.dydv*(TPATCH_W-1);
23 | int lr_x = ll_x+bpos.dxdu*(TPATCH_W-1);
24 | int lr_y = ll_y+bpos.dydu*(TPATCH_W-1);
25 | int bw16 = (b->w-1)<<16, bh16 = (b->h-1)<<16;
26 | if ((unsigned) ul_x >= (unsigned) bw16 ||
27 | (unsigned) ul_y >= (unsigned) bh16 ||
28 | (unsigned) ur_x >= (unsigned) bw16 ||
29 | (unsigned) ur_y >= (unsigned) bh16 ||
30 | (unsigned) ll_x >= (unsigned) bw16 ||
31 | (unsigned) ll_y >= (unsigned) bh16 ||
32 | (unsigned) lr_x >= (unsigned) bw16 ||
33 | (unsigned) lr_y >= (unsigned) bh16) { return INT_MAX-4096; }
34 |
35 | int ans = 0;
36 | int bx_row = bpos.x0, by_row = bpos.y0;
37 | #if SAMPLE_NN
38 | bx_row += 32768;
39 | by_row += 32768;
40 | #endif
41 | for (int dy = 0; dy < TPATCH_W; dy++) {
42 | int bx = bx_row, by = by_row;
43 | for (int dx = 0; dx < TPATCH_W; dx++) {
44 | unsigned int c1 = adata[dx];
45 | int r2, g2, b2;
46 | #if SAMPLE_BILINEAR_EXACT
47 | // getpixel_bilin(b, bx, by, r2, g2, b2); // A slower and safer method is to do bounds checking in the inner loop
48 | int bxi = bx>>16, byi = by>>16;
49 | int bxf = bx&((1<<16)-1), byf = by&((1<<16)-1);
50 |
51 | int *row1 = ((int *) b->line[byi])+bxi;
52 | int *row2 = ((int *) b->line[byi+1])+bxi;
53 | int cul = row1[0], cur = row1[1];
54 | int cll = row2[0], clr = row2[1];
55 |
56 | int rul = cul&255, rur = cur&255;
57 | int rll = cll&255, rlr = clr&255;
58 |
59 | int gul = (cul>>8)&255, gur = (cur>>8)&255;
60 | int gll = (cll>>8)&255, glr = (clr>>8)&255;
61 |
62 | int bul = cul>>16, bur = cur>>16;
63 | int bll = cll>>16, blr = clr>>16;
64 |
65 | int rt = rul+(((rur-rul)*bxf)>>16);
66 | int rb = rll+(((rlr-rll)*bxf)>>16);
67 | r2 = rt+(((rb-rt)*byf)>>16);
68 |
69 | int gt = gul+(((gur-gul)*bxf)>>16);
70 | int gb = gll+(((glr-gll)*bxf)>>16);
71 | g2 = gt+(((gb-gt)*byf)>>16);
72 |
73 | int bt = bul+(((bur-bul)*bxf)>>16);
74 | int bb = bll+(((blr-bll)*bxf)>>16);
75 | b2 = bt+(((bb-bt)*byf)>>16);
76 | #else
77 | int c2 = ((int *) b->line[(by)>>16])[(bx)>>16];
78 | r2 = (c2&255);
79 | g2 = (c2>>8)&255;
80 | b2 = (c2>>16);
81 | #endif
82 | int dr = (c1&255)-r2;
83 | int dg = ((c1>>8)&255)-g2;
84 | int db = (c1>>16)-b2;
85 | ans += DELTA_TERM; //dr*dr+dg*dg+db*db;
86 | if (DO_BRANCH && ans > maxval) { return ans; }
87 | bx += bpos.dxdu;
88 | by += bpos.dydu;
89 | }
90 | adata += TPATCH_W;
91 | bx_row += bpos.dxdv;
92 | by_row += bpos.dydv;
93 | }
94 | return ans;
95 | }
96 |
97 | template<int PATCH_W>
98 | void sim_attempt_n(int &err, int &xbest, int &ybest, int &sbest, int &tbest, int *adata, BITMAP *b, XFORM bpos, int bx, int by, int bs, int bt, Params *p) {
99 | //int h = PATCH_W/2;
100 | if ((bx != xbest || by != ybest || bs != sbest || bt != tbest) &&
101 | (unsigned) (bx) < (unsigned) (b->w-PATCH_W+1) &&
102 | (unsigned) (by) < (unsigned) (b->h-PATCH_W+1))
103 | {
104 | //XFORM bpos = get_xform(p, bx, by, bs, bt);
105 | int current = sim_fast_patch_dist<PATCH_W, 1>(adata, b, bpos, err);
106 | if (current < err) {
107 | err = current;
108 | xbest = bx;
109 | ybest = by;
110 | sbest = bs;
111 | tbest = bt;
112 | }
113 | }
114 | }
115 |
116 | template<int TPATCH_W, int DO_BRANCH>
117 | int sim_patch_dist_ab(Params *p, BITMAP *a, int ax, int ay, BITMAP *b, int bx, int by, int bs, int bt, int maxval) {
118 | int adata[TPATCH_W*TPATCH_W];
119 | int *ptr = adata;
120 | for (int dy = 0; dy < TPATCH_W; dy++) {
121 | int *row = ((int *) a->line[ay+dy])+ax;
122 | for (int dx = 0; dx < TPATCH_W; dx++) {
123 | *ptr++ = *row++;
124 | }
125 | }
126 | XFORM bpos = get_xform(p, bx, by, bs, bt);
127 | return sim_fast_patch_dist<TPATCH_W, DO_BRANCH>(adata, b, bpos, maxval);
128 | }
129 |
130 | #endif
131 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/test_descriptor_mex.m:
--------------------------------------------------------------------------------
1 | 9i8u%------------------------------------------------------------------------%
2 | % Copyright 2008-2009 Adobe Systems Inc., for noncommercial use only.
3 | % Citation:
4 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
5 | % PatchMatch: A Randomized Correspondence Algorithm for Structural Image
6 | % Editing. ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
7 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
8 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
9 | % Version: 1.0, 21-June-2008
10 | %------------------------------------------------------------------------%
11 |
12 | % Test 'image mode' nnmex, and votemex (see test_square.m and test_daisy.m for tests of 'descriptor mode' nnmex).
13 |
14 | cores = 2; % Use more cores for more speed
15 |
16 | disp('start testing descriptor mode');
17 |
18 | if cores==1
19 | algo = 'cpu';
20 | else
21 | algo = 'cputiled';
22 | end
23 |
24 |
25 | A=imread('a.png');
26 | B=imread('b.png');
27 | B=B(1:end-1,1:end-1,:);
28 |
29 | SA = A; SA(:,:,4:6) = A; SA(:,:,7:9) = A; SA = double(SA);
30 | SB = B; SB(:,:,4:6) = B; SB(:,:,7:9) = B; SB = double(SB);
31 | disp('data generated');
32 |
33 | patch_w = 7;
34 | ann0 = nnmex(SA, SB, algo, [], [], [], [], [], [], cores); % Warm up
35 | disp('warm-up finished');
36 |
37 | % Benchmark
38 | tic;
39 | nnmex(SA, SB, algo, [], [], [], [], [], [], cores);
40 | nnmex(SB, SA, algo, [], [], [], [], [], [], cores);
41 | disp(['NN A <-> B time: ', num2str(toc), ' sec']);
42 |
43 | % Display field
44 | ann = nnmex(SA, SB, algo, [], [], [], [], [], [], cores);
45 | bnn = nnmex(SB, SA, algo, [], [], [], [], [], [], cores);
46 | imshow(ann(:,:,1), []);
47 | figure
48 | imshow(sqrt(abs(double(ann(1:end-patch_w,1:end-patch_w,3)))), []);
49 | figure
50 |
51 | % Display reconstruction
52 | imshow(votemex(B, ann)) % Coherence
53 | figure
54 | imshow(votemex(B, ann, bnn)) % BDS
55 |
56 | % Test 3x3 patches, and clipping
57 | patch_w = 3;
58 | ann = nnmex(SA, SB, algo, patch_w, [], [], [], [], [], cores);
59 | bnn = nnmex(SB, SA, algo, patch_w, [], [], [], [], [], cores);
60 | % figure
61 | ann(1,1,1) = -2000;
62 | ann(2,1,1) = 2000;
63 | ann(3,1,2) = 2000;
64 | ann(4,1,2) = -2000;
65 | bnn(1,1,1) = -2000;
66 | bnn(2,1,1) = 2000;
67 | bnn(3,1,2) = 2000;
68 | bnn(4,1,2) = -2000;
69 | % imshow(votemex(B, ann, bnn, algo, patch_w));
70 |
71 | % Test initial guess
72 | tic;
73 | annp = nnmex(SA, SB, algo, [], 0, [], [], [], [], cores, [], [], ann0);
74 | disp(['Initial guess mode run for 0 iterations: ', num2str(toc), ' sec']);
75 | figure
76 | imshow(votemex(B, annp)) % Coherence
77 |
78 | ann0(1:200,1:200) = 0;
79 | tic;
80 | annp = nnmex(SA, SB, algo, [], [], [], [], [], [], cores, [], [], ann0);
81 | disp(['Initial guess mode run for 5 iterations: ', num2str(toc), ' sec']);
82 | figure
83 | imshow(votemex(B, annp)) % Coherence
84 |
85 |
86 | close all;
87 | %% Test for memory leaks
88 | %A=imresize(A,0.25);
89 | %B=imresize(B,0.25);
90 | %B=B(1:end-1,1:end-1,:);
91 | %ann = nnmex(A, B); bnn = nnmex(B, A);
92 | %
93 | %user = memory;
94 | %disp(['before memory leak test: ', num2str(user.MemUsedMATLAB/1e6), ' MB']);
95 | %for i=1:100
96 | % ann = nnmex(A, B); bnn = nnmex(B, A);
97 | %end
98 | %user = memory;
99 | %disp(['after memory leak test: ', num2str(user.MemUsedMATLAB/1e6), ' MB']);
100 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/test_knn.m:
--------------------------------------------------------------------------------
1 |
2 | cores = 2;
3 |
4 | % kNN without enrichment
5 | b=imread('b.png');
6 | c=imread('c.png');
7 | tic;
8 | cnn=nnmex(b, b, 'cputiled', 7, 16, [], [], [], [], cores, [], [], [], [], [], 4);
9 | toc
10 | imshow(cnn(:,:,1,1),'test17.png', []); figure
11 | imshow(cnn(:,:,1,2),'test18.png', []); figure
12 | imshow(cnn(:,:,1,3),'test19.png', []); figure
13 | imshow(cnn(:,:,1,4),'test20.png', []); figure
14 | D = sqrt(double(cnn(:,:,3,:)));
15 | format long;
16 | disp(['Average dist (no enrichment):', num2str(mean(D(:)))]);
17 |
18 | % kNN with enrichment -- both images must be the same. Enrichment requires the number of NN iterations to be even -- if not it will round down to the next even number.
19 | tic;
20 | cnn=nnmex(b, b, 'enrich', 7, 6, [], [], [], [], cores, [], [], [], [], [], 4);
21 | toc
22 | imshow(cnn(:,:,1,1),'test21.png', []); figure
23 | imshow(cnn(:,:,1,2),'test22.png', []); figure
24 | imshow(cnn(:,:,1,3),'test23.png', []); figure
25 | imshow(cnn(:,:,1,4),'test24.png', [])
26 | D = sqrt(double(cnn(:,:,3,:)));
27 | disp(['Average dist (enrichment):', num2str(mean(D(:)))]);
28 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/test_mex.m:
--------------------------------------------------------------------------------
1 | %------------------------------------------------------------------------%
2 | % Copyright 2008-2009 Adobe Systems Inc., for noncommercial use only.
3 | % Citation:
4 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
5 | % PatchMatch: A Randomized Correspondence Algorithm for Structural Image
6 | % Editing. ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
7 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
8 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
9 | % Version: 1.0, 21-June-2008
10 | %------------------------------------------------------------------------%
11 |
12 | % Test 'image mode' nnmex, and votemex (see test_square.m and test_daisy.m for tests of 'descriptor mode' nnmex).
13 |
14 | cores = 2; % Use more cores for more speed
15 |
16 | if cores==1
17 | algo = 'cpu';
18 | else
19 | algo = 'cputiled';
20 | end
21 |
22 | A=imread('a.jpg');
23 | B=imread('b.jpg');
24 | B=B(1:end-1,1:end-1,:);
25 |
26 | patch_w = 3;
27 | ann0 = nnmex(A, B, algo, [], [], [], [], [], [], cores); % Warm up
28 |
29 | % Benchmark
30 | tic;
31 | nnmex(A, B, algo, [], [], [], [], [], [], cores);
32 | nnmex(B, A, algo, [], [], [], [], [], [], cores);
33 | disp(['NN A <-> B time: ', num2str(toc), ' sec']);
34 |
35 | % Display field
36 | ann = nnmex(A, B, algo, [], [], [], [], [], [], cores);
37 | bnn = nnmex(B, A, algo, [], [], [], [], [], [], cores);
38 | imshow(ann(:,:,1), []);
39 | figure
40 | imshow(sqrt(double(ann(1:end-patch_w,1:end-patch_w,3))), []);
41 | figure
42 |
43 | % Display reconstruction
44 | imshow(votemex(B, ann)) % Coherence
45 | figure
46 | imshow(votemex(B, ann, bnn)) % BDS
47 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/test_rot_scale.m:
--------------------------------------------------------------------------------
1 |
2 | b=imread('b.png');
3 | c=imread('c.png');
4 | tic; cnn=nnmex(c,b,'rotscale',7,20); toc
5 | imshow(cnn(:,:,1),[])
6 | figure
7 | imshow(cnn(:,:,4),[])
8 |
9 | % Limiting to only unity scales on this input still works, as the object hasn't changed size too much
10 | tic; cnn=nnmex(c,b,'rotscale',7,20, [], [], [], [], [], [], [], [], [], [], [], 1); toc
11 | figure
12 | imshow(cnn(:,:,1),[])
13 | figure
14 | imshow(cnn(:,:,4),[])
15 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/vecnn.cpp:
--------------------------------------------------------------------------------
1 |
2 | #include "vecnn.h"
3 | #include "simnn.h"
4 |
5 | template<> int get_maxval() { return INT_MAX; }
6 | template<> float get_maxval() { return FLT_MAX; }
7 | template<> double get_maxval() { return DBL_MAX; }
8 | template<> long long get_maxval() { return LLONG_MAX; }
9 |
10 | VECBITMAP<unsigned char> *bitmap_to_patches(Params *p, BITMAP *a) {
11 | //int n = -1, d = -1;
12 | //unsigned char *data = im2patches_ub(a, p->patch_w, n, d);
13 | //VECBITMAP<unsigned char> *ans = new VECBITMAP<unsigned char>();
14 | //ans->w = a->w;
15 | //ans->h = a->h;
16 | //ans->n = d;
17 | //ans->data = data;
18 | if (p->vec_len != p->patch_w*p->patch_w*3) { fprintf(stderr, "vec_len (%d) != 3*patch_w**2 (%d)\n", p->vec_len, p->patch_w*p->patch_w*3); exit(1); }
19 | VECBITMAP<unsigned char> *ans = new VECBITMAP<unsigned char>(a->w, a->h, p->vec_len);
20 | unsigned char *ptr = ans->data;
21 | for (int y = 0; y < a->h; y++) {
22 | for (int x = 0; x < a->w; x++) {
23 | for (int dy = 0; dy < p->patch_w; dy++) {
24 | for (int dx = 0; dx < p->patch_w; dx++) {
25 | int xp = x+dx, yp = y+dy;
26 | if (xp >= a->w) { xp = a->w - 1; }
27 | if (yp >= a->h) { yp = a->h - 1; }
28 | int c = _getpixel32(a, xp, yp);
29 | *ptr++ = getr32(c);
30 | *ptr++ = getg32(c);
31 | *ptr++ = getb32(c);
32 | }
33 | }
34 | }
35 | }
36 | int npatches = ptr - ans->data;
37 | int n = a->w * a->h * p->vec_len;
38 | if (n != npatches) { fprintf(stderr, "n != npatches (%d != %d)\n", n, npatches); exit(1); }
39 |
40 | return ans;
41 | }
42 |
43 | BITMAP *vecbitmap_to_bitmap(VECBITMAP<int> *a) {
44 | if (a->n != 1) { fprintf(stderr, "vecbitmap n != 1 (%d)\n", a->n); exit(1); }
45 | BITMAP *ans = create_bitmap(a->w, a->h);
46 | for (int y = 0; y < a->h; y++) {
47 | int *a_row = a->line_n1(y);
48 | int *ans_row = (int *) ans->line[y];
49 | for (int x = 0; x < a->w; x++) {
50 | ans_row[x] = a_row[x];
51 | }
52 | }
53 | return ans;
54 | }
55 |
56 | VECBITMAP<int> *bitmap_to_vecbitmap(BITMAP *a) {
57 | VECBITMAP<int> *ans = new VECBITMAP<int>(a->w, a->h, 1);
58 | for (int y = 0; y < a->h; y++) {
59 | int *a_row = (int *) a->line[y];
60 | int *ans_row = ans->line_n1(y);
61 | for (int x = 0; x < a->w; x++) {
62 | ans_row[x] = a_row[x];
63 | }
64 | }
65 | return ans;
66 | }
67 |
68 | BITMAP *vecwrap_init_nn(int vec_mode, Params *p, BITMAP *a, BITMAP *b, BITMAP *bmask, RegionMasks *region_masks, RegionMasks *amask, BITMAP **ann_sim) {
69 | if (vec_mode == VEC_MODE_PATCH) {
70 | return init_nn(p, a, b, bmask, region_masks, amask);
71 | }
72 | else if (vec_mode == VEC_MODE_DESC) {
73 | VECBITMAP<unsigned char> *av = bitmap_to_patches(p, a);
74 | VECBITMAP<unsigned char> *bv = bitmap_to_patches(p, b);
75 | BITMAP *ans = vec_init_nn<unsigned char>(p, av, bv, bmask, region_masks, amask);
76 | delete av;
77 | delete bv;
78 | return ans;
79 | }
80 | else if (vec_mode == VEC_MODE_SIM) {
81 | if (!ann_sim) { fprintf(stderr, "vecwrap_init_nn: expected argument ann_sim\n"); exit(1); }
82 | return sim_init_nn(p, a, b, *ann_sim);
83 | }
84 | else {
85 | fprintf(stderr, "vecwrap_init_nn: unknown mode %d\n", vec_mode); exit(1);
86 | }
87 | }
88 |
89 | BITMAP *vecwrap_init_dist(int vec_mode, Params *p, BITMAP *a, BITMAP *b, BITMAP *ann, BITMAP *bmask, RegionMasks *region_masks, RegionMasks *amask, BITMAP *ann_sim) {
90 | if (vec_mode == VEC_MODE_PATCH) {
91 | return init_dist(p, a, b, ann, bmask, region_masks, amask);
92 | }
93 | else if (vec_mode == VEC_MODE_DESC) {
94 | VECBITMAP<unsigned char> *av = bitmap_to_patches(p, a);
95 | VECBITMAP<unsigned char> *bv = bitmap_to_patches(p, b);
96 |
97 | VECBITMAP<int> *ans = vec_init_dist<unsigned char, int>(p, av, bv, ann, bmask, region_masks, amask);
98 |
99 | delete av;
100 | delete bv;
101 | BITMAP *ansd = vecbitmap_to_bitmap(ans);
102 | delete ans;
103 | return ansd;
104 | }
105 | else if (vec_mode == VEC_MODE_SIM) {
106 | if (!ann_sim) { fprintf(stderr, "vecwrap_init_dist: expected argument ann_sim\n"); exit(1); }
107 | return sim_init_dist(p, a, b, ann, ann_sim);
108 | }
109 | else {
110 | fprintf(stderr, "vecwrap_init_dist: unknown mode %d\n", vec_mode); exit(1);
111 | }
112 | }
113 |
114 | void vecwrap_nn(int vec_mode, Params *p, BITMAP *a, BITMAP *b,
115 | BITMAP *ann, BITMAP *annd,
116 | RegionMasks *amask, BITMAP *bmask,
117 | int level, int em_iter, RecomposeParams *rp, int offset_iter, int update_type, int cache_b,
118 | RegionMasks *region_masks, int tiles, BITMAP *ann_sim)
119 | {
120 | if (vec_mode == VEC_MODE_PATCH) {
121 | return nn(p, a, b, ann, annd, amask, bmask, level, em_iter, rp, offset_iter, update_type, cache_b, region_masks, tiles);
122 | }
123 | else if (vec_mode == VEC_MODE_DESC) {
124 | VECBITMAP<unsigned char> *av = bitmap_to_patches(p, a);
125 | VECBITMAP<unsigned char> *bv = bitmap_to_patches(p, b);
126 | VECBITMAP<int> *anndv = bitmap_to_vecbitmap(annd);
127 |
128 | vec_nn<unsigned char, int>(p, av, bv, ann, anndv, amask, bmask, level, em_iter, rp, offset_iter, update_type, cache_b, region_masks, tiles);
129 |
130 | BITMAP *anndp = vecbitmap_to_bitmap(anndv);
131 | if (anndp->w != annd->w || anndp->h != annd->h) { fprintf(stderr, "Sizes differ in vecwrap_nn: %dx%d, %dx%d\n", anndp->w, anndp->h, annd->w, annd->h); exit(1); }
132 | blit(anndp, annd, 0, 0, 0, 0, anndp->w, anndp->h);
133 |
134 | delete av;
135 | delete bv;
136 | delete anndv;
137 | destroy_bitmap(anndp);
138 | }
139 | else if (vec_mode == VEC_MODE_SIM) {
140 | if (!ann_sim) { fprintf(stderr, "vecwrap_nn: expected argument ann_sim\n"); exit(1); }
141 | return sim_nn(p, a, b, ann, ann_sim, annd, amask, bmask, level, em_iter, rp, offset_iter, update_type, cache_b, region_masks, tiles);
142 | }
143 | else {
144 | fprintf(stderr, "vecwrap_nn: unknown mode %d\n", vec_mode); exit(1);
145 | }
146 | }
147 |
148 | BITMAP *vecwrap_vote(int vec_mode, Params *p, BITMAP *b,
149 | BITMAP *ann, BITMAP *ann_sim, BITMAP *bnn,
150 | BITMAP *bmask, BITMAP *bweight,
151 | double coherence_weight, double complete_weight,
152 | RegionMasks *amask, BITMAP *aweight, BITMAP *ainit, RegionMasks *region_masks, BITMAP *aconstraint, int mask_self_only)
153 | {
154 | if (vec_mode == VEC_MODE_PATCH || vec_mode == VEC_MODE_DESC) {
155 | return vote(p, b, ann, bnn, bmask, bweight, coherence_weight, complete_weight, amask, aweight, ainit, region_masks, aconstraint, mask_self_only);
156 | }
157 | else if (vec_mode == VEC_MODE_SIM) {
158 | if (!ann_sim) { fprintf(stderr, "vecwrap_vote: expected argument ann_sim\n"); exit(1); }
159 | return sim_vote(p, b, ann, ann_sim, bnn, NULL, bmask, bweight, coherence_weight, complete_weight, amask, aweight, ainit, region_masks, aconstraint, mask_self_only);
160 | }
161 | else {
162 | fprintf(stderr, "vecwrap_vote: unknown mode %d\n", vec_mode); exit(1);
163 | }
164 | }
165 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/vecpatch.h:
--------------------------------------------------------------------------------
1 |
2 | /* Patch distance templates for vector input. */
3 |
4 | #ifndef _vecpatch_h
5 | #define _vecpatch_h
6 |
7 | #include "vecnn.h"
8 |
9 | template<class T, class ACCUM, int IS_WINDOW>
10 | ACCUM vec_fast_patch_dist(T *apatch, T *bpatch, ACCUM maxval, Params *p) {
11 | ACCUM ans = 0;
12 | for (int i = 0; i < p->vec_len; i++) {
13 | ACCUM d = ((ACCUM) apatch[i])-((ACCUM) bpatch[i]);
14 | ans += d*d;
15 | if (ans > maxval) { return ans; }
16 | }
17 | return ans;
18 | }
19 |
20 | template<class T, class ACCUM, int IS_MASK, int IS_WINDOW>
21 | void vec_attempt_n(ACCUM &err, int &xbest, int &ybest, T *apatch, VECBITMAP<T> *b, int bx, int by, BITMAP *bmask, RegionMasks *region_masks, int src_mask, Params *p) {
22 | if ((bx != xbest || by != ybest) &&
23 | (unsigned) bx < (unsigned) (b->w) &&
24 | (unsigned) by < (unsigned) (b->h))
25 | {
26 | if (IS_MASK && region_masks && src_mask != ((int *) region_masks->bmp->line[by])[bx]) { return; }
27 | if (IS_MASK && bmask && ((int *) bmask->line[by])[bx]) { return; }
28 | ACCUM current = vec_fast_patch_dist<T, ACCUM, IS_WINDOW>(apatch, b->get(bx, by), err, p);
29 | if (current < err) {
30 | err = current;
31 | xbest = bx;
32 | ybest = by;
33 | }
34 | }
35 | }
36 |
37 | template<class T, class ACCUM, int IS_WINDOW>
38 | ACCUM vec_fast_patch_nobranch(T *apatch, T *bpatch, Params *p) {
39 | ACCUM ans = 0;
40 | for (int i = 0; i < p->vec_len; i++) {
41 | ACCUM d = ((ACCUM) apatch[i])-((ACCUM) bpatch[i]);
42 | ans += d*d;
43 | }
44 | return ans;
45 | }
46 |
47 | template<class T, class ACCUM, int IS_WINDOW, int HAS_MASKS>
48 | ACCUM vec_patch_dist_ab(Params *p, VECBITMAP<T> *a, int ax, int ay, VECBITMAP<T> *b, int bx, int by, ACCUM maxval, RegionMasks *region_masks) {
49 | if (HAS_MASKS && region_masks && ((int *) region_masks->bmp->line[ay])[ax] != ((int *) region_masks->bmp->line[by])[bx])
50 | return get_maxval<ACCUM>();
51 | return vec_fast_patch_nobranch<T, ACCUM, IS_WINDOW>(a->get(ax, ay), b->get(bx, by), p);
52 | }
53 |
54 |
55 | // -----------------------------
56 | // XC version
57 | // -----------------------------
58 |
59 | template<class T, class ACCUM, int IS_WINDOW, int PATCH_W>
60 | ACCUM XCvec_fast_patch_nobranch(T *adata[PATCH_W][PATCH_W], VECBITMAP<T> *b, int bx, int by, Params *p)
61 | {
62 | ACCUM ans = 0;
63 |
64 | // aggregate a patch of vectors for b, assume we have trimed patches to possible choices
65 | T *bdata[PATCH_W][PATCH_W];
66 | for (int dy = 0; dy < PATCH_W; dy++) {
67 | for (int dx = 0; dx < PATCH_W; dx++) {
68 | bdata[dy][dx] = b->get(bx+dx, by+dy);
69 | }
70 | }
71 |
72 | // patch distance
73 | for (int dy = 0; dy < PATCH_W; dy++) {
74 | for (int dx = 0; dx < PATCH_W; dx++) {
75 | T *apatch = adata[dy][dx];
76 | T *bpatch = bdata[dy][dx];
77 | for(int i = 0; i < p->vec_len; i ++) {
78 | ACCUM d = ((ACCUM) apatch[i])-((ACCUM) bpatch[i]);
79 | ans += d*d;
80 | }
81 | }
82 | }
83 |
84 | return ans;
85 | }
86 |
87 |
88 | template<class T, class ACCUM, int IS_WINDOW, int PATCH_W>
89 | ACCUM XCvec_fast_patch_dist(T *adata[PATCH_W][PATCH_W], VECBITMAP<T> *b, int bx, int by, ACCUM maxval, Params *p) {
90 | ACCUM ans = 0;
91 |
92 | // aggregate a patch of vectors for b, assume we have trimed patches to possible choices
93 | T *bdata[PATCH_W][PATCH_W];
94 | for (int dy = 0; dy < PATCH_W; dy++) {
95 | for (int dx = 0; dx < PATCH_W; dx++) {
96 | bdata[dy][dx] = b->get(bx+dx, by+dy);
97 | }
98 | }
99 |
100 | // patch distance
101 | for (int dy = 0; dy < PATCH_W; dy++) {
102 | for (int dx = 0; dx < PATCH_W; dx++) {
103 | T *apatch = adata[dy][dx];
104 | T *bpatch = bdata[dy][dx];
105 | for(int i = 0; i < p->vec_len; i ++) {
106 | ACCUM d = ((ACCUM) apatch[i])-((ACCUM) bpatch[i]);
107 | ans += d*d;
108 | if (ans > maxval) { return ans; }
109 | }
110 | }
111 | }
112 |
113 | return ans;
114 | }
115 |
116 |
117 | template<class T, class ACCUM, int IS_MASK, int IS_WINDOW, int PATCH_W>
118 | void XCvec_attempt_n(ACCUM &err, int &xbest, int &ybest, T *adata[PATCH_W][PATCH_W], VECBITMAP<T> *b, int bx, int by, BITMAP *bmask, RegionMasks *region_masks, int src_mask, Params *p) {
119 | if ((bx != xbest || by != ybest) &&
120 | (unsigned) bx < (unsigned) (b->w-PATCH_W+1) &&
121 | (unsigned) by < (unsigned) (b->h-PATCH_W+1))
122 | {
123 | if (IS_MASK && region_masks && src_mask != ((int *) region_masks->bmp->line[by])[bx]) { return; }
124 | if (IS_MASK && bmask && ((int *) bmask->line[by])[bx]) { return; }
125 | ACCUM current = XCvec_fast_patch_dist<T, ACCUM, IS_WINDOW, PATCH_W>(adata, b, bx, by, err, p);
126 | if (current < err) {
127 | err = current;
128 | xbest = bx;
129 | ybest = by;
130 | }
131 | }
132 | }
133 |
134 |
135 | template<class T, class ACCUM, int IS_WINDOW, int HAS_MASKS, int PATCH_W>
136 | ACCUM XCvec_patch_dist_ab(Params *p, VECBITMAP<T> *a, int ax, int ay, VECBITMAP<T> *b, int bx, int by, ACCUM maxval, RegionMasks *region_masks) {
137 | if (HAS_MASKS && region_masks && ((int *) region_masks->bmp->line[ay])[ax] != ((int *) region_masks->bmp->line[by])[bx])
138 | return get_maxval<ACCUM>();
139 |
140 | // aggregate a patch of vectors for a, assume we have trimed patches to possible choices
141 | T *adata[PATCH_W][PATCH_W];
142 | for (int dy = 0; dy < PATCH_W; dy++) {
143 | for (int dx = 0; dx < PATCH_W; dx++) {
144 | adata[dy][dx] = a->get(ax+dx, ay+dy);
145 | }
146 | }
147 |
148 | return XCvec_fast_patch_nobranch<T, ACCUM, IS_WINDOW, PATCH_W>(adata, b, bx, by, p);
149 | }
150 |
151 |
152 | #endif
153 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/votemex.cpp:
--------------------------------------------------------------------------------
1 |
2 | // MATLAB interface, for vote().
3 |
4 | #include "allegro_emu.h"
5 | #include "mex.h"
6 | #include "nn.h"
7 | //#include "matrix.h"
8 | #include "mexutil.h"
9 |
10 | void mexFunction(int nout, mxArray *pout[], int nin, const mxArray *pin[]) {
11 | if (nin < 2) { mexErrMsgTxt("votemex called with < 2 input arguments"); }
12 | const mxArray *B = pin[0], *Ann = pin[1], *Bnn = NULL;
13 |
14 | BITMAP *b = convert_bitmap(B);
15 |
16 | Params *p = new Params();
17 | // [bnn=[]], [algo='cpu'], [patch_w=7], [bmask=[]], [bweight=[]], [coherence_weight=1], [complete_weight=1], [amask=[]], [aweight=[]]
18 | BITMAP *bmask = NULL, *bweight = NULL, *amask = NULL, *aweight = NULL, *ainit = NULL;
19 | double coherence_weight = 1, complete_weight = 1;
20 | int i = 2;
21 | if (nin > i && !mxIsEmpty(pin[i])) { Bnn = pin[i]; } i++;
22 | if (nin > i && !mxIsEmpty(pin[i])) {
23 | if (mxStringEquals(pin[i], "cpu")) { p->algo = ALGO_CPU; }
24 | else if (mxStringEquals(pin[i], "gpucpu")) { p->algo = ALGO_GPUCPU; }
25 | else if (mxStringEquals(pin[i], "cputiled")) { p->algo = ALGO_CPUTILED; }
26 | else { mexErrMsgTxt("Unknown algorithm"); }
27 | } i++;
28 | if (nin > i && !mxIsEmpty(pin[i])) { p->patch_w = int(mxGetScalar(pin[i])); } i++;
29 | if (nin > i && !mxIsEmpty(pin[i])) { bmask = convert_bitmap(pin[i]); } i++;
30 | if (nin > i && !mxIsEmpty(pin[i])) { bweight = convert_bitmapf(pin[i]); } i++;
31 | if (nin > i && !mxIsEmpty(pin[i])) { coherence_weight = mxGetScalar(pin[i]); } i++;
32 | if (nin > i && !mxIsEmpty(pin[i])) { complete_weight = mxGetScalar(pin[i]); } i++;
33 | if (nin > i && !mxIsEmpty(pin[i])) { amask = convert_bitmap(pin[i]); } i++;
34 | if (nin > i && !mxIsEmpty(pin[i])) { aweight = convert_bitmapf(pin[i]); } i++;
35 | if (nin > i && !mxIsEmpty(pin[i])) { ainit = convert_bitmap(pin[i]); } i++;
36 |
37 | int aclip = 0, bclip = 0;
38 | BITMAP *ann = convert_field(p, Ann, b->w, b->h, aclip);
39 | BITMAP *bnn = Bnn ? convert_field(p, Bnn, ann->w, ann->h, bclip): NULL;
40 |
41 | int nclip = aclip + bclip;
42 | //if (nclip) printf("Warning: clipped %d votes (%d a -> b, %d b -> a)\n", nclip, aclip, bclip);
43 |
44 | RegionMasks *amaskm = amask ? new RegionMasks(p, amask): NULL;
45 | //BITMAP *a = vote(p, b, ann, bnn, bmask, bweight, coherence_weight, complete_weight, amaskm, aweight, ainit);
46 | // David Jacobs -- Added mask_self_only as true for multiple.
47 | BITMAP *a = vote(p, b, ann, bnn, bmask, bweight, coherence_weight, complete_weight, amaskm, aweight, ainit, NULL, NULL, 1);
48 |
49 | destroy_region_masks(amaskm);
50 |
51 | if(nout >= 1) {
52 | mxArray *ans = bitmap_to_array(a);
53 | pout[0] = ans;
54 | }
55 |
56 | delete p;
57 | destroy_bitmap(a);
58 | destroy_bitmap(ainit);
59 | destroy_bitmap(b);
60 | destroy_bitmap(ann);
61 | destroy_bitmap(bnn);
62 | destroy_bitmap(bmask);
63 | destroy_bitmap(bweight);
64 | // destroy_bitmap(amask);
65 | destroy_bitmap(aweight);
66 | }
67 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/votemex.m:
--------------------------------------------------------------------------------
1 | %function A = votemex(B, ann, [bnn=[]], [algo='cpu'], [patch_w=7],
2 | %[bmask=[]], [bweight=[]], [coherence_weight=1], [complete_weight=1],
3 | %[amask=[]], [aweight=[]]), [A0=[]]
4 | %
5 | %
6 | %Input image B is hxwx3, floats in [0, 1] or uint8.
7 | %Input fields are:
8 | % ann = nnmex(A, B)
9 | % bnn = nnmex(B, A)
10 | %If bnn unspecified, uses coherence only.
11 | %
12 | %Options are:
13 | %algo - One of 'cpu', 'cputiled'
14 | %patch_w - Width (and height) of patch, currently support sizes up to 32
15 | %bmask - B mask image, votes made only where mask is zero
16 | % (affects both 'coherence' and 'completeness'
17 | %bweight - B weight image, weights are 32 bit floats, and
18 | % correspond to the *center* of the patch
19 | %coherence_weight - Weight multiplier for coherence
20 | %complete_weight - Weight multiplier for completeness
21 | %amask - A mask image, votes made only where mask is zero
22 | % (affects only 'coherence')
23 | %aweight - A weight image, if not given uses inverse mapping
24 | % and B weight image. Corresponds to the *center* of the
25 | % patch.
26 | %A0 - Initial guess for A. It is used as default in regions
27 | % with no votes - outside the output mask 'amask' and
28 | % with no votes from the input.
29 | %
30 | %NOTE: all NN fields and masks correspond to the upper-left corner of the
31 | %patches, and the field indeces are in C coordinates (start with [0,0]),
32 | %wheras the weight maps correspond to the *center* of the patch.
33 | %
34 | %
35 | %------------------------------------------------------------------------%
36 | % Copyright 2008-2009 Adobe Systems Inc., for noncommercial use only.
37 | % Citation:
38 | % Connelly Barnes, Eli Shechtman, Adam Finkelstein, and Dan B Goldman.
39 | % PatchMatch: A Randomized Correspondence Algorithm for Structural Image
40 | % Editing. ACM Transactions on Graphics (Proc. SIGGRAPH), 28(3), 2009
41 | % http://www.cs.princeton.edu/gfx/pubs/Barnes_2009_PAR/
42 | % Main contact: csbarnes@cs.princeton.edu (Connelly)
43 | % Version: 1.0, 21-June-2008
44 | %------------------------------------------------------------------------%
45 |
46 |
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/votemex.mexa64:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/votemex.mexa64
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/votemex.mexw64:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/3rdparty/patchmatch-2.0/votemex.mexw64
--------------------------------------------------------------------------------
/3rdparty/patchmatch-2.0/writeim.m:
--------------------------------------------------------------------------------
1 | function out = writeim(A, filename)
2 |
3 | a = min(A(:));
4 | b = max(A(:));
5 | A = uint8((A-a)*(255/(b-a)));
6 | imwrite(A, filename);
7 |
--------------------------------------------------------------------------------
/README2.md:
--------------------------------------------------------------------------------
1 |
2 | ## Disclaimer
3 | **We don't own the copyright of some images below. Use with caution.**
4 |
5 | ## Examples
6 | Here are some results from our algorithm (from left to right are original painting, naive composite and our output):
7 | <p align='center'>
8 | <img src='data/0_target.jpg' width='290'/>
9 | <img src='data/0_naive.jpg' width='290'/>
10 | <img src='results/0_final_res2.png' width='290'/>
11 | </p>
12 | <p align='center'>
13 | <img src='data/1_target.jpg' width='290'/>
14 | <img src='data/1_naive.jpg' width='290'/>
15 | <img src='results/1_final_res2.png' width='290'/>
16 | </p>
17 | <p align='center'>
18 | <img src='data/2_target.jpg' width='290'/>
19 | <img src='data/2_naive.jpg' width='290'/>
20 | <img src='results/2_final_res2.png' width='290'/>
21 | </p>
22 | <p align='center'>
23 | <img src='data/3_target.jpg' width='290'/>
24 | <img src='data/3_naive.jpg' width='290'/>
25 | <img src='results/3_final_res2.png' width='290'/>
26 | </p>
27 | <p align='center'>
28 | <img src='data/4_target.jpg' width='290'/>
29 | <img src='data/4_naive.jpg' width='290'/>
30 | <img src='results/4_final_res2.png' width='290'/>
31 | </p>
32 | <p align='center'>
33 | <img src='data/5_target.jpg' width='290'/>
34 | <img src='data/5_naive.jpg' width='290'/>
35 | <img src='results/5_final_res2.png' width='290'/>
36 | </p>
37 | <p align='center'>
38 | <img src='data/6_target.jpg' width='290'/>
39 | <img src='data/6_naive.jpg' width='290'/>
40 | <img src='results/6_final_res2.png' width='290'/>
41 | </p>
42 |
43 | <p align='center'>
44 | <img src='data/7_target.jpg' width='290'/>
45 | <img src='data/7_naive.jpg' width='290'/>
46 | <img src='results/7_final_res2.png' width='290'/>
47 | </p>
48 |
49 | <p align='center'>
50 | <img src='results/7_target.jpg' width='290'/>
51 | <img src='results/7_naive_popeye.png' width='290'/>
52 | <img src='results/7_result_popeye.jpg' width='290'/>
53 | </p>
54 |
55 |
56 | <p align='center'>
57 | <img src='data/8_target.jpg' width='290'/>
58 | <img src='data/8_naive.jpg' width='290'/>
59 | <img src='results/8_final_res.png' width='290'/>
60 | </p>
61 | <p align='center'>
62 | <img src='data/8_target.jpg' width='290'/>
63 | <img src='results/8_naive_balloon.jpg' width='290'/>
64 | <img src='results/8_result_balloon.jpg' width='290'/>
65 | </p>
66 |
67 |
68 | <p align='center'>
69 | <img src='data/8_target.jpg' width='290'/>
70 | <img src='results/8_naive_aircraft.png' width='290'/>
71 | <img src='results/8_result_aircraft.png' width='290'/>
72 | </p>
73 |
74 |
75 | <p align='center'>
76 | <img src='data/9_target.jpg' width='290'/>
77 | <img src='data/9_naive.jpg' width='290'/>
78 | <img src='results/9_final_res2.png' width='290'/>
79 | </p>
80 |
81 | <p align='center'>
82 | <img src='data/9_target.jpg' width='290'/>
83 | <img src='results/9_naive_dragon.png' width='290'/>
84 | <img src='results/9_result_dragon.jpg' width='290'/>
85 | </p>
86 |
87 | <p align='center'>
88 | <img src='data/10_target.jpg' width='290'/>
89 | <img src='data/10_naive.jpg' width='290'/>
90 | <img src='results/10_final_res2.png' width='290'/>
91 | </p>
92 | <p align='center'>
93 | <img src='data/11_target.jpg' width='290'/>
94 | <img src='data/11_naive.jpg' width='290'/>
95 | <img src='results/11_final_res2.png' width='290'/>
96 | </p>
97 | <p align='center'>
98 | <img src='data/12_target.jpg' width='290'/>
99 | <img src='data/12_naive.jpg' width='290'/>
100 | <img src='results/12_final_res2.png' width='290'/>
101 | </p>
102 | <p align='center'>
103 | <img src='data/13_target.jpg' width='290'/>
104 | <img src='data/13_naive.jpg' width='290'/>
105 | <img src='results/13_final_res2.png' width='290'/>
106 | </p>
107 | <p align='center'>
108 | <img src='data/14_target.jpg' width='290'/>
109 | <img src='data/14_naive.jpg' width='290'/>
110 | <img src='results/14_final_res2.png' width='290'/>
111 | </p>
112 | <p align='center'>
113 | <img src='data/15_target.jpg' width='290'/>
114 | <img src='data/15_naive.jpg' width='290'/>
115 | <img src='results/15_final_res2.png' width='290'/>
116 | </p>
117 | <p align='center'>
118 | <img src='data/16_target.jpg' width='290'/>
119 | <img src='data/16_naive.jpg' width='290'/>
120 | <img src='results/16_final_res2.png' width='290'/>
121 | </p>
122 |
123 |
124 | <p align='center'>
125 | <img src='data/17_target.jpg' width='290'/>
126 | <img src='data/17_naive.jpg' width='290'/>
127 | <img src='results/17_final_res2.png' width='290'/>
128 | </p>
129 |
130 |
131 |
132 | <p align='center'>
133 | <img src='data/17_target.jpg' width='290'/>
134 | <img src='results/17_naive_sherlock.jpg' width='290'/>
135 | <img src='results/17_result_sherlock.jpg' width='290'/>
136 | </p>
137 | <p align='center'>
138 | <img src='data/18_target.jpg' width='290'/>
139 | <img src='data/18_naive.jpg' width='290'/>
140 | <img src='results/18_final_res2.png' width='290'/>
141 | </p>
142 | <p align='center'>
143 | <img src='data/19_target.jpg' width='290'/>
144 | <img src='data/19_naive.jpg' width='290'/>
145 | <img src='results/19_final_res2.png' width='290'/>
146 | </p>
147 | <p align='center'>
148 | <img src='data/20_target.jpg' width='290'/>
149 | <img src='data/20_naive.jpg' width='290'/>
150 | <img src='results/20_final_res2.png' width='290'/>
151 | </p>
152 | <p align='center'>
153 | <img src='data/21_target.jpg' width='290'/>
154 | <img src='data/21_naive.jpg' width='290'/>
155 | <img src='results/21_final_res2.png' width='290'/>
156 | </p>
157 | <p align='center'>
158 | <img src='data/22_target.jpg' width='290'/>
159 | <img src='data/22_naive.jpg' width='290'/>
160 | <img src='results/22_final_res2.png' width='290'/>
161 | </p>
162 | <p align='center'>
163 | <img src='data/23_target.jpg' width='290'/>
164 | <img src='data/23_naive.jpg' width='290'/>
165 | <img src='results/23_final_res2.png' width='290'/>
166 | </p>
167 | <p align='center'>
168 | <img src='data/24_target.jpg' width='290'/>
169 | <img src='data/24_naive.jpg' width='290'/>
170 | <img src='results/24_final_res2.png' width='290'/>
171 | </p>
172 | <p align='center'>
173 | <img src='data/25_target.jpg' width='290'/>
174 | <img src='data/25_naive.jpg' width='290'/>
175 | <img src='results/25_final_res2.png' width='290'/>
176 | </p>
177 | <p align='center'>
178 | <img src='data/26_target.jpg' width='290'/>
179 | <img src='data/26_naive.jpg' width='290'/>
180 | <img src='results/26_final_res2.png' width='290'/>
181 | </p>
182 | <p align='center'>
183 | <img src='data/27_target.jpg' width='290'/>
184 | <img src='data/27_naive.jpg' width='290'/>
185 | <img src='results/27_final_res2.png' width='290'/>
186 | </p>
187 |
188 |
189 | <p align='center'>
190 | <img src='data/28_target.jpg' width='290'/>
191 | <img src='data/28_naive.jpg' width='290'/>
192 | <img src='results/28_final_res2.png' width='290'/>
193 | </p>
194 |
195 | <p align='center'>
196 | <img src='data/28_target.jpg' width='290'/>
197 | <img src='results/28_naive_cow.png' width='290'/>
198 | <img src='results/28_result_cow.jpg' width='290'/>
199 | </p>
200 |
201 | <p align='center'>
202 | <img src='data/29_target.jpg' width='290'/>
203 | <img src='data/29_naive.jpg' width='290'/>
204 | <img src='results/29_final_res2.png' width='290'/>
205 | </p>
206 | <p align='center'>
207 | <img src='data/30_target.jpg' width='290'/>
208 | <img src='data/30_naive.jpg' width='290'/>
209 | <img src='results/30_final_res2.png' width='290'/>
210 | </p>
211 | <p align='center'>
212 | <img src='data/31_target.jpg' width='290'/>
213 | <img src='data/31_naive.jpg' width='290'/>
214 | <img src='results/31_final_res2.png' width='290'/>
215 | </p>
216 | <p align='center'>
217 | <img src='data/32_target.jpg' width='290'/>
218 | <img src='data/32_naive.jpg' width='290'/>
219 | <img src='results/32_final_res2.png' width='290'/>
220 | </p>
221 |
222 | <p align='center'>
223 | <img src='data/35_target.jpg' width='290'/>
224 | <img src='results/35_naive.jpg' width='290'/>
225 | <img src='results/35_result.png' width='290'/>
226 | </p>
227 |
228 |
229 | <p align='center'>
230 | <img src='data/33_target.jpg' width='290'/>
231 | <img src='results/33_naive_buddha.png' width='290'/>
232 | <img src='results/33_result_buddha.jpg' width='290'/>
233 | </p>
234 |
235 | <p align='center'>
236 | <img src='data/33_target.jpg' width='290'/>
237 | <img src='data/33_naive.jpg' width='290'/>
238 | <img src='results/33_final_res2.png' width='290'/>
239 | </p>
240 |
241 | <p align='center'>
242 | <img src='data/34_target.jpg' width='290'/>
243 | <img src='data/34_naive.jpg' width='290'/>
244 | <img src='results/34_final_res2.png' width='290'/>
245 | </p>
246 |
--------------------------------------------------------------------------------
/data/0_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/0_c_mask.jpg
--------------------------------------------------------------------------------
/data/0_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/0_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/0_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/0_naive.jpg
--------------------------------------------------------------------------------
/data/0_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/0_target.jpg
--------------------------------------------------------------------------------
/data/10_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/10_c_mask.jpg
--------------------------------------------------------------------------------
/data/10_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/10_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/10_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/10_naive.jpg
--------------------------------------------------------------------------------
/data/10_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/10_target.jpg
--------------------------------------------------------------------------------
/data/11_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/11_c_mask.jpg
--------------------------------------------------------------------------------
/data/11_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/11_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/11_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/11_naive.jpg
--------------------------------------------------------------------------------
/data/11_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/11_target.jpg
--------------------------------------------------------------------------------
/data/12_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/12_c_mask.jpg
--------------------------------------------------------------------------------
/data/12_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/12_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/12_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/12_naive.jpg
--------------------------------------------------------------------------------
/data/12_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/12_target.jpg
--------------------------------------------------------------------------------
/data/13_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/13_c_mask.jpg
--------------------------------------------------------------------------------
/data/13_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/13_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/13_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/13_naive.jpg
--------------------------------------------------------------------------------
/data/13_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/13_target.jpg
--------------------------------------------------------------------------------
/data/14_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/14_c_mask.jpg
--------------------------------------------------------------------------------
/data/14_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/14_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/14_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/14_naive.jpg
--------------------------------------------------------------------------------
/data/14_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/14_target.jpg
--------------------------------------------------------------------------------
/data/15_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/15_c_mask.jpg
--------------------------------------------------------------------------------
/data/15_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/15_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/15_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/15_naive.jpg
--------------------------------------------------------------------------------
/data/15_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/15_target.jpg
--------------------------------------------------------------------------------
/data/16_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/16_c_mask.jpg
--------------------------------------------------------------------------------
/data/16_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/16_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/16_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/16_naive.jpg
--------------------------------------------------------------------------------
/data/16_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/16_target.jpg
--------------------------------------------------------------------------------
/data/17_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/17_c_mask.jpg
--------------------------------------------------------------------------------
/data/17_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/17_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/17_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/17_naive.jpg
--------------------------------------------------------------------------------
/data/17_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/17_target.jpg
--------------------------------------------------------------------------------
/data/18_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/18_c_mask.jpg
--------------------------------------------------------------------------------
/data/18_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/18_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/18_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/18_naive.jpg
--------------------------------------------------------------------------------
/data/18_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/18_target.jpg
--------------------------------------------------------------------------------
/data/19_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/19_c_mask.jpg
--------------------------------------------------------------------------------
/data/19_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/19_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/19_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/19_naive.jpg
--------------------------------------------------------------------------------
/data/19_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/19_target.jpg
--------------------------------------------------------------------------------
/data/1_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/1_c_mask.jpg
--------------------------------------------------------------------------------
/data/1_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/1_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/1_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/1_naive.jpg
--------------------------------------------------------------------------------
/data/1_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/1_target.jpg
--------------------------------------------------------------------------------
/data/20_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/20_c_mask.jpg
--------------------------------------------------------------------------------
/data/20_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/20_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/20_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/20_naive.jpg
--------------------------------------------------------------------------------
/data/20_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/20_target.jpg
--------------------------------------------------------------------------------
/data/21_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/21_c_mask.jpg
--------------------------------------------------------------------------------
/data/21_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/21_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/21_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/21_naive.jpg
--------------------------------------------------------------------------------
/data/21_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/21_target.jpg
--------------------------------------------------------------------------------
/data/22_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/22_c_mask.jpg
--------------------------------------------------------------------------------
/data/22_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/22_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/22_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/22_naive.jpg
--------------------------------------------------------------------------------
/data/22_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/22_target.jpg
--------------------------------------------------------------------------------
/data/23_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/23_c_mask.jpg
--------------------------------------------------------------------------------
/data/23_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/23_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/23_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/23_naive.jpg
--------------------------------------------------------------------------------
/data/23_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/23_target.jpg
--------------------------------------------------------------------------------
/data/24_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/24_c_mask.jpg
--------------------------------------------------------------------------------
/data/24_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/24_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/24_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/24_naive.jpg
--------------------------------------------------------------------------------
/data/24_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/24_target.jpg
--------------------------------------------------------------------------------
/data/25_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/25_c_mask.jpg
--------------------------------------------------------------------------------
/data/25_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/25_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/25_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/25_naive.jpg
--------------------------------------------------------------------------------
/data/25_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/25_target.jpg
--------------------------------------------------------------------------------
/data/26_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/26_c_mask.jpg
--------------------------------------------------------------------------------
/data/26_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/26_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/26_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/26_naive.jpg
--------------------------------------------------------------------------------
/data/26_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/26_target.jpg
--------------------------------------------------------------------------------
/data/27_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/27_c_mask.jpg
--------------------------------------------------------------------------------
/data/27_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/27_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/27_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/27_naive.jpg
--------------------------------------------------------------------------------
/data/27_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/27_target.jpg
--------------------------------------------------------------------------------
/data/28_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/28_c_mask.jpg
--------------------------------------------------------------------------------
/data/28_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/28_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/28_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/28_naive.jpg
--------------------------------------------------------------------------------
/data/28_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/28_target.jpg
--------------------------------------------------------------------------------
/data/29_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/29_c_mask.jpg
--------------------------------------------------------------------------------
/data/29_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/29_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/29_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/29_naive.jpg
--------------------------------------------------------------------------------
/data/29_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/29_target.jpg
--------------------------------------------------------------------------------
/data/2_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/2_c_mask.jpg
--------------------------------------------------------------------------------
/data/2_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/2_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/2_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/2_naive.jpg
--------------------------------------------------------------------------------
/data/2_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/2_target.jpg
--------------------------------------------------------------------------------
/data/30_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/30_c_mask.jpg
--------------------------------------------------------------------------------
/data/30_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/30_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/30_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/30_naive.jpg
--------------------------------------------------------------------------------
/data/30_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/30_target.jpg
--------------------------------------------------------------------------------
/data/31_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/31_c_mask.jpg
--------------------------------------------------------------------------------
/data/31_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/31_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/31_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/31_naive.jpg
--------------------------------------------------------------------------------
/data/31_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/31_target.jpg
--------------------------------------------------------------------------------
/data/32_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/32_c_mask.jpg
--------------------------------------------------------------------------------
/data/32_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/32_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/32_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/32_naive.jpg
--------------------------------------------------------------------------------
/data/32_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/32_target.jpg
--------------------------------------------------------------------------------
/data/33_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/33_c_mask.jpg
--------------------------------------------------------------------------------
/data/33_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/33_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/33_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/33_naive.jpg
--------------------------------------------------------------------------------
/data/33_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/33_target.jpg
--------------------------------------------------------------------------------
/data/34_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/34_c_mask.jpg
--------------------------------------------------------------------------------
/data/34_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/34_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/34_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/34_naive.jpg
--------------------------------------------------------------------------------
/data/34_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/34_target.jpg
--------------------------------------------------------------------------------
/data/35_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/35_target.jpg
--------------------------------------------------------------------------------
/data/3_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/3_c_mask.jpg
--------------------------------------------------------------------------------
/data/3_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/3_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/3_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/3_naive.jpg
--------------------------------------------------------------------------------
/data/3_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/3_target.jpg
--------------------------------------------------------------------------------
/data/4_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/4_c_mask.jpg
--------------------------------------------------------------------------------
/data/4_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/4_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/4_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/4_naive.jpg
--------------------------------------------------------------------------------
/data/4_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/4_target.jpg
--------------------------------------------------------------------------------
/data/5_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/5_c_mask.jpg
--------------------------------------------------------------------------------
/data/5_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/5_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/5_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/5_naive.jpg
--------------------------------------------------------------------------------
/data/5_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/5_target.jpg
--------------------------------------------------------------------------------
/data/6_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/6_c_mask.jpg
--------------------------------------------------------------------------------
/data/6_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/6_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/6_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/6_naive.jpg
--------------------------------------------------------------------------------
/data/6_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/6_target.jpg
--------------------------------------------------------------------------------
/data/7_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/7_c_mask.jpg
--------------------------------------------------------------------------------
/data/7_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/7_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/7_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/7_naive.jpg
--------------------------------------------------------------------------------
/data/7_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/7_target.jpg
--------------------------------------------------------------------------------
/data/8_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/8_c_mask.jpg
--------------------------------------------------------------------------------
/data/8_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/8_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/8_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/8_naive.jpg
--------------------------------------------------------------------------------
/data/8_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/8_target.jpg
--------------------------------------------------------------------------------
/data/9_c_mask.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/9_c_mask.jpg
--------------------------------------------------------------------------------
/data/9_c_mask_dilated.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/9_c_mask_dilated.jpg
--------------------------------------------------------------------------------
/data/9_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/9_naive.jpg
--------------------------------------------------------------------------------
/data/9_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/data/9_target.jpg
--------------------------------------------------------------------------------
/data/wikiart_output.txt:
--------------------------------------------------------------------------------
1 | idx=0, classified label=Art Nouveau (Modern), weight=1.203930
2 | idx=1, classified label=Impressionism, weight=5.323582
3 | idx=2, classified label=Impressionism, weight=4.234591
4 | idx=3, classified label=Post-Impressionism, weight=5.498451
5 | idx=4, classified label=Impressionism, weight=4.910163
6 | idx=5, classified label=Cubism, weight=9.982656
7 | idx=6, classified label=Post-Impressionism, weight=5.731922
8 | idx=7, classified label=Expressionism, weight=9.862515
9 | idx=8, classified label=Post-Impressionism, weight=5.019980
10 | idx=9, classified label=Expressionism, weight=8.189465
11 | idx=10, classified label=Art Nouveau (Modern), weight=1.340035
12 | idx=11, classified label=Expressionism, weight=9.331765
13 | idx=12, classified label=Post-Impressionism, weight=7.318527
14 | idx=13, classified label=Post-Impressionism, weight=5.106254
15 | idx=14, classified label=Symbolism, weight=1.295220
16 | idx=15, classified label=Baroque, weight=1.001454
17 | idx=16, classified label=Impressionism, weight=1.018541
18 | idx=17, classified label=High Renaissance, weight=1.000000
19 | idx=18, classified label=Impressionism, weight=4.999322
20 | idx=19, classified label=Mannerism (Late Renaissance), weight=1.002947
21 | idx=20, classified label=Romanticism, weight=1.000424
22 | idx=21, classified label=Post-Impressionism, weight=4.953209
23 | idx=22, classified label=Impressionism, weight=4.997079
24 | idx=23, classified label=Expressionism, weight=9.995778
25 | idx=24, classified label=Impressionism, weight=4.800576
26 | idx=25, classified label=Post-Impressionism, weight=5.062284
27 | idx=26, classified label=Realism, weight=1.001287
28 | idx=27, classified label=Post-Impressionism, weight=5.000851
29 | idx=28, classified label=Impressionism, weight=4.977097
30 | idx=29, classified label=Romanticism, weight=2.303986
31 | idx=30, classified label=Post-Impressionism, weight=5.017763
32 | idx=31, classified label=Impressionism, weight=4.983986
33 | idx=32, classified label=Post-Impressionism, weight=4.999594
34 | idx=33, classified label=Realism, weight=1.512768
35 | idx=34, classified label=Baroque, weight=1.036211
36 |
--------------------------------------------------------------------------------
/dilate_mask.m:
--------------------------------------------------------------------------------
1 | for i = 0 : 34
2 | close all;
3 | fn = [int2str(i) '_c_mask.png']
4 | I = im2double(imread(fn));
5 | [h w c] = size(I);
6 | if c == 3
7 | I = I(:,:,1);
8 | end
9 |
10 | h1 = h;
11 | w1 = w;
12 | if h ~= 700 && w ~= 700
13 | if h > w
14 | h1 = 700;
15 | w1 = floor(h1 * w/h);
16 | else
17 | w1 = 700;
18 | h1 = floor(w1 * h/w);
19 | end
20 | end
21 |
22 | figure; imshow(I)
23 | r = 35;
24 | h = fspecial('gaussian', [r r], r/3);
25 | J = imfilter(I, h, 'same');
26 |
27 | figure; imshow(J)
28 | J2 = J;
29 | J2(J>0.1) = 1;
30 | J2(J<=0.1) = 0;
31 | figure; imshow(J2)
32 | imwrite(J2, [int2str(i) '_c_mask_dilated.png']);
33 | end
--------------------------------------------------------------------------------
/filt_cnn_artifact.m:
--------------------------------------------------------------------------------
1 |
2 | for i = 0:34
3 | close all
4 |
5 | in_fn = ['results/' int2str(i) '_final_res.png'];
6 | out_fn = ['results/' int2str(i) '_final_res2.png'];
7 |
8 | if exist(in_fn, 'file') ~= 2
9 | fprintf('file doesn''t exist: %s\n', in_fn);
10 | continue
11 | end
12 | if exist(out_fn, 'file') == 2
13 | fprintf('result already exists: %s\n', out_fn);
14 | continue
15 | end
16 |
17 | I = im2double(imread(in_fn));
18 |
19 | G = im2double(imread(['data/' int2str(i) '_naive.jpg']));
20 | M = im2double(imread(['data/' int2str(i) '_c_mask.jpg']));
21 | B = im2double(imread(['data/' int2str(i) '_target.jpg']));
22 |
23 |
24 | tr= 3;
25 | h = fspecial('gaussian', [2*tr+1 2*tr+1], tr);
26 | sM = imfilter(M, h, 'same');
27 | sM(sM > 0.01) = 1; % dialte
28 | sM(sM < 0.01) = 0;
29 | sM = imfilter(sM, h, 'same');
30 |
31 | addpath 3rdparty/colorspace
32 | I_lab = colorspace('rgb->lab', I);
33 |
34 | addpath 3rdparty/guided_filter
35 | addpath 3rdparty/patchmatch-2.0
36 |
37 | r = 2; % try r=2, 4, or 8
38 | eps = 0.1^2; % try eps=0.1^2, 0.2^2, 0.4^2
39 |
40 | O_lab = I_lab;
41 | O_lab(:,:,2) = guidedfilter_color(G, I_lab(:,:,2), r, eps);
42 | O_lab(:,:,3) = guidedfilter_color(G, I_lab(:,:,3), r, eps);
43 |
44 | % runs here, deconvolution CNN artifact removed
45 | O1 = colorspace('lab->rgb', O_lab);
46 | % one patchmatch pass to further remove color artifact
47 | cores = 4;
48 | algo = 'cputiled';
49 | patch_w = 7;
50 | ann = nnmex(O1, B, algo, patch_w, [], [], [], [], [], cores);
51 | O2_base = im2double(votemex(B, ann));
52 |
53 | r = 3;
54 | h = fspecial('gaussian', [2*r+1 2*r+1], r/3);
55 | O1_base = imfilter(O1, h, 'same');
56 | O2 = O2_base + O1 - O1_base;
57 |
58 | O2 = O2.*sM + B.*(1-sM);
59 | figure; imshow(I)
60 | figure; imshow(O2)
61 |
62 | imwrite(O2, out_fn);
63 | end
64 |
65 |
66 |
--------------------------------------------------------------------------------
/gen_all.py:
--------------------------------------------------------------------------------
1 | import os
2 | import math
3 |
4 | numImgs = 35
5 | # numGpus = 16
6 | numGpus = 1
7 |
8 | if os.path.exists('results') == 0:
9 | os.mkdir('results')
10 |
11 | N = int(math.ceil(float(numImgs)/numGpus))
12 |
13 | for j in range(1, numGpus+1):
14 | cmd = ''
15 | for i in range(1, N+1):
16 | idx = (i-1) * numGpus + (j-1)
17 | if idx >= 0 and idx < numImgs:
18 | print('Working on image idx = ', idx)
19 | part_cmd1 =' th neural_gram.lua '\
20 | ' -content_image data/' + str(idx) + '_naive.jpg '\
21 | ' -style_image data/' + str(idx) + '_target.jpg '\
22 | ' -tmask_image data/' + str(idx) + '_c_mask.jpg '\
23 | ' -mask_image data/' + str(idx) + '_c_mask_dilated.jpg '\
24 | ' -gpu ' + str(j-1) + ' -original_colors 0 -image_size 700 '\
25 | ' -output_image results/' + str(idx) + '_inter_res.jpg'\
26 | ' -print_iter 100 -save_iter 100 && '
27 | part_cmd2 =' th neural_paint.lua '\
28 | ' -content_image data/' + str(idx) + '_naive.jpg '\
29 | ' -style_image data/' + str(idx) + '_target.jpg '\
30 | ' -tmask_image data/' + str(idx) + '_c_mask.jpg '\
31 | ' -mask_image data/' + str(idx) + '_c_mask_dilated.jpg '\
32 | ' -cnnmrf_image results/' + str(idx) + '_inter_res.jpg '\
33 | ' -gpu ' + str(j-1) + ' -original_colors 0 -image_size 700 '\
34 | ' -index ' + str(idx) + ' -wikiart_fn data/wikiart_output.txt '\
35 | ' -output_image results/' + str(idx) + '_final_res.jpg' \
36 | ' -print_iter 100 -save_iter 100 '\
37 | ' -num_iterations 1000 &&'
38 | cmd = cmd + part_cmd1 + part_cmd2
39 | cmd = cmd[1:len(cmd)-1]
40 | print(cmd)
41 | os.system(cmd)
42 |
--------------------------------------------------------------------------------
/makefile:
--------------------------------------------------------------------------------
1 | # Modify PREFIX and NVCC_PREFIX based on your machine environment
2 | PREFIX=/home/ubuntu/torch/install
3 | NVCC_PREFIX=/usr/local/cuda-8.0/bin
4 | CFLAGS=-I$(PREFIX)/include/THC -I$(PREFIX)/include/TH -I$(PREFIX)/include
5 | LDFLAGS_NVCC=-L$(PREFIX)/lib -Xlinker -rpath,$(PREFIX)/lib -lluaT -lTHC -lTH -lpng -lluajit -lgomp
6 |
7 | all: libcuda_utils.so
8 |
9 | libcuda_utils.so: cuda_utils.cu
10 | $(NVCC_PREFIX)/nvcc -arch sm_35 -O3 -DNDEBUG -Xcompiler -fopenmp --compiler-options '-fPIC' -o libcuda_utils.so --shared cuda_utils.cu $(CFLAGS) $(LDFLAGS_NVCC)
11 |
12 | clean:
13 | find . -type f | xargs -n 5 touch
14 | rm -f libcuda_utils.so
15 |
--------------------------------------------------------------------------------
/models/download_models.sh:
--------------------------------------------------------------------------------
1 | cd models
2 | wget -c https://gist.githubusercontent.com/ksimonyan/3785162f95cd2d5fee77/raw/bb2b4fe0a9bb0669211cf3d0bc949dfdda173e9e/VGG_ILSVRC_19_layers_deploy.prototxt
3 | wget -c --no-check-certificate https://bethgelab.org/media/uploads/deeptextures/vgg_normalised.caffemodel
4 | wget -c http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel
5 | cd ..
6 |
--------------------------------------------------------------------------------
/results/0_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/0_final_res.png
--------------------------------------------------------------------------------
/results/0_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/0_final_res2.png
--------------------------------------------------------------------------------
/results/0_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/0_inter_res.jpg
--------------------------------------------------------------------------------
/results/10_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/10_final_res.png
--------------------------------------------------------------------------------
/results/10_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/10_final_res2.png
--------------------------------------------------------------------------------
/results/10_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/10_inter_res.jpg
--------------------------------------------------------------------------------
/results/11_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/11_final_res.png
--------------------------------------------------------------------------------
/results/11_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/11_final_res2.png
--------------------------------------------------------------------------------
/results/11_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/11_inter_res.jpg
--------------------------------------------------------------------------------
/results/12_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/12_final_res.png
--------------------------------------------------------------------------------
/results/12_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/12_final_res2.png
--------------------------------------------------------------------------------
/results/12_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/12_inter_res.jpg
--------------------------------------------------------------------------------
/results/13_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/13_final_res.png
--------------------------------------------------------------------------------
/results/13_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/13_final_res2.png
--------------------------------------------------------------------------------
/results/13_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/13_inter_res.jpg
--------------------------------------------------------------------------------
/results/14_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/14_final_res.png
--------------------------------------------------------------------------------
/results/14_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/14_final_res2.png
--------------------------------------------------------------------------------
/results/14_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/14_inter_res.jpg
--------------------------------------------------------------------------------
/results/15_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/15_final_res.png
--------------------------------------------------------------------------------
/results/15_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/15_final_res2.png
--------------------------------------------------------------------------------
/results/15_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/15_inter_res.jpg
--------------------------------------------------------------------------------
/results/16_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/16_final_res.png
--------------------------------------------------------------------------------
/results/16_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/16_final_res2.png
--------------------------------------------------------------------------------
/results/16_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/16_inter_res.jpg
--------------------------------------------------------------------------------
/results/17_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/17_final_res.png
--------------------------------------------------------------------------------
/results/17_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/17_final_res2.png
--------------------------------------------------------------------------------
/results/17_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/17_inter_res.jpg
--------------------------------------------------------------------------------
/results/17_naive_sherlock.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/17_naive_sherlock.jpg
--------------------------------------------------------------------------------
/results/17_result_sherlock.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/17_result_sherlock.jpg
--------------------------------------------------------------------------------
/results/18_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/18_final_res.png
--------------------------------------------------------------------------------
/results/18_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/18_final_res2.png
--------------------------------------------------------------------------------
/results/18_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/18_inter_res.jpg
--------------------------------------------------------------------------------
/results/19_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/19_final_res.png
--------------------------------------------------------------------------------
/results/19_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/19_final_res2.png
--------------------------------------------------------------------------------
/results/19_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/19_inter_res.jpg
--------------------------------------------------------------------------------
/results/1_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/1_final_res.png
--------------------------------------------------------------------------------
/results/1_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/1_final_res2.png
--------------------------------------------------------------------------------
/results/1_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/1_inter_res.jpg
--------------------------------------------------------------------------------
/results/20_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/20_final_res.png
--------------------------------------------------------------------------------
/results/20_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/20_final_res2.png
--------------------------------------------------------------------------------
/results/20_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/20_inter_res.jpg
--------------------------------------------------------------------------------
/results/21_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/21_final_res.png
--------------------------------------------------------------------------------
/results/21_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/21_final_res2.png
--------------------------------------------------------------------------------
/results/21_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/21_inter_res.jpg
--------------------------------------------------------------------------------
/results/22_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/22_final_res.png
--------------------------------------------------------------------------------
/results/22_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/22_final_res2.png
--------------------------------------------------------------------------------
/results/22_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/22_inter_res.jpg
--------------------------------------------------------------------------------
/results/23_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/23_final_res.png
--------------------------------------------------------------------------------
/results/23_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/23_final_res2.png
--------------------------------------------------------------------------------
/results/23_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/23_inter_res.jpg
--------------------------------------------------------------------------------
/results/24_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/24_final_res.png
--------------------------------------------------------------------------------
/results/24_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/24_final_res2.png
--------------------------------------------------------------------------------
/results/24_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/24_inter_res.jpg
--------------------------------------------------------------------------------
/results/25_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/25_final_res.png
--------------------------------------------------------------------------------
/results/25_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/25_final_res2.png
--------------------------------------------------------------------------------
/results/25_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/25_inter_res.jpg
--------------------------------------------------------------------------------
/results/26_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/26_final_res.png
--------------------------------------------------------------------------------
/results/26_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/26_final_res2.png
--------------------------------------------------------------------------------
/results/26_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/26_inter_res.jpg
--------------------------------------------------------------------------------
/results/27_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/27_final_res.png
--------------------------------------------------------------------------------
/results/27_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/27_final_res2.png
--------------------------------------------------------------------------------
/results/27_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/27_inter_res.jpg
--------------------------------------------------------------------------------
/results/28_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/28_final_res.png
--------------------------------------------------------------------------------
/results/28_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/28_final_res2.png
--------------------------------------------------------------------------------
/results/28_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/28_inter_res.jpg
--------------------------------------------------------------------------------
/results/28_naive_cow.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/28_naive_cow.png
--------------------------------------------------------------------------------
/results/28_result_cow.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/28_result_cow.jpg
--------------------------------------------------------------------------------
/results/29_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/29_final_res.png
--------------------------------------------------------------------------------
/results/29_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/29_final_res2.png
--------------------------------------------------------------------------------
/results/29_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/29_inter_res.jpg
--------------------------------------------------------------------------------
/results/2_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/2_final_res.png
--------------------------------------------------------------------------------
/results/2_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/2_final_res2.png
--------------------------------------------------------------------------------
/results/2_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/2_inter_res.jpg
--------------------------------------------------------------------------------
/results/30_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/30_final_res.png
--------------------------------------------------------------------------------
/results/30_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/30_final_res2.png
--------------------------------------------------------------------------------
/results/30_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/30_inter_res.jpg
--------------------------------------------------------------------------------
/results/31_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/31_final_res.png
--------------------------------------------------------------------------------
/results/31_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/31_final_res2.png
--------------------------------------------------------------------------------
/results/31_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/31_inter_res.jpg
--------------------------------------------------------------------------------
/results/32_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/32_final_res.png
--------------------------------------------------------------------------------
/results/32_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/32_final_res2.png
--------------------------------------------------------------------------------
/results/32_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/32_inter_res.jpg
--------------------------------------------------------------------------------
/results/33_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/33_final_res.png
--------------------------------------------------------------------------------
/results/33_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/33_final_res2.png
--------------------------------------------------------------------------------
/results/33_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/33_inter_res.jpg
--------------------------------------------------------------------------------
/results/33_naive_buddha.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/33_naive_buddha.png
--------------------------------------------------------------------------------
/results/33_result_buddha.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/33_result_buddha.jpg
--------------------------------------------------------------------------------
/results/34_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/34_final_res.png
--------------------------------------------------------------------------------
/results/34_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/34_final_res2.png
--------------------------------------------------------------------------------
/results/34_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/34_inter_res.jpg
--------------------------------------------------------------------------------
/results/35_naive.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/35_naive.jpg
--------------------------------------------------------------------------------
/results/35_result.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/35_result.png
--------------------------------------------------------------------------------
/results/3_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/3_final_res.png
--------------------------------------------------------------------------------
/results/3_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/3_final_res2.png
--------------------------------------------------------------------------------
/results/3_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/3_inter_res.jpg
--------------------------------------------------------------------------------
/results/4_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/4_final_res.png
--------------------------------------------------------------------------------
/results/4_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/4_final_res2.png
--------------------------------------------------------------------------------
/results/4_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/4_inter_res.jpg
--------------------------------------------------------------------------------
/results/5_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/5_final_res.png
--------------------------------------------------------------------------------
/results/5_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/5_final_res2.png
--------------------------------------------------------------------------------
/results/5_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/5_inter_res.jpg
--------------------------------------------------------------------------------
/results/6_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/6_final_res.png
--------------------------------------------------------------------------------
/results/6_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/6_final_res2.png
--------------------------------------------------------------------------------
/results/6_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/6_inter_res.jpg
--------------------------------------------------------------------------------
/results/7_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_final_res.png
--------------------------------------------------------------------------------
/results/7_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_final_res2.png
--------------------------------------------------------------------------------
/results/7_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_inter_res.jpg
--------------------------------------------------------------------------------
/results/7_naive_popeye.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_naive_popeye.png
--------------------------------------------------------------------------------
/results/7_result_popeye.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_result_popeye.jpg
--------------------------------------------------------------------------------
/results/7_target.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/7_target.jpg
--------------------------------------------------------------------------------
/results/8_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_final_res.png
--------------------------------------------------------------------------------
/results/8_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_final_res2.png
--------------------------------------------------------------------------------
/results/8_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_inter_res.jpg
--------------------------------------------------------------------------------
/results/8_naive_aircraft.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_naive_aircraft.png
--------------------------------------------------------------------------------
/results/8_naive_balloon.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_naive_balloon.jpg
--------------------------------------------------------------------------------
/results/8_result_aircraft.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_result_aircraft.png
--------------------------------------------------------------------------------
/results/8_result_balloon.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/8_result_balloon.jpg
--------------------------------------------------------------------------------
/results/9_final_res.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/9_final_res.png
--------------------------------------------------------------------------------
/results/9_final_res2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/9_final_res2.png
--------------------------------------------------------------------------------
/results/9_inter_res.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/9_inter_res.jpg
--------------------------------------------------------------------------------
/results/9_naive_dragon.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/9_naive_dragon.png
--------------------------------------------------------------------------------
/results/9_result_dragon.jpg:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/luanfujun/deep-painterly-harmonization/a33a9a70366b6baff1cc0291f857b5895b271fc1/results/9_result_dragon.jpg
--------------------------------------------------------------------------------