├── .gitignore
├── .gitmodules
├── LICENSE
├── README.md
├── VCLFD.m
├── angularDiffusion
├── diffuseAngular.m
├── drawVisibleLines.m
├── points2ulines.m
├── points2vlines.m
├── splatEPIu.m
└── splatEPIv.m
├── compile_mex.m
├── const.m
├── cviewDepthEstim
├── diffuseSpatial.m
├── movePts2Surface.m
├── neighborPtsAlongEdge.m
├── planeSweep.m
├── wmf.m
└── wmf
│ ├── boxfilter.m
│ ├── demo_jpeg.m
│ ├── demo_stereo_refine.m
│ ├── guidedfilter_color_precompute.m
│ ├── guidedfilter_color_runfilter.m
│ ├── guidedfilter_color_runfilter_mask.m
│ ├── guidedfilter_rgbd_precompute.m
│ ├── guidedfilter_rgbd_runfilter.m
│ ├── img_jpeg
│ ├── 20.jpg
│ ├── 28.jpg
│ └── 31.jpg
│ ├── weighted_median_filter.m
│ ├── weighted_median_filter_approx.m
│ └── weighted_median_filter_mask.m
├── depth
├── reproj.m
├── reproj2offcenter.m
├── splat.m
└── trilatFilt.m
├── eval
├── badpixels.m
├── mse.m
└── pairwiseconst.m
├── lahbpcg_mex.cpp
├── lahbpcg_mex.mexmaci64
├── lines
├── edges2lines.m
├── epis2edges.m
├── filterOutliers.m
├── findEdgesEPI.m
├── fitLinesEPI.m
├── genFilters.m
├── lf2edges4d.m
├── merge.m
├── nms.m
├── pvisible.m
├── refineLineDepth.m
├── visibility.m
└── wu.m
├── parameters.m
├── run.sh
├── runOnLightfields.m
├── util
├── expspace.m
└── loadLF.m
└── view-consistent-depth.gif
/.gitignore:
--------------------------------------------------------------------------------
1 | results/*
2 | *.psd
3 | *.mp4
4 | *.png
5 | *~
6 | *#
7 | *.avi
8 | *.mov
9 | *.mat
10 | *.txt
11 | .DS_Store
12 | */.DS_Store
13 | *.mexa64
14 | *#*
--------------------------------------------------------------------------------
/.gitmodules:
--------------------------------------------------------------------------------
1 | [submodule "ImageStack"]
2 | path = ImageStack
3 | url = https://github.com/abadams/ImageStack.git
4 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 | # View-Consistent 4D Light Field Depth Estimation
3 | [Numair Khan](https://cs.brown.edu/~nkhan6)1,
4 | [Min H. Kim](http://vclab.kaist.ac.kr/minhkim/)2,
5 | [James Tompkin](http://www.jamestompkin.com)1
6 | 1Brown, 2KAIST
7 | BMVC 2020 & BMVC 2021
8 | [Project Homepage](http://visual.cs.brown.edu/lightfielddepth/)
9 |
10 | ### [View Consistency Paper](https://www.bmvc2020-conference.com/assets/papers/0395.pdf) | [Edge-aware Bi-directional Diffusion Paper](https://www.bmvc2021-virtualconference.com/assets/papers/0637.pdf) | [Presentation Video](http://visual.cs.brown.edu/projects/lightfielddepth-webpage/video/presentation.mp4) | [Supplemental Results Video](https://www.bmvc2020-conference.com/assets/supp/0395_supp.mp4)
11 |
12 | 
13 |
14 | ## Citation
15 | If you use this code in your work, please cite the following works:
16 |
17 | ```
18 | @article{khan2021edgeaware,
19 | title={Edge-aware Bidirectional Diffusion for Dense Depth Estimation from Light Fields},
20 | author={Numair Khan and Min H. Kim and James Tompkin},
21 | journal={British Machine Vision Conference},
22 | year={2021},
23 | }
24 |
25 | @article{khan2020vclfd,
26 | title={View-consistent {4D} Lightfield Depth Estimation},
27 | author={Numair Khan, Min H. Kim, James Tompkin},
28 | journal={British Machine Vision Conference},
29 | year={2020},
30 | }
31 | ```
32 |
33 | ## Running the MATLAB Code
34 | * [Installing ImageStack](#installing-imagestack)
35 | * [Generating Depth](#generating-depth)
36 | * [Troubleshooting](#troubleshooting)
37 |
38 | ### Installing ImageStack
39 | The code uses ImageStack's implementation of Richard Szeliski's LAHBPCG solver. Along with this repo, you will also have to clone the ImageStack submodule:
40 |
41 | ```
42 | $ git clone https://github.com/brownvc/lightfielddepth.git
43 | $ cd lightfielddepth
44 | $ git submodule init
45 | $ git submodule update
46 | ```
47 |
48 | You may have to install the FFTW3 library for ImageStack:
49 |
50 | ```
51 | $ sudo apt-get install fftw3
52 | ```
53 |
54 | Then compile the MEX interface to ImageStack:
55 |
56 | ```
57 | $ matlab -nodisplay -r "compile_mex; exit"
58 | ```
59 |
60 | ### Generating Depth
61 | To generate disparity estimates for all views of a light field, use `run.sh` followed by the path to the light field file:
62 |
63 | ```$ sudo ./run.sh ```
64 |
65 | The light field is provided as a `.mat` file containing a 5D array. The dimensions of the 5D array should be ordered as (y, x, rgb, v, u) where "rgb" denotes the color channels.
66 |
67 | ```
68 | u
69 | ---------------------------->
70 | | ________ ________
71 | | | x | | x |
72 | | | | | |
73 | v | | y | | y | ....
74 | | | | | |
75 | | |________| |________|
76 | | :
77 | | :
78 | v
79 | ```
80 |
81 | Alternatively, a path to a directory of images may be provided to `run.sh`. The directory should contain a file called `config.txt` with the dimensions of the light field on the first line in format `y, x, v, u`.
82 |
83 | Make sure to set the camera movement direction for both u and v in `parameters.m`.
84 |
85 | The depth estimation results are output to a 4D MATLAB array in `./results//`.
86 |
87 | ### Troubleshooting
88 | - Code fails with error `Index exceeds the number of array elements`: Make sure you are following the correct dimensional
89 | ordering; for light field images this should be `(y, x, rgb, v, u)` and for depth labels `(y, x, v, u)`.
90 | - The output has very high error: Make sure you specify the direction in which the camera moves in u and v. This can be done by setting the boolean variables `uCamMovingRight` and `vCamMovingRight` in `parameters.m`. The camera movement direction determines the occlusion order of EPI lines, and is important for edge detection and depth ordering.
91 | - The code has been run and tested in MATLAB 2019b. Older version of MATLAB may throw errors on some functions.
92 |
--------------------------------------------------------------------------------
/VCLFD.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Generate *View Consistent Light Field Depth* for the input light field using
3 | %% the parameter settings in param
4 | %%
5 | function VCLFD (fin, fout, param)
6 |
7 | LF = loadLF( fin, param.uCamMovingRight, param.vCamMovingRight, 'lab');
8 |
9 | param.szLF = [size(LF, 1) size(LF, 2) size(LF, 4) size(LF, 5)]; % light field size in y, x, v, u
10 | param.szEPI = [param.szLF(4) param.szLF(2) param.szLF(1)]; % EPI size
11 | param.cviewIdx = ceil(param.szLF(3)/2);
12 |
13 | LFrgb = single(loadLF( fin, param.uCamMovingRight, param.vCamMovingRight, 'rgb')) ./ 255;
14 | LFrgb = num2cell(cat(4, squeeze(LFrgb(:, :, :, :, param.cviewIdx)), squeeze(LFrgb(:, :, :, param.cviewIdx, :))), [1 2 3]);
15 |
16 | LFg = single(loadLF( fin, param.uCamMovingRight, param.vCamMovingRight, 'gray')) ./ 255;
17 | LFg = num2cell(cat(3, squeeze(LFg(:, :, 1, :, param.cviewIdx)), squeeze(LFg(:, :, 1, param.cviewIdx, :))), [1 2]);
18 |
19 | % Get the central row of LF images, and their EPIs
20 | LFuc = squeeze(LF(:, :, :, param.cviewIdx, :));
21 |
22 | % Filter the views to remove noise
23 | for i = 1:size(LFuc, 4)
24 | LFuc(:, :, :, i) = imgaussfilt( squeeze(LFuc(:, :, :, i)), 0.85);
25 | end
26 | EPIuc = permute(LFuc, [4 2 3 1]);
27 |
28 | % Get the central column of LF images, and their EPIs
29 | LFvc = squeeze(LF(:, :, :, :, param.cviewIdx));
30 | LFvc = permute(LFvc, [2 1 3 4]);
31 | for i = 1:size(LFvc, 4)
32 | LFvc(:, :, :, i) = imgaussfilt( squeeze(LFvc(:, :, :, i)), 0.85);
33 | end
34 | EPIvc = permute(LFvc, [4 2 3 1]);
35 |
36 | t0 = tic;
37 |
38 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
39 | % EDGE DETECTION & LINE FITTING %
40 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
41 |
42 | % Get 4D edges
43 | %
44 | tic
45 | [P, V] = lf2edges4d(EPIuc, EPIvc, param);
46 | [P, ~] = trilatFilt(P, V, LFrgb, param);
47 | t = toc;
48 | disp(['4D edge detection completed in ' num2str(t) 's']);
49 |
50 |
51 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
52 | % CENTRAL VIEW DEPTH ESTIMATION %
53 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
54 | tic
55 | [O, W, dheuristic] = movePts2Surface(P, V, LFg, param);
56 | P(:, [1 2]) = P(:, [1 2]) + O;
57 | t = toc;
58 | disp(['Point offset completed in ' num2str(t) 's']);
59 |
60 | [P, urIdx] = trilatFilt(P, V, LFrgb, param);
61 | idx = isnan(W) | urIdx;
62 | P(idx, :) = [];
63 | V(idx, :) = [];
64 | W(idx, :) = [];
65 | O(idx, :) = [];
66 |
67 | % Select a specified percentage of highest-confidence points
68 | [W, o] = sort(W, 'descend');
69 | W = W(1:round(size(o, 1) * const.sparsifyFactor), :);
70 | P = P(o(1:round(size(o, 1) * const.sparsifyFactor)), :);
71 | V = V(o(1:round(size(o, 1) * const.sparsifyFactor)), :);
72 | O = O(o(1:round(size(o, 1) * const.sparsifyFactor)), :);
73 |
74 | % Improve depth at remaining points by performing a plane sweep in a small
75 | % oriented window around each point, and discard points with low alignment confidence
76 | tic
77 | [P, ~, lowConfIdx] = planeSweep(P, O, V, LFg, param);
78 | P(lowConfIdx, :) = [];
79 | V(lowConfIdx, :) = [];
80 | W(lowConfIdx) = [];
81 | t = toc;
82 | disp(['Plane sweep completed in ' num2str(t) 's']);
83 |
84 | tic
85 | dheuristic = wmf(dheuristic, lab2rgb(LF(:, :, :, param.cviewIdx, param.cviewIdx)), 256, 0.01^2, 5);
86 | t = toc;
87 | disp(['Weighted median filtering completed in ' num2str(t) 's']);
88 |
89 | tic
90 | o = diffuseSpatial(P(V(:, param.cviewIdx), :), W(V(:, param.cviewIdx)), LFg{param.cviewIdx}, dheuristic, param);
91 | t = toc;
92 | disp(['Diffusion completed in ' num2str(t) 's']);
93 |
94 | % Remove outliers and sharpen edges
95 | tic
96 | om = medfilt2(o, [3 3]);
97 | mn = min(min(om));
98 | mx = max(max(om));
99 | o(o > mx) = mx;
100 | o(o < mn) = mn;
101 | o = wmf(o, lab2rgb(LF(:, :, :, param.cviewIdx, param.cviewIdx)), 256, 0.001^2, 7);
102 | t = toc;
103 | disp(['Weighted median filtering completed in ' num2str(t) 's']);
104 |
105 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
106 | % CROSS-HAIR VIEW PROJECTION %
107 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
108 | v = [1:param.szLF(3) repelem(param.cviewIdx, 1, param.szLF(4))];
109 | u = [repelem(param.cviewIdx, 1, param.szLF(3)) 1:param.szLF(4)];
110 | R = zeros(param.szLF(1), param.szLF(2), length(u));
111 | M = zeros(param.szLF(1), param.szLF(2), length(u));
112 |
113 | tic;
114 | parfor (i = 1:length(u), 18)
115 | [R(:, :, i), M(:, :, i)] = reproj(o, param.cviewIdx - v(i), param.cviewIdx - u(i));
116 | end
117 | t = toc;
118 | disp(['Crosshair views projection completed in ' num2str(t) 's']);
119 |
120 | %%%%%%%%%%%%%%%%%%%%%%
121 | % Angular Diffusion %
122 | %%%%%%%%%%%%%%%%%%%%%%
123 |
124 | % In U...
125 | tic;
126 |
127 | dEPIu = permute(R(:, :, param.szLF(3) + 1:end), [3 2 1]);
128 | dEPIu(isnan(dEPIu)) = 0;
129 | mEPIu = permute(M(:, :, param.szLF(3) + 1:end), [3 2 1]);
130 |
131 | % Select points occluded in the central view
132 | idx = ~V(:, param.cviewIdx);
133 | [wEPIu, lEPIu, ~] = splatEPIu(P(idx, :), V(idx, param.szLF(3) + 1:end), W(idx, :), param.szEPI);
134 |
135 | % Diffuse along horizontal EPIs
136 | U = diffuseAngular(dEPIu, mEPIu, lEPIu, wEPIu, o', EPIuc, param);
137 |
138 | dEPIv = permute(permute(R(:, :, 1:param.szLF(3)), [2 1 3]), [3 2 1]);
139 | dEPIv(isnan(dEPIv)) = 0;
140 | mEPIv = permute(permute(M(:, :, 1:param.szLF(3)), [2 1 3]), [3 2 1]);
141 |
142 | idx = ~V(:, param.cviewIdx);
143 | [wEPIv, lEPIv, ~] = splatEPIv(P(idx, :), V(idx, 1:param.szLF(3)), W(idx, :), param.szEPI);
144 |
145 | % Diffuse along vertical EPIs
146 | V = diffuseAngular(dEPIv, mEPIv, lEPIv, wEPIv, o, EPIvc, param);
147 | V = permute(V, [2 1 3]);
148 |
149 | t = toc;
150 | disp(['EPI propagation completed in ' num2str(t) 's']);
151 |
152 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
153 | % Non-Cross Hair View Projection %
154 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
155 |
156 | tic;
157 | D = [];
158 | jv = [1:param.cviewIdx-1 param.cviewIdx + 1:param.szLF(3)];
159 | for i = [1:param.cviewIdx-1 param.cviewIdx + 1:param.szLF(3)]
160 | parfor (j = 1:length(jv), 12)
161 | [di, m] = reproj2offcenter(V, U, i, j, param);
162 |
163 | g = imgradient(LF(:, :, 1, i, j));
164 | g = 1./(1000 .* g.^2 + 0.0001);
165 | o = lahbpcg_mex(di, m .* 15000, g, g, 10, 0.00001);
166 |
167 | o(o < min(min(di))) = min(min(di));
168 | o(o > max(max(di))) = max(max(di));
169 | Dt(:, :, j) = o;
170 | end
171 | D(:, :, i, jv) = Dt;
172 | end
173 |
174 | D(:, :, param.cviewIdx, :) = U;
175 | D(:, :, :, param.cviewIdx) = V;
176 |
177 | t = toc;
178 | disp(['All views projection completed in ' num2str(t) 's']);
179 | disp(['Depth estimation for ' num2str(param.szLF(3) * param.szLF(4)) ' views completed in ' num2str(toc(t0)) 's']);
180 | disp(['Output saved in ' fout '.mat']);
181 |
182 | save([fout '.mat'], 'D');
183 | end
184 |
--------------------------------------------------------------------------------
/angularDiffusion/diffuseAngular.m:
--------------------------------------------------------------------------------
1 | function O = diffuseAngular(d, m, l, w, dmapc, EPI, param)
2 |
3 | szEPI = size(EPI);
4 |
5 | parfor i = 1:size(EPI, 4)
6 | e(:, :, i) = imgradient(EPI(:, :, 1, i));
7 | m(:, :, i) = medfilt2(m(:, :, i), [1 3]);
8 | end
9 |
10 | m = m .* 15;
11 | d(m == 0) = 10000;
12 |
13 | d(w > 0) = l(w > 0);
14 | m(w > 0) = w(w > 0);
15 |
16 | d(param.cviewIdx, :, :) = dmapc;
17 | m(param.cviewIdx, :, :) = 5;
18 |
19 | nWorkers = 12;
20 | uEpisPerWorker = ceil(szEPI(4) ./ nWorkers);
21 |
22 | M = accumarray( repelem([1:nWorkers]', uEpisPerWorker, 1), ...
23 | 1:nWorkers * uEpisPerWorker, [], @(r){m(:, :, min(r, szEPI(4)))});
24 | D = accumarray( repelem([1:nWorkers]', uEpisPerWorker, 1), ...
25 | 1:nWorkers * uEpisPerWorker, [], @(r){d(:, :, min(r, szEPI(4)))});
26 | E = accumarray( repelem([1:nWorkers]', uEpisPerWorker, 1), ...
27 | 1:nWorkers * uEpisPerWorker, [], @(r){e(:, :, min(r, szEPI(4)))});
28 |
29 | O = {};
30 | parfor (i = 1:size(M, 1), nWorkers)
31 | mi = M{i};
32 | di = D{i};
33 | ei = E{i};
34 | oi = zeros( szEPI(1), szEPI(2), uEpisPerWorker );
35 |
36 | for j = 1:uEpisPerWorker
37 | d = padarray(double(di(:, :, j)), [1 1], 0);
38 | w = padarray(double(mi(:, :, j)), [1 1], 0);
39 | m = max(max(ei(:, :, j)));
40 | g = padarray(ei(:, :, j), [1 1], m);
41 | g = 1 ./ (10 .* g.^2 + 0.0001);
42 | o = lahbpcg_mex(d, w, g, g, 1000, 0.00001);
43 | oi(:, :, j) = o( 2:end - 1, 2:end - 1);
44 | end
45 | O(i) = {oi};
46 | end
47 |
48 | O = cellfun(@(u) permute(u, [3 2 1]), O, 'UniformOutput', false);
49 | O = vertcat(O{:});
50 | O = O(1:size(EPI, 4), :, :);
51 | end
52 |
--------------------------------------------------------------------------------
/angularDiffusion/drawVisibleLines.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Rasterize EPI lines
3 | %%
4 | function bw = drawVisibleLines(lines, c, v, emptyVal, epiSz)
5 |
6 | [~, o] = sort(lines(:, 2) - lines(:, 1));
7 | lines = lines(o, :);
8 | c = c(o, :);
9 | v = v(o, :);
10 |
11 | bw = ones(epiSz) .* emptyVal;
12 | for i = 1:size(lines, 1)
13 |
14 | x = [lines(i, 1) lines(i, 2)];
15 | y = [1 epiSz(1)];
16 |
17 | nPoints = epiSz(1);
18 | rIndex = [y(1):y(2)];
19 | cIndex = max(1, min(round(linspace(x(1), x(2), nPoints)), epiSz(2)));
20 | index = sub2ind(epiSz, rIndex, cIndex);
21 | index = index(logical(v(i, :)));
22 | bw(index) = c(i, 1);
23 | end
24 | end
25 |
--------------------------------------------------------------------------------
/angularDiffusion/points2ulines.m:
--------------------------------------------------------------------------------
1 | %
2 | % Convert points to lines defined by their top and bottom intercepts on
3 | % horizontal (u) EPIs.
4 | %
5 | function [L, O] = points2ulines(P, szEPI)
6 |
7 | % Group points into lines by rounded y-coordinate
8 | [P, o] = sortrows(P, 2);
9 | idx = P(:, 2) < 1 | P(:, 2) > szEPI(3);
10 | P(idx, :) = [];
11 | o(idx, :) = [];
12 |
13 | [~, ~, X] = unique( round(P(:, 2)) );
14 | A = accumarray(X, 1:size(P, 1), [], @(r){[P(r, :) o(r, :)]});
15 |
16 | L = cell(szEPI(3), 1);
17 | for i = 1:size(A, 1)
18 | a = A{i};
19 | y = round(a(1, 2));
20 | L(y) = {[a(:, 1) - floor(szEPI(1) ./ 2) .* a(:, 3) ...
21 | a(:, 1) + floor(szEPI(1) ./ 2) .* a(:, 3)]};
22 | O(y) = {a(:, 4)};
23 | end
24 |
25 | end
26 |
--------------------------------------------------------------------------------
/angularDiffusion/points2vlines.m:
--------------------------------------------------------------------------------
1 | %
2 | % Convert points to lines defined by their top and bottom intercepts on
3 | % vertical (v) EPIs.
4 | %
5 | function [L, O] = points2vlines(P, szEPI)
6 |
7 | % Group points into lines by rounded x-coordinate
8 | [P, o] = sortrows(P, 1);
9 | idx = P(:, 1) < 1 | P(:, 1) > szEPI(3);
10 | P(idx, :) = [];
11 | o(idx, :) = [];
12 |
13 | [~, ~, X] = unique( round(P(:, 1)) );
14 | A = accumarray(X, 1:size(P, 1), [], @(r){[P(r, :) o(r, :)]});
15 |
16 | L = cell(szEPI(3), 1);
17 | for i = 1:size(A, 1)
18 | a = A{i};
19 | x = round(a(1, 1));
20 | L(x) = {[a(:, 2) - floor(szEPI(1) ./ 2) .* a(:, 3) ...
21 | a(:, 2) + floor(szEPI(1) ./ 2) .* a(:, 3)]};
22 | O(x) = {a(:, 4)};
23 | end
24 |
25 | end
26 |
--------------------------------------------------------------------------------
/angularDiffusion/splatEPIu.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Splat points as lines onto a horizontal (u) EPI with visibility V and weight W
3 | %%
4 | function [M, D, I] = splatEPIu(P, V, W, szEPI)
5 |
6 | [L, o] = points2ulines(P, szEPI);
7 | Lv = cellfun(@(i) V(i, :), o, 'UniformOutput', false);
8 | Lw = cellfun(@(i) W(i, :), o, 'UniformOutput', false);
9 | Ld = cellfun(@(i) P(i, 3), o, 'UniformOutput', false);
10 |
11 | M = zeros(szEPI);
12 | D = zeros(szEPI);
13 | I = zeros(szEPI);
14 |
15 | for i = 1:szEPI(3)
16 | if isempty(L{i})
17 | continue;
18 | end
19 |
20 | M(:, :, i) = drawVisibleLines(L{i}, Lw{i}, Lv{i}, 0, szEPI([1 2]));
21 | D(:, :, i) = drawVisibleLines(L{i}, Ld{i}, Lv{i}, 0, szEPI([1 2]));
22 | end
23 |
24 | end
25 |
--------------------------------------------------------------------------------
/angularDiffusion/splatEPIv.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Splat points as lines onto a vertical (v) EPI with visibility V and weight W
3 | %%
4 | function [M, D, I] = splatEPIv(P, V, W, szEPI)
5 |
6 | [L, o] = points2vlines(P, szEPI);
7 | Lv = cellfun(@(i) V(i, :), o, 'UniformOutput', false);
8 | Lw = cellfun(@(i) W(i, :), o, 'UniformOutput', false);
9 | Ld = cellfun(@(i) P(i, 3), o, 'UniformOutput', false);
10 |
11 | M = zeros(szEPI);
12 | D = zeros(szEPI);
13 | I = zeros(szEPI);
14 |
15 | for i = 1:szEPI(3)
16 | if isempty(L{i})
17 | continue;
18 | end
19 | M(:, :, i) = drawVisibleLines(L{i}, Lw{i}, Lv{i}, 0, szEPI([1 2]));
20 | D(:, :, i) = drawVisibleLines(L{i}, Ld{i}, Lv{i}, 0, szEPI([1 2]));
21 | %I(:, :, i) = drawLines2(L{i}, o{i}, V{i}, 0, szEPI([1 2]));
22 | end
23 |
24 | end
25 |
--------------------------------------------------------------------------------
/compile_mex.m:
--------------------------------------------------------------------------------
1 | filelist = dir('./ImageStack/src/');
2 | filelist = filelist(~startsWith({filelist.name}, '.'));
3 | filelist = filelist(~startsWith({filelist.name}, 'Func'));
4 | filelist = filelist(endsWith({filelist.name}, '.cpp'));
5 |
6 | filelist = cellfun(@(a) strcat('./ImageStack/src/', a), {filelist.name}, 'UniformOutput', false);
7 | filelist = join(cellfun(@string, filelist));
8 |
9 | eval(strcat("mex ./lahbpcg_mex.cpp ", filelist, " -I/usr/local/include -L/usr/local/lib/ -DNO_SDL -DNO_OPENEXR -DNO_TIFF -DNO_JPEG -DNO_PNG -lfftw3f -ldl -R2018a"))
10 |
--------------------------------------------------------------------------------
/const.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Constants used by the algorithms
3 | %% These should not be changed. Any run specific parameters can
4 | %% be set in parameters.m
5 | %%
6 | classdef const
7 | properties (Constant = true)
8 | SegMinWidthMultiplier = 0.1;
9 |
10 | filtOutliersGradientDirectionThresh = 0.97;
11 | filtOutliersMinContigPixels = 4;
12 |
13 | visibilityMinViews = 3;
14 | visibilityAlignmentThreshold = 0.95 % this is a cos(theta) where theta is the alignment angle
15 |
16 | refineIterCount = 10;
17 | refineTempStart = 0.15;
18 | refineTempAnnealFactor = 0.88;
19 |
20 | trilatFiltWinSz = 10;
21 |
22 | sparsifyFactor = 0.9;
23 |
24 | diffusionScale = 2.0;
25 |
26 | planeSweepMaxViews = 8;
27 | planeSweepPatchSz = [5 5];
28 | planeSweepMaxDispOffset = 0.25;
29 | planeSweepNumDispOffset = 9; % This should be an odd number
30 |
31 | end
32 | end
33 |
--------------------------------------------------------------------------------
/cviewDepthEstim/diffuseSpatial.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Dense depth diffusion in the spatial domain
3 | %%
4 | function o = diffuseSpatial(P, W, I, dheuristic, param)
5 |
6 | P(:, [1 2]) = P(:, [1 2]) * const.diffusionScale;
7 |
8 | % Gradients of the depth heuristic
9 | [gxd, gyd] = imgradientxy( imresize( dheuristic, const.diffusionScale) );
10 | gd = sqrt( gxd.^2 + gyd.^2 );
11 | gd(:, [1 end]) = max(max(gd));
12 | gd([1 end], :) = max(max(gd));
13 |
14 | gd = imgaussfilt(gd, 1);
15 |
16 | % Gradients of the guidance image
17 | [gx, gy] = imgradientxy( imresize( I, const.diffusionScale) );
18 | g = sqrt( gx.^2 + gy.^2 );
19 | g(:, [1 end]) = max(max(g));
20 | g([1 end], :) = max(max(g));
21 |
22 | [w, d] = splat(P, W, param.szLF([1 2]) * const.diffusionScale);
23 |
24 | % Weights are determined empirically and work for all light fields
25 | wg = 100./255 ./ sqrt(0.01);
26 | g = wg .* g .* gd;
27 | g = double(1 ./ (10 * g.^2 + 0.0001));
28 | w = w .* 1500;
29 |
30 | o = lahbpcg_mex(d, w, g, g, 20, 0.00001);
31 | o = imresize(o, param.szLF([1 2]));
32 | end
33 |
--------------------------------------------------------------------------------
/cviewDepthEstim/movePts2Surface.m:
--------------------------------------------------------------------------------
1 | %
2 | % Move depth points from edges to surfaces.
3 | %
4 | % In theory, the depth at an edge is undefined -- it is a transition between two surfaces.
5 | % In order to perform diffusion correctly, we want our depth labels to lie on surfaces,
6 | % not edges. So we need to move our points by a small amount off edges. But since we only
7 | % have a sparse set of depth labels, it is not trivial to determine which surface the edge
8 | % depth label corresponds to.
9 | %
10 | % This function moves points off edges by performing diffusion in both surface directions,
11 | % and then checking which direction generates the stronger gradient at the edge. It also
12 | % returns a heuristic estimate of the final disparity.
13 | %
14 | function [O, W, dheuristic] = movePts2Surface(P, V, LFg, param)
15 |
16 | % For each point, get the index of a view in the central cross-hair in which it is visible.
17 | % For points visible in multiple views, the one closest to the central view is selected
18 | [~, viewIdx] = min(abs(V .* [1:param.szLF(3) 1:param.szLF(4)] - param.cviewIdx), [], 2);
19 |
20 | % Get the unique set of views with visible points, ...
21 | [uniqueViewIdx, ~, X] = unique(viewIdx);
22 | % ... and the set of points for each of those views
23 | visiblePtsIdx = accumarray(X, 1:size(V, 1), [], @(r){r});
24 |
25 | O = zeros(size(P, 1), 1); % The offsets
26 | W = zeros(size(P, 1), 1); % The confidence
27 | Gx = zeros(size(P, 1), 1); % Gradient along x at each point..
28 | Gy = zeros(size(P, 1), 1); % Gradient along y.
29 |
30 | Oi = {};
31 | Wi = {};
32 | Gxi = {};
33 | Gyi = {};
34 |
35 | [m, d, idx] = splat(P, 1, [512 512]);
36 |
37 | parfor (i = 1:length(uniqueViewIdx), 18)
38 | view = uniqueViewIdx(i);
39 | ui = (view <= param.szLF(3)) * param.cviewIdx + (view > param.szLF(3)) * (view - param.szLF(3));
40 | vi = (view <= param.szLF(3)) * view + (view > param.szLF(3)) * param.cviewIdx;
41 |
42 | % Project visible points to current view
43 | vis = V(:, view);
44 | Pi = P;
45 | Pi(:, [1 2]) = [Pi(:, 1) + (ui - param.cviewIdx) .* Pi(:, 3) Pi(:, 2) + (vi - param.cviewIdx) .* Pi(:, 3)];
46 |
47 | % Find the gradient direction at each points
48 | [lfgx, lfgy] = imgradientxy( LFg{view} );
49 | pgx = interp2(lfgx, Pi(:, 1), Pi(:, 2));
50 | pgy = interp2(lfgy, Pi(:, 1), Pi(:, 2));
51 | pgm = 1 ./ sqrt( pgx.^2 + pgy.^2 );
52 | pgx = pgx .* pgm;
53 | pgy = pgy .* pgm;
54 | pgx(isnan(pgx)) = 0;
55 | pgy(isnan(pgy)) = 0;
56 |
57 | [g, ~, ~] = splat(Pi(vis, :), 1, [param.szLF(1) param.szLF(2)]);
58 | g = double(1 ./ (g.^2 + 0.0001));
59 |
60 | % 1. Offset points in gradient direction and diffuse
61 | Po = Pi;
62 | Po(:, [1 2]) = Po(:, [1 2]) + [pgx pgy];
63 |
64 | [w, d] = splat(Po(vis, :), 1, [param.szLF(1) param.szLF(2)]);
65 | w = double(w .* 100000);
66 | Duf(:, :, i) = lahbpcg_mex(d, w, g, g, 1000, 0.00001);
67 |
68 | % 2. Offset points opposite to the gradient direction and diffuse
69 | Po(:, [1 2]) = Po(:, [1 2]) - 2 * [pgx pgy];
70 |
71 | [w, d] = splat(Po(vis, :), 1, [param.szLF(1) param.szLF(2)]);
72 | w = double(w .* 100000);
73 | Dub(:, :, i) = lahbpcg_mex(d, w, g, g, 1000, 0.00001);
74 |
75 | ptsIdx = visiblePtsIdx{i};
76 |
77 | %
78 | % Select the offset direction which generates the sharper gradient at the current edge point
79 | samplesRange = 3;
80 | samplesOffset = [-samplesRange:samplesRange];
81 | edgeSampleIdx = ceil(length(samplesOffset) ./ 2);
82 | ptsx = Pi(ptsIdx, 1) + pgx(ptsIdx) .* samplesOffset;
83 | ptsy = Pi(ptsIdx, 2) + pgy(ptsIdx) .* samplesOffset;
84 |
85 | df = interp2( Duf(:, :, i), ptsx, ptsy );
86 | db = interp2( Dub(:, :, i), ptsx, ptsy );
87 |
88 | idxf = abs(df(:, edgeSampleIdx ) - df(:, edgeSampleIdx - 1)) > abs(df(:, edgeSampleIdx) - df(:, edgeSampleIdx + 1));
89 | dfc = df(:, 2:end);
90 | dfc(idxf, :) = df(idxf, 1:end-1);
91 |
92 | idxb = abs(db(:, edgeSampleIdx ) - db(:, edgeSampleIdx - 1)) > abs(db(:, edgeSampleIdx) - db(:, edgeSampleIdx + 1));
93 | dbc = db(:, 2:end);
94 | dbc(idxf, :) = db(idxf, 1:end-1);
95 |
96 | df = dfc;
97 | db = dbc;
98 |
99 | ao = abs(df * [ linspace(-1, -2, samplesRange) linspace(2, 1, samplesRange) ]');
100 | bo = abs(db * [ linspace(-1, -2, samplesRange) linspace(2, 1, samplesRange) ]');
101 | Wi(i) = {exp(1.3 * abs(bo - ao))};
102 |
103 | % Normalize the response across the edge to the range [-1, 1]
104 | df(isnan(df)) = 0;
105 | db(isnan(db)) = 0;
106 | df = df - min(df, [], 2);
107 | df = (df ./ max(df, [], 2)) * 2 - 1;
108 | db = db - min(db, [], 2);
109 | db = (db ./ max(db, [], 2)) * 2 - 1;
110 |
111 | df = abs(df * [ linspace(-1, -2, samplesRange) linspace(2, 1, samplesRange) ]');
112 | db = abs(db * [ linspace(-1, -2, samplesRange) linspace(2, 1, samplesRange) ]');
113 | fidx = df > db;
114 |
115 | oi = zeros(size(ptsIdx, 1), 1);
116 | oi(fidx, :) = 1; % Move these points along the gradient direction
117 | oi(~fidx, :) = -1; % Move these points opposite to the gradient direction
118 |
119 | Oi(i) = {oi};
120 | Gxi(i) = {pgx(ptsIdx)};
121 | Gyi(i) = {pgy(ptsIdx)};
122 | end
123 |
124 | cidx = find( uniqueViewIdx == param.cviewIdx);
125 | dheuristic = (Duf(:, :, cidx) + Dub(:, :, cidx));
126 |
127 | ptsIdx = vertcat(visiblePtsIdx{:});
128 | O(ptsIdx, :) = vertcat(Oi{:});
129 | W(ptsIdx, :) = vertcat(Wi{:});
130 | Gx(ptsIdx, :) = vertcat(Gxi{:});
131 | Gy(ptsIdx, :) = vertcat(Gyi{:});
132 |
133 | % Median filter the offsets of neighboring points along an edge
134 | [m, ~, idx] = splat(P, 1, param.szLF([1 2]));
135 | [gx, gy] = imgradientxy(LFg{param.cviewIdx});
136 | gm = sqrt(gx.^2 + gy.^2);
137 | gx = gx ./ gm;
138 | gy = gy ./ gm;
139 |
140 | O2 = O;
141 | for i = 1:size(P, 1)
142 | p = round(P(i, [1 2]));
143 | neighbors = neighborPtsAlongEdge(m, gx, gy, p, 60);
144 | if ~isempty(neighbors)
145 | O2(i) = median( [O(idx(neighbors))' O(i)] );
146 | else
147 | O2(i) = O(i);
148 | end
149 | end
150 |
151 | % Offset the points in the determined direction
152 | O = O2 .* [Gx Gy];
153 | end
154 |
--------------------------------------------------------------------------------
/cviewDepthEstim/neighborPtsAlongEdge.m:
--------------------------------------------------------------------------------
1 | %
2 | % Return the neighbors for a point p in the edge image E.
3 | % An point q in E is considered a neighbor of another point r if it lies on an edge (E(q) == 1), and
4 | % it lies along the tangent at r. The set N is constructed by adding neighbors recursively starting
5 | % at p, upto a maximum of maxCount
6 | %
7 | function N = neighborPtsAlongEdge(E, gx, gy, p, maxCount)
8 |
9 | po = p;
10 | N = zeros(maxCount, 2);
11 | i = 1;
12 |
13 | for k = 1:floor(maxCount/2)
14 |
15 | % Move in the tangent direction
16 | tx = -gy(p(2), p(1));
17 | ty = gx(p(2), p(1));
18 |
19 | if abs(ty) < abs(tx)
20 | px1 = p(1) + round(tx);
21 | px2 = px1;
22 | py1 = p(2) + ceil(ty);
23 | py2 = p(2) + floor(ty);
24 | else
25 | px1 = p(1) + ceil(tx);
26 | px2 = p(1) + floor(tx);
27 | py1 = p(2) + round(ty);
28 | py2 = py1;
29 | end
30 |
31 | % If an edge point exists in the tangent direction, add it to the list of neighbors
32 | if py1 < size(E, 1) & py1 > 0 & px1 < size(E, 2) & px1 > 0 & E(py1, px1) == 1
33 | p = [px1 py1];
34 | N(i, :) = p;
35 | i = i + 1;
36 | elseif py2 < size(E, 1) & py2 > 0 & px2 < size(E, 2) & px2 > 0 & E(py2, px2) == 1
37 | p = [px2 py2];
38 | N(i, :) = p;
39 | i = i + 1;
40 | else
41 | break;
42 | end
43 | end
44 |
45 | p = po;
46 | for k = 1:floor(maxCount/2)
47 |
48 | % Move in the opposite direction of the tangent
49 | tx = gy(p(2), p(1));
50 | ty = -gx(p(2), p(1));
51 |
52 | if abs(ty) < abs(tx)
53 | px1 = p(1) + round(tx);
54 | px2 = px1;
55 | py1 = p(2) + ceil(ty);
56 | py2 = p(2) + floor(ty);
57 | else
58 | px1 = p(1) + ceil(tx);
59 | px2 = p(1) + floor(tx);
60 | py1 = p(2) + round(ty);
61 | py2 = py1;
62 | end
63 |
64 | if py1 < size(E, 1) & py1 > 0 & px1 < size(E, 2) & px1 > 0 & E(py1, px1) == 1
65 | p = [px1 py1];
66 | N(i, :) = p;
67 | i = i + 1;
68 | elseif py2 < size(E, 1) & py2 > 0 & px2 < size(E, 2) & px2 > 0 & E(py2, px2) == 1
69 | p = [px2 py2];
70 | N(i, :) = p;
71 | i = i + 1;
72 | else
73 | break;
74 | end
75 | end
76 |
77 | N = sub2ind( size(E), N(1:i-1, 2), N(1:i-1, 1) );
78 | end
79 |
--------------------------------------------------------------------------------
/cviewDepthEstim/planeSweep.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Fine-tune the depth at points in P by performing a plane-sweep around each point using a subset of views.
3 | %%
4 | function [P, conf, pUnreliableIdx] = planeSweep(P, O, V, LFg, param)
5 |
6 | npoints = size(P, 1);
7 | doff = P(:, 3) + linspace(-const.planeSweepMaxDispOffset, const.planeSweepMaxDispOffset, const.planeSweepNumDispOffset);
8 |
9 | % For each point get at most const.planeSweepMaxViews views in which it is visible
10 | %
11 | fv = cellfun(@(vi) find(vi), num2cell(V, 2), 'UniformOutput', false);
12 | pviews = cellfun(@(f, i) uint32([repelem(i, min(const.planeSweepMaxViews, length(f)), 1) ...
13 | f( randperm(length(f), min(const.planeSweepMaxViews, length(f)) ))']), ...
14 | fv, num2cell([1:npoints]'), 'UniformOutput', false);
15 | pviews = vertcat(pviews{:});
16 | Vc = zeros(size(V), 'logical');
17 | Vc( sub2ind(size(Vc), pviews(:, 1), pviews(:, 2)) ) = 1;
18 |
19 | C = zeros( [const.planeSweepPatchSz(1), const.planeSweepPatchSz(2), const.planeSweepNumDispOffset, npoints, const.planeSweepMaxViews], 'single');
20 |
21 | % Select the set of views from which we want to project onto our reference plane.
22 | vIdx = single(unique(pviews(:, 2)));
23 | pNextViewIdx = ones(npoints, 1, 'uint8');
24 |
25 | for i = 1:length(vIdx)
26 | u = (vIdx(i) <= param.szLF(3)) .* param.cviewIdx + (vIdx(i) > param.szLF(3)) .* (vIdx(i) - param.szLF(3));
27 | v = (vIdx(i) > param.szLF(3)) .* param.cviewIdx + (vIdx(i) <= param.szLF(3)) .* vIdx(i);
28 | s2t = [u - param.cviewIdx v - param.cviewIdx];
29 |
30 | pIdx = find(Vc(:, vIdx(i)));
31 | ci = costVolumeForImage( LFg{vIdx(i)}, P(pIdx, :), O(pIdx, :), ...
32 | s2t, doff(pIdx,:), const.planeSweepPatchSz );
33 | for k = 1:length(pIdx)
34 | C(:, :, :, pIdx(k), pNextViewIdx(pIdx(k))) = ci(:, :, :, k);
35 | end
36 | pNextViewIdx(pIdx) = pNextViewIdx(pIdx) + 1;
37 | end
38 |
39 | C(isnan(C)) = 0; % A bit hacky; Does it affect the variance?
40 |
41 | Cvar = var(C, [], 5); % The variance along the views for each disparity estimate
42 | [conf, dmin] = min(Cvar, [], 3); % We use a winner-takes-all strategy for the best disparity
43 | dmin = squeeze(dmin);
44 |
45 | conf = squeeze(conf); % A const.planeSweepPatchSz x const.planeSweepPatchSz x npoints matrix of confidence at each patch pixel for a point
46 |
47 | % Normalize the confidence
48 | varsum = squeeze(sum(Cvar, 3));
49 | conf = 1 - conf ./ varsum;
50 | idx = conf > 0.99;
51 | didx = cellfun(@(d, i) mode(d(i)), num2cell(dmin, [1 2]), num2cell(idx, [1 2]) );
52 | didx = didx(:);
53 |
54 | conf = cellfun(@(cf, i) mean(cf(i)), num2cell(conf, [1 2]), num2cell(idx, [1 2]) );
55 | conf = conf(:);
56 |
57 | idx = sub2ind( size(doff), [1:npoints]', didx );
58 | pUnreliableIdx = isnan(idx); % These NaNs occur for patches where all pixels' confidence is lower than the threshold
59 |
60 | P(~pUnreliableIdx, 3) = doff( idx(~pUnreliableIdx) );
61 | end
62 |
63 | %%
64 | %% Create a cost volume
65 | %%
66 | function v = costVolumeForImage(I, ps, o, s2t, doff, patchSz)
67 | ndoff = size(doff, 2);
68 |
69 | % Generate oriented patches at each point
70 | px = ps(:, 1) + s2t(:, 1) .* doff;
71 | py = ps(:, 2) + s2t(:, 2) .* doff;
72 | px = px';
73 | py = py';
74 | px = px(:);
75 | py = py(:);
76 |
77 | [patchx, patchy] = orientedPatchAtPoint( [px, py], repelem(o, ndoff, 1), patchSz);
78 |
79 | % Interpolate image values at patch points
80 | v = interp2(I, patchx, patchy);
81 |
82 | % Set NaN values to the mean.
83 | % NaNs occur when the patch exceeds the image bounds.
84 | % Note that even after the following steps we may have NaNs in cases where the
85 | % entire patch is zero.
86 | nanIdx = isnan(v);
87 | vprime = v;
88 | vprime(nanIdx) = 0;
89 | vmean = sum(vprime, 2) ./ sum(~nanIdx, 2);
90 | v = vprime + nanIdx .* vmean;
91 | v = reshape(v', patchSz(1), patchSz(2), ndoff, size(ps, 1));
92 | end
93 |
94 | %%
95 | %% Return coordinates of a patch along point p oriented along the direction o
96 | %%
97 | function [x, y] = orientedPatchAtPoint(p, o, patchSz)
98 |
99 | nSamplesu = patchSz(1);
100 | nSamplesv = patchSz(2);
101 |
102 | t = [o(:, 2) -o(:, 1)]; % The tangent at the point
103 |
104 | pux = repelem( linspace(-patchSz(1)/2, patchSz(1)/2, nSamplesu) .* t(:, 1), nSamplesv, 1);
105 | puy = repelem( linspace(-patchSz(2)/2, patchSz(2)/2, nSamplesu) .* t(:, 2), nSamplesv, 1);
106 |
107 | pvx = [linspace(0, patchSz(1), nSamplesv) .* o(:, 1)]';
108 | pvy = [linspace(0, patchSz(2), nSamplesv) .* o(:, 2)]';
109 | pvx = repelem( pvx(:), 1, nSamplesu);
110 | pvy = repelem( pvy(:), 1, nSamplesu);
111 |
112 | x = pvx + pux + repelem(p(:, 1), nSamplesv, nSamplesu);
113 | y = pvy + puy + repelem(p(:, 2), nSamplesv, nSamplesu);
114 | x = x';
115 | y = y';
116 | x = x(:);
117 | y = y(:);
118 | x = reshape(x, nSamplesu * nSamplesv, []);
119 | y = reshape(y, nSamplesu * nSamplesv, []);
120 | x = x';
121 | y = y';
122 | end
123 |
--------------------------------------------------------------------------------
/cviewDepthEstim/wmf.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Perform weighted median filtering of the input depth image using the method
3 | %% described in 'Constant Time Weighted Median Filtering for Stereo Matching and Beyond.'
4 | %% The code for filtering in ./wmf was downloaded from Kaiming He's homepage:
5 | %% http://kaiminghe.com/iccv13wmf/matlab_wmf_release_v1.rar
6 | %%
7 | function D = wmf(D, I, q, epsilon, r)
8 | mn = min(min(D));
9 | mx = max(max(D));
10 | D = (D - mn) ./ (mx - mn);
11 | D = round(D .* (q - 1));
12 | D = weighted_median_filter(D, I, [0:q], r, epsilon);
13 | D = medfilt2(D, [3 3]);
14 | D = D ./ q .* (mx - mn) + mn;
15 | end
16 |
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/cviewDepthEstim/wmf/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 |
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/cviewDepthEstim/wmf/demo_jpeg.m:
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1 | % demo_jpeg.m
2 |
3 | close all;
4 | clear all;
5 |
6 | imgInput = imread('img_jpeg/20.jpg');
7 | %imgInput = imread('img_jpeg/28.jpg');
8 | %imgInput = imread('img_jpeg/31.jpg');
9 |
10 |
11 | %--------------------------------------------------------------------------
12 | % JPEG Artifact removal example (without downsampling)
13 |
14 | % imgOutput = zeros(size(imgInput), class(imgInput));
15 | % for c = 1 : size(imgInput,3)
16 | % imgOutput(:,:,c) = ...
17 | % weighted_median_filter(imgInput(:,:,c), imgInput, 0:255, 5, 0.01);
18 | % end
19 |
20 | %--------------------------------------------------------------------------
21 | % JPEG Artifact removal example (with downsampling)
22 |
23 | nBins = 16;
24 | imgOutput = weighted_median_filter_approx(imgInput, imgInput, 5, 0.01, nBins);
25 |
26 | figure; imshow(imgInput ); title('Input image:');
27 | figure; imshow(imgOutput); title('Output image:');
28 |
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/cviewDepthEstim/wmf/demo_stereo_refine.m:
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1 | % demo_stereo_refine
2 |
3 | close all;
4 | clear all;
5 |
6 | pair_name = 'tsukuba';
7 | %pair_name = 'venus';
8 |
9 | if strcmp(pair_name, 'tsukuba')
10 | num_disp = 15;
11 | disp_scale = 16; %% a constant scale for displaying only
12 | elseif strcmp(pair_name, 'venus')
13 | num_disp = 32;
14 | disp_scale = 8;
15 | end
16 |
17 | imgGuide = imread(['img_stereo/',pair_name,'_left.png']);
18 | dispMapInput = imread(['img_stereo/',pair_name,'_boxagg.png']) / disp_scale;
19 |
20 | eps = 0.01^2;
21 | r = ceil(max(size(imgGuide, 1), size(imgGuide, 2)) / 40);
22 |
23 | dispMapOutput = weighted_median_filter(dispMapInput, imgGuide, 1:num_disp, r, eps);
24 | dispMapOutput = medfilt2(dispMapOutput,[3,3]);
25 |
26 | figure; imshow(dispMapInput * disp_scale); title('Input disparity map');
27 | figure; imshow(dispMapOutput * disp_scale); title('Output disparity map');
28 |
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/cviewDepthEstim/wmf/guidedfilter_color_precompute.m:
--------------------------------------------------------------------------------
1 | function gfobj = guidedfilter_color_precompute(I, r, eps)
2 | % guidedfilter_color_precompute Precomputation of the O(1) time guided filter using a color image as the guidance.
3 | %
4 | % - guidance image: I (should be a color (RGB) image)
5 | % - local window radius: r
6 | % - regularization parameter: eps
7 |
8 | %global gfobj;
9 |
10 | gfobj.I = I;
11 | gfobj.r = r;
12 | gfobj.eps = eps;
13 |
14 | hei = size(I,1);
15 | wid = size(I,2);
16 | gfobj.N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
17 |
18 | gfobj.mean_I_r = boxfilter(I(:, :, 1), r) ./ gfobj.N;
19 | gfobj.mean_I_g = boxfilter(I(:, :, 2), r) ./ gfobj.N;
20 | gfobj.mean_I_b = boxfilter(I(:, :, 3), r) ./ gfobj.N;
21 |
22 | % variance of I in each local patch: the matrix Sigma in Eqn (14).
23 | % Note the variance in each local patch is a 3x3 symmetric matrix:
24 | % rr, rg, rb
25 | % Sigma = rg, gg, gb
26 | % rb, gb, bb
27 | gfobj.var_I_rr = boxfilter(I(:, :, 1).*I(:, :, 1), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_r;
28 | gfobj.var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_g;
29 | gfobj.var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_b;
30 | gfobj.var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ gfobj.N - gfobj.mean_I_g .* gfobj.mean_I_g;
31 | gfobj.var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_g .* gfobj.mean_I_b;
32 | gfobj.var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_b .* gfobj.mean_I_b;
33 |
34 | gfobj.invSigma = cell(hei, wid);
35 | for y=1:hei
36 | for x=1:wid
37 | Sigma = [gfobj.var_I_rr(y, x), gfobj.var_I_rg(y, x), gfobj.var_I_rb(y, x);
38 | gfobj.var_I_rg(y, x), gfobj.var_I_gg(y, x), gfobj.var_I_gb(y, x);
39 | gfobj.var_I_rb(y, x), gfobj.var_I_gb(y, x), gfobj.var_I_bb(y, x)];
40 | %Sigma = Sigma + eps * eye(3);
41 |
42 | gfobj.invSigma{y, x} = inv(Sigma + eps * eye(3)); % Eqn. (14) in the paper;
43 | end
44 | end
45 |
--------------------------------------------------------------------------------
/cviewDepthEstim/wmf/guidedfilter_color_runfilter.m:
--------------------------------------------------------------------------------
1 | function q = guidedfilter_color_runfilter(p, gfobj)
2 | % guidedfilter_color_runfilter Run O(1) time implementation of guided filter
3 | %
4 | % NOTE: you must call guidedfilter_color_precompute first!
5 | %
6 | % - filtering input image: p (should be a gray-scale/single channel image)
7 |
8 | %global gfobj;
9 |
10 | r = gfobj.r;
11 |
12 | [hei, wid] = size(p);
13 |
14 | mean_p = boxfilter(p, r) ./ gfobj.N;
15 |
16 | mean_Ip_r = boxfilter(gfobj.I(:, :, 1).*p, r) ./ gfobj.N;
17 | mean_Ip_g = boxfilter(gfobj.I(:, :, 2).*p, r) ./ gfobj.N;
18 | mean_Ip_b = boxfilter(gfobj.I(:, :, 3).*p, r) ./ gfobj.N;
19 |
20 | % covariance of (I, p) in each local patch.
21 | cov_Ip_r = mean_Ip_r - gfobj.mean_I_r .* mean_p;
22 | cov_Ip_g = mean_Ip_g - gfobj.mean_I_g .* mean_p;
23 | cov_Ip_b = mean_Ip_b - gfobj.mean_I_b .* mean_p;
24 |
25 | a = zeros(hei, wid, 3);
26 | for y=1:hei
27 | for x=1:wid
28 | cov_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x)];
29 | a(y, x, :) = cov_Ip * gfobj.invSigma{y, x}; % Eqn. (14) in the paper;
30 | end
31 | end
32 |
33 | b = mean_p - a(:, :, 1) .* gfobj.mean_I_r - a(:, :, 2) .* gfobj.mean_I_g - a(:, :, 3) .* gfobj.mean_I_b; % Eqn. (15) in the paper;
34 |
35 | q = (boxfilter(a(:, :, 1), r).* gfobj.I(:, :, 1)...
36 | + boxfilter(a(:, :, 2), r).* gfobj.I(:, :, 2)...
37 | + boxfilter(a(:, :, 3), r).* gfobj.I(:, :, 3)...
38 | + boxfilter(b, r)) ./ gfobj.N; % Eqn. (16) in the paper;
39 |
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/cviewDepthEstim/wmf/guidedfilter_color_runfilter_mask.m:
--------------------------------------------------------------------------------
1 | function q = guidedfilter_color_runfilter_mask(p, mask, gfobj)
2 | % guidedfilter_color_runfilter Run O(1) time implementation of guided filter
3 | %
4 | % NOTE: you must call guidedfilter_color_precompute first!
5 | %
6 | % - filtering input image: p (should be a gray-scale/single channel image)
7 |
8 | %global gfobj;
9 |
10 | r = gfobj.r;
11 |
12 | [hei, wid] = size(p);
13 |
14 | mean_p = boxfilter(p, r) ./ gfobj.N;
15 |
16 | mean_Ip_r = boxfilter(gfobj.I(:, :, 1).*p, r) ./ gfobj.N;
17 | mean_Ip_g = boxfilter(gfobj.I(:, :, 2).*p, r) ./ gfobj.N;
18 | mean_Ip_b = boxfilter(gfobj.I(:, :, 3).*p, r) ./ gfobj.N;
19 |
20 | % covariance of (I, p) in each local patch.
21 | cov_Ip_r = mean_Ip_r - gfobj.mean_I_r .* mean_p;
22 | cov_Ip_g = mean_Ip_g - gfobj.mean_I_g .* mean_p;
23 | cov_Ip_b = mean_Ip_b - gfobj.mean_I_b .* mean_p;
24 |
25 | [i, j] = find(mask);
26 | a = zeros(hei, wid, 3);
27 |
28 | for k = 1:length(i)
29 | x = j(k);
30 | y = i(k);
31 | cov_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x)];
32 | a(y, x, :) = cov_Ip * gfobj.invSigma{y, x}; % Eqn. (14) in the paper;
33 | end
34 |
35 | b = mean_p - a(:, :, 1) .* gfobj.mean_I_r - a(:, :, 2) .* gfobj.mean_I_g - a(:, :, 3) .* gfobj.mean_I_b; % Eqn. (15) in the paper;
36 |
37 | q = (boxfilter(a(:, :, 1), r).* gfobj.I(:, :, 1)...
38 | + boxfilter(a(:, :, 2), r).* gfobj.I(:, :, 2)...
39 | + boxfilter(a(:, :, 3), r).* gfobj.I(:, :, 3)...
40 | + boxfilter(b, r)) ./ gfobj.N; % Eqn. (16) in the paper;
41 |
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/cviewDepthEstim/wmf/guidedfilter_rgbd_precompute.m:
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1 | function gfobj = guidedfilter_rgbd_precompute(I, r, eps)
2 | % guidedfilter_color_precompute Precomputation of the O(1) time guided filter using a color image as the guidance.
3 | %
4 | % - guidance image: I (should be a color (RGB) image)
5 | % - local window radius: r
6 | % - regularization parameter: eps
7 |
8 | %global gfobj;
9 |
10 | gfobj.I = I;
11 | gfobj.r = r;
12 | gfobj.eps = eps;
13 |
14 | hei = size(I,1);
15 | wid = size(I,2);
16 | gfobj.N = boxfilter(ones(hei, wid), r); % the size of each local patch; N=(2r+1)^2 except for boundary pixels.
17 |
18 | gfobj.mean_I_r = boxfilter(I(:, :, 1), r) ./ gfobj.N;
19 | gfobj.mean_I_g = boxfilter(I(:, :, 2), r) ./ gfobj.N;
20 | gfobj.mean_I_b = boxfilter(I(:, :, 3), r) ./ gfobj.N;
21 | gfobj.mean_I_d = boxfilter(I(:, :, 4), r) ./ gfobj.N;
22 |
23 | % variance of I in each local patch: the matrix Sigma in Eqn (14).
24 | % Note the variance in each local patch is a 3x3 symmetric matrix:
25 | % rr, rg, rb
26 | % Sigma = rg, gg, gb
27 | % rb, gb, bb
28 | gfobj.var_I_rr = boxfilter(I(:, :, 1).*I(:, :, 1), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_r;
29 | gfobj.var_I_rg = boxfilter(I(:, :, 1).*I(:, :, 2), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_g;
30 | gfobj.var_I_rb = boxfilter(I(:, :, 1).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_b;
31 | gfobj.var_I_rd = boxfilter(I(:, :, 1).*I(:, :, 4), r) ./ gfobj.N - gfobj.mean_I_r .* gfobj.mean_I_d;
32 | gfobj.var_I_gg = boxfilter(I(:, :, 2).*I(:, :, 2), r) ./ gfobj.N - gfobj.mean_I_g .* gfobj.mean_I_g;
33 | gfobj.var_I_gb = boxfilter(I(:, :, 2).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_g .* gfobj.mean_I_b;
34 | gfobj.var_I_gd = boxfilter(I(:, :, 2).*I(:, :, 4), r) ./ gfobj.N - gfobj.mean_I_g .* gfobj.mean_I_d;
35 | gfobj.var_I_bb = boxfilter(I(:, :, 3).*I(:, :, 3), r) ./ gfobj.N - gfobj.mean_I_b .* gfobj.mean_I_b;
36 | gfobj.var_I_bd = boxfilter(I(:, :, 3).*I(:, :, 4), r) ./ gfobj.N - gfobj.mean_I_b .* gfobj.mean_I_d;
37 | gfobj.var_I_dd = boxfilter(I(:, :, 4).*I(:, :, 4), r) ./ gfobj.N - gfobj.mean_I_d .* gfobj.mean_I_d;
38 |
39 | gfobj.invSigma = cell(hei, wid);
40 | for y=1:hei
41 | for x=1:wid
42 | Sigma = [gfobj.var_I_rr(y, x), gfobj.var_I_rg(y, x), gfobj.var_I_rb(y, x), gfobj.var_I_rd(y, x);
43 | gfobj.var_I_rg(y, x), gfobj.var_I_gg(y, x), gfobj.var_I_gb(y, x), gfobj.var_I_gd(y, x);
44 | gfobj.var_I_rb(y, x), gfobj.var_I_gb(y, x), gfobj.var_I_bb(y, x), gfobj.var_I_bd(y, x);
45 | gfobj.var_I_rd(y, x), gfobj.var_I_gd(y, x), gfobj.var_I_bd(y, x), gfobj.var_I_dd(y, x)];
46 | %Sigma = Sigma + eps * eye(3);
47 |
48 | gfobj.invSigma{y, x} = inv(Sigma + eps * eye(4)); % Eqn. (14) in the paper;
49 | end
50 | end
51 |
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/cviewDepthEstim/wmf/guidedfilter_rgbd_runfilter.m:
--------------------------------------------------------------------------------
1 | function q = guidedfilter_rgbd_runfilter(p, gfobj)
2 | % guidedfilter_color_runfilter Run O(1) time implementation of guided filter
3 | %
4 | % NOTE: you must call guidedfilter_color_precompute first!
5 | %
6 | % - filtering input image: p (should be a gray-scale/single channel image)
7 |
8 | %global gfobj;
9 |
10 | r = gfobj.r;
11 |
12 | [hei, wid] = size(p);
13 |
14 | mean_p = boxfilter(p, r) ./ gfobj.N;
15 |
16 | mean_Ip_r = boxfilter(gfobj.I(:, :, 1).*p, r) ./ gfobj.N;
17 | mean_Ip_g = boxfilter(gfobj.I(:, :, 2).*p, r) ./ gfobj.N;
18 | mean_Ip_b = boxfilter(gfobj.I(:, :, 3).*p, r) ./ gfobj.N;
19 | mean_Ip_d = boxfilter(gfobj.I(:, :, 4).*p, r) ./ gfobj.N;
20 |
21 | % covariance of (I, p) in each local patch.
22 | cov_Ip_r = mean_Ip_r - gfobj.mean_I_r .* mean_p;
23 | cov_Ip_g = mean_Ip_g - gfobj.mean_I_g .* mean_p;
24 | cov_Ip_b = mean_Ip_b - gfobj.mean_I_b .* mean_p;
25 | cov_Ip_d = mean_Ip_d - gfobj.mean_I_d .* mean_p;
26 |
27 | a = zeros(hei, wid, 4);
28 | for y=1:hei
29 | for x=1:wid
30 | cov_Ip = [cov_Ip_r(y, x), cov_Ip_g(y, x), cov_Ip_b(y, x) cov_Ip_d(y, x)];
31 | a(y, x, :) = cov_Ip * gfobj.invSigma{y, x}; % Eqn. (14) in the paper;
32 | end
33 | end
34 |
35 | b = mean_p - a(:, :, 1) .* gfobj.mean_I_r - a(:, :, 2) .* gfobj.mean_I_g - a(:, :, 3) .* gfobj.mean_I_b - a(:, :, 4) .* gfobj.mean_I_d; % Eqn. (15) in the paper;
36 |
37 | q = (boxfilter(a(:, :, 1), r).* gfobj.I(:, :, 1)...
38 | + boxfilter(a(:, :, 2), r).* gfobj.I(:, :, 2)...
39 | + boxfilter(a(:, :, 3), r).* gfobj.I(:, :, 3)...
40 | + boxfilter(a(:, :, 4), r).* gfobj.I(:, :, 4)...
41 | + boxfilter(b, r)) ./ gfobj.N; % Eqn. (16) in the paper;
42 |
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/cviewDepthEstim/wmf/img_jpeg/20.jpg:
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https://raw.githubusercontent.com/brownvc/lightfielddepth/9e6344c9fe6122cc1e6d1c83cf26e8bd4d28d3ed/cviewDepthEstim/wmf/img_jpeg/20.jpg
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/cviewDepthEstim/wmf/img_jpeg/28.jpg:
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https://raw.githubusercontent.com/brownvc/lightfielddepth/9e6344c9fe6122cc1e6d1c83cf26e8bd4d28d3ed/cviewDepthEstim/wmf/img_jpeg/28.jpg
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/cviewDepthEstim/wmf/img_jpeg/31.jpg:
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https://raw.githubusercontent.com/brownvc/lightfielddepth/9e6344c9fe6122cc1e6d1c83cf26e8bd4d28d3ed/cviewDepthEstim/wmf/img_jpeg/31.jpg
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/cviewDepthEstim/wmf/weighted_median_filter.m:
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1 | function dispOut = weighted_median_filter(dispIn, imgGuide, vecDisps, r, epsilon)
2 | %weighted_median_filter - Weighted median filter with guided filter weights
3 | %
4 | % dispOut = weighted_median_filter(dispIn, imgGuide, vecDisps, r, epsilon)
5 | %
6 | % INPUT:
7 | %
8 | % dispIn - Input 1-channel discrete disparity map, disparities must
9 | % come from vecDisps
10 | % imgGuide - Input guidance image, should be 3-channel RGB
11 | % vecDisps - Vector of disparities in consideration, must be intergers
12 | % r - Local window radius for guided filter weights
13 | % epsilon - Regularization parameter for guided filter weights
14 | %
15 |
16 | if ~exist('epsilon', 'var')
17 | epsilon = 0.01;
18 | end
19 |
20 | imgGuide = im2double(imgGuide);
21 |
22 | dispOut = zeros( size(dispIn) );
23 | imgAccum = zeros( size(dispIn) );
24 |
25 | gfObj = guidedfilter_color_precompute(imgGuide, r, epsilon);
26 |
27 | I = zeros( size(dispOut, 1), size(dispOut, 2), length(vecDisps));
28 |
29 | parfor (d = 1:numel(vecDisps), 18)
30 | img01 = guidedfilter_color_runfilter(double(dispIn == vecDisps(d)), gfObj);
31 | I(:, :, d) = img01;
32 | end
33 |
34 | % TODO: use cumsum
35 | for d = 1 : numel(vecDisps)
36 | % accumulation to find median disp. for each pixel
37 | imgAccum = imgAccum + I(:, :, d);
38 | idxSelected = (imgAccum > 0.5) & (dispOut == 0);
39 | dispOut(idxSelected) = d;
40 | end
41 |
42 | dispOut = cast(dispOut, class(dispIn));
43 |
44 |
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/cviewDepthEstim/wmf/weighted_median_filter_approx.m:
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1 | function imgOut = weighted_median_filter_approx(imgIn, imgGuide, r, epsilon, nBins, sigmaHist)
2 | %weighted_median_filter_approx - Approximated version of weighted median filter with guided filter weights for images
3 | %
4 | % imgOut = weighted_median_filter_approx(imgIn, imgGuide, r, epsilon, nBins, sigmaHist)
5 | %
6 | % INPUT:
7 | %
8 | % imgIn - Input image
9 | % imgGuide - Input guidance image, should be 3-channel RGB
10 | % r - Local window radius for guided filter weights
11 | % epsilon - Regularization parameter for guided filter weights
12 | % nBins - Number of bins for quantizing
13 | % sigmaHist - Sigma for Gaussian smoothed histogram
14 | %
15 | %
16 | % Algorithm:
17 | %
18 | % This approximated version is for filtering images (usually with 256 labels) only. It constructs a
19 | % Gaussian smoothed weighted histogram, then the median can be found via integrating this histogram.
20 | % This is done in our code as convolving each slice with the erf function, which is essentially a low-
21 | % pass filtering, hence downsampling in range space can be used.
22 | %
23 |
24 | if ~exist('epsilon', 'var')
25 | epsilon = 0.01;
26 | end
27 |
28 | if ~exist('nBins', 'var')
29 | nBins = 32;
30 | end
31 |
32 | if ~exist('sigmaHist', 'var')
33 | sigmaHist = 0.06;
34 | end
35 |
36 | imgIn = im2double(imgIn);
37 | imgGuide = im2double(imgGuide);
38 | imgOut = zeros( size(imgIn) );
39 |
40 | vecDisps = linspace(0, 1, nBins);
41 |
42 | gfObj = guidedfilter_color_precompute(imgGuide, r, epsilon);
43 |
44 | hei = size(imgIn,1);
45 | wid = size(imgIn,2);
46 | weightedIntegralHist = zeros(hei, wid, nBins);
47 |
48 | for c = 1 : size(imgIn,3)
49 | fprintf('Computing channel: %d\n', c);
50 | for d = 1 : nBins
51 | fprintf('%d of %d\n', d, nBins);
52 |
53 | % apply guided filter to the Gaussian integral slice
54 | weightedIntegralHist(:,:,d) = ...
55 | guidedfilter_color_runfilter(...
56 | gaussian_integral(vecDisps(d), imgIn(:,:,c), sigmaHist), gfObj);
57 | end
58 |
59 | % find the interpolated median
60 |
61 | targetVal = 0.49;
62 |
63 | imgOutc = zeros(hei, wid);
64 |
65 | for d = 1 : nBins-1
66 | imgBin1 = weightedIntegralHist(:,:,d);
67 | imgBin2 = weightedIntegralHist(:,:,d+1);
68 |
69 | bin1Val = vecDisps(d);
70 | bin2Val = vecDisps(d+1);
71 |
72 | frac = (targetVal-imgBin1) ./ (imgBin2-imgBin1);
73 | interpolated = bin1Val + frac * (bin2Val - bin1Val);
74 |
75 | idx = imgBin1=targetVal;
76 | imgOutc(idx) = interpolated(idx);
77 | end
78 |
79 | imgOut(:,:,c) = imgOutc;
80 | end
81 |
82 | end
83 |
84 | %--------------------------------------------------------------------------------------
85 | function y = gaussian_integral(x, mu, sigma)
86 | y = 0.5 * ( 1 + erf((x-mu) / (sigma * 1.41421356237)) );
87 | end
88 |
--------------------------------------------------------------------------------
/cviewDepthEstim/wmf/weighted_median_filter_mask.m:
--------------------------------------------------------------------------------
1 | function dispOut = weighted_median_filter_mask(dispIn, imgGuide, mask, vecDisps, r, epsilon)
2 | %weighted_median_filter - Weighted median filter with guided filter weights
3 | %
4 | % dispOut = weighted_median_filter(dispIn, imgGuide, vecDisps, r, epsilon)
5 | %
6 | % INPUT:
7 | %
8 | % dispIn - Input 1-channel discrete disparity map, disparities must
9 | % come from vecDisps
10 | % imgGuide - Input guidance image, should be 3-channel RGB
11 | % vecDisps - Vector of disparities in consideration, must be intergers
12 | % r - Local window radius for guided filter weights
13 | % epsilon - Regularization parameter for guided filter weights
14 | %
15 |
16 | if ~exist('epsilon', 'var')
17 | epsilon = 0.01;
18 | end
19 |
20 | imgGuide = im2double(imgGuide);
21 |
22 | dispOut = zeros( size(dispIn) );
23 | imgAccum = zeros( size(dispIn) );
24 |
25 | gfObj = guidedfilter_color_precompute(imgGuide, r, epsilon);
26 |
27 | I = zeros( size(dispOut, 1), size(dispOut, 2), length(vecDisps));
28 |
29 | parfor d = 1:numel(vecDisps)
30 | img01 = guidedfilter_color_runfilter_mask(double(dispIn == vecDisps(d)), mask, gfObj);
31 | I(:, :, d) = img01;
32 | end
33 |
34 | for d = 1 : numel(vecDisps)
35 | %fprintf('%d of %d\n', d, numel(vecDisps));
36 |
37 | % apply guided filter to each slice
38 | % img01 = guidedfilter_color_runfilter(double(dispIn == vecDisps(d)));
39 |
40 | % accumulation to find median disp. for each pixel
41 | imgAccum = imgAccum + I(:, :, d); %img01;
42 | idxSelected = (imgAccum > 0.5) & (dispOut == 0);
43 | dispOut(idxSelected) = d;
44 | end
45 |
46 | dispOut = cast(dispOut, class(dispIn));
47 |
48 |
--------------------------------------------------------------------------------
/depth/reproj.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% This method was adapted from Jiang et al.'s code for performing forward
3 | %% projection of depth and color. We only use it to reproject depth.
4 | %% Jiang et al.'s code can be found at:
5 | %% http://clim.inria.fr/research/DepthEstim/DepthEstim.zip
6 | %%
7 | function [outputFW, maskFW] = reproj(disparity_ref, delY, delX)
8 |
9 | [h, w] = size(disparity_ref);
10 | [X, Y] = meshgrid(1:w, 1:h);
11 |
12 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
13 | %%% Forward propagation from disparity map of the knwon view %%%
14 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
15 | curX_forward = round(X - delX * disparity_ref);
16 | curY_forward = round(Y - delY * disparity_ref);
17 |
18 | maskFW = false(h,w);
19 | %DispFW = zeros(h, w);
20 | DispFW = ones(h, w)*(-1000);
21 | DispFwIdx = zeros(h, w);
22 | NonOverlapped = false(h,w);
23 |
24 | for H = 1:h
25 | for W = 1:w
26 |
27 | if curY_forward(H,W)<=h && curX_forward(H,W)<=w && curY_forward(H,W)>=1 && curX_forward(H,W)>=1
28 | if(~maskFW(curY_forward(H,W),curX_forward(H,W)) )
29 | maskFW(curY_forward(H,W),curX_forward(H,W)) = true;
30 | DispFW(curY_forward(H,W),curX_forward(H,W)) = disparity_ref(H,W);
31 | DispFwIdx( curY_forward(H, W), curX_forward(H, W) ) = sub2ind( [h, w], H, W);
32 | NonOverlapped(H, W) = true;
33 |
34 | elseif (disparity_ref(H,W) > DispFW(curY_forward(H,W),curX_forward(H,W)) )
35 | maskFW(curY_forward(H,W),curX_forward(H,W)) = true;
36 | DispFW(curY_forward(H,W),curX_forward(H,W)) = disparity_ref(H,W);
37 |
38 | if DispFwIdx( curY_forward(H, W), curX_forward(H, W) ) ~= 0
39 | NonOverlapped( DispFwIdx( curY_forward(H, W), curX_forward(H, W) ) ) = false;
40 | end
41 | DispFwIdx( curY_forward(H, W), curX_forward(H, W) ) = sub2ind( [h, w], H, W);
42 | NonOverlapped(H, W) = true;
43 | end
44 |
45 | end
46 | end
47 | end
48 |
49 | curX_forward = double(X - delX * disparity_ref);
50 | curY_forward = double(Y - delY * disparity_ref);
51 | curX_forward = curX_forward(NonOverlapped);
52 | curY_forward = curY_forward(NonOverlapped);
53 |
54 | outputFW = zeros(h,w);
55 | ImgRefVec = disparity_ref(NonOverlapped);
56 | outputFW = griddata(curX_forward, curY_forward, ImgRefVec, X, Y, 'linear');
57 | outputFW = reshape(outputFW,[h,w]);
58 | maskFW = maskFW & ~isnan(outputFW);
59 | end
60 |
--------------------------------------------------------------------------------
/depth/reproj2offcenter.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Reproject depth to a non-crosshair view (which can be a novel view)
3 | %% using the depth of the cross hair views provided in U and V
4 | %%
5 | function [D, M] = reproj2offcenter(V, U, vo, uo, param)
6 |
7 | uIdx = min(max(1, round(uo)), param.szLF(4));
8 | vIdx = min(max(1, round(vo)), param.szLF(3));
9 |
10 | [ur, um] = reproj(U(:, :, uIdx), param.cviewIdx - vo, 0);
11 | [vr, vm] = reproj(V(:, :, vIdx), 0, param.cviewIdx - uo);
12 |
13 | vm = medfilt2(vm, [3 3]);
14 | um = medfilt2(um, [3 3]);
15 |
16 | M = cat(3, um, vm);
17 | R = cat(3, ur, vr);
18 | R(isnan(R)) = 0;
19 | mc = num2cell(M, 3);
20 | rc = num2cell(R, 3);
21 |
22 | D = cellfun(@(ri, mi) mean(ri(mi ~= 0)), rc, mc);
23 | D(isnan(D)) = 0;
24 | M = sum(M, 3);
25 | end
26 |
27 |
--------------------------------------------------------------------------------
/depth/splat.m:
--------------------------------------------------------------------------------
1 | %
2 | % Splat depth labels, weights, and point ids onto an image
3 | %
4 | function [M, S, Id] = splat(D, W, sz)
5 | [D, o] = sortrows(D, 3); % sort so that foreground points are drawn after
6 |
7 | D(:, [1 2]) = round(D(:, [1 2]));
8 | oIdx = D(:, 1) < 1 | D(:, 1) > sz(2) | D(:, 2) < 1 | D(:, 2) > sz(1);
9 |
10 | if size(D, 1) == size(W, 1)
11 | W = W(o);
12 | W(oIdx, :) = [];
13 | end
14 | D(oIdx, :) = [];
15 | sIdx = sub2ind( sz, D(:, 2), D(:, 1) );
16 |
17 | S = zeros(sz);
18 | S(sIdx) = D(:, 3);
19 |
20 | M = zeros(sz);
21 | M(sIdx) = W;
22 |
23 | Id = zeros(sz);
24 | Id(sIdx) = o(~oIdx);
25 | end
26 |
--------------------------------------------------------------------------------
/depth/trilatFilt.m:
--------------------------------------------------------------------------------
1 | %
2 | % Perform trilateral filtering of points in color, depth, and spatial domains
3 | %
4 | function [Q, urIdx] = trilatFilt(P, V, LF, param)
5 |
6 | % Get the color of each point from a view it is visible in.
7 | %
8 |
9 | % We select the visible view closest to the center
10 | [~, v] = min(abs(V .* [1:param.szLF(3) 1:param.szLF(4)] - 5), [], 2);
11 |
12 | % Group points by their visible view
13 | [U, ~, X] = unique(v);
14 | A = accumarray(X, 1:size(P,1), [], @(r) {P(r, :)});
15 | O = accumarray(X, 1:size(P,1), [], @(r) {r} ); % This is used to unsort A later
16 | C = {};
17 |
18 | % Calculate color by interpolating at any subpixel locations
19 | for i = 1:length(U)
20 | ui = (U(i) > param.szLF(3)) * (U(i) - param.szLF(3)) + (U(i) <= param.szLF(3)) * (param.cviewIdx);
21 | vi = (U(i) > param.szLF(3)) * (param.cviewIdx) + (U(i) <= param.szLF(3)) * U(i);
22 |
23 | % project points to current view
24 | pi = A{i};
25 | pi(:, [1 2]) = [ pi(:, 1) + pi(:, 3) .* (ui - param.cviewIdx) ...
26 | pi(:, 2) + pi(:, 3) .* (vi - param.cviewIdx) ];
27 | lf = LF{U(i)};
28 |
29 | ci1 = interp2( lf(:, :, 1), pi(:, 1), pi(:, 2) );
30 | ci2 = interp2( lf(:, :, 2), pi(:, 1), pi(:, 2) );
31 | ci3 = interp2( lf(:, :, 3), pi(:, 1), pi(:, 2) );
32 | C(i) = {[ci1 ci2 ci3]};
33 | end
34 |
35 | Q = vertcat(A{:});
36 | C = vertcat(C{:});
37 | X = Q;
38 |
39 | % Rescale the spatial coordinates to accomodate points that overflow the center view
40 | viewportSz = [max(X(:, 2)) - min(X(:, 2)) max(X(:, 1)) - min(X(:, 1))] + 1;
41 | X(:, 1) = X(:, 1) - min(X(:, 1)) + 1;
42 | X(:, 2) = X(:, 2) - min(X(:, 2)) + 1;
43 |
44 | upscaleFactor = 2;
45 | sigmas = const.trilatFiltWinSz ./ 1;
46 | sigmad = param.maxAbsDisparity / 20;
47 | sigmac = 0.5;
48 | winSz = ceil(const.trilatFiltWinSz / 2) * upscaleFactor;
49 |
50 | sz = param.szLF([1 2]) .* upscaleFactor;
51 | X(:, 1) = X(:, 1) .* upscaleFactor;
52 | X(:, 2) = X(:, 2) .* upscaleFactor;
53 | [~, ~, idx] = splat(X, 1, sz);
54 |
55 | d = X(:, 3);
56 | urIdx = zeros(size(d), 'logical');
57 |
58 | parfor (i = 1:size(X, 1), 6)
59 | pi = X(i, :);
60 | pc = C(i, :);
61 |
62 | win = idx( max(1, round(pi(2)) - winSz): min(sz(1), round(pi(2)) + winSz), ...
63 | max(1, round(pi(1)) - winSz): min(sz(2), round(pi(1)) + winSz) );
64 | nidx = win(find(win));
65 | nidx = nidx(nidx ~= i); % Exclude the current point
66 |
67 | if sum(sum(nidx > 0)) < 5 %(isempty(nidx))
68 | urIdx(i) = true;
69 | continue;
70 | end
71 |
72 | pn = X(nidx, :);
73 | nd = pn(:, 3); % The neighbors' depth
74 | ns = pn(:, [1 2]); % The neighbors' spatial position
75 | nc = C(nidx, :); % The neighbors' (interpolated) color
76 |
77 | wd = normpdf( nd - pi(3), 0, sigmad );
78 | ws = normpdf( sqrt(sum((ns - pi([1 2])).^2, 2)), 0, sigmas);
79 | wc = normpdf( sqrt(sum((nc - pc).^2, 2)), 0, sigmac);
80 |
81 | w = max(wd .* ws .* wc, eps);
82 |
83 | d(i) = sum(w .* nd) ./ sum(w);
84 | end
85 |
86 | Q(:, 3) = d;
87 | O = vertcat(O{:});
88 | [~, S] = sort(O);
89 | Q = Q(S, :);
90 | urIdx = urIdx(S, :);
91 | end
92 |
--------------------------------------------------------------------------------
/eval/badpixels.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Bad pixels is defined as the percentage of pixels with an error below
3 | %% a specified threshold t
4 | %%
5 | function e = badpixels(dmap, dgt, t)
6 | e = sum(sum(abs(dmap - dgt) > t)) ./ (size(dmap, 1) * size(dmap, 2));
7 | end
8 |
--------------------------------------------------------------------------------
/eval/mse.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Mean squared error times 100
3 | %%
4 | function e = mse(dmap, dgt)
5 | e = mean((dmap - dgt).^2, 'all') * 100;
6 | end
7 |
--------------------------------------------------------------------------------
/eval/pairwiseconst.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Return a symmetric num_views x num_views matrix representing the view consistency
3 | %% between each pair of views in the light field
4 | %%
5 | function C = pairwiseconst(D)
6 | szLF = size(D);
7 | numViews = szLF(3) * szLF(4);
8 |
9 | C = zeros( numViews, numViews);
10 | Dc = reshape(num2cell(D, [1 2]), 1, numViews);
11 |
12 | for i = 1:numViews
13 | [vs, us] = ind2sub( szLF([3 4]), i); % source view indices
14 | di = D(:, :, vs, us);
15 | ci = zeros(1, numViews);
16 |
17 | parfor (j = 1:numViews, 24)
18 |
19 | if i >= j
20 | ci(j) = 0;
21 | continue;
22 | end
23 |
24 | [vt, ut] = ind2sub( [szLF(3) szLF(4)], j); % target view indices
25 |
26 | % Project target onto source
27 | [r, m] = reproj( Dc{j}, 0, vt - vs, ut - us);
28 |
29 | R = [r(:) di(:)];
30 | R(isnan(R)) = 0;
31 | R(~m(:), :) = 0;
32 | vi = var(R, [], 2);
33 | vi(~m(:)) = 0; % ignore disoccluded pixels
34 |
35 | ci(j) = mean(vi, 'all');
36 | end
37 | C(:, i) = ci;
38 | end
39 |
40 | end
41 |
--------------------------------------------------------------------------------
/lahbpcg_mex.cpp:
--------------------------------------------------------------------------------
1 | #include
2 | #include "mex.h"
3 | #include "./ImageStack/src/main.h"
4 | #include "./ImageStack/src/Image.h"
5 | #include "./ImageStack/src/LAHBPCG.h"
6 |
7 | using namespace std;
8 |
9 | void lahbpcg_mex(double *d, double *w, double *gx, double *gy, double *o, double niter, double merr, mwSize m, mwSize n) {
10 | ImageStack::Image data(n, m, 1, 1);
11 | ImageStack::Image grad(n, m, 1, 1);
12 | ImageStack::Image data_weight(n, m, 1, 1);
13 | ImageStack::Image gradx_weight(n, m, 1, 1);
14 | ImageStack::Image grady_weight(n, m, 1, 1);
15 |
16 | //cout << "[n, m] = [" << int(n) << ", " << int(m) << "]" <apply(data, grad, grad, data_weight, gradx_weight, grady_weight, niter, merr);
32 |
33 | for (int x = 0; x < n; x++) {
34 | for (int y = 0; y < m; y++) {
35 | int idx = x * m + y;
36 | o[idx] = result(x, y);
37 | }
38 | }
39 |
40 | delete oplahbpcg;
41 | }
42 |
43 |
44 | void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {
45 | if(nrhs != 6)
46 | mexErrMsgIdAndTxt("MexLAHBPCG:nrhs", "Six inputs required.");
47 |
48 | if(nlhs != 1) {
49 | mexErrMsgIdAndTxt("MexLAHBPCG:nlhs", "One output required.");
50 | }
51 |
52 | double *d = mxGetDoubles(prhs[0]);
53 | double *w = mxGetDoubles(prhs[1]);
54 | double *gx = mxGetDoubles(prhs[2]);
55 | double *gy = mxGetDoubles(prhs[3]);
56 | double niter = mxGetScalar(prhs[4]);
57 | double merr = mxGetScalar(prhs[5]);
58 | plhs[0] = mxCreateDoubleMatrix(mxGetM(prhs[0]), mxGetN(prhs[0]), mxREAL);
59 | double *o = mxGetDoubles(plhs[0]);
60 |
61 |
62 | //cout << "Calling" << endl;
63 | lahbpcg_mex(d, w, gx, gy, o, niter, merr, mxGetM(prhs[0]), mxGetN(prhs[0]) );
64 | }
65 |
66 |
67 |
68 |
--------------------------------------------------------------------------------
/lahbpcg_mex.mexmaci64:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/brownvc/lightfielddepth/9e6344c9fe6122cc1e6d1c83cf26e8bd4d28d3ed/lahbpcg_mex.mexmaci64
--------------------------------------------------------------------------------
/lines/edges2lines.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Given an edge confidence map E and a slope map Z, fit a line
3 | %% to the edges. The slope of each line is provided in m.
4 | %%
5 | %% The function returns a line set L, where each line is defined by its
6 | %% top and bottom intercept on an EPI.
7 | %%
8 | function L = edges2lines( E, Z, m )
9 | szEpi = size(E);
10 |
11 | L = cell(szEpi(3), 1);
12 |
13 | parfor i = 1:szEpi(3)
14 | lines = fitLinesEPI( E(:, :, i), Z(:, :, i), m);
15 |
16 | if isempty(lines)
17 | lines = [0 0];
18 | end
19 | L(i) = {lines};
20 | end
21 |
22 | end
23 |
--------------------------------------------------------------------------------
/lines/epis2edges.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Detect edges by directional filtering with the filters provided
3 | %% in F. The gradient of each filter is specified in gx, and gy
4 | %%
5 | function [E, Z] = epis2edges( EPI, F, m, gx, gy )
6 | szEpi = [size(EPI, 1) size(EPI, 2) size(EPI, 4)];
7 | E = zeros( szEpi );
8 | Z = zeros( szEpi );
9 |
10 | parfor i = 1:szEpi(3)
11 | [E(:, :, i), Z(:, :, i), ~] = findEdgesEPI( EPI(:, :, :, i), F, m, gx, gy);
12 | end
13 |
14 | end
15 |
--------------------------------------------------------------------------------
/lines/filterOutliers.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Remove outliers from the set of EPI lines L.
3 | %% An outlier is a line whos gradient doesn't match the gradient of the corresponding EPI
4 | %% at a minimum specified number of sample points
5 | %%
6 | function L = filterOutliers(L, EPI, gxEPI, gyEPI, param)
7 |
8 | szEPI = size(EPI);
9 |
10 | for i = 1:length(L)
11 | lines = L{i};
12 | if isempty(lines)
13 | continue;
14 | end
15 |
16 | %
17 | % Remove lines who's gradients don't match EPI gradients over a minimum number of views
18 |
19 | % Get EPI gradients
20 | imgx = gxEPI(:, :, i);
21 | imgy = gyEPI(:, :, i);
22 |
23 | % Calculate line gradients
24 | gx = szEPI(1);
25 | gy = lines(:, 1) - lines(:, 2);
26 | gm = 1 ./ sqrt(gx.^2 + gy.^2);
27 | gx = gx .* gm;
28 | gy = gy .* gm;
29 |
30 | idx = ones(size(lines, 1), 1, 'logical');
31 |
32 | for j = 1:size(lines, 1)
33 | x = [lines(j, 1) lines(j, 2)];
34 | y = [1 szEPI(1)];
35 |
36 | nPoints = szEPI(1);
37 | rIndex = [y(1):y(2)];
38 | cIndex = max(1, min(round(linspace(x(1), x(2), nPoints)), szEPI(2)));
39 | index = sub2ind([szEPI(1) szEPI(2)], rIndex, cIndex);
40 |
41 | c = abs(gx(j) .* imgx(index) + gy(j) .* imgy(index));
42 |
43 | c = c > const.filtOutliersGradientDirectionThresh;
44 | if max(accumarray( [cumsum(diff([0 c]) == 1) .* c + 1]', c)) < const.filtOutliersMinContigPixels
45 | idx(j) = 0;
46 | end
47 |
48 | end
49 | L(i) = {lines(idx, :)};
50 | end
51 |
52 | end
53 |
--------------------------------------------------------------------------------
/lines/findEdgesEPI.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Find edges in a single EPI E by directional filtering with
3 | %% the filters provided in F.
4 | %%
5 | function [e, z, c] = findEdgesEPI(E, F, m, gx, gy)
6 |
7 | % Directional filtering
8 | l = zeros( size(E, 1), size(E, 2), size(F, 3));
9 | a = zeros(size(l));
10 | b = zeros(size(l));
11 |
12 | for i = 1:size(F, 3)
13 | l(:, :, i) = abs(conv2(E(:, :, 1), F(:, :, i), 'same'));
14 | a(:, :, i) = abs(conv2(E(:, :, 2), F(:, :, i), 'same'));
15 | b(:, :, i) = abs(conv2(E(:, :, 3), F(:, :, i), 'same'));
16 | end
17 |
18 | % Select filter value with maximum response at each pixel
19 | [ml, mlIdx] = max(l, [], 3);
20 | [ma, maIdx] = max(a, [], 3);
21 | [mb, mbIdx] = max(b, [], 3);
22 |
23 | c = max(cat(3, ml, ma, mb), [], 3);
24 |
25 | % Non-maximal suppression
26 | nl = nms(ml, [gx(mlIdx(:))' gy(mlIdx(:))']) .* ml;
27 | na = nms(ma, [gx(maIdx(:))' gy(maIdx(:))']) .* ma;
28 | nb = nms(mb, [gx(mbIdx(:))' gy(mbIdx(:))']) .* mb;
29 |
30 | % Standard-deviation based modulation
31 | vl = stdfilt( E(:, :, 1) ) .* nl;
32 | va = stdfilt( E(:, :, 2) ) .* na;
33 | vb = stdfilt( E(:, :, 3) ) .* nb;
34 | vl = nl; va = na; vb = nb;
35 |
36 | % Select as edge value the maximum across all channels...
37 | [e, eIdx] = max(cat(3, vl, va, vb), [], 3);
38 |
39 | % ... setting the corresponding pixel values in the slope map
40 | z = (eIdx == 1) .* mlIdx + (eIdx == 2) .* maIdx + (eIdx == 3) .* mbIdx;
41 | end
42 |
--------------------------------------------------------------------------------
/lines/fitLinesEPI.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Fit a line to each non-zero pixel in c according to the slope map z
3 | %% The lines are specified by their top and bottom intercepts on the EPI
4 | %%
5 | function l = fitLinesEPI(c, z, m, EPI)
6 |
7 | l = [];
8 |
9 | while sum(sum(c)) > 0
10 |
11 | % We start by fitting the most confident lines
12 | [~, maxIdx] = max(c(:));
13 | [i, j] = ind2sub( size(c), maxIdx );
14 |
15 | % Disregard values along the top and bottom edges of the EPI as
16 | % these are usually noisy
17 | if(i == 1 | i == size(c, 1)) | (j == 1 | j == size(c, 2))
18 | c(i, j) = 0;
19 | continue;
20 | end
21 | p = [i, j];
22 |
23 | %
24 | % Fit a line at point p.
25 | % A line template is fit according to the slope at point p in the slope map z.
26 | tIdx = z( p(1), p(2) );
27 |
28 | %
29 | % Clear all pixels in the edge map around the fit line.
30 | % To do this, we calculate the perpendicular distance of each non-zero pixel in
31 | % c from the line with slope m(tIdx) passing through p.
32 |
33 | % A point on the line, in addition to p
34 | q = [0; p(2) + p(1) ./ m(tIdx)];
35 |
36 | % The pixels for which we want to calculate the distance from the line
37 | [Y, X] = find(c > 0);
38 |
39 | % Calculate perpendical distance from our fit line
40 | D = abs(X .* (q(1) - p(1)) - Y .* (q(2) - p(2)) + q(2) * p(1) - q(1) * p(2)) ./ ...
41 | sqrt( (q(1) - p(1)) .^ 2 + (q(2) - p(2)) .^ 2 );
42 |
43 | % Select pixels with distance less than desired threshold
44 | D = D < round((const.SegMinWidthMultiplier * size(c, 1)));
45 | idx = sub2ind(size(c), Y, X);
46 | proximalPtsIdx = D .* idx;
47 | proximalPtsIdx = proximalPtsIdx( proximalPtsIdx > 0);
48 |
49 | l = cat(1, l, [tIdx i j]);
50 |
51 | c(i, j) = 0;
52 | c(proximalPtsIdx) = 0;
53 | end
54 |
55 | if ~isempty(l)
56 | % Get the intercepts of the templates on the top and bottom of the EPI
57 | m = m(l(:, 1))';
58 | xt = l(:, 3) + (l(:, 2) - 1) ./ m;
59 | xb = l(:, 3) + (l(:, 2) - size(c, 1)) ./ m;
60 | l = [xt xb];
61 | end
62 | end
63 |
--------------------------------------------------------------------------------
/lines/genFilters.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Generate 2 * param.nDisparity symmetric directional Prewitt filters.
3 | %% The filters are twice as high as the height of the EPI.
4 | %%
5 | %% The gradient of each filter along the x, and y directions is returned in gx,
6 | %% and gy respectively. The slope in m.
7 | %%
8 | function [F, m, gx, gy] = genFilters(param)
9 |
10 | n = ceil(param.nDisparity/2);
11 | s = expspace( -param.maxAbsDisparity, 0, -1, n);
12 |
13 | h = 2 * param.szEPI(1) + 1;
14 | w = ceil(max(h, max(abs(s)) * h + 2));
15 | if mod(w, 2) == 0
16 | w = w + 1;
17 | end
18 | xc = ceil(w/2);
19 | yc = ceil(h/2);
20 |
21 | F = zeros(h, w, n * 2 - 1);
22 | gx = zeros(1, n * 2 - 1);
23 | gy = zeros(1, n * 2 - 1);
24 | m = zeros(1, n * 2 - 1);
25 |
26 | [X, Y] = meshgrid(1:w, 1:h);
27 |
28 | for i = 1:n
29 | x = [xc + s(i) * (yc - 1) xc + s(i) * (yc - h)];
30 | y = [1 h];
31 |
32 | bwPos = zeros(h, w);
33 | bwPos = wu( bwPos, x(1) - 1, y(1), x(2) - 1, y(2));
34 | bwPos = bwPos / sum(sum(bwPos));
35 |
36 | bwNeg = zeros(h, w);
37 | bwNeg = wu( bwNeg, x(1) + 1, y(1), x(2) + 1, y(2));
38 | bwNeg = (bwNeg / sum(sum(bwNeg))) .* -1;
39 |
40 | F(:, :, i) = bwNeg + bwPos;
41 |
42 | % Calculate the slope and the gradients of the filters
43 | dy = h - 1;
44 | dx = x(1) - x(2);
45 | dm = 1 ./ sqrt(dx.^2 + dy.^2);
46 | gy(i) = -dx .* dm;
47 | gx(i) = dy .* dm;
48 | m(i) = dy ./ dx;
49 | end
50 |
51 | % the next n filters are generated by flipping the existing ones
52 | F(:, :, n+1:end) = fliplr(F(:, :, n-1:-1:1)) .* -1;
53 | gx(n+1:end) = gx(n-1:-1:1);
54 | gy(n+1:end) = -gy(n-1:-1:1);
55 | m(n+1:end) = -m(n-1:-1:1);
56 | end
57 |
58 |
59 |
--------------------------------------------------------------------------------
/lines/lf2edges4d.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Edge detection and line fitting
3 | %%
4 | function [P, V] = lf2edges4d(EPIuc, EPIvc, param)
5 |
6 | % Get EPI gradient images
7 | for i = 1:size(EPIvc, 4)
8 | [gxEPIv(:, :, i) gyEPIv(:, :, i)] = imgradientxy(EPIvc(:, :, 1, i));
9 | gm = sqrt(gxEPIv(:, :, i).^2 + gyEPIv(:, :, i).^2);
10 | gxEPIv(:, :, i) = gxEPIv(:, :, i) ./ gm;
11 | gyEPIv(:, :, i) = gyEPIv(:, :, i) ./ gm;
12 | end
13 |
14 | for i = 1:size(EPIuc, 4)
15 | [gxEPIu(:, :, i) gyEPIu(:, :, i)] = imgradientxy(EPIuc(:, :, 1, i));
16 | gm = sqrt(gxEPIu(:, :, i).^2 + gyEPIu(:, :, i).^2);
17 | gxEPIu(:, :, i) = gxEPIu(:, :, i) ./ gm;
18 | gyEPIu(:, :, i) = gyEPIu(:, :, i) ./ gm;
19 | end
20 |
21 | % Generate the filters for edge detection, ...
22 | [F, m, gx, gy] = genFilters(param);
23 |
24 | % Calculate the edge confidence and slope maps
25 | [Eu, Zu] = epis2edges( EPIuc, F, m, gx, gy);
26 | [Ev, Zv] = epis2edges( EPIvc, F, m, gx, gy);
27 |
28 | % Fit lines to the edges
29 | Lu = edges2lines(Eu, Zu, m);
30 | Lv = edges2lines(Ev, Zv, m);
31 |
32 | % Remove outliers
33 | Lu = filterOutliers(Lu, EPIuc, gxEPIu, gyEPIu, param);
34 | Lv = filterOutliers(Lv, EPIvc, gxEPIv, gyEPIv, param);
35 |
36 | % Gradient-based line slope(depth)refinement
37 | Lu = refineLineDepth(Lu, gxEPIu, gyEPIu, EPIuc);
38 | Lv = refineLineDepth(Lv, gxEPIv, gyEPIv, EPIvc);
39 |
40 | % Merge lines from vertical and horizontal EPIs
41 | P = merge(Lu, Lv, param.szLF);
42 |
43 | % Determine the visibility of points/lines in each cross-hair view as a boolean matrix V.
44 | % The rows of V represents points/lines; the columns represent the cross-hair views (ordered linearly as vu)
45 | V = pvisible(P, gxEPIu, gyEPIu, gxEPIv, gyEPIv);
46 |
47 | % The central light field view is represented twice - once in the central column and once in the central row.
48 | % If an EPI line/point detected in the *central column* of views is hidden in the central view,
49 | % it is set as hidden in the entire *central row*, and vice versa
50 | idxv = V(:, param.cviewIdx) == 0;
51 | idxu = V(:, param.cviewIdx + param.szLF(3)) == 0;
52 | V(idxv, param.szLF(3) + 1 : end) = 0;
53 | V(idxu, 1:param.szLF(3)) = 0;
54 |
55 | % Remove points visible in less than a minimum number of views as outliers
56 | idx = sum(V, 2) < const.visibilityMinViews;
57 | P(idx, :) = [];
58 | V(idx, :) = [];
59 | end
60 |
--------------------------------------------------------------------------------
/lines/merge.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Merge horizontal and vertical EPI lines into a single set of 3D points.
3 | %% Points in the set P are represented by their spatial position in the central view,
4 | %% along with their depth.
5 | %%
6 | function [P, puIdx, pvIdx] = merge(Lu, Lv, szLF)
7 |
8 | Lu = cellfun(@(l, i) [sum(l, 2) ./ 2, ... % x-coordinate
9 | repelem(i, size(l, 1), 1), ... % y-coordinate
10 | (l(:, 2) - l(:, 1)) ./ (szLF(3) - 1)], ... % disparity
11 | Lu, num2cell([1:length(Lu)]'), 'UniformOutput', false);
12 |
13 | Lv = cellfun(@(l, i) [repelem(i, size(l, 1), 1), ... % x-coordinate
14 | sum(l, 2) ./ 2, ... % y-coordinate
15 | (l(:, 2) - l(:, 1)) ./ (szLF(3) - 1)], ... % disparity
16 | Lv, num2cell([1:length(Lv)]'), 'UniformOutput', false);
17 |
18 | Lu = vertcat(Lu{:});
19 | Lv = vertcat(Lv{:});
20 |
21 | P = [Lu; Lv];
22 | puIdx = zeros(size(P, 1), 1, 'logical');
23 | pvIdx = zeros(size(P, 1), 1, 'logical');
24 | puIdx([1:size(Lu, 1)]) = 1;
25 | pvIdx([size(Lu, 1) + 1:size(Lu, 1) + size(Lv, 1)]) = 1;
26 | end
27 |
--------------------------------------------------------------------------------
/lines/nms.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Non-Maximal Suppression
3 | %%
4 | function s = nms(im, g)
5 | h = size(im, 1);
6 | w = size(im, 2);
7 |
8 | g(:, 2) = -g(:, 2);
9 |
10 | % Get the direction of the two closest pixels in the gradient
11 | % direction on an integer grid.
12 | a = ceil(abs(g)) .* sign(g + eps);
13 | b = (abs(g(:, 1)) > abs(g(:, 2))) .* repmat([1 0], size(g, 1), 1) .* sign(g(:, 1) + eps) + ...
14 | (abs(g(:, 1)) <= abs(g(:, 2))) .* repmat([0 1], size(g, 1), 1) .* sign(g(:, 2) + eps);
15 | theta = atan( abs(g(:, 2)) ./ abs(g(:, 1)) );
16 |
17 | [X, Y] = meshgrid(1:w, 1:h);
18 | A = sub2ind( size(im), min( h, max(1, Y(:) + a(:, 2))), min( w, max(1, X(:) + a(:, 1))) );
19 | B = sub2ind( size(im), min( h, max(1, Y(:) + b(:, 2))), min( w, max(1, X(:) + b(:, 1))) );
20 |
21 | l1 = abs(pi/2 - theta)/ (pi/2);
22 | v = (1 - l1) .* im(A) + l1 .* im(B);
23 |
24 | % The closest pixels to the negative of the gradient
25 | c = -a;
26 | d = -b;
27 | C = sub2ind( size(im), min( h, max(1, Y(:) + c(:, 2))), min( w, max(1, X(:) + c(:, 1))) );
28 | D = sub2ind( size(im), min( h, max(1, Y(:) + d(:, 2))), min( w, max(1, X(:) + d(:, 1))) );
29 | w = (1 - l1) .* im(C) + l1 .* im(D);
30 |
31 | s = reshape( (im(:) > v) & (im(:) > w) , size(im, 1), size(im, 2) );
32 | end
33 |
--------------------------------------------------------------------------------
/lines/pvisible.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Get the cross-hair view indices in which each point is visible
3 | %%
4 | function V = pvisible(P, gxEPIu, gyEPIu, gxEPIv, gyEPIv)
5 |
6 | % Group points into lines by rounded y-coordinate
7 | [P, o] = sortrows(P, 2);
8 | [~, ~, X] = unique( round(P(:, 2)) );
9 | A = accumarray(X, 1:size(P, 1), [], @(r){P(r, :)});
10 |
11 | U = cell(size(A, 1), 1);
12 | for i = 1:size(A, 1)
13 | a = A{i};
14 | y = round(a(1, 2));
15 | if y < 1 | y > size(gxEPIu, 3)
16 | U(i) = {zeros( size(a, 1), size(gxEPIu, 1) )};
17 | continue;
18 | end
19 |
20 | lines = [a(:, 1) - floor(size(gxEPIu, 1) ./ 2) .* a(:, 3) ...
21 | a(:, 1) + floor(size(gxEPIu, 1) ./ 2) .* a(:, 3)];
22 |
23 | U(i) = {visibility(lines, gxEPIu(:, :, y), gyEPIu(:, :, y))};
24 | end
25 |
26 | % Reverse U to original point order
27 | [~, o] = sort(o);
28 | U = vertcat(U{:});
29 | P = P(o, :);
30 | U = U(o, :);
31 |
32 | % Group points into lines by rounded x-coordinate
33 | [P, o] = sortrows(P, 1);
34 | [~, ~, X] = unique( round(P(:, 1)) );
35 | A = accumarray(X, 1:size(P, 1), [], @(r){P(r, :)});
36 |
37 | V = cell(size(A, 1), 1);
38 | for i = 1:size(A, 1)
39 | a = A{i};
40 | x = round(a(1, 1));
41 | if x < 1 | x > size(gxEPIv, 3)
42 | V(i) = {zeros( size(a, 1), size(gxEPIv, 1) )};
43 | continue;
44 | end
45 |
46 | lines = [a(:, 2) - floor(size(gxEPIv, 1) ./ 2) .* a(:, 3) ...
47 | a(:, 2) + floor(size(gxEPIv, 1) ./ 2) .* a(:, 3)];
48 |
49 | V(i) = {visibility(lines, gxEPIv(:, :, x), gyEPIv(:, :, x))};
50 | end
51 |
52 | % Reverse V to original point order
53 | [~, o] = sort(o);
54 | V = vertcat(V{:});
55 | V = V(o, :);
56 |
57 | V = logical([V U]);
58 | end
59 |
--------------------------------------------------------------------------------
/lines/refineLineDepth.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Refine the depth of each EPI line by minimizing the entropy of a set of points
3 | %% sampled along the line.
4 | %%
5 | function [L, C] = refineLineDepth(L, gxEPI, gyEPI, EPI)
6 |
7 | parfor k = 1:length(L)
8 |
9 | lk = L{k};
10 |
11 | epigx = gxEPI(:, :, k);
12 | epigy = gyEPI(:, :, k);
13 |
14 | cprev = linesCost(lk, EPI(:, :, :, k));
15 |
16 | for j = 1:const.refineIterCount
17 | ti = const.refineTempStart * (const.refineTempAnnealFactor .^ (j - 1));
18 | dxt = rand(size(lk, 1), 1) * 2 * ti - ti;
19 | dxb = rand(size(lk, 1), 1) * 2 * ti - ti;
20 |
21 | lines = [lk(:, 1) + dxt lk(:, 2) + dxb];
22 | %c = refineCost(lines, epigx, epigy);
23 | c = linesCost(lines, EPI(:, :, :, k));
24 | % Update lines for which cost has reduced
25 | idx = c < cprev;
26 |
27 | lk(idx, :) = lines(idx, :);
28 | cprev(idx) = c(idx);
29 | end
30 | L(k) = {lk};
31 | C(k) = {cprev};
32 | end
33 |
34 | end
35 |
36 | %%
37 | %% The entropy-based cost of a given line depth assignment
38 | %%
39 | function c = linesCost(lines, epi)
40 | szEpi = [size(epi, 1) size(epi, 2)];
41 | c = zeros(size(lines, 1), 1);
42 | nSamples = 15;
43 |
44 | % The fractional x values of each line at integer y coordinates
45 | %
46 | m = (lines(:, 1) - lines(:, 2)) ./ (szEpi(1) - 1);
47 | x = min(max(1, lines(:, 1) - m * linspace(0, szEpi(1) - 1, nSamples)) , szEpi(2));
48 |
49 | xf = floor(x);
50 | xc = ceil(x);
51 | y = repelem(linspace(1, szEpi(1), nSamples), size(lines,1), 1);
52 | w = x - xf; % linear interpolation weights
53 |
54 | % cubic interpolation
55 | [p, q] = meshgrid([1:szEpi(2)], [1:szEpi(1)]);
56 | li = interp2(p, q, epi(:, :, 1), x, y, 'cubic');
57 |
58 | % Linearly interpolate the luminance value of the line
59 | li = li ./ max(li, [], 2);
60 | c = entropy(li);
61 | end
62 |
--------------------------------------------------------------------------------
/lines/visibility.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Get the view indices in which an EPI line is visible.
3 | %%
4 | function b = visibility(l, epigx, epigy)
5 |
6 | szEPI = [size(epigx, 1), size(epigx,2)];
7 |
8 | b = ones( size(l, 1), size(epigx, 1), 'logical');
9 | n = size(l, 1);
10 |
11 | % Find all pairs of intersecting lines...
12 | %
13 | u = repmat(l(:, 1), 1, n);
14 | v = repmat(l(:, 2), 1, n);
15 | s = repmat(l(:, 1)', n, 1);
16 | r = repmat(l(:, 2)', n, 1);
17 |
18 | % The foreground/background line is determined by slope
19 | N = ((r <= v & s >= u) | (r >= v & s <= u));
20 | [lb, lf] = find(N .*((r - s) > (v - u)) > 0);
21 |
22 | % Also get lines that extend outside the visible EPI region
23 | lo = find((l(:, 1) < 1 | l(:, 1) > szEPI(2)) | (l(:, 2) < 1 | l(:, 2) > szEPI(2)));
24 | lb = unique([lb; lo]);
25 |
26 | % Get pixel positions to sample along each background line
27 | %
28 | lbu = l(lb, :);
29 |
30 | m = (lbu(:, 1) - lbu(:, 2)) ./ (szEPI(1) - 1);
31 | x = lbu(:, 1) - m * linspace(0, szEPI(1) - 1, szEPI(1));
32 | y = repelem(linspace(1, szEPI(1), szEPI(1)), size(lbu, 1), 1);
33 |
34 | % Calculate the gradient of each background line
35 | gx = szEPI(1) - 1;
36 | gy = lbu(:, 1) - lbu(:, 2);
37 | gm = 1 ./ sqrt(gx.^2 + gy.^2);
38 | gx = gx .* gm;
39 | gy = gy .* gm;
40 |
41 | imgx_interp = interp2(epigx, x, y, 'cubic');
42 | imgy_interp = interp2(epigy, x, y, 'cubic');
43 |
44 | img_m = 1 ./ sqrt(imgx_interp.^2 + imgy_interp.^2);
45 | imgx_interp = imgx_interp .* img_m;
46 | imgy_interp = imgy_interp .* img_m;
47 |
48 | a = imgx_interp .* gx + imgy_interp .* gy;
49 | b(unique(lb), :) = medfilt2(abs(imgx_interp .* gx + imgy_interp .* gy) > const.visibilityAlignmentThreshold, [1 3]);
50 | end
51 |
--------------------------------------------------------------------------------
/lines/wu.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Xiaolin Wu's antialiased line drawing algorithm.
3 | %% Code downloaded from Wikipedia
4 | %%
5 |
6 | function I = wu(I, x0, y0, x1, y1)
7 |
8 | idx = [];
9 |
10 | ipart = @(x) floor(x); % integer part of x
11 | fpart = @(x) x - floor(x); % fractional part of x
12 | rfpart = @(x) 1 - fpart(x);
13 |
14 | steep = abs(y1 - y0) > abs(x1 - x0);
15 |
16 | if steep
17 | [x0, y0] = deal(y0, x0);
18 | [x1, y1] = deal(y1, x1);
19 | end
20 | if x0 > x1
21 | [x0, x1] = deal(x1, x0);
22 | [y0, y1] = deal(y1, y0);
23 | end
24 |
25 | dx = x1 - x0;
26 | dy = y1 - y0;
27 | gradient = dy / dx;
28 |
29 | if dx == 0.0
30 | gradient = 1.0
31 | end
32 |
33 | % first endpoint
34 | xend1 = round(x0);
35 | yend1 = y0 + gradient * (xend1 - x0);
36 | xgap1 = rfpart(x0 + 0.5);
37 | xpxl1 = xend1; % this will be used in the main loop
38 | ypxl1 = ipart(yend1);
39 |
40 | % second endpoint
41 | xend2 = round(x1);
42 | yend2 = y1 + gradient * (xend2 - x1);
43 | xgap2 = fpart(x1 + 0.5);
44 | xpxl2 = xend2; %this will be used in the main loop
45 | ypxl2 = ipart(yend2);
46 |
47 | idx = ones( xpxl2 - 1 - xpxl1, 3);
48 | n = 1;
49 |
50 | if steep
51 | idx(n, :) = [xpxl1, ypxl1, rfpart(yend1) * xgap1];
52 | idx(n + 1, :) = [xpxl1, ypxl1 + 1, fpart(yend1) * xgap1];
53 | else
54 | idx(n, :) = [ypxl1, xpxl1, rfpart(yend1) * xgap1];
55 | idx(n + 1, :) = [ypxl1 + 1, xpxl1, fpart(yend1) * xgap1];
56 | end
57 | n = n + 2;
58 | intery = yend1 + gradient; % first y-intersection for the main loop
59 |
60 | if steep
61 | idx(n, :) = [xpxl2, ypxl2, rfpart(yend2) * xgap2];
62 | idx(n + 1, :) = [xpxl2, ypxl2 + 1, fpart(yend2) * xgap2];
63 | else
64 | idx(n, :) = [ypxl2, xpxl2, rfpart(yend2) * xgap2];
65 | idx(n + 1, :) = [ypxl2 + 1, xpxl2, fpart(yend2) * xgap2];
66 | end
67 | n = n + 2;
68 |
69 | % main loop
70 | if steep
71 | for x = xpxl1+1:xpxl2-1
72 | idx(n, :) = [x, ipart(intery), rfpart(intery)];
73 | idx(n + 1, :) = [x, ipart(intery) + 1, fpart(intery)];
74 | n = n + 2;
75 | intery = intery + gradient;
76 | end
77 | else
78 | for x = xpxl1+1:xpxl2-1
79 | idx(n, :) = [ipart(intery), x, rfpart(intery)];
80 | idx(n + 1, :) = [ipart(intery) + 1, x, fpart(intery)];
81 | n = n + 2;
82 | intery = intery + gradient;
83 | end
84 | end
85 |
86 | idx = idx( (idx(:, 1) > 0 & idx(:, 1) <= size(I, 1)) & ...
87 | (idx(:, 2) > 0 & idx(:, 2) <= size(I, 2)), :);
88 | f = sub2ind( size(I), idx(:, 1), idx(:, 2));
89 | I(f) = idx(:, 3);
90 | end
91 |
--------------------------------------------------------------------------------
/parameters.m:
--------------------------------------------------------------------------------
1 |
2 | classdef parameters
3 | properties
4 | % Multithreading
5 | nWorkers = 3;
6 |
7 | szLF = [];
8 | szEPI = [];
9 | cviewIdx = 0;
10 |
11 | % The code requires the camera to move towards the RIGHT in both the horizontal (u)
12 | % and vertical (v) directions. This ensures a uniform occlusion order for lines
13 | % in an EPI based on slope.
14 | % Set the following parameters to ensure the light field is loaded in the
15 | % correct order.
16 | uCamMovingRight = false;
17 | vCamMovingRight = true;
18 |
19 | % The maximum absolute disparity between two adjacent light field views
20 | maxAbsDisparity = 2.0;
21 | nDisparity = 60;
22 | end
23 | end
24 |
--------------------------------------------------------------------------------
/run.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | lightfields="'$*'"
4 | matlab -nodisplay -r "runOnLightfields(strsplit($lightfields, ','));exit"
5 |
--------------------------------------------------------------------------------
/runOnLightfields.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Run the depth estimation code on multiple lightfields,
3 | %% saving results to file.
4 | %%
5 | function runOnLightfields (D)
6 | addpath('./util', './lines', './depth', './cviewDepthEstim', './cviewDepthEstim/wmf', ...
7 | './angularDiffusion');
8 | if isempty(D{1})
9 | disp('Error! No input specified. Please see README.md for help.');
10 | return;
11 | end
12 |
13 | % Results are saved in a a folder with the current timestamp as the name
14 | %
15 | tstamp = datestr(datetime, 'dd-mmm-yyyy HHMM');
16 | tstamp( isspace(tstamp) ) = '_';
17 | folder = ['./results/' tstamp 'hrs'];
18 | mkdir(folder);
19 |
20 | % The output file the name is the name of the light field
21 | fout = {};
22 | for i = 1:length(D)
23 | [filepath, name, ext] = fileparts( D{i} );
24 | if isempty(ext)
25 | str = D{i};
26 | if str(end) == '/'
27 | str(end) = [];
28 | end
29 | s = strsplit(str, '/');
30 | fout{i} = s{end};
31 | else
32 | disp('Input Error! Please provide a path to the folder containing light field images');
33 | end
34 | end
35 |
36 | % Run parameters
37 | % Change these to evaluate the output with different settings for different light fields
38 | param = parameters;
39 |
40 | % Initialize Matlab's parpool
41 | cluster = parcluster('local');
42 | cluster.NumWorkers = param.nWorkers;
43 | saveProfile(cluster);
44 | delete(gcp('nocreate'));
45 | parpool(param.nWorkers);
46 |
47 | % Run...
48 | for i = 1:length(D)
49 | VCLFD( D{i}, [folder '/' fout{i}], param);
50 | end
51 | end
52 |
--------------------------------------------------------------------------------
/util/expspace.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Return n exponentially spaced points between v0 and v1
3 | %%
4 | function v = expspace(v0, v1, d, n)
5 | v = exp(d .* linspace(0, 1, n));
6 | v = v - min(v);
7 | v = (v ./ max(v)) .* (v0 - v1);
8 | v = v + v1;
9 | end
10 |
--------------------------------------------------------------------------------
/util/loadLF.m:
--------------------------------------------------------------------------------
1 | %%
2 | %% Load the light field images into a 5D array: (y, x, rgb, v, u)
3 | %% Indices of the returned array follow Matlab's convention of (row, column):
4 | %% u
5 | %% ---------------------------->
6 | %% | ________ ________
7 | %% | | x | | x |
8 | %% | | | | |
9 | %% v | | y | | y | ....
10 | %% | | | | |
11 | %% | |________| |________|
12 | %% | :
13 | %% | :
14 | %% v
15 | %%
16 | %% The input files may be in .mat, or
17 | %% alternately, a folder with the light field images (PNGs) may be provided.
18 |
19 | function [LF, dmin, dmax] = loadLF( fin, uCameraMovingRight, vCameraMovingRight, cspace)
20 |
21 | [folder, name, ext] = fileparts(fin);
22 | LF = [];
23 |
24 | if isempty(ext)
25 | % Read the light field from a directory of images.
26 | % Assumes the images in the directory are named in row-major order.
27 | % A file with the name config.txt must be included in the image
28 | % directory which lists the size of the light field as v, u, y, x
29 |
30 | config = fopen(fullfile(fin, 'config.txt'), 'r');
31 | assert( config > 0, ['Error: No config.txt found at ' fin]);
32 | pm = textscan(config, '%f %f %f %f %f %f', 1);
33 | sz = pm(1:4);
34 | dmin = pm(5);
35 | dmax = pm(6);
36 | fclose(config);
37 |
38 | v = sz{3}; u = sz{4}; y = sz{1}; x = sz{2};
39 | files = dir(fullfile(fin, '*.png'));
40 | assert( length(files) == u * v, 'Error: Lightfield size mismatch');
41 |
42 | if strcmp(cspace, 'lab')
43 | LF = zeros(y, x, 3, v, u);
44 | elseif strcmp(cspace, 'gray')
45 | %LF = zeros(y, x, 1, v, u, 'uint8');
46 | LF = zeros(y, x, 1, v, u);
47 | else
48 | LF = zeros(y, x, 3, v, u);
49 | end
50 |
51 | for i = 1:v
52 | for j = 1:u
53 | filename = fullfile(fin, files((i - 1) * u + j).name);
54 |
55 | if strcmp(cspace, 'lab')
56 | LF(:, :, :, i, j) = rgb2lab(imread(filename));
57 | elseif strcmp(cspace, 'ycbcr')
58 | LF(:, :, :, i, j) = rgb2ycbcr(imread(filename));
59 | elseif strcmp(cspace, 'gray')
60 | LF(:, :, 1, i, j) = rgb2gray(imread(filename));
61 | else
62 | LF(:, :, :, i, j) = (imread(filename));
63 | end
64 | end
65 | end
66 | elseif strcmp(ext, '.mat')
67 | % Read light field from a .mat file
68 | LF = struct2cell(load(fin));
69 | LF = LF{1};
70 | else
71 | disp(['Error: Unrecognized light field file extension ' ext]);
72 | disp("Hint: Use utility function HCIloadLF in runOnLightfields.m to load HCI dataset light fields.");
73 | return;
74 | end
75 |
76 | % Flipping ensures a uniform occlusion order in EPIs
77 | if uCameraMovingRight
78 | LF = flip(LF, 5);
79 | end
80 | if vCameraMovingRight
81 | LF = flip(LF, 4);
82 | end
83 |
84 | end
85 |
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https://raw.githubusercontent.com/brownvc/lightfielddepth/9e6344c9fe6122cc1e6d1c83cf26e8bd4d28d3ed/view-consistent-depth.gif
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