done!

RANSAC.m

@@ -1,4 +1,4 @@
-function [T] = RANSAC(confidence, inliner_Ratio, Npairs, data, epsilon)
+function [T,MaxInliers] = RANSAC(confidence, inliner_Ratio, Npairs, data, epsilon)
 
 m = ceil(log(1 - confidence) / log(1 - inliner_Ratio^Npairs)); % calculate number of loops
 NPoints = size(data, 1);

README.md

@@ -1,15 +1,25 @@
 ### panorama
-
-##### features
-- [x] use SIFT features  
+This is my panorama creator, images files are already in ./imgs
+
+##### bonus  
+I implemented all of the elaborate features described in **BONUS** part.  
+- I'm able to handle 360 panorama.
+- Random sequences is ok.
+- I use color blending and smoothing to make the image more continuous.  
+*details of my algorithms are shown below:*  
+##### getting features
+- [x] use SIFT features(using VLFeat library, professor allowed)  
 - [x] SURF features, (SIFT is better)  
 
+##### 360 panorama
+- [x] mapping image to cylindrical coordinate
+
 ##### transformation
-- [x] boost translate
-- [x] homography is not robust
+- [x] homography transformation.
+- [x] translation transformation.( This is more robust)
 
 ##### matching
-- [x] boost RANSAC
+- [x] RANSAC
 - [ ] exposure matching  
 
 ##### global adjustment
@@ -22,6 +32,9 @@
 - [x] Noblend
 
 ##### recognize panorama(random inputs)
-- [ ] hard  
+As described in Brown's paper, I use N_inlier>k\*N_pairs+b to compute whether a pair of images match or not  
+k,b are const. Set to 5.9 and 0.22 respectively.  
+
+
 
 

computeTrans.m

@@ -14,7 +14,7 @@
     d1 = d2;
     [f2, d2] = getSIFTFeatures(imgs(:, :, :, i), Thresh);
     [matches, ~] = getMatches(f1, d1, f2, d2);
-    T(:, :, i) = RANSAC(confidence, inlierRatio, 1, matches, epsilon);
+    [T(:, :, i),~] = RANSAC(confidence, inlierRatio, 1, matches, epsilon);
 end
 end
 

create.m

@@ -26,8 +26,8 @@
 % end to end adjustment
 width = size(cylindricalimages, 2);
 height = size(cylindricalimages, 1);
-panorama_w = abs(round(absoluteTrans(2, 3, end))) + width;
 if full360
+    panorama_w = abs(round(absoluteTrans(2, 3, end))) + width;
     % \delta x / \delta y
     driftSlope = absoluteTrans(1, 3, end) / absoluteTrans(2, 3, end);
 
@@ -45,12 +45,16 @@
     maxY = height;
     minY = 1;
     minX = 1;
+    maxX=width;
     for i = 2 : nImgs 
         maxY = max(maxY, absoluteTrans(1,3,i)+height);
+        maxX = max(maxX, absoluteTrans(2,3,i)+width);
         minY = min(minY, absoluteTrans(1,3,i));
         minX=min(minX,absoluteTrans(2,3,i));
     end
     panorama_h = ceil(maxY) - floor(minY) + 1;
+    panorama_w = ceil(maxX)-floor(minX) +1;
+
     absoluteTrans(2, 3, :) = absoluteTrans(2, 3, :) - floor(minX);
     absoluteTrans(1, 3, :) = absoluteTrans(1, 3, :) - floor(minY);
 end

imorder.m

@@ -0,0 +1,88 @@
+function [sorted_imgs]=imorder(imgs)
+Thresh = 5;
+confidence = 0.999;
+inlierRatio = 0.1;
+epsilon = 1.5;
+
+nImgs = size(imgs, 4);
+
+T = zeros(3, 3, nImgs);
+T(:, :, 1) = eye(3);
+imgs_feat{nImgs}={};
+imgs_dist{nImgs}={};
+for i=1: nImgs
+    [f, d] = getSIFTFeatures(imgs(:, :, :, i), Thresh);
+    imgs_feat{i}=f;
+    imgs_dist{i}=d;
+end
+ifmatch=zeros(nImgs,nImgs);
+transforms{nImgs,nImgs}={};
+for i = 1 : nImgs
+    for j = 1 : nImgs
+        if j==i
+            continue
+        end
+        [matches, ~] = getMatches(imgs_feat{i}, imgs_dist{i},...
+            imgs_feat{j}, imgs_dist{j});
+        [T,nInliers] = ...
+        RANSAC(confidence, inlierRatio, 1, matches, epsilon);
+        if nInliers>5.9+.22*length(matches)
+            ifmatch(i,j)=1;
+            transforms{i,j}=T;
+        end
+    end
+end
+sequence=[];
+sequence(1)=1;
+%% forward matching
+for i=2:nImgs
+    nextIdx=find(ifmatch(sequence(i-1),:)==1);
+    if size(nextIdx,2)==0
+        break
+    end
+    failed=true;
+    for matched=1:size(nextIdx,2)
+        if size(find(sequence==nextIdx(matched)),2)==1
+            continue
+        end
+        real_idx=matched;
+        failed=false;
+        break
+    end
+
+	if failed==false
+        sequence(i)=nextIdx(real_idx);
+    else
+        break
+    end
+end
+%% backward matching
+for i=2:nImgs
+    nextIdx=find(ifmatch(sequence(1),:)==1);
+    if size(nextIdx,2)==0
+        break
+    end
+    failed=true;
+    for matched=1:size(nextIdx,2)
+        if size(find(sequence==nextIdx(matched)),2)==1
+            continue
+        end
+        real_idx=matched;
+        failed=false;
+        break
+    end
+
+	if failed==false
+        sequence=[nextIdx(real_idx),sequence];
+    else
+        break
+    end
+end
+%% reorder
+disp(['using ',int2str(length(sequence)),' of ',int2str(length(imgs(1,1,1,:))),' unordered imgs']);
+sorted_imgs=zeros(size(imgs,1),size(imgs,2),size(imgs,3),length(sequence),'like',imgs);
+for i=1:length(sequence)
+    sorted_imgs(:,:,:,i)=imgs(:,:,:,sequence(i));
+end
+
+

imorder.m~

@@ -0,0 +1,60 @@
+function [sorted_imgs]=imorder(imgs)
+Thresh = 10;
+confidence = 0.99;
+inlierRatio = 0.3;
+epsilon = 1.5;
+
+nImgs = size(imgs, 4);
+
+T = zeros(3, 3, nImgs);
+T(:, :, 1) = eye(3);
+imgs_feat{nImgs}={};
+imgs_dist{nImgs}={};
+for i=1: nImgs
+    [f, d] = getSIFTFeatures(imgs(:, :, :, i), Thresh);
+    imgs_feat{i}=f;
+    imgs_dist{i}=d;
+end
+ifmatch=zeros(nImgs,nImgs);
+transforms{nImgs,nImgs}={};
+for i = 1 : nImgs
+    for j = 1 : nImgs
+        if j==i
+            continue
+        end
+        [matches, ~] = getMatches(imgs_feat{i}, imgs_dist{i},...
+            imgs_feat{j}, imgs_dist{j});
+        [T,nInliers] = ...
+        RANSAC(confidence, inlierRatio, 1, matches, epsilon);
+        if nInliers>5.9+.22*length(matches)
+            ifmatch(i,j)=1;
+            transforms{i,j}=T;
+        end
+    end
+end
+sequence=[];
+sequence(1)=1;
+for i=2:nImgs
+    nextIdx=find(ifmatch(sequence(i-1),:)==1);
+    if size(nextIdx,2)==0
+        break
+    end
+    failed=false;
+    for i=1:size(nextIdx,2)
+        if size(find(sequence==nextIdx(i)),2)==1
+            continue
+        end
+        real_idx=i;
+
+        break
+
+    sequence(i)=nextIdx(1);
+end
+disp(length(imgs(1,1,1,:)))
+disp(length(sequence))
+sorted_imgs=zeros(size(imgs,1),size(imgs,2),size(imgs,3),length(sequence),'like',imgs);
+for i=1:length(sequence)
+    sorted_imgs(:,:,:,i)=imgs(:,:,:,sequence(i));
+end
+
+

main.m

@@ -1,32 +1,41 @@
 clear all
 %% params
 
-k1=-0;
-k2=0;
-
-which=11;
+which=8;
 path='imgs';
-
-%%
-%             1       2bad          3           4             5
+%             1       2unorder          3           4             5
 datasets={'ucsb4','family_house','glacier4','yellowstone2','GrandCanyon1',...
     'yellowstone5','yellowstone4','west_campus1','redrock','intersection',...
-...%     6              7               8             9failed    10failed
+...%     6              7               8unorder            9      10
 'GrandCanyon2'};
-% 11  failed
-
-focus=[595,400,2000,1000,1000,1000,1000,1000,1000,1000,1000];
-Full360=[0,1,0,0,0,0,0,0,1000,1000,1000];
+% 11  
+%       1   2  3    4     5   6    7    8    9    10   11
+focus=[595,400,2000,1000,1000,1000,1000,1000,2000,2000,2000];
+Full360=[0,0,0,0,0,0,0,0,0,0,0];
+unordered=[0,1,0,0,0,0,0,1,0,0,0];
+%%
 full=Full360(which);
 f=focus(which);
 run('lib/vlfeat-0.9.20/toolbox/vl_setup');
-
+disp(['creating panorama for ',datasets{which}])
 s=imageSet(fullfile(path,datasets{which}));
 img=read(s,1);
 imgs=zeros(size(img,1),size(img,2),size(img,3),s.Count,'like',img);
 for i=1:s.Count
     imgs(:,:,:,i)=read(s,i);
 end
 
+
+if unordered(which)
+    t=cputime;
+    disp('ordering unordered images');
+    imgs=imorder(imgs);
+    disp([int2str(cputime-t),' sec']);
+end
+
+
 panorama=create( imgs, f, full);
-imwrite(panorama,['./results/',datasets{which},'.jpg']);
+imwrite(panorama,['./results/',datasets{which},'.jpg']);
+if unordered(which)
+    imwrite(panorama,['./results/',datasets{which},'from unordered.jpg']);
+end