From 97b4c1453377e6257f6b5efc32e2d5bc472e9323 Mon Sep 17 00:00:00 2001 From: Ronghang Hu Date: Sat, 30 May 2015 09:37:32 +0800 Subject: [PATCH] Update ilsvrc_2012_mean.mat to W x H x C, update demo and add comments Update previously ilsvrc_2012_mean.mat stores 'image_mean' variable in H x W x C with BGR channels, which is inconsistent with Caffe's data format and inconsistent with caffe.io.read_mean(..). Replace 'image_mean' with 'mean_data' variable in W x H x C and update classification_demo.m. Also add some comments. --- matlab/+caffe/imagenet/ilsvrc_2012_mean.mat | Bin 786640 -> 606799 bytes matlab/+caffe/io.m | 10 ++- matlab/demo/classification_demo.m | 64 ++++++++++---------- 3 files changed, 42 insertions(+), 32 deletions(-) diff --git a/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat index f1da25c84a1a4bdd6d32aee386bcdbed48bba99c..21df3d39aaa3c43b9c2b41f88bd2e6f48585b8c5 100644 GIT binary patch literal 606799 zcma%?=Q|sY8}+}cMN4U`wFRY=+IvJ4wO8%E_ZB;Li)w9x*jjt5y`{Bcr($nKs7;I@ z$n(3d=X%~h=hgimoa=nfeKchC)Me$EMR@s{HDvX89Gu+9E2(Q5 z$%+bdGs}D1Tl?DEF?)JQGV5CVGHX~vn1%S6`NbrK`6UJUnFaX;M410S_22&!frXGmU58Ro0}SXDVd?OK=a@#B=_uD8rw z70PjsmV+friPNcWTMF=Um%Qc>1+EcIK)7{`D4t|Jdgt6KiCYiqc>5+o15uS%CmKshugLv~IQ+&x` zPoFJ4vs!QdFL>|Kf{f5m>N&INyQrZ?t`E2BUv>2R+zu%n5DWb1{3y;v-rml$*TOxt zKSF!n1`jIKQz58E)VGrp8jn*QK*9a1O60L5&7&f99Fg^rPcsgL#UcGx9gbeFtA_ix z*>^f~4)D` z+y?V>Djkmi7+r&!T#p5+B9+oxfvD3=3B9H4plO=Fr;5}`u|+^dr|xt0q|Y}V6J$y2 z%1IZb(xT3^L2H}PZva!7KVVyF3zOYR_pgZWlK|bD;PaWlA4!Ob-+%z$8c(7?GS7dL zmC;Hom4Fz(@$C+n+=VoV_vjQtnbf5K)UmuAf~{?)%;o_B*9E6TyPdwFvzt*FM~pDk zoSDn*mf)T%Oq2LL2iCs4bkyLJt0tP@MFIP3ZuUgjpR;5q!(h6^3q+%x7gJO1MnC7+@~UgRp?WjOZZJrS`?T;Ft#=w0~Cyc6Y$+u1pD&Tgo?MU{JF zbJTcQ5Hhci3VzfSc$%!R&KS4FAGfK`-NWfI*)JjyI%T)c4()Q)aA_79wwNjoC!&nMB}nj(41~XCXs7x6SL(Uy zY&<`bd@9T^lgZ@Eu%5u!972vepTS1q_p>{XM-@$MLvc?(pFbwfY<~CaR+V79#8GCM!ju^M@iHjt6{Jx8;1&9`ydejot--V- zoME$K1e~oIf3}(-{whISp^-h)lH>Qn>f^9mLD`bv#~aO12D;R(lR>tLNw!ud_IAtn z$j&O+Z;y#e{OQ@kMdx$Ii?wN?3*GA3S|ZnH<-^}CHG;OMgL&+=YoP;IkExadc31AL zz&gjbuw~I!*M%lGd!e8P4V%32LeCQHJJIFlh1&M16aAR9Qt}jSa#{z8mnJ(OL+a!W z#=F4+F%8I$E~AMOt6&&?w;2)7G(&oZl&fZ))J`f{+nAtv*vxHs$#;b3OmWd3U`<7a z-XvIxPnrsJmH^d{G3ld|Gi*Y3MkYime;*I}b;8*)@Ags$sYD6|q8#R=LLEIc@MOEjGv)p&)(WIDf|n6 z-TQX8)+vu9y*7sS#5$AJuQ#`csqm`h8BXepcrHu-*G*Y}UlMON!{h+?lRs|05nez3 zixzz=O;r4RJY}$iVAq$EW_`Q(zw{}>c-~jVe}KF9X2~4U^0(*yUth1ql;5alW*7Bt zNxo#yV3qrE`MhV5l12A*@1%qFmyl;ATM2KkNfG}*uN8Nj1tzitCa7W?U6gar6Tq10 zjh7?&-)+hsY9*dO+HV4-f#K@+mE0F1Q&}?Np^B-jrutaJ` ztACjl$&&e1JYZvwGlur;vY+noNTxM9&5he!_{OEA7Vp=(*7)%dOcIL?Wl+UQ$?KNVz4OC#@3t>1>J1`Ud z!nn?DbswWLRAR7PVn>yvJJR!Xfu9q!0 z&VHK|X}tSc z=U^rtFX2PJ^nR^O`+7BZl+NT@?2u1iA}Y*G3~R6b|{ z=@mQ$dGK(mj?Ji(Up8{$aqsnubG5jsFpb<6 z^9{EVfFov{h9{%nS4N=r{`s4`_i0P=Ma%E__}Zv`>}QZrW-AQAIOX>8`Ktz_Ds;rh zP-iSAT2Ce`RKFg=iNOYsDlUSOH)$ic7HGZ?9{S#1JD)qHDSndJU3Po%9rydc(_2{q zeO`|6)Y|HVp)J%K- zjiztAzucF3Fe@Hx5y+#tq5`J#h;3v6l*#^mIDi|*_kXYtSN_SK##|DVy6pMpItUKb z3Bl)I-5UIL8K-m#%T(T*}SGvN6l^D=Ddj@6d5_Nv-@HFS%(kp5-0ma*@r* zF=Na9AwQalIJS+f_Q1OnwAc6gITbSVKy1qR3ZzARq*N(%R|cwa7Bg~JL^V2RI`)c~CU1r^T?o7jpXOtq zpkVKYW!lj@=p4U--ehT1t_c2vxt#RTL@r7{9aP45x{dE6p<|Y5#c?;kt5sF*F(ugd za`)Nmv;rq<3dO{R*H^~5t%LoLlEB<|0k&pq&o^OV0xot50*7X4(LP1DA7xZ~tfKy| zefTHwwij8#$^c?wm*7(u0ss0b#WLdYIK|KDtFf^hHx4uufsO345K-QzB%pG9@(~T}7y`V@AV)+#}Q-ZgaUA^a3#s6Qi@t1>~%v zFqSvr)nS4TUTJ3qABGw9J)dLwa$juLm z7aWw;e}poPg;W~zRc^lGeDX9f!*^^;ao;Y8>AF~%82yLVHrYoR1%X`6L zSRT?Xc~|O9-drg)_OGb=**gnU^iPjQ`P*#yqnt#^RQcuu-hh#g<;offj!UHYi2XJZ z44Tu-OVJi)Y<2@11z!KEdLx}*u|BLOt=e*4F7q+}JJ~xCjcz00Nok-}ykM<(6^@fAj0z}JM&(C~$|Y6$J@heH=Py$6)XP~!mij{N=H1;Vg>f?pT#&D7+-0%FvL^Ny_ z%j=I?Pw4WNZ4uT6uD6Qv9;QiE?X(y3crSGI*lnl+d_UOUJ;roz<|fxN4C6`E`pU;`wqaG; zzP@bvRZLff?P#1zqD8uB`Y1FF$B!fJ zsTQT2!E+oop0vC*95gli=QP<^JTT}e-0G2;d%d`|%mp1h6QRh-Ip^b|`dU$xuWxB? 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zR`5CeeA9x^@4)d(J*e%(cAiZ(=e@C)`yyUGe0+}U+1D|%V1DmlGj@66;s0qyvxCPQ zdRHfR@b51oz6;OL$ztN$43BS9g`J!hMH;?PWCI+28+`f~7AX=#P5&Z_1k=mUN4MAD z$#;)s*9Lyv2BWlM>WFWzmR711rN+P2NPRGUkt29>!!+!((?~IQ4Srm;xYN&%ja5s( zP_^_CwXCY9mPKIt)d|$~b@cOtRr08$N|wIOloHhUkBc*UuH1lmLYcs jWJqs^44MDp8~pG$nbYT+bSnntPf3>%tJ1}`O1k_HUTyRW diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m index c9e07aee9..af8369ddf 100644 --- a/matlab/+caffe/io.m +++ b/matlab/+caffe/io.m @@ -7,16 +7,24 @@ % load an image from disk into Caffe-supported data format % switch channels from RGB to BGR, make width the fastest dimension % and convert to single + % returns im_data in W x H x C. For colored images, C = 3 in BGR + % channels, and for grayscale images, C = 1 CHECK(ischar(im_file), 'im_file must be a string'); CHECK_FILE_EXIST(im_file); im_data = imread(im_file); - im_data = im_data(:, :, [3, 2, 1]); + % permute channels from RGB to BGR for colored images + if size(im_data, 3) == 3 + im_data = im_data(:, :, [3, 2, 1]); + end + % flip width and height to make width the fastest dimension im_data = permute(im_data, [2, 1, 3]); + % convert from uint8 to single im_data = single(im_data); end function mean_data = read_mean(mean_proto_file) % mean_data = read_mean(mean_proto_file) % read image mean data from binaryproto file + % returns mean_data in W x H x C with BGR channels CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string'); CHECK_FILE_EXIST(mean_proto_file); mean_data = caffe_('read_mean', mean_proto_file); diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m index 453582476..2b6033297 100644 --- a/matlab/demo/classification_demo.m +++ b/matlab/demo/classification_demo.m @@ -40,15 +40,17 @@ % [width, height, channels, images] % where width is the fastest dimension. % Here is the rough matlab for putting image data into the correct -% format: -% % convert from uint8 to single -% im = single(im); -% % reshape to a fixed size (e.g., 227x227) -% im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); -% % permute from RGB to BGR and subtract the data mean (already in BGR) -% im = im(:,:,[3 2 1]) - data_mean; +% format in W x H x C with BGR channels: +% % permute channels from RGB to BGR +% im_data = im(:, :, [3, 2, 1]); % % flip width and height to make width the fastest dimension -% im = permute(im, [2 1 3]); +% im_data = permute(im_data, [2, 1, 3]); +% % convert from uint8 to single +% im_data = single(im_data); +% % reshape to a fixed size (e.g., 227x227). +% im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); +% % subtract mean_data (already in W x H x C with BGR channels) +% im_data = im_data - mean_data; % If you have multiple images, cat them with cat(4, ...) @@ -94,7 +96,7 @@ toc; % do forward pass to get scores -% scores are now Width x Height x Channels x Num +% scores are now Channels x Num, where Channels == 1000 tic; % The net forward function. It takes in a cell array of N-D arrays % (where N == 4 here) containing data of input blob(s) and outputs a cell @@ -103,43 +105,43 @@ toc; scores = scores{1}; -size(scores) -scores = squeeze(scores); -scores = mean(scores,2); +scores = mean(scores, 2); % take average scores over 10 crops -[~,maxlabel] = max(scores); +[~, maxlabel] = max(scores); % call caffe.reset_all() to reset caffe caffe.reset_all(); % ------------------------------------------------------------------------ -function images = prepare_image(im) +function crops_data = prepare_image(im) % ------------------------------------------------------------------------ +% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that +% is already in W x H x C with BGR channels d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat'); -IMAGE_MEAN = d.image_mean; +mean_data = d.mean_data; IMAGE_DIM = 256; CROPPED_DIM = 227; -% resize to fixed input size -im = single(im); -im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear'); -% permute from RGB to BGR (IMAGE_MEAN is already BGR) -im = im(:,:,[3 2 1]) - IMAGE_MEAN; +% Convert an image returned by Matlab's imread to im_data in caffe's data +% format: W x H x C with BGR channels +im_data = im(:, :, [3, 2, 1]); % permute channels from RGB to BGR +im_data = permute(im_data, [2, 1, 3]); % flip width and height +im_data = single(im_data); % convert from uint8 to single +im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear'); % resize im_data +im_data = im_data - mean_data; % subtract mean_data (already in W x H x C, BGR) % oversample (4 corners, center, and their x-axis flips) -images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); +crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single'); indices = [0 IMAGE_DIM-CROPPED_DIM] + 1; -curr = 1; +n = 1; for i = indices for j = indices - images(:, :, :, curr) = ... - permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]); - images(:, :, :, curr+5) = images(end:-1:1, :, :, curr); - curr = curr + 1; + crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :); + crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n); + n = n + 1; end end -center = floor(indices(2) / 2)+1; -images(:,:,:,5) = ... - permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ... - [2 1 3]); -images(:,:,:,10) = images(end:-1:1, :, :, curr); +center = floor(indices(2) / 2) + 1; +crops_data(:,:,:,5) = ... + im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:); +crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);

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