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Event_context_DEMOuav_rmax7by7rect_FASTwithDet.m
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Event_context_DEMOuav_rmax7by7rect_FASTwithDet.m
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clear;
close all;
warning('off', 'MATLAB:MKDIR:DirectoryExists');
tic;
% Add VLFeat toolbox to MATLAB working path
run('vlfeat-0.9.17-bin/vlfeat-0.9.17/toolbox/vl_setup.m');
addpath(genpath('getNmnistDesc'));
% Put all the folders (classes) in a single folder
train_dataset_path = '../N-SOD Dataset/Train';
filenamesA = dir2(train_dataset_path);
num_classes=numel(filenamesA);
classnames = cell(1, num_classes);
for i=1:num_classes
classnames{i}=filenamesA(i).name;
end
% create training labels later
train_label=[];
num_test= 2;
filename = 'traindata_binTD4cl_CYnewobs.dat';
testfiles_save = 'testfiles_binTD4cl_CYnewobs';
show_detect = 0;
try
canUseGPU = parallel.gpu.GPUDevice.isAvailable;
catch ME
canUseGPU = false;
end
param.descsize = 7; % denotes a N by N region sampled from the subsampled count matrix
param.queuesize = 5000;
param.countmatsubsamp = 2; % a 2 by 2 cell region for the count matrix
param.minNumEvents = 200; % wait for this many events before getting desc from queue
disp(param);
if exist(filename, 'file') ~= 2
% Training and testing set filenames
for i=1:num_classes
folder_path = fullfile(train_dataset_path,classnames{i});
subfolder_names = dir(folder_path);
isdir_tags = [subfolder_names.isdir];
subfolder_names = subfolder_names(~isdir_tags);
subfolder_names = {subfolder_names.name};
randomize_files{i} = randperm(numel(subfolder_names));
num_train = numel(randomize_files{i});
index= [];
for ii = randomize_files{i}
filepath = fullfile(folder_path, subfolder_names{ii});
img = read_linux(filepath, 0);
if size(img.x,2) > 5000
index = [index ii];
end
if numel(index) > 80
break
end
end
train_filenames{i} = vl_colsubset(subfolder_names(index),num_train, 'beginning');
test_filenames{i} = vl_colsubset(subfolder_names(index),num_test, 'beginning');
end
fid = fopen(filename, 'w');
% Write into file
for i = 1: numel(train_filenames)
classi = train_filenames{i};
for j = 1: numel(classi)
classi_objectj = classi{j};
fprintf(fid, '%s\n', classi_objectj );
end
end
save(testfiles_save,'test_filenames');
end
fid = fopen(filename, 'r');
A = textscan(fid,'%s', 'Delimiter','\n');
all_filenames= A{1};
fclose(fid);
load(testfiles_save);
% TD Noise Filter
% Assumes Garrick's AER functions are available (www.garrickorchard.com)
us_time_filter = 5e3; % in micro-seconds
% Debug purpose --
training_desc_done = 0; % for debug purposes only
svmtraining_done = 0;
testing_desc_done = 0;
% The training phase is here so that it is easy to change parameters
% and check the code of the FPGA implmentation. The testing stage will be
% made modular like how the FPGA implementation is intended to be, fixed
% precision and memory constraints closely followed.
count = 0;
train_desc_savefolder = './ECtraindata_FASTWithDet/';
mkdir(train_desc_savefolder);
fprintf('\n\n------------------------------Starting Training Stage------------------------------\n\n');
if training_desc_done == 0
fprintf('Getting events and saving descriptors for training...\n\n');
for class_i=1:num_classes
classi_name = cell2mat(classnames(class_i));
folder_path = fullfile(train_dataset_path,classi_name);
subfolder_names = dir(folder_path);
isdir_tags = [subfolder_names.isdir];
subfolder_names = subfolder_names(~isdir_tags);
subfolder_names = {subfolder_names.name};
mkdir(fullfile(train_desc_savefolder, classi_name));
for train_classi = 1:numel(subfolder_names)
count = count + 1;
filename = all_filenames{count};
filepath = fullfile(train_dataset_path,classi_name, filename);
TD = read_linux(filepath, 0);
TD = FilterTD(TD, us_time_filter);
trainimage_sizes(count,:) = [max(TD.y) max(TD.x)];
savepath_file = fullfile(train_desc_savefolder, classi_name, filename(1:end-4));
try
load(savepath_file);
catch
[frames, desc] = getDescs_RECT_FPGA(TD, param);
savepath_file = fullfile(train_desc_savefolder, classi_name, filename(1:end-4));
disp(savepath_file);
save(savepath_file,'frames','desc');
end
train_label = [train_label class_i];
fprintf('File num: %d\n\n', count);
end
end
save('./NDEMO4desc_7x7subsamp2x2_ustime5e3.mat',...
'train_label','trainimage_sizes','-v7.3');
if canUseGPU == 1
poolobj = gcp('nocreate');
delete(poolobj);
end
else
fprintf('Loading descriptors...\n\n');
load('./NDEMO4desc_7x7subsamp2x2_ustime5e3.mat');
end
histopts.num_bins = 150; % codebook size
histopts.numSpatialX = [1] ; % ignore but required for code, if [1 2] performs spatial pyramid matching (not needed)
histopts.numSpatialY = [1] ; % ignore
% Initialize detection vector
det_vector = zeros(histopts.num_bins, 1);
det_class = find(strcmp(classnames, 'Thumper')); % the integer denoting the Thumper class
% SVM params
svmOpts.C = 10 ;
svmOpts.biasMultiplier = 1 ;
% Build the codebook
clearvars model net
model_str_stringname = 'modelTD4cl_DEMO_FASTWithDet';
fprintf("Loading previously generated codebook...");
try
load(['./ECtrainmodels/' model_str_stringname ...
num2str(histopts.num_bins) num2str(param.countmatsubsamp) num2str(param.descsize)]);
model_done = 1;
fprintf(" Found!\n\n");
catch
model_done = 0;
fprintf(" Not Found!\n\n");
end
if model_done == 0
fprintf("Generating codebook...\n\n");
[train_data, loctrain_label, new_train_label] = readDescs(train_desc_savefolder,8,'nonorm');
[model.vocab, model.assoc] = vl_kmeans(vl_colsubset(single([train_data.desc]), 4e6), histopts.num_bins, 'verbose','algorithm', 'ANN') ;
model.kdtree = vl_kdtreebuild(model.vocab, 'Distance','L1') ;
model.vocab = double(model.vocab);
mkdir('./ECtrainmodels/');
save(['./ECtrainmodels/' model_str_stringname ...
num2str(histopts.num_bins) num2str(param.countmatsubsamp) num2str(param.descsize)],'model');
disp(' ');
end
% For possbile integer comparisons on FPGA
model.kdtree.trees.nodes.splitThreshold = floor(model.kdtree.trees.nodes.splitThreshold);
% Get hists
for repeat= 1
clearvars hists*
train_label = [];
count_sep = 0;
count = 0;
event_chunk_period = 1e4; % get a histogram for classification every 10k events, same for testing
if svmtraining_done == 0
for class_i=1:num_classes
classname=classnames{class_i};
folder_path = fullfile(train_dataset_path,classname);
subfolder_names = dir(folder_path);
isdir_tags = [subfolder_names.isdir];
subfolder_names = subfolder_names(~isdir_tags);
subfolder_names = {subfolder_names.name};
for j=1:numel(subfolder_names)
count = count + 1;
filename = all_filenames{count};
filepath=fullfile(train_desc_savefolder, classname, [filename(1:end-4) '.mat']);
fprintf('Binning: %s\n', filepath);
[train_data, loctrain_label, label] = readDescs(filepath, [], 'nonorm');
start_chunk = 1;
end_chunk = event_chunk_period;
for event_c = 1:ceil(size(train_data.frames,2)/event_chunk_period)
count_sep= count_sep + 1;
try
desc = train_data.desc(:, start_chunk:end_chunk);
frames = train_data.frames(:,start_chunk:end_chunk);
catch
desc = train_data.desc(:, start_chunk:end);
frames = train_data.frames(:,start_chunk:end);
end
hists{count_sep} = get_imagehist(model,trainimage_sizes(count,:),...
double(frames), double(desc), histopts, 'nonorm');
% Evaluate the codewords assigned to the Thumper class
if class_i == det_class
det_vector = det_vector + hists{count_sep};
elseif class_i == 1 || class_i == 4 % Background class
det_vector = det_vector - hists{count_sep};
end
train_label = [train_label class_i];
start_chunk = end_chunk + 1;
end_chunk = end_chunk + event_chunk_period;
if end_chunk > size(train_data.frames,2)
end_chunk = size(train_data.frames,2);
end
end
end
end
detector_codewords = find(det_vector > 0 );
hists = cat(2, hists{:}) ;
hists=cat(1,hists);
psix = hists; % kernel removed, above line applies kernel
%SVM Calculation
lambda = 1 / (svmOpts.C * length(train_label)) ;
w = [] ;
for ci = 1:length(unique(train_label))
perm = randperm(length(train_label)) ;
y = 2 * (train_label == ci) - 1 ;
[w(:,ci) b(:, ci) info(ci)] = vl_svmtrain(psix(:,perm), ...
y(perm), lambda, ...
'MaxNumIterations', 50/lambda, ...
'BiasMultiplier', svmOpts.biasMultiplier) ;
end
svmmodel.b = b ;
svmmodel.w = w;
save('svvmodel_DEMO_FPGAver2_150codebokNONORM_det.mat','svmmodel','detector_codewords');
else
fprintf("Loading previously generated SVM model...\n\n");
load('svvmodel_DEMO_FPGAver2_150codebokNONORM_det.mat');
end
%% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% testing stage
count = 0;
test_label = [];
test_desc_savefolder = './ECtestdata_FASTWithDet/';
mkdir(test_desc_savefolder);
if testing_desc_done == 0
fprintf('\nGetting events and saving descriptors for testing...\n');
for class_i=1:num_classes
classi_name = cell2mat(classnames(class_i));
test_classi = test_filenames{class_i};
mkdir(fullfile(test_desc_savefolder, classi_name));
for image_classi = 1:numel(test_classi)
count = count + 1;
filename = test_classi{image_classi};
filepath = fullfile(train_dataset_path,classi_name, filename);
savepath_file = fullfile(test_desc_savefolder, classi_name, filename(1:end-4));
TD = read_linux(filepath, 0);
TD = FilterTD(TD, us_time_filter);
testimage_sizes(count,:) = [max(TD.y) max(TD.x)];
try
load(savepath_file);
catch
[frames, desc] = getDescs_RECT_FPGA(TD, param);
disp(savepath_file);
save(savepath_file,'frames','desc');
end
test_label = [test_label class_i];
fprintf('File num: %d\n\n', count);
end
end
testing_desc_done =1;
save('./NDEMO4testdesc_7x7subsamp2x2_ustime5e3.mat',...
'test_label','testimage_sizes','-v7.3');
if canUseGPU == 1
poolobj = gcp('nocreate');
delete(poolobj);
end
else
disp('Loading test descriptors...'); % nneds modification of code if desc_done=0
load('./NDEMO4testdesc_7x7subsamp2x2_ustime5e3.mat');
end
%% ---------------------------------------------------------------------------
%---------------------------------------------------------------------------
% Testing phase
% Detection matrix init
det_matrix = zeros(trainimage_sizes(1,1),trainimage_sizes(1,2)); % trainimage_sizes is in [y x] order already
temp_count_matrix = det_matrix;
count = 0;
count_sep =0;
save_for_majvot = [];
test_label = [];
fprintf('\n\n------------------------------Starting Testing Stage------------------------------\n\n');
for class_i=1:num_classes
classi_name = cell2mat(classnames(class_i));
test_classi = test_filenames{class_i};
for image_classi = 1:numel(test_classi)
count = count + 1;
filename = test_classi{image_classi};
filepath = fullfile(test_desc_savefolder, classi_name, [filename(1:end-4) '.mat']);
[test_data, loctest_label, testlabel] = readDescs(filepath, [], 'nonorm');
% Read original TD for detection matrix update
filepath = fullfile(train_dataset_path,classi_name, filename);
TD = read_linux(filepath, 0);
TD = FilterTD(TD, us_time_filter);
start_chunk = 1;
end_chunk = event_chunk_period;
for event_c = 1:ceil(size(test_data.frames,2)/event_chunk_period)
count_sep= count_sep + 1;
try
desc = test_data.desc(:, start_chunk:end_chunk);
frames = test_data.frames(:,start_chunk:end_chunk);
catch
desc = test_data.desc(:, start_chunk:end);
frames = test_data.frames(:,start_chunk:end);
end
hists = get_imagehist(model,testimage_sizes(count,:),...
double(frames), double(desc), histopts, 'nonorm');
psix = hists;
%SVM Classification
scores = svmmodel.w' * psix + svmmodel.b' * ones(1,size(psix,2)) ;
[obj_cat(count_sep,1), obj_cat(count_sep,2)] = max(scores);
if obj_cat(count_sep,2) == det_class
binsa = double(vl_kdtreequery(model.kdtree, double(model.vocab), ...
double(desc), 'MaxComparisons', 15)) ;
[ind, ~]=ismember(binsa, detector_codewords);
index_desc = find(ind == 1);
detected_locations_x = TD.x(index_desc + (event_c - 1) * event_chunk_period + param.minNumEvents);
detected_locations_y = TD.y(index_desc + (event_c - 1) * event_chunk_period + param.minNumEvents);
linear_indices = sub2ind(size(det_matrix), detected_locations_y, detected_locations_x);
det_matrix(linear_indices) = det_matrix(linear_indices) + 1;
% Display purposes only % Fun fun fun fun looking forward to the detection
try
all_locations_x = TD.x(1 + (event_c - 1) * event_chunk_period + param.minNumEvents: event_chunk_period + (event_c - 1) * event_chunk_period + param.minNumEvents);
all_locations_y = TD.y(1 + (event_c - 1) * event_chunk_period + param.minNumEvents: event_chunk_period + (event_c - 1) * event_chunk_period + param.minNumEvents);
catch
all_locations_x = TD.x(1 + (event_c - 1) * event_chunk_period + param.minNumEvents: end);
all_locations_y = TD.y(1 + (event_c - 1) * event_chunk_period + param.minNumEvents: end);
end
all_linear_indices = sub2ind(size(det_matrix), all_locations_y, all_locations_x);
temp_count_matrix(all_linear_indices) = temp_count_matrix(all_linear_indices) + 1;
if show_detect == 1
figure(1),
imshow(temp_count_matrix)
hold on
plot(ceil(mean(detected_locations_x)), ceil(mean(detected_locations_y)), 'g+', 'MarkerSize',30);
hold off
drawnow()
end
%Reset count matrix and detection matrix
det_matrix = zeros(trainimage_sizes(1,1),trainimage_sizes(1,2)); % trainimage_sizes is in [y x] order already
temp_count_matrix = det_matrix;
end
test_label = [test_label class_i];
start_chunk = end_chunk + 1;
end_chunk = end_chunk + event_chunk_period;
if end_chunk > size(test_data.frames,2)
end_chunk = size(test_data.frames,2);
end
end
save_for_majvot(count) = count_sep;
fprintf('File num: %d\n\n', count);
end
end
error_locs=find(obj_cat(:,2)'~=test_label);
error_per=length(error_locs);
accuracy(repeat)= 100*(numel(test_label)- error_per)/numel(test_label)
end
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