我正在尝试运行一些从图像创建草图的代码：
https://github.com/panly099/sketchSynthesis
在Windows上运行之后，我遇到了我认为的性能问题。该程序应该在5分钟内完成任务，但我的计算机根本不会完成任务（我等了至少30分钟）
我得到了一些建议，我需要调查一下＆＃34; parfor＆＃34;和多处理，以加快/完成任务
我想知道是否有人可以向我解释我是如何能够在5分钟内加快完成图像的速度。
我应该编辑的代码是：
https://github.com/panly099/sketchSynthesis/blob/master/functions/sketchDetect.m
function [ detection ] = sketchDetect(sample, strokeModel, configurations, searchRatio, threshold, appGeoWeight)
% This function anchors the stroke model on the given input sketch or edge
% map sample via a dynamic programming process.
% Input :
% sample : the input sketch or edge map sample.
% strokeModel : the stroke model.
% configurations : the sampled configurations of the object.
% searchRatio : the search ratio of the original stroke bounding box.
% threshold : the chamfer matching threshold.
% appGeoWeight : the balance between the appearance and the geometry.
% Output :
% detection : the detected model instance.
% Author :
% panly099@gmail.com
% Version :
% 1.1 07/06/2016
if nargin < 5
threshold = 0.5;
end
if nargin < 6
appGeoWeight = 0.5;
end
baseScale = 1;
baseAspect = 1;
overlapWeight = 0.001;
mst = strokeModel.mst;
avgWidth = strokeModel.avgWidth;
avgHeight = strokeModel.avgHeight;
clusterBbox = strokeModel.clusterBbox;
[height, width] = size(sample);
numConf = length(configurations);
detections = cell(1, numConf);
energy = zeros(1,numConf);
for a = 1 : numConf
configuration = configurations{a};
%% sampling by fast directional chamfer matching (fdcm)
numCluster = length(configuration);
strokeCandidates = cell(1, numCluster);
parfor j = 1 : numCluster
fprintf('current stroke id: %d\n', j);
strokeImg = configuration{j};
strBbox = getBoundingBox(strokeImg,0);
strokeImg = strokeImg(strBbox(2):strBbox(4), strBbox(1):strBbox(3));
% compute the search region
curBbox = clusterBbox(:,j)';
% curBbox = strBbox;
curWidth = curBbox(3) - curBbox(1);
curHeight = curBbox(4) - curBbox(2);
searchRegion = [curBbox(1) - ceil((searchRatio - 1) / 2 * curWidth), curBbox(2) - ceil((searchRatio - 1) / 2 * curHeight), ...
curBbox(3) + ceil((searchRatio - 1) / 2 * curWidth), curBbox(4) + ceil((searchRatio - 1) / 2 * curHeight)];
if searchRegion(1) < 1
searchRegion(1) = 1;
end
if searchRegion(2) < 1
searchRegion(2) = 1;
end
if searchRegion(3) > width
searchRegion(3) = width;
end
if searchRegion(4) > height
searchRegion(4) = height;
end
img = sample(searchRegion(2):searchRegion(4), searchRegion(1):searchRegion(3));
% fdcm anchoring
[strokeMatched, cost] = chamferLocate(img, strokeImg, baseScale, baseAspect, threshold);
% figure;imshow(~strokeImg);figure;imshow(~img);
if size(strokeMatched,1) > 1000
stop = 1;
end
for s = 1 : length(strokeMatched)
tmpSample = zeros(height, width);
tmpSample(searchRegion(2):searchRegion(4), searchRegion(1):searchRegion(3)) = strokeMatched{s};
if cost(s) <= 0
cost(s) = 1;
end
strokeCandidates{j}(s,:) = [{tmpSample}, cost(s)];
% figure;imshow(~strokeMatched{s});
end
% figure;imshow(sample);
end
%% energy minimization by dynamic programming
% backward
fprintf('Backward propogation\n');
for i = length(mst) : -1 : 2
fprintf('Scanning MST layer: %d\n', i);
curLayer = mst{i};
for j = 1 : length(curLayer)
% parents = [];
curEdge = curLayer{j};
curParent = curEdge{1}(2);
% parents = [parents curParent];
curChild = curEdge{1}(1);
curParam = curEdge{2};
curParentChildIdx = size(strokeCandidates{curParent},2) + 1;
curParentCandidates = strokeCandidates{curParent}(:,1);
curChildCandidates = strokeCandidates{curChild};
for p = 1 : length(curParentCandidates)
tmpBbox = getBoundingBox(curParentCandidates{p}, 0);
curParentCenter = ceil([(tmpBbox(2)+tmpBbox(4))/2, (tmpBbox(1)+tmpBbox(3))/2]);
childCosts = ones(1, size(curChildCandidates, 1))*Inf;
for c = 1 : size(curChildCandidates, 1)
tmpBbox = getBoundingBox(curChildCandidates{c,1},0);
curChildCenter = ceil([(tmpBbox(2)+tmpBbox(4))/2, (tmpBbox(1)+tmpBbox(3))/2]);
geoNorm = 1/40;
curCost = appGeoWeight * curChildCandidates{c,2} - (1-appGeoWeight) * geoNorm * log(mvnpdf(curChildCenter-curParentCenter, ...
curParam{2},curParam{3}));
for gc = 3 : size(curChildCandidates,2)
curCost = curCost + curChildCandidates{c,gc}(2);
end
childCosts(c) = curCost;
end
[minCost, minIdx] = min(childCosts);
if minCost == Inf
stop = 1;
end
strokeCandidates{curParent}{p, curParentChildIdx} = [curChild, minCost, minIdx];
end
% % adding overlapping penalty
% parents = unique(parents);
% for u = 1 : length(parents)
% curParent = parents(u);
% for p = 1 : size(strokeCandidates{curParent}, 1)
% curParentImg = strokeCandidates{curParent}{p,1};
% overlapCost = 0;
% for c = 3 : size(strokeCandidates{curParent}, 2)
% curChild = strokeCandidates{curParent}{p,c}(1);
% curChildIdx = strokeCandidates{curParent}{p,c}(3);
% curChildImg = strokeCandidates{curChild}{curChildIdx,1};
% curOverlap = curParentImg.*curChildImg;
% overlapCost = overlapCost + sum(curOverlap(:)) * overlapWeight;
% end
% strokeCandidates{curParent}{p,2} = strokeCandidates{curParent}{p,2} + overlapCost;
% end
% end
end
end
% forward
fprintf('forward propogation\n');
fprintf('Scanning MST layer: %d\n', 1);
detection = cell(1, numCluster);
root = mst{1};
rootCosts = [];
for i = 1 : size(strokeCandidates{root},1)
cost = strokeCandidates{root}{i,2};
for j = 3 : size(strokeCandidates{root},2)
cost = cost + strokeCandidates{root}{i,j}(2);
end
rootCosts(end+1) = cost;
end
[~,rootSelected] = min(rootCosts);
minCost = rootCosts(rootSelected);
detection{root} = strokeCandidates{root}{rootSelected, 1};
for i = 2 : length(mst)
fprintf('Scanning MST layer: %d\n', i);
curLayer = mst{i};
for j = 1 : length(curLayer)
curEdge = curLayer{j};
curParent = curEdge{1}(2);
curChild = curEdge{1}(1);
curParam = curEdge{2};
curParentCandidate = detection{curParent};
curChildCandidates = strokeCandidates{curChild};
tmpBbox = getBoundingBox(curParentCandidate, 0);
curParentCenter = ceil([(tmpBbox(2)+tmpBbox(4))/2, (tmpBbox(1)+tmpBbox(3))/2]);
childCosts = ones(1, size(curChildCandidates, 1))*Inf;
for c = 1 : size(curChildCandidates, 1)
tmpBbox = getBoundingBox(curChildCandidates{c,1},0);
curChildCenter = ceil([(tmpBbox(2)+tmpBbox(4))/2, (tmpBbox(1)+tmpBbox(3))/2]);
geoNorm = 1/40;
curCost = appGeoWeight * curChildCandidates{c,2} - (1-appGeoWeight) * geoNorm * log(mvnpdf(curChildCenter-curParentCenter, ...
curParam{2},curParam{3}));
for gc = 3 : size(curChildCandidates,2)
curCost = curCost + curChildCandidates{c,gc}(2);
end
childCosts(c) = curCost;
end
[~, minId] = min(childCosts);
detection{curChild} = curChildCandidates{minId,1};
end
end
detections{a} = detection;
energy(a) = minCost;
end
[~,minIdx] = min(energy);
detection = detections{minIdx};
% visualize the detection
% synthesized = zeros(height, width);
% for i = 1 : length(detection)
% if sum(detection{i}(:))>1
% synthesized = synthesized + detection{i};
% end
% end
% figure;imshow(~sample);
% figure;imshow(~synthesized);
end