matlab实现YOLOV3车辆识别目标识别

function [boxDeltaTarget, objectnessTarget, classTarget, maskTarget, boxErrorScaleTarget] = generateTargets(YPredCellGathered, groundTruth, inputImageSize, anchorBoxes, anchorBoxMask, penaltyThreshold) % generateTargets creates target array for every prediction element % x, y, width, height, confidence scores and class probabilities. boxDeltaTarget = cell(size(YPredCellGathered,1),4); objectnessTarget = cell(size(YPredCellGathered,1),1); classTarget = cell(size(YPredCellGathered,1),1); maskTarget = cell(size(YPredCellGathered,1),3); boxErrorScaleTarget = cell(size(YPredCellGathered,1),1); % Normalize the ground truth boxes w.r.t image input size. gtScale = [inputImageSize(2) inputImageSize(1) inputImageSize(2) inputImageSize(1)]; groundTruth(:,1:4,:,:) = groundTruth(:,1:4,:,:)./gtScale; for numPred = 1:size(YPredCellGathered,1) % Select anchor boxes based on anchor box mask indices. anchors = anchorBoxes(anchorBoxMask{numPred},:); bx = YPredCellGathered{numPred,2}; by = YPredCellGathered{numPred,3}; bw = YPredCellGathered{numPred,4}; bh = YPredCellGathered{numPred,5}; predClasses = YPredCellGathered{numPred,6}; gridSize = size(bx); if numel(gridSize)== 3 gridSize(4) = 1; end numClasses = size(predClasses,3)./size(anchors,1); % Initialize the required variables. mask = single(zeros(size(bx))); confMask = single(ones(size(bx))); classMask = single(zeros(size(predClasses))); tx = single(zeros(size(bx))); ty = single(zeros(size(by))); tw = single(zeros(size(bw))); th = single(zeros(size(bh))); tconf = single(zeros(size(bx))); tclass = single(zeros(size(predClasses))); boxErrorScale = single(ones(size(bx))); % Get the IOU of predictions with groundtruth. iou = getMaxIOUPredictedWithGroundTruth(bx,by,bw,bh,groundTruth); % Donot penalize the predictions which has iou greater than penalty % threshold. confMask(iou > penaltyThreshold) = 0; for batch = 1:gridSize(4) truthBatch = groundTruth(:,1:5,:,batch); truthBatch = truthBatch(all(truthBatch,2),:); % Get boxes with center as 0. gtPred = [0-truthBatch(:,3)/2,0-truthBatch(:,4)/2,truthBatch(:,3),truthBatch(:,4)]; anchorPrior = [0-anchorBoxes(:,2)/(2*inputImageSize(2)),0-anchorBoxes(:,1)/(2*inputImageSize(1)),anchorBoxes(:,2)/inputImageSize(2),anchorBoxes(:,1)/inputImageSize(1)]; % Get the iou of best matching anchor box. overLap = bboxOverlapRatio(gtPred,anchorPrior); [~,bestAnchorIdx] = max(overLap,[],2); % Select gt that are within the mask. index = ismember(bestAnchorIdx,anchorBoxMask{numPred}); truthBatch = truthBatch(index,:); bestAnchorIdx = bestAnchorIdx(index,:); bestAnchorIdx = bestAnchorIdx - anchorBoxMask{numPred}(1,1) + 1; if ~isempty(truthBatch) % Convert top left position of ground-truth to centre coordinates. truthBatch = [truthBatch(:,1)+truthBatch(:,3)./2,truthBatch(:,2)+truthBatch(:,4)./2,truthBatch(:,3),truthBatch(:,4),truthBatch(:,5)]; errorScale = 2 - truthBatch(:,3).*truthBatch(:,4); truthBatch = [truthBatch(:,1)*gridSize(2),truthBatch(:,2)*gridSize(1),truthBatch(:,3)*inputImageSize(2),truthBatch(:,4)*inputImageSize(1),truthBatch(:,5)]; for t = 1:size(truthBatch,1) % Get the position of ground-truth box in the grid. colIdx = ceil(truthBatch(t,1)); colIdx(colIdx<1) = 1; colIdx(colIdx>gridSize(2)) = gridSize(2); rowIdx = ceil(truthBatch(t,2)); rowIdx(rowIdx<1) = 1; rowIdx(rowIdx>gridSize(1)) = gridSize(1); pos = [rowIdx,colIdx]; anchorIdx = bestAnchorIdx(t,1); mask(pos(1,1),pos(1,2),anchorIdx,batch) = 1; confMask(pos(1,1),pos(1,2),anchorIdx,batch) = 1; % Calculate the shift in ground-truth boxes. tShiftX = truthBatch(t,1)-pos(1,2)+1; tShiftY = truthBatch(t,2)-pos(1,1)+1; tShiftW = log(truthBatch(t,3)/anchors(anchorIdx,2)); tShiftH = log(truthBatch(t,4)/anchors(anchorIdx,1)); % Update the target box. tx(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftX; ty(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftY; tw(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftW; th(pos(1,1),pos(1,2),anchorIdx,batch) = tShiftH; boxErrorScale(pos(1,1),pos(1,2),anchorIdx,batch) = errorScale(t); tconf(rowIdx,colIdx,anchorIdx,batch) = 1; classIdx = (numClasses*(anchorIdx-1))+truthBatch(t,5); tclass(rowIdx,colIdx,classIdx,batch) = 1; classMask(rowIdx,colIdx,(numClasses*(anchorIdx-1))+(1:numClasses),batch) = 1; end end end boxDeltaTarget(numPred,:) = [{tx} {ty} {tw} {th}]; objectnessTarget{numPred,1} = tconf; classTarget{numPred,1} = tclass; maskTarget(numPred,:) = [{mask} {confMask} {classMask}]; boxErrorScaleTarget{numPred,:} = boxErrorScale; end end function iou = getMaxIOUPredictedWithGroundTruth(predx,predy,predw,predh,truth) % getMaxIOUPredictedWithGroundTruth computes the maximum intersection over % union scores for every pair of predictions and ground-truth boxes. [h,w,c,n] = size(predx); iou = zeros([h w c n],'like',predx); % For each batch prepare the predictions and ground-truth. for batchSize = 1:n truthBatch = truth(:,1:4,1,batchSize); truthBatch = truthBatch(all(truthBatch,2),:); predxb = predx(:,:,:,batchSize); predyb = predy(:,:,:,batchSize); predwb = predw(:,:,:,batchSize); predhb = predh(:,:,:,batchSize); predb = [predxb(:),predyb(:),predwb(:),predhb(:)]; % Convert from center xy coordinate to topleft xy coordinate. predb = [predb(:,1)-predb(:,3)./2, predb(:,2)-predb(:,4)./2, predb(:,3), predb(:,4)]; % Compute and extract the maximum IOU of predictions with ground-truth. overlap = bboxOverlapRatio(predb,truthBatch); maxOverlap = max(overlap,[],2); iou(:,:,:,batchSize) = reshape(maxOverlap,h,w,c); end end