hi everyone
i am facing probwm while finding precision recall curve .i got precision values but my recall values are zero i change threshold valusbut didnot get recall values this is my code
clear
load('C:\Users\ZBook\OneDrive\Desktop\detector\detectorFasterRCNN.mat');
dataRoad = load("D:\this is.mat");
img = imageDatastore(dataRoad.gTruth.DataSource.Source);
labeldata = dataRoad.gTruth.LabelData;
blds = boxLabelDatastore(labeldata);
cds = combine(img, blds);
preview(cds)
tbl = countEachLabel(blds);
% Define the split ratios (e.g., 70% training, 15% validation, 15% testing)
trainRatio = 0.2;
valRatio = 0.10;
testRatio = 0.7;
% Count the total number of images
numImages = numel(img.Files);
% Calculate the number of images for each split
numTrain = round(trainRatio * numImages);
numVal = round(valRatio * numImages);
numTest = numImages - numTrain - numVal;
% Shuffle the datastore
cds = shuffle(cds);
inputSize = [224 224 3];
% Split the datastore into training, validation, and testing sets
trainingData = subset(cds, 1:numTrain);
validationData = subset(cds, numTrain+1:numTrain+numVal);
testData = subset(cds, numTrain+numVal+1:numTrain+numVal+numTest);
testdata = transform(testData, @(data) preprocessData(data, inputSize));
% Make predictions
detectionResults = detect(detector,testData,'MinibatchSize',1,'Threshold', 0.4);
numClasses = 8; % Replace with your specific number of classes
% Initialize arrays to store precision and recall for each class
precision = zeros(numClasses, 1);
recall = zeros(numClasses, 1);
for classID = 1:numClasses
% Evaluate object detection metrics
metrics = evaluateObjectDetection(detectionResults, testData);
% Get precision and recall for the current class
precision(classID) = mean(metrics.ClassMetrics.Precision{classID});
recall(classID) = mean(metrics.ClassMetrics.Recall{classID});
fprintf('Class %d - Average Precision: %.4f, Average Recall: %.4f\n', classID, precision(classID), recall(classID));
end
% Optionally, you can plot the precision-recall curves for each class
figure;
plot(recall, precision, 'o-');
xlabel('Recall');
ylabel('Precision');
title('Precision-Recall Curves for Each Class');
legend('Class 1', 'Class 2', 'Class 3', 'Class 4', 'Class 5', 'Class 6', 'Class 7', 'Class 8');
grid on;
function data = preprocessData(data, targetSize)
% Resize image and bounding boxes to the targetSize.
sz = size(data{1}, [1, 2]);
scale = targetSize(1:2) ./ sz;
data{1} = imresize(data{1}, targetSize(1:2));
% Pass imageSize to helperSanitizeBoxes
imageSize = targetSize; % You may adjust this based on your needs
data{2} = helperSanitizeBoxes(data{2}, imageSize);
% Resize boxes to new image size.
data{2} = bboxresize(data{2}, scale);
end
