如何在视频中将检测到的对象存储在Matlab中每一帧的文件夹中？

Question

I want to store the detected object that is inside the yellow box and store each object with his label name in a separate file like object 1 in folder 1 object 2 in folder 2 only I want to save the detected object in the frame.

Image:

    function [centroids, bboxes, mask] = detectObjects(frame)

        % Detect foreground.
        mask = obj.detector.step(frame);

        % Apply morphological operations to remove noise and fill in holes.
        mask = imopen(mask, strel('rectangle', [3,3]));
        mask = imclose(mask, strel('rectangle', [15, 15])); 
        mask = imfill(mask, 'holes');

        % Perform blob analysis to find connected components.
        [~, centroids, bboxes] = obj.blobAnalyser.step(mask);
    end

%% Predict New Locations of Existing Tracks
% Use the Kalman filter to predict the centroid of each track in the
% current frame, and update its bounding box accordingly.

    function predictNewLocationsOfTracks()
        for i = 1:length(tracks)
            bbox = tracks(i).bbox;

            % Predict the current location of the track.
            predictedCentroid = predict(tracks(i).kalmanFilter);

            % Shift the bounding box so that its center is at 
            % the predicted location.
            predictedCentroid = int32(predictedCentroid) - bbox(3:4) / 2;
            tracks(i).bbox = [predictedCentroid, bbox(3:4)];
        end
    end

%% Assign Detections to Tracks
% Assigning object detections in the current frame to existing tracks is
% done by minimizing cost. The cost is defined as the negative
% log-likelihood of a detection corresponding to a track.  
%
% The algorithm involves two steps: 
%
% Step 1: Compute the cost of assigning every detection to each track using
% the |distance| method of the |vision.KalmanFilter| System object(TM). The 
% cost takes into account the Euclidean distance between the predicted
% centroid of the track and the centroid of the detection. It also includes
% the confidence of the prediction, which is maintained by the Kalman
% filter. The results are stored in an MxN matrix, where M is the number of
% tracks, and N is the number of detections.   
%
% Step 2: Solve the assignment problem represented by the cost matrix using
% the |assignDetectionsToTracks| function. The function takes the cost 
% matrix and the cost of not assigning any detections to a track.  
%
% The value for the cost of not assigning a detection to a track depends on
% the range of values returned by the |distance| method of the 
% |vision.KalmanFilter|. This value must be tuned experimentally. Setting 
% it too low increases the likelihood of creating a new track, and may
% result in track fragmentation. Setting it too high may result in a single 
% track corresponding to a series of separate moving objects.   
%
% The |assignDetectionsToTracks| function uses the Munkres' version of the
% Hungarian algorithm to compute an assignment which minimizes the total
% cost. It returns an M x 2 matrix containing the corresponding indices of
% assigned tracks and detections in its two columns. It also returns the
% indices of tracks and detections that remained unassigned. 

    function [assignments, unassignedTracks, unassignedDetections] = ...
            detectionToTrackAssignment()

        nTracks = length(tracks);
        nDetections = size(centroids, 1);

        % Compute the cost of assigning each detection to each track.
        cost = zeros(nTracks, nDetections);
        for i = 1:nTracks
            cost(i, :) = distance(tracks(i).kalmanFilter, centroids);
        end

        % Solve the assignment problem.
        costOfNonAssignment = 20;
        [assignments, unassignedTracks, unassignedDetections] = ...
            assignDetectionsToTracks(cost, costOfNonAssignment);
    end

%% Update Assigned Tracks
% The |updateAssignedTracks| function updates each assigned track with the
% corresponding detection. It calls the |correct| method of
% |vision.KalmanFilter| to correct the location estimate. Next, it stores
% the new bounding box, and increases the age of the track and the total
% visible count by 1. Finally, the function sets the invisible count to 0. 

    function updateAssignedTracks()
        numAssignedTracks = size(assignments, 1);
        for i = 1:numAssignedTracks
            trackIdx = assignments(i, 1);
            detectionIdx = assignments(i, 2);
            centroid = centroids(detectionIdx, :);
            bbox = bboxes(detectionIdx, :);

            % Correct the estimate of the object's location
            % using the new detection.
            correct(tracks(trackIdx).kalmanFilter, centroid);

            % Replace predicted bounding box with detected
            % bounding box.
            tracks(trackIdx).bbox = bbox;

            % Update track's age.
            tracks(trackIdx).age = tracks(trackIdx).age + 1;

            % Update visibility.
            tracks(trackIdx).totalVisibleCount = ...
                tracks(trackIdx).totalVisibleCount + 1;
            tracks(trackIdx).consecutiveInvisibleCount = 0;
        end
    end

%% Update Unassigned Tracks
% Mark each unassigned track as invisible, and increase its age by 1.

    function updateUnassignedTracks()
        for i = 1:length(unassignedTracks)
            ind = unassignedTracks(i);
            tracks(ind).age = tracks(ind).age + 1;
            tracks(ind).consecutiveInvisibleCount = ...
                tracks(ind).consecutiveInvisibleCount + 1;
        end
    end

%% Delete Lost Tracks
% The |deleteLostTracks| function deletes tracks that have been invisible
% for too many consecutive frames. It also deletes recently created tracks
% that have been invisible for too many frames overall. 

    function deleteLostTracks()
        if isempty(tracks)
            return;
        end

        invisibleForTooLong = 20;
        ageThreshold = 8;

        % Compute the fraction of the track's age for which it was visible.
        ages = [tracks(:).age];
        totalVisibleCounts = [tracks(:).totalVisibleCount];
        visibility = totalVisibleCounts ./ ages;

        % Find the indices of 'lost' tracks.
        lostInds = (ages < ageThreshold & visibility < 0.6) | ...
            [tracks(:).consecutiveInvisibleCount] >= invisibleForTooLong;

        % Delete lost tracks.
        tracks = tracks(~lostInds);
    end

%% Create New Tracks
% Create new tracks from unassigned detections. Assume that any unassigned
% detection is a start of a new track. In practice, you can use other cues
% to eliminate noisy detections, such as size, location, or appearance.

    function createNewTracks()
        centroids = centroids(unassignedDetections, :);
        bboxes = bboxes(unassignedDetections, :);

        for i = 1:size(centroids, 1)

            centroid = centroids(i,:);
            bbox = bboxes(i, :);

            % Create a Kalman filter object.
            kalmanFilter = configureKalmanFilter('ConstantVelocity', ...
                centroid, [200, 50], [100, 25], 100);

            % Create a new track.
            newTrack = struct(...
                'id', nextId, ...
                'bbox', bbox, ...
                'kalmanFilter', kalmanFilter, ...
                'age', 1, ...
                'totalVisibleCount', 1, ...
                'consecutiveInvisibleCount', 0);

            % Add it to the array of tracks.
            tracks(end + 1) = newTrack;

            % Increment the next id.
            nextId = nextId + 1;
        end
    end

%% Display Tracking Results
% The |displayTrackingResults| function draws a bounding box and label ID 
% for each track on the video frame and the foreground mask. It then 
% displays the frame and the mask in their respective video players. 

    function displayTrackingResults()
        % Convert the frame and the mask to uint8 RGB.
        frame = im2uint8(frame);
        mask = uint8(repmat(mask, [1, 1, 3])) .* 255;

        minVisibleCount = 8;
        if ~isempty(tracks)

            % Noisy detections tend to result in short-lived tracks.
            % Only display tracks that have been visible for more than 
            % a minimum number of frames.
            reliableTrackInds = ...
                [tracks(:).totalVisibleCount] > minVisibleCount;
            reliableTracks = tracks(reliableTrackInds);

            % Display the objects. If an object has not been detected
            % in this frame, display its predicted bounding box.
            if ~isempty(reliableTracks)
                % Get bounding boxes.
                bboxes = cat(1, reliableTracks.bbox);

                % Get ids.
                ids = int32([reliableTracks(:).id]);

                % Create labels for objects indicating the ones for 
                % which we display the predicted rather than the actual 
                % location.
                labels = cellstr(int2str(ids'));
                predictedTrackInds = ...
                    [reliableTracks(:).consecutiveInvisibleCount] > 0;
                isPredicted = cell(size(labels));
                isPredicted(predictedTrackInds) = {' predicted'};
                labels = strcat(labels, isPredicted);

                % Draw the objects on the frame.
                frame = insertObjectAnnotation(frame, 'rectangle', ...
                    bboxes, labels);

                % Draw the objects on the mask.
                mask = insertObjectAnnotation(mask, 'rectangle', ...
                    bboxes, labels);
            end
        end

        % Display the mask and the frame.
        obj.maskPlayer.step(mask);        
        obj.videoPlayer.step(frame);
    end

%% Summary
% This example created a motion-based system for detecting and
% tracking multiple moving objects. Try using a different video to see if
% you are able to detect and track objects. Try modifying the parameters
% for the detection, assignment, and deletion steps.  
%
% The tracking in this example was solely based on motion with the
% assumption that all objects move in a straight line with constant speed.
% When the motion of an object significantly deviates from this model, the
% example may produce tracking errors. Notice the mistake in tracking the
% person labeled #12, when he is occluded by the tree. 
%
% The likelihood of tracking errors can be reduced by using a more complex
% motion model, such as constant acceleration, or by using multiple Kalman
% filters for every object. Also, you can incorporate other cues for
% associating detections over time, such as size, shape, and color. 

displayEndOfDemoMessage(mfilename)
  end

Answer 1

使用imcrop将检测到的对象从图像中剪切出来，然后使用imwrite将其保存到文件中。