我正在进行视频监视中的跟踪和对象分类项目，而从前景对象删除阴影时，我遇到了一些困难。问题是我不能只使用我想要的任何方法，我必须使用本文中描述的相同方法，并使用基于斑点的形态重构来删除阴影。
在文章中，他们使用了混合阴影去除方法 - 基于RGB颜色的检测和基于纹理的检测。基于颜色和纹理的过程均并行使用，然后是一个结合两个结果的断言过程。
所以我想在删除阴影MATLAB代码方面获得一些帮助。
到目前为止我的代码
function Tracking_Objects()
% Create System objects used for reading video, detecting moving objects,
% and displaying the results.
obj = setupSystemObjects();
tracks = initializeTracks(); % Create an empty array of tracks.
nextId = 1; % ID of the next track
% Detect moving objects, and track them across video frames.
while ~isDone(obj.reader)
frame = readFrame();
[centroids, bboxes, mask] = detectObjects(frame);
predictNewLocationsOfTracks();
[assignments, unassignedTracks, unassignedDetections] = ...
detectionToTrackAssignment();
updateAssignedTracks();
updateUnassignedTracks();
deleteLostTracks();
createNewTracks();
displayTrackingResults();
end
%% Create System Objects
function obj = setupSystemObjects()
% Initialize Video I/O
% Create objects for reading a video from a file, drawing the tracked
% objects in each frame, and playing the video.
% Create a video file reader.
obj.reader = vision.VideoFileReader('input.avi');
% Create two video players, one to display the video,
% and one to display the foreground mask.
obj.videoPlayer = vision.VideoPlayer('Position', [10, 250, 700, 400]);
obj.maskPlayer = vision.VideoPlayer('Position', [720, 250, 700, 400]);
obj.detector = vision.ForegroundDetector('NumGaussians', 3, ...
'NumTrainingFrames', 40, 'MinimumBackgroundRatio', 0.7);
obj.blobAnalyser = vision.BlobAnalysis('BoundingBoxOutputPort', true, ...
'AreaOutputPort', true, 'CentroidOutputPort', true, ...
'MinimumBlobArea', 75);
end
%% Initialize Tracks
function tracks = initializeTracks()
% create an empty array of tracks
tracks = struct(...
'id', {}, ...
'bbox', {}, ...
'kalmanFilter', {}, ...
'age', {}, ...
'totalVisibleCount', {}, ...
'consecutiveInvisibleCount', {});
end
%% Read a Video Frame
% Read the next video frame from the video file.
function frame = readFrame()
frame = obj.reader.step();
end
%% Detect Objects
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
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
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
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
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
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
end
似乎您需要使用代码修改detectObjects函数以消除阴影。目前，它所做的只是从背景扣除（vision.ForegroundDetector）中取出前景面具，消除了一些噪音，并使用形态填充了小间隙，然后找到了连接的组件，也就是" blobs"。通常，斑点确实包括阴影。
如果您的阴影去除算法在单个帧上运行，那么代码将进行的位置。
我认为您的问题在您的对象检测中。您的代码使用imopen和imclose。但是我认为您想使用imdilate和imerode
%% Detect Objects
function [centroids, bboxes, mask] = detectObjects(frame)
% Detect foreground.
mask = obj.detector.step(frame);
% Apply morphological operations to remove noise and fill in holes.
%this makes the white space smaller to remove small dots/noise
mask = imerode(mask, strel('rectangle', [3,3]));
%this makes the white regions larger to restore the original size
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
我更改了您的imopen和imclose，您可能还需要更改结构元素。如果您查看MATLAB文档iMopen与侵蚀相同，然后是扩张，或imopened = imdilate(imerode(im,se)));Imclose是扩张的，然后是侵蚀或imcloseed = imerode(imdilate(im,se))
纸张使用侵蚀，然后进行扩张（膜片操作）。这就是我在代码中放置的内容。如果您想对两个操作使用相同的结构元素，则可以更改
%this makes the white space smaller to remove small dots/noise
mask = imerode(mask, strel('rectangle', [3,3]));
%this makes the white regions larger to restore the original size
mask = imclose(mask, strel('rectangle', [15, 15]));
to
mask = imopen(mask,strel('rectangle',[15 15]));
由于您没有包含图片，所以我无法尝试，但是我认为使用strel功能后应该可以使用