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2.部分仿真图预览
3.算法概述
猫作为一种受欢迎的宠物，其图像在互联网上大量存在。对猫脸和猫眼进行准确检测和定位，在宠物识别、情感分析等领域具有广泛的应用价值。然而，由于猫脸和猫眼的多样性以及复杂背景的干扰，传统的图像处理方法往往难以取得理想的效果。因此，本文提出了一种基于Faster-RCNN网络的猫脸检测和猫眼定位系统，以提高猫脸和猫眼检测的准确性和鲁棒性。
4.部分源码
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global im;
load mat\net1.mat
In_layer_Size = [224 224 3];
I = im;
I = imresize(I,In_layer_Size(1:2));
[bboxes,scores] = detect(detector1,I);
[Vs,Is] = max(scores);
if isempty(bboxes)==0
I1 = insertObjectAnnotation(I,'rectangle',bboxes(Is,:),Vs);
axes(handles.axes2);
imshow(I1)
text(160,20,'检测到猫脸');
set(handles.edit2,'string','有猫脸');
set(handles.edit5,'string',num2str(Vs));
else
I1 = I;
axes(handles.axes2);
imshow(I1)
text(160,20,'未检到猫脸');
set(handles.edit2,'string','无猫脸');
set(handles.edit5,'string',num2str(0));
end
load mat\net2.mat
In_layer_Size = [224 224 3];
I = im;
I = imresize(I,In_layer_Size(1:2));
[bboxes2,scores] = detect(detector2,I);
[Vs,Is] = max(bboxes2);
N = length(bboxes2);
bboxes2
if isempty(bboxes2)==0
[index_km,center_km]=kmeans(bboxes2(:,1:2),2);
bboxes3(1,:) = [center_km(1,:),bboxes2(1,3:end)];
bboxes3(2,:) = [center_km(2,:),bboxes2(2,3:end)];
I2 = insertObjectAnnotation(I1,'rectangle',bboxes3,scores(1:2),'color','cyan');
axes(handles.axes2);
imshow(I2)
else
I2 = I1;
axes(handles.axes2);
imshow(I2)
end
0Y_009m
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