63 lines
2.3 KiB
Mathematica
63 lines
2.3 KiB
Mathematica
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function [ bboxes, shapes ] = detect_faces( image, types )
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%DETECT_FACES Detecting faces in a given image using one of the three detectors
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% INPUT:
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% image - the image to detect the faces on
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% type - cell array of the face detectors to use: 'zhu', 'yu', 'cascade'
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% OUTPUT:
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% bboxes - a set of bounding boxes describing the detected faces 4 x
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% num_faces, the format is [min_x; min_y; max_x; max_y];
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% shapes - if the face detector detects landmarks as well, output them
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% n_points x 2 x num_faces
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use_zhu = any(strcmp('zhu', types));
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use_yu = any(strcmp('yu', types));
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use_cascade = any(strcmp('cascade', types));
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% Start with fastest one
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shapes = [];
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bboxes = [];
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if(use_cascade)
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% Check if vision toolbox is available
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if(~isempty(ver('vision')))
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face_detector = vision.CascadeObjectDetector();
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bboxes_det = step(face_detector, image);
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if(~isempty(bboxes_det))
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% Convert to the right format
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bboxes = bboxes_det';
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% Correct the bounding box to be around face outline
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% horizontally and from brows to chin vertically
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% The scalings were learned using the Face Detections on LFPW, Helen, AFW and iBUG datasets
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% using ground truth and detections from openCV
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% Correct the widths
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bboxes(3,:) = bboxes(3,:) * 0.8534;
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bboxes(4,:) = bboxes(4,:) * 0.8972;
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% Correct the location
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bboxes(1,:) = bboxes(1,:) + bboxes_det(:,3)'*0.0266;
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bboxes(2,:) = bboxes(2,:) + bboxes_det(:,4)'*0.1884;
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bboxes(3,:) = bboxes(1,:) + bboxes(3,:);
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bboxes(4,:) = bboxes(2,:) + bboxes(4,:);
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end
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else
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fprintf('Vision toolbox not present in Matlab, it is necessary for Cascade face detection\n');
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end
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end
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if(use_yu && isempty(bboxes))
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od = cd('../face_detection/face_detection_yu/');
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[bboxes, shapes] = Detect_tree_based_yu_multi(image);
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cd(od);
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end
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if(use_zhu && isempty(bboxes))
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[bboxes, shapes] = Detect_tree_based_zhu(image);
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end
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end
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