sustaining_gazes/matlab_version/face_detection/detect_faces.m
2016-04-28 15:40:36 -04:00

62 lines
2.3 KiB
Matlab

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