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function detect(img::OpenCV.InputArray, cascade)
rects = OpenCV.detectMultiScale(cascade, img)
return (rects[1].x, rects[1].y, rects[1].width+rects[1].x, rects[1].height+rects[1].y)
end
function IOU(boxA, boxB)
xA = max(boxA[1], boxB[1])
yA = max(boxA[2], boxB[2])
xB = min(boxA[3], boxB[3])
yB = min(boxA[4], boxB[4])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[3] - boxA[1] + 1) * (boxA[4] - boxA[2] + 1)
boxBArea = (boxB[3] - boxB[1] + 1) * (boxB[4] - boxB[2] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
end
cascade = OpenCV.CascadeClassifier(joinpath(test_dir, "cascadeandhog", "cascades", "haarcascade_frontalface_alt.xml"))
img = OpenCV.imread(joinpath(test_dir, "cascadeandhog", "images", "mona-lisa.png"), OpenCV.IMREAD_GRAYSCALE)
rect = detect(img, cascade)
expected_rect = (164,119,306,261)
@test IOU(rect, expected_rect) > 0.95
print("objdetect test passed\n")
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