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