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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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const cv = OpenCV
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net = cv.dnn.DetectionModel(joinpath(ENV["OPENCV_TEST_DATA_PATH"], "dnn", "opencv_face_detector.pbtxt"),joinpath(ENV["OPENCV_TEST_DATA_PATH"], "dnn", "opencv_face_detector_uint8.pb"))
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size0 = 300
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cv.dnn.setPreferableTarget(net, cv.dnn.DNN_TARGET_CPU)
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cv.dnn.setInputMean(net, (104, 177, 123))
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cv.dnn.setInputScale(net, 1.)
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cv.dnn.setInputSize(net, size0, size0)
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img = OpenCV.imread(joinpath(test_dir, "cascadeandhog", "images", "mona-lisa.png"))
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classIds, confidences, boxes = cv.dnn.detect(net, img, confThreshold=0.5)
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box = (boxes[1].x, boxes[1].y, boxes[1].x+boxes[1].width, boxes[1].y+boxes[1].height)
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expected_rect = (185,101,129+185,169+101)
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@test IOU(box, expected_rect) > 0.8
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print("dnn test passed\n")
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