vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
commit 12022378a3
3872 changed files with 2513409 additions and 0 deletions
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+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
+
+const cv = OpenCV
+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"))
+size0 = 300
+
+cv.dnn.setPreferableTarget(net, cv.dnn.DNN_TARGET_CPU)
+cv.dnn.setInputMean(net, (104, 177, 123))
+cv.dnn.setInputScale(net, 1.)
+cv.dnn.setInputSize(net, size0, size0)
+
+
+img = OpenCV.imread(joinpath(test_dir, "cascadeandhog", "images", "mona-lisa.png"))
+
+classIds, confidences, boxes = cv.dnn.detect(net, img, confThreshold=0.5)
+
+box = (boxes[1].x, boxes[1].y, boxes[1].x+boxes[1].width, boxes[1].y+boxes[1].height)
+expected_rect = (185,101,129+185,169+101)
+
+@test IOU(box, expected_rect) > 0.8
+
+print("dnn test passed\n")