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#393, SnakeImage
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@ -1499,6 +1499,15 @@ KalmanCorrect ROCvMat*
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KalmanPredict ROCvMat*
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CvKalman* kalman
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CvMat control NULL
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SnakeImage points
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IplImage image
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CvPoints points
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floats alpha
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floats beta
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floats gamma
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CvSize win
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CvTermCriteria criteria
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int calc_gradient 1
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# Optical Flow
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CalcOpticalFlowLK
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@ -1812,16 +1812,24 @@ struct floats {
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};
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static int convert_to_floats(PyObject *o, floats *dst, const char *name = "no_name")
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{
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PyObject *fi = PySequence_Fast(o, name);
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if (fi == NULL)
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return 0;
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dst->count = PySequence_Fast_GET_SIZE(fi);
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dst->f = new float[dst->count];
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for (Py_ssize_t i = 0; i < PySequence_Fast_GET_SIZE(fi); i++) {
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PyObject *item = PySequence_Fast_GET_ITEM(fi, i);
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dst->f[i] = (float)PyFloat_AsDouble(item);
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if (PySequence_Check(o)) {
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PyObject *fi = PySequence_Fast(o, name);
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if (fi == NULL)
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return 0;
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dst->count = PySequence_Fast_GET_SIZE(fi);
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dst->f = new float[dst->count];
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for (Py_ssize_t i = 0; i < PySequence_Fast_GET_SIZE(fi); i++) {
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PyObject *item = PySequence_Fast_GET_ITEM(fi, i);
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dst->f[i] = (float)PyFloat_AsDouble(item);
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}
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Py_DECREF(fi);
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} else if (PyNumber_Check(o)) {
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dst->count = 1;
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dst->f = new float[1];
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dst->f[0] = PyFloat_AsDouble(o);
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} else {
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return failmsg("Expected list of floats, or float for argument '%s'", name);
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}
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Py_DECREF(fi);
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return 1;
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}
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@ -2552,6 +2560,16 @@ static PyObject *FROM_CvSubdiv2DEdge(CvSubdiv2DEdge r)
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return (PyObject*)m;
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}
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static PyObject *FROM_CvPoints(CvPoints src)
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{
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PyObject *pr;
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pr = PyList_New(src.count);
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for (int i = 0; i < src.count; i++) {
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PyList_SetItem(pr, i, FROM_CvPoint(src.p[i]));
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}
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return pr;
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}
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/************************************************************************/
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/* A few functions are too odd to be generated,
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@ -3763,6 +3781,17 @@ static int zero = 0;
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#define constCvMat const CvMat
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#define FROM_constCvMatPTR(x) FROM_CvMatPTR((CvMat*)x)
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#define cvSnakeImage(image, points, length, a, b, g, win, criteria, calc_gradient) \
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do { \
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int coeff_usage; \
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if ((alpha.count == 1) && (beta.count == 1) && (gamma.count == 1)) \
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coeff_usage = CV_VALUE; \
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else if ((length == alpha.count) && (alpha.count == beta.count) && (beta.count == gamma.count)) \
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coeff_usage = CV_ARRAY; \
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else \
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return (PyObject*)failmsg("SnakeImage weights invalid"); \
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cvSnakeImage(image, points, length, a, b, g, coeff_usage, win, criteria, calc_gradient); \
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} while (0)
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#include "generated0.i"
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@ -770,6 +770,26 @@ class FunctionTests(OpenCVTests):
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expected = 0.0
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self.assertEqual(M[rj,cj], expected)
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def test_SnakeImage(self):
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src = self.get_sample("samples/c/lena.jpg", 0)
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pts = [ (512-i,i) for i in range(0, 512, 8) ]
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# Make sure that weight arguments get validated
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self.assertRaises(TypeError, lambda: cv.SnakeImage(cv.GetImage(src), pts, [1,2], .01, .01, (7,7), (cv.CV_TERMCRIT_ITER, 100, 0.1)))
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# Smoke by making sure that points are changed by call
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r = cv.SnakeImage(cv.GetImage(src), pts, .01, .01, .01, (7,7), (cv.CV_TERMCRIT_ITER, 100, 0.1))
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if 0:
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cv.PolyLine(src, [ r ], 0, 255)
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self.snap(src)
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self.assertEqual(len(r), len(pts))
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self.assertNotEqual(r, pts)
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# Ensure that list of weights is same as scalar weight
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w = [.01] * len(pts)
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r2 = cv.SnakeImage(cv.GetImage(src), pts, w, w, w, (7,7), (cv.CV_TERMCRIT_ITER, 100, 0.1))
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self.assertEqual(r, r2)
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def test_Sum(self):
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for r in range(1,11):
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for c in range(1, 11):
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