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16b9514543
`PyObject*` to `std::vector<T>` conversion logic:
- If user passed Numpy Array
- If array is planar and T is a primitive type (doesn't require
constructor call) that matches with the element type of array, then
copy element one by one with the respect of the step between array
elements. If compiler is lucky (or brave enough) copy loop can be
vectorized.
For classes that require constructor calls this path is not
possible, because we can't begin an object lifetime without hacks.
- Otherwise fall-back to general case
- Otherwise - execute the general case:
If PyObject* corresponds to Sequence protocol - iterate over the
sequence elements and invoke the appropriate `pyopencv_to` function.
`std::vector<T>` to `PyObject*` conversion logic:
- If `std::vector<T>` is empty - return empty tuple.
- If `T` has a corresponding `Mat` `DataType` than return
Numpy array instance of the matching `dtype` e.g.
`std::vector<cv::Rect>` is returned as `np.ndarray` of shape `Nx4` and
`dtype=int`.
This branch helps to optimize further evaluations in user code.
- Otherwise - execute the general case:
Construct a tuple of length N = `std::vector::size` and insert
elements one by one.
Unnecessary functions were removed and code was rearranged to allow
compiler select the appropriate conversion function specialization.
95 lines
3.9 KiB
Python
95 lines
3.9 KiB
Python
#!/usr/bin/env python
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from __future__ import print_function
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import numpy as np
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import cv2 as cv
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from tests_common import NewOpenCVTests
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class Hackathon244Tests(NewOpenCVTests):
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def test_int_array(self):
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a = np.array([-1, 2, -3, 4, -5])
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absa0 = np.abs(a)
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self.assertTrue(cv.norm(a, cv.NORM_L1) == 15)
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absa1 = cv.absdiff(a, 0)
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self.assertEqual(cv.norm(absa1, absa0, cv.NORM_INF), 0)
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def test_imencode(self):
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a = np.zeros((480, 640), dtype=np.uint8)
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flag, ajpg = cv.imencode("img_q90.jpg", a, [cv.IMWRITE_JPEG_QUALITY, 90])
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self.assertEqual(flag, True)
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self.assertEqual(ajpg.dtype, np.uint8)
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self.assertTrue(isinstance(ajpg, np.ndarray), "imencode returned buffer of wrong type: {}".format(type(ajpg)))
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self.assertEqual(len(ajpg.shape), 1, "imencode returned buffer with wrong shape: {}".format(ajpg.shape))
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self.assertGreaterEqual(len(ajpg), 1, "imencode length of the returned buffer should be at least 1")
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self.assertLessEqual(
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len(ajpg), a.size,
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"imencode length of the returned buffer shouldn't exceed number of elements in original image"
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)
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def test_projectPoints(self):
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objpt = np.float64([[1,2,3]])
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imgpt0, jac0 = cv.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), np.float64([]))
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imgpt1, jac1 = cv.projectPoints(objpt, np.zeros(3), np.zeros(3), np.eye(3), None)
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self.assertEqual(imgpt0.shape, (objpt.shape[0], 1, 2))
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self.assertEqual(imgpt1.shape, imgpt0.shape)
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self.assertEqual(jac0.shape, jac1.shape)
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self.assertEqual(jac0.shape[0], 2*objpt.shape[0])
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def test_estimateAffine3D(self):
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pattern_size = (11, 8)
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pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
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pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
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pattern_points *= 10
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(retval, out, inliers) = cv.estimateAffine3D(pattern_points, pattern_points)
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self.assertEqual(retval, 1)
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if cv.norm(out[2,:]) < 1e-3:
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out[2,2]=1
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self.assertLess(cv.norm(out, np.float64([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])), 1e-3)
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self.assertEqual(cv.countNonZero(inliers), pattern_size[0]*pattern_size[1])
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def test_fast(self):
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fd = cv.FastFeatureDetector_create(30, True)
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img = self.get_sample("samples/data/right02.jpg", 0)
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img = cv.medianBlur(img, 3)
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keypoints = fd.detect(img)
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self.assertTrue(600 <= len(keypoints) <= 700)
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for kpt in keypoints:
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self.assertNotEqual(kpt.response, 0)
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def check_close_angles(self, a, b, angle_delta):
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self.assertTrue(abs(a - b) <= angle_delta or
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abs(360 - abs(a - b)) <= angle_delta)
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def check_close_pairs(self, a, b, delta):
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self.assertLessEqual(abs(a[0] - b[0]), delta)
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self.assertLessEqual(abs(a[1] - b[1]), delta)
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def check_close_boxes(self, a, b, delta, angle_delta):
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self.check_close_pairs(a[0], b[0], delta)
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self.check_close_pairs(a[1], b[1], delta)
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self.check_close_angles(a[2], b[2], angle_delta)
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def test_geometry(self):
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npt = 100
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np.random.seed(244)
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a = np.random.randn(npt,2).astype('float32')*50 + 150
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be = cv.fitEllipse(a)
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br = cv.minAreaRect(a)
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mc, mr = cv.minEnclosingCircle(a)
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be0 = ((150.2511749267578, 150.77322387695312), (158.024658203125, 197.57696533203125), 37.57804489135742)
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br0 = ((161.2974090576172, 154.41793823242188), (207.7177734375, 199.2301483154297), 80.83544921875)
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mc0, mr0 = (160.41790771484375, 144.55152893066406), 136.713500977
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self.check_close_boxes(be, be0, 5, 15)
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self.check_close_boxes(br, br0, 5, 15)
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self.check_close_pairs(mc, mc0, 5)
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self.assertLessEqual(abs(mr - mr0), 5)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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