def test_cast_to_opencv_keypoints(self):
"""Tests conversion of GTSFM's keypoints to OpenCV's keypoints."""
gtsfm_keypoints = Keypoints(
coordinates=np.array([[1.3, 5], [20, 10]]),
scales=np.array([1.0, 5.2]),
responses=np.array([4.2, 3.2]),
)
results = gtsfm_keypoints.cast_to_opencv_keypoints()
# check the length of the result
self.assertEqual(len(results), len(gtsfm_keypoints))
# check all the keypoint values
for idx in range(len(gtsfm_keypoints)):
opencv_kp = results[idx]
self.assertAlmostEqual(opencv_kp.pt[0],
gtsfm_keypoints.coordinates[idx, 0],
places=5)
self.assertAlmostEqual(opencv_kp.pt[1],
gtsfm_keypoints.coordinates[idx, 1],
places=5)
self.assertAlmostEqual(opencv_kp.size,
gtsfm_keypoints.scales[idx],
places=5)
self.assertAlmostEqual(opencv_kp.response,
gtsfm_keypoints.responses[idx],
places=5)
def describe(self, image: Image, keypoints: Keypoints) -> np.ndarray:
"""Assign descriptors to detected features in an image.
Arguments:
image: the input image.
keypoints: the keypoints to describe, of length N.
Returns:
Descriptors for the input features, of shape (N, D) where D is the dimension of each descriptor.
"""
if len(keypoints) == 0:
return np.array([])
gray_image = image_utils.rgb_to_gray_cv(image)
opencv_obj = cv.SIFT_create()
# TODO(ayush): what to do about new set of keypoints
_, descriptors = opencv_obj.compute(
gray_image.value_array, keypoints.cast_to_opencv_keypoints())
return descriptors