def find_features(pyr):
#############################################################
# finds features in an image given by its pyramid pyr
#############################################################
pos = sad.spread_out_corners(pyr[0], 7, 7, 12)
desc = sample_descriptor(pyr[2], pos, 3)
return pos, desc
def find_features(pyr: list) -> tuple:
"""
get simplified version of the MOPS descriptors from the given pyramid and the positions of them
:param pyr: Gaussian pyramid of a grayscale image having 3 levels
:return: the positions of the descriptors and array of them [with shape (1+2*desc_rad,1+2*desc_rad,N)]
"""
pos = sol4_add.spread_out_corners(pyr[0], N, M, RADIUS)
desc = sample_descriptor(pyr[2], pos / 4, DEFAULT_DESC_RAD)
return pos, desc
def find_features(pyr):
"""
pyr − Gaussian pyramid of a grayscale image having 3 levels.
pos − An array with shape (N,2) of [x,y] feature location per row found in the (third pyramid level of the) image. These
coordinates are provided at the pyramid level pyr[0].
desc − A feature descriptor array with shape (K,K,N).
"""
pos = sol4_add.spread_out_corners(pyr[0], 7, 7, 3)
pos_3 = pos[:] / 4
desc = sample_descriptor(pyr[2], pos_3)
return pos, desc
def find_features(pyr):
"""
Takes in a gaussian pyramid of an image with 3 levels, find intersting features and return their location,
as well as their corresponding descriptors
:param pyr: Gaussian pyramid of a grayscale image having 3 levels.
:return:
pos − An array with shape (N,2) of [x,y] feature location per row found in the (third pyramid level of the)
image. These coordinates are provided at the pyramid level pyr[0].
desc − A feature descriptor array with shape (K,K,N).
"""
pos = sol4_add.spread_out_corners(pyr[0], M, N, SPOC_RADIUS)
pos_flipped = np.fliplr(pos)
pos_in_l3 = map_coord_2_level(pos_flipped)
desc = sample_descriptor(pyr[2], pos_in_l3, DESC_RADIUS)
# remove false descriptors (constant)
k = desc.shape[0]
mask = np.where(
np.sum(np.sum((
0 == desc).astype(np.uint8), axis=0), axis=0) == k**2)[0]
desc = np.delete(desc, mask, axis=2)
pos = np.delete(pos, mask, axis=0)
return pos, desc
def test_harris_detector():
im = read_image('external/oxford2.jpg', 1)
plt.figure()
res = spread_out_corners(im, 7, 7, 15)
plt.imshow(im, cmap=plt.cm.gray)
plt.scatter(res[:, 0], res[:, 1])
# plt.show(block=True)
for pth in [
'external/oxford2.jpg', 'external/office1.jpg',
'external/office2.jpg'
]:
plt.figure()
im = read_image(pth, 1)
res = sol4.harris_corner_detector(im)
plt.imshow(im, cmap=plt.cm.gray)
plt.scatter(res[:, 0], res[:, 1])
pyr, f = build_gaussian_pyramid(im, 3, 3)
plt.figure()
im = pyr[2]
res = sol4.harris_corner_detector(im)
plt.imshow(im, cmap=plt.cm.gray)
plt.scatter(res[:, 0], res[:, 1])
plt.show(block=True)
def find_features(pyr):
desc_rad = 3
pos = sol4_add.spread_out_corners(pyr[0], 7, 7, desc_rad * 4)
desc = sample_descriptor(pyr[2], pos, desc_rad)
return pos, desc
def find_features(pyr, radius=3, m=5, n=5):
points = spread_out_corners(pyr[0], m, n,
radius * 4) # im using radius times 4 because in the next function im using orig radius
return points, sample_descriptor(pyr[2], points / 4, radius) # how cool is it to write 2 lines function :)
def find_features(pyr):
pos = add.spread_out_corners(pyr[0],7,7,12)
#TODO should we use 3?
return pos,sample_descriptor(pyr[2],pos,3)