def _process_training(
coords: np.ndarray,
targets: np.ndarray,
feature_source: H5Features,
image_spec: ImageSpec,
halfwidth: int,
) -> DataArrays:
coords_x, coords_y = coords.T
indices_x = world_to_image(coords_x, image_spec.x_coordinates)
indices_y = world_to_image(coords_y, image_spec.y_coordinates)
patch_reads, mask_reads = patch.patches(indices_x, indices_y, halfwidth,
image_spec.width,
image_spec.height)
npatches = indices_x.shape[0]
patchwidth = 2 * halfwidth + 1
con_marray, cat_marray = None, None
if feature_source.continuous:
con_marray = _direct_read(feature_source.continuous, patch_reads,
mask_reads, npatches, patchwidth)
if feature_source.categorical:
cat_marray = _direct_read(feature_source.categorical, patch_reads,
mask_reads, npatches, patchwidth)
indices = np.vstack((indices_x, indices_y)).T
output = DataArrays(con_marray, cat_marray, targets, coords, indices)
return output
def _process_query(indices: np.ndarray, feature_source: H5Features,
image_spec: ImageSpec, halfwidth: int) -> DataArrays:
indices_x, indices_y = indices.T
coords_x = image_to_world(indices_x, image_spec.x_coordinates)
coords_y = image_to_world(indices_y, image_spec.y_coordinates)
patch_reads, mask_reads = patch.patches(indices_x, indices_y, halfwidth,
image_spec.width,
image_spec.height)
patch_data_slices = _slices_from_patches(patch_reads)
npatches = indices_x.shape[0]
patchwidth = 2 * halfwidth + 1
con_marray, cat_marray = None, None
if feature_source.continuous:
con_data_cache = _get_rows(patch_data_slices,
feature_source.continuous)
con_marray = _cached_read(con_data_cache, feature_source.continuous,
patch_reads, mask_reads, npatches,
patchwidth)
if feature_source.categorical:
cat_data_cache = _get_rows(patch_data_slices,
feature_source.categorical)
cat_marray = _cached_read(cat_data_cache, feature_source.categorical,
patch_reads, mask_reads, npatches,
patchwidth)
coords = np.vstack((coords_x, coords_y)).T
output = DataArrays(con_marray, cat_marray, None, coords, indices)
return output
def test_patch_20():
"""Check patch code in y edge."""
halfwidth = 1
im_width = 5
im_height = 5
image = np.arange((im_height * im_width)).reshape((im_height, im_width))
n = 2 * halfwidth + 1
# 2,0 edge
p_data = np.zeros((n, n), dtype=int) - 1
x = np.array([2])
y = np.array([0])
patch_rws, mask_ws = patch.patches(x, y, halfwidth, im_width, im_height)
for r in patch_rws:
p_data[r.yp, r.xp] = image[r.y, r.x]
true_answer = np.array([[-1, -1, -1], [1, 2, 3], [6, 7, 8]], dtype=int)
assert np.all(true_answer == p_data)
def test_patch_44():
"""Check patch code in outer corner."""
halfwidth = 1
im_width = 5
im_height = 5
image = np.arange((im_height * im_width)).reshape((im_height, im_width))
n = 2 * halfwidth + 1
# 4,4 corner
p_data = np.zeros((n, n), dtype=int) - 1
x = np.array([4])
y = np.array([4])
patch_rws, mask_ws = patch.patches(x, y, halfwidth, im_width, im_height)
for r in patch_rws:
p_data[r.yp, r.xp] = image[r.y, r.x]
true_answer = np.array([[18, 19, -1], [23, 24, -1], [-1, -1, -1]], dtype=int)
assert np.all(true_answer == p_data)
def test_patch_44_mask():
"""Check that patches are correctly created from points in 00 corner."""
halfwidth = 1
im_width = 5
im_height = 5
n = 2 * halfwidth + 1
# 0,0 corner
p_mask = np.zeros((n, n), dtype=bool)
x = np.array([4])
y = np.array([4])
patch_rws, mask_ws = patch.patches(x, y, halfwidth, im_width, im_height)
for r in mask_ws:
p_mask[r.yp, r.xp] = True
true_answer = np.array(
[[False, False, True], [False, False, True], [True, True, True]],
dtype=bool)
assert np.all(true_answer == p_mask)
def test_patch_00():
"""Check that patches are correctly created from points in 00 corner."""
halfwidth = 1
im_width = 5
im_height = 5
image = np.arange((im_height * im_width)).reshape((im_height, im_width))
n = 2 * halfwidth + 1
# 0,0 corner
p_data = np.zeros((n, n), dtype=int) - 1
x = np.array([0])
y = np.array([0])
patch_rws, mask_ws = patch.patches(x, y, halfwidth, im_width, im_height)
for r in patch_rws:
p_data[r.yp, r.xp] = image[r.y, r.x]
true_answer = np.array([[-1, -1, -1], [-1, 0, 1], [-1, 5, 6]], dtype=int)
assert np.all(true_answer == p_data)
