def cc_locs(mask):
ccs = cv2.connectedComponents(mask.astype(np.uint8), connectivity=4)[1]
rc_locs = np.where(mask > 0)
rc_ids = ccs[rc_locs]
rc_arrs = np.ascontiguousarray(np.vstack(rc_locs).T)
cc_to_loc = util.group_items(rc_arrs, rc_ids, axis=0)
return cc_to_loc
def cc_locs(mask):
"""
Grouped row/col locations of 4-connected-components
"""
from clab import util
import cv2
ccs = cv2.connectedComponents(mask.astype(np.uint8), connectivity=4)[1]
rc_locs = np.where(mask > 0)
rc_ids = ccs[rc_locs]
rc_arrs = np.ascontiguousarray(np.vstack(rc_locs).T)
cc_to_loc = util.group_items(rc_arrs, rc_ids, axis=0)
return cc_to_loc
def instance_contours(gti):
""" Extracts a contour for each (non-overlapping) instance label in a mask """
# TODO: move to somewhere better
import cv2
rc_locs = np.where(gti > 0)
grouped_cc_rcs = util.group_items(
np.ascontiguousarray(np.vstack(rc_locs).T),
gti[rc_locs], axis=0
)
def bounding_box(rcs):
rc1 = rcs.min(axis=0)
rc2 = rcs.max(axis=0)
return rc1, rc2
# slice out a bounding region around each instance, detect the contour and
# then offset it back into image coordinates
grouped_contours = {}
for label, rcs in grouped_cc_rcs.items():
rc1, rc2 = bounding_box(rcs)
sl = (slice(rc1[0], rc2[0] + 2), slice(rc1[1], rc2[1] + 2))
submask = (gti[sl] == label).astype(np.uint8)
xy_offset = rc1[::-1]
offset = xy_offset + [-2, -2]
border = cv2.copyMakeBorder(submask, 2, 2, 2, 2, cv2.BORDER_CONSTANT, value=0 )
_, contors, hierarchy = cv2.findContours(border, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE,
offset=tuple(offset))
"""
offset = [0, 0]
BGR_GREEN = (0, 255, 0)
x = np.ascontiguousarray(util.ensure_alpha_channel(submask)[:, :, 0:3]).astype(np.uint8)
draw_img = cv2.drawContours(
image=x, contours=contors,
contourIdx=-1, color=BGR_GREEN, thickness=2)
"""
# note when len(contours > 1, there is a hole in the building)
# assert len(contors) == 1
grouped_contours[label] = contors
return grouped_contours
def instance_submasks(gti):
"""
Iterate over a cropped mask for each instance in an instance segmentation
Example:
>>> gti = np.array([
>>> [0, 0, 5, 5, 5, 2, 2],
>>> [0, 0, 5, 5, 5, 2, 2],
>>> [0, 0, 0, 0, 0, 0, 0],
>>> [0, 0, 0, 1, 9, 0, 0],
>>> [3, 0, 0, 1, 1, 0, 0],
>>> ], dtype=np.uint8)
>>> first, *rest = instance_submasks(gti)
>>> (label, submask, rc_off, rc_sl) = first
>>> submask
array([[1, 0],
[1, 1]], dtype=uint8)
"""
rc_locs = np.where(gti > 0)
grouped_cc_rcs = util.group_items(
np.ascontiguousarray(np.vstack(rc_locs).T),
gti[rc_locs], axis=0
)
def bounding_box(rcs):
rc1 = rcs.min(axis=0)
rc2 = rcs.max(axis=0)
return rc1, rc2
for label, rcs in grouped_cc_rcs.items():
rc1, rc2 = bounding_box(rcs)
r_slice = slice(rc1[0], rc2[0] + 1)
c_slice = slice(rc1[1], rc2[1] + 1)
rc_sl = (r_slice, c_slice)
subimg = gti[rc_sl]
submask = (subimg == label).astype(np.uint8)
rc_off = rc1
yield label, submask, rc_off, rc_sl