def slice_blob(dert__, mask, crit__, AveB, verbose=False, render=False):
frame = dict(rng=1, dert__=dert__, mask=None, I=0, Dy=0, Dx=0, G=0, M=0, Dyy=0, Dyx=0, Dxy=0, Dxx=0, Ga=0, Ma=0, blob__=[])
stack_ = deque() # buffer of running vertical stacks of Ps
height, width = dert__[0].shape
if render:
def output_path(input_path, suffix):
return str(Path(input_path).with_suffix(suffix))
streamer = BlobStreamer(CBlob, dert__[1],
record_path=output_path(arguments['image'],
suffix='.im2blobs.avi'))
if verbose:
start_time = time()
print("Converting to image to blobs...")
for y, dert_ in enumerate(zip(*dert__)): # first and last row are discarded
if verbose:
print(f"\rProcessing line {y + 1}/{height}, ", end="")
print(f"{len(frame['blob__'])} blobs converted", end="")
sys.stdout.flush()
P_ = form_P_(zip(*dert_), crit__[y], mask[y]) # horizontal clustering
if render:
render = streamer.update_blob_conversion(y, P_)
P_ = scan_P_(P_, stack_, frame) # vertical clustering, adds P up_connects and _P down_connect_cnt
stack_ = form_stack_(P_, frame, y)
while stack_: # frame ends, last-line stacks are merged into their blobs
form_blob(stack_.popleft(), frame)
# evaluate P blobs
comp_slice_blob(frame['blob__'], AveB)
if verbose: # print out at the end
nblobs = len(frame['blob__'])
print(f"\rImage has been successfully converted to "
f"{nblobs} blob{'s' if nblobs != 1 else 0} in "
f"{time() - start_time:.3} seconds", end="")
blob_ids = [blob_id for blob_id in range(CBlob.instance_cnt)]
merged_percentage = len([*filter(lambda bid: CBlob.get_instance(bid) is None, blob_ids)]) / len(blob_ids)
print(f"\nPercentage of merged blobs: {merged_percentage}")
if render: # rendering mode after blob conversion
path = output_path(arguments['image'],
suffix='.im2blobs.jpg')
streamer.end_blob_conversion(y, img_out_path=path)
return frame # frame of blobs
def cluster_derts(dert__,
mask,
Ave,
fcr,
fig,
render=False): # similar to frame_to_blobs
if fcr: # comp_r output; form clustering criterion:
if fig:
crit__ = dert__[0] + dert__[
6] - Ave # eval by i + m, accum in rng; dert__[:,:,0] if not transposed
else:
crit__ = Ave - dert__[3] # eval by -g, accum in rng
else: # comp_g output
crit__ = dert__[
6] - Ave # comp_g output eval by m, or clustering is always by m?
root_dert__ = dert__ # derts after the comps operation, which is the root_dert__
dert__ = [*zip(*dert__)] # transpose dert__ into shape [y, params, x]
sub_blobs = [] # from form_blob:
stack_ = deque() # buffer of running vertical stacks of Ps
stack_binder = AdjBinder(CDeepStack)
if render:
streamer = BlobStreamer(CDeepBlob, crit__, mask)
if render:
streamer = BlobStreamer(CDeepBlob, crit__, mask)
for y, dert_ in enumerate(
dert__
): # in height, first and last row are discarded; print(f'Processing intra line {y}...')
# if False in mask[i]: # [y,x,params], there is at least one dert in line
P_binder = AdjBinder(
CDeepP) # binder needs data about clusters of the same level
P_ = form_P_(zip(*dert_), crit__[y], mask[y],
P_binder) # horizontal clustering, adds a row of Ps
if render:
render = streamer.update_blob_conversion(
y, P_) # if return False, stop rendering
P_ = scan_P_(
P_, stack_, root_dert__, sub_blobs, P_binder
) # vertical clustering, adds up_connects per P and down_connect_cnt per stack
stack_ = form_stack_(P_, root_dert__, sub_blobs, y)
stack_binder.bind_from_lower(P_binder)
while stack_: # frame ends, last-line stacks are merged into their blobs:
form_blob(stack_.popleft(), root_dert__, sub_blobs)
blob_binder = AdjBinder(CDeepBlob)
blob_binder.bind_from_lower(stack_binder)
assign_adjacents(blob_binder) # add adj_blobs to each blob
# sub_blobs = find_adjacent(sub_blobs)
if render: # rendering mode after blob conversion
streamer.end_blob_conversion(y)
return sub_blobs