def comp_angle(blob, render, verbose):
AveB = int(aveB * blob.rdn)
if render: # don't render small blobs
if blob.A < 100: render = False
spliced_layers = [] # to extend root_blob sublayers
# root fork is frame_blobs or comp_r
ext_dert__, ext_mask__ = extend_dert(
blob) # dert__ boundaries += 1, for cross-comp in larger kernels
if -blob.M > AveB: # comp_a fork, replace with borrow_M if known
blob.rng = 0
blob.f_comp_a = 1
adert__, mask__ = comp_a(ext_dert__,
ext_mask__) # compute abs ma, no indep eval
if verbose: print('\na fork\n')
blob.prior_forks.extend('a')
if mask__.shape[0] > 2 and mask__.shape[
1] > 2 and False in mask__: # min size in y and x, least one dert in dert__
sign__ = (
-adert__[3] * adert__[9]
) > ave * pcoef # -m * ma: variable value of comp_slice_, no ave_ma in comp_a
cluster_sub_eval(
blob, adert__, sign__, mask__, render,
verbose) # forms sub_blobs of fork p sign in unmasked area
spliced_layers = [
spliced_layers + sublayers for spliced_layers, sublayers in
zip_longest(spliced_layers, blob.sublayers, fillvalue=[])
]
return spliced_layers
def intra_blob(
blob, **kwargs
): # slice_blob or recursive input rng+ | angle cross-comp within input blob
Ave = int(ave * blob.rdn)
AveB = int(aveB * blob.rdn)
verbose = kwargs.get('verbose')
if kwargs.get('render') is not None: # don't render small blobs
if blob.A < 100: kwargs['render'] = False
spliced_layers = [] # to extend root_blob sub_layers
# root fork is frame_blobs or comp_r
ext_dert__, ext_mask__ = extend_dert(
blob) # dert__ boundaries += 1, for cross-comp in larger kernels
if -blob.M > AveB: # comp_a fork, replace with borrow_M if known
blob.rng = 0
blob.f_comp_a = 1
adert__, mask__ = comp_a(ext_dert__,
ext_mask__) # compute abs ma, no indep eval
if kwargs.get('verbose'): print('\na fork\n')
blob.prior_forks.extend('a')
if mask__.shape[0] > 2 and mask__.shape[
1] > 2 and False in mask__: # min size in y and x, least one dert in dert__
sign__ = (
-adert__[3] * adert__[9]
) > ave * pcoef # -m * ma: variable value of comp_slice_, no ave_ma in comp_a
cluster_sub_eval(
blob, adert__, sign__, mask__,
**kwargs) # forms sub_blobs of fork p sign in unmasked area
spliced_layers = [
spliced_layers + sub_layers for spliced_layers, sub_layers in
zip_longest(spliced_layers, blob.sub_layers, fillvalue=[])
]
elif blob.M > AveB: # comp_r fork
blob.rng += 1
blob.f_comp_a = 0
dert__, mask__ = comp_r(ext_dert__, Ave, blob.rng, ext_mask__)
if kwargs.get('verbose'): print('\na fork\n')
blob.prior_forks.extend('r')
if mask__.shape[0] > 2 and mask__.shape[
1] > 2 and False in mask__: # min size in y and x, at least one dert in dert__
sign__ = dert__[3] > 0 # m__: inverse deviation of g
cluster_sub_eval(
blob, dert__, sign__, mask__,
**kwargs) # forms sub_blobs of sign in unmasked area
spliced_layers = [
spliced_layers + sub_layers for spliced_layers, sub_layers in
zip_longest(spliced_layers, blob.sub_layers, fillvalue=[])
]
return spliced_layers
def intra_blob(
blob, **kwargs): # recursive input rng+ | angle cross-comp within blob
if kwargs.get(
'render'
) is not None: # stop rendering sub-blobs when blob is too small
if blob.S < 100:
kwargs['render'] = False
spliced_layers = [] # to extend root_blob sub_layers
ext_dert__, ext_mask = extend_dert(blob)
if blob.fia: # comp_a -> P_blobs or comp_aga
dert__, mask = comp_a(
ext_dert__,
ext_mask) # -> ga sub_blobs -> P_blobs (comp_d, comp_P)
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
# P_blobs eval, tentative:
if blob.G * (1 - blob.Ga / 6 * blob.S) - aveB * blob.rdn > 0:
# G reduced by Ga value, max_ga=6?
# G is second deviation, or replace with Adj_blobs borrow value?
# flatten day and dax: this should done for both if blob.fia forks, or a general default?
dert__ = list(dert__)
dert__ = (dert__[0], dert__[1], dert__[2], dert__[3],
dert__[4], dert__[5][0], dert__[5][1], dert__[6][0],
dert__[6][1], dert__[7], dert__[8])
crit__ = dert__[3] * (
1 - dert__[7] /
6) - ave * blob.rdn # max_ga=6, separate from g and ga?
sub_frame = cluster_derts_P(dert__, crit__, mask,
ave * blob.rdn)
sub_blobs = sub_frame['blob__']
blob.Ls = len(sub_blobs) # for visibility and next-fork rd
blob.sub_layers = [sub_blobs] # 1st layer of sub_blobs
# comp_aga eval, tentative:
elif blob.G / (1 - blob.Ga / 6 * blob.S) - aveB > 0: # max_ga=6
# G increased by Ga value,
# G is second deviation or specific borrow value?
# flatten day and dax?
crit__ = dert__[3] / (1 - dert__[7] / 100) - ave * blob.rdn
# similar to eval per blob, replace 100 with max_ga value?
# record separately from g and ga?
sub_blobs = cluster_derts(dert__, crit__, mask)
blob.Ls = len(sub_blobs) # for visibility and next-fork rd
blob.sub_layers = [sub_blobs] # 1st layer of sub_blobs
for sub_blob in sub_blobs: # evaluate for comp_aga only, not comp_r | P_blobs?
G = blob.G
adj_G = blob.adj_blobs[2]
borrow = min(abs(G),
abs(adj_G) /
2) # or adjacent M if negative sign?
# same eval as in root elif?:
if sub_blob.G + borrow > ave * blob.rdn: # also if +Ga, +Gaga, +Gr, +Gagr, +Grr...
# comp_aga: runnable but not correct, the issue of nested day and dax need to be fixed first
sub_blob.fia = 1
sub_blob.rdn = sub_blob.rdn + 1 + 1 / blob.Ls
blob.sub_layers += intra_blob(sub_blob, **kwargs)
spliced_layers = [
spliced_layers + sub_layers for spliced_layers, sub_layers in
zip_longest(spliced_layers, blob.sub_layers, fillvalue=[])
]
else: # comp_r -> comp_r or comp_a
if blob.M > aveB:
dert__, mask = comp_r(ext_dert__, blob.fca, ext_mask)
crit__ = dert__[4] - ave * blob.rdn
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
sub_blobs = cluster_derts(dert__,
crit__,
mask,
verbose=False,
**kwargs) # -> m sub_blobs
# replace lines below with generic sub_eval()?
blob.Ls = len(sub_blobs) # for visibility and next-fork rdn
blob.sub_layers = [sub_blobs] # 1st layer of sub_blobs
for sub_blob in sub_blobs: # evaluate for intra_blob comp_a | comp_r | P_blobs:
G = blob.G
adj_G = blob.adj_blobs[2]
borrow = min(abs(G),
abs(adj_G) /
2) # or adjacent M if negative sign?
if sub_blob.G + borrow > ave * blob.rdn: # also if +Ga, +Gaga, +Gr, +Gagr, +Grr...
# comp_a:
sub_blob.rdn = sub_blob.rdn + 1 + 1 / blob.Ls
blob.sub_layers += intra_blob(sub_blob, **kwargs)
elif sub_blob.M > ave * blob.rdn: # if +Mr, +Mrr...
# comp_r:
sub_blob.rdn = sub_blob.rdn + 1 + 1 / blob.Ls
sub_blob.fcr = 1
sub_blob.rng = blob.rng * 2
blob.sub_layers += intra_blob(sub_blob, **kwargs)
spliced_layers = [
spliced_layers + sub_layers
for spliced_layers, sub_layers in zip_longest(
spliced_layers, blob.sub_layers, fillvalue=[])
]
elif blob.G > aveB:
dert__, mask = comp_a(ext_dert__, ext_mask) # -> m sub_blobs
crit__ = dert__[3] - ave * blob.rdn
# call new sub_eval(), same as in comp_r fork
return spliced_layers
def intra_blob(
blob, **kwargs
): # slice_blob or recursive input rng+ | angle cross-comp within input blob
Ave = int(ave * blob.rdn)
AveB = int(aveB * blob.rdn)
verbose = kwargs.get('verbose')
if kwargs.get('render') is not None: # don't render small blobs
if blob.A < 100: kwargs['render'] = False
spliced_layers = [] # to extend root_blob sub_layers
if blob.f_root_a:
# root fork is comp_a -> slice_blob
if blob.mask__.shape[0] > 2 and blob.mask__.shape[
1] > 2 and False in blob.mask__: # min size in y and x, at least one dert in dert__
if (
-blob.Dert.M * blob.Dert.Ma - AveB > 0
) and blob.Dert.Dx: # vs. G reduced by Ga: * (1 - Ga / (4.45 * A)), max_ga=4.45
blob.f_comp_a = 0
blob.prior_forks.extend('p')
if kwargs.get('verbose'): print('\nslice_blob fork\n')
segment_by_direction(blob, verbose=True)
# derP_ = slice_blob(blob, []) # cross-comp of vertically consecutive Ps in selected stacks
# blob.PP_ = derP_2_PP_(derP_, blob.PP_) # form vertically contiguous patterns of patterns
else:
# root fork is frame_blobs or comp_r
ext_dert__, ext_mask__ = extend_dert(
blob) # dert__ boundaries += 1, for cross-comp in larger kernels
if blob.Dert.G > AveB: # comp_a fork, replace G with borrow_M when known
adert__, mask__ = comp_a(ext_dert__, Ave, blob.prior_forks,
ext_mask__) # compute ma and ga
blob.f_comp_a = 1
if kwargs.get('verbose'): print('\na fork\n')
blob.prior_forks.extend('a')
if mask__.shape[0] > 2 and mask__.shape[
1] > 2 and False in mask__: # min size in y and x, least one dert in dert__
sign__ = adert__[3] * adert__[
8] > 0 # g * (ma / ave: deviation rate, no independent value, not co-measurable with g)
dert__ = tuple([
adert__[0], adert__[1], adert__[2], adert__[3], adert__[4],
adert__[5][0], adert__[5][1], adert__[6][0], adert__[6][1],
adert__[7], adert__[8]
]) # flatten adert
cluster_sub_eval(
blob, dert__, sign__, mask__,
**kwargs) # forms sub_blobs of sign in unmasked area
spliced_layers = [
spliced_layers + sub_layers
for spliced_layers, sub_layers in zip_longest(
spliced_layers, blob.sub_layers, fillvalue=[])
]
elif blob.Dert.M > AveB * 1.2: # comp_r fork, ave M = ave G * 1.2
dert__, mask__ = comp_r(ext_dert__, Ave, blob.f_root_a, ext_mask__)
blob.f_comp_a = 0
if kwargs.get('verbose'): print('\na fork\n')
blob.prior_forks.extend('r')
if mask__.shape[0] > 2 and mask__.shape[
1] > 2 and False in mask__: # min size in y and x, at least one dert in dert__
sign__ = dert__[4] > 0 # m__ is inverse deviation of SAD
cluster_sub_eval(
blob, dert__, sign__, mask__,
**kwargs) # forms sub_blobs of sign in unmasked area
spliced_layers = [
spliced_layers + sub_layers
for spliced_layers, sub_layers in zip_longest(
spliced_layers, blob.sub_layers, fillvalue=[])
]
return spliced_layers
def intra_blob(blob,
**kwargs): # recursive input rng+ | der+ cross-comp within blob
# fig: flag input is g | p, fcr: flag comp over rng+ | der+
if kwargs.get(
'render'
) is not None: # stop rendering sub-blobs when blob is too small
if blob.S < 100:
kwargs['render'] = False
spliced_layers = [] # to extend root_blob sub_layers
ext_dert__, ext_mask = extend_dert(blob)
if blob.fca: # comp_a
dert__, mask = comp_a(ext_dert__,
ext_mask) # -> xy_blobs (comp_d, comp_P)
else: # comp_r
dert__, mask = comp_r(ext_dert__, blob.fcr, ext_mask) # -> m sub_blobs
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
sub_blobs = cluster_derts(dert__,
mask,
ave * blob.rdn,
blob.fcr,
blob.fca,
verbose=False,
**kwargs)
# fork params:
blob.Ls = len(sub_blobs) # for visibility and next-fork rdn
blob.sub_layers = [sub_blobs] # 1st layer of sub_blobs
for sub_blob in sub_blobs: # evaluate for intra_blob comp_a | comp_r | xy_blobs:
G = blob.G
adj_G = blob.adj_blobs[2]
borrow = min(abs(G),
abs(adj_G) / 2) # or adjacent M if negative sign?
# +Ga, +Gaga, +Gr, +Gagr, +Grr and so on
if sub_blob.G + borrow > ave * blob.rdn: # comp_a
sub_blob.rdn = sub_blob.rdn + 1 + 1 / blob.Ls
blob.sub_layers += intra_blob(sub_blob, **kwargs)
# +Ma, +Maga, +Magr and so on (root fork of xy_blobs always = comp_a)
elif blob.fca == 1 and sub_blob.M > ave * blob.rdn: # xy_blobs
image_to_blobs(sub_blob.root_dert__,
verbose=False,
render=False)
# +Mr, +Mrr and so on
elif sub_blob.M > ave * blob.rdn: # comp_r
sub_blob.rdn = sub_blob.rdn + 1 + 1 / blob.Ls
sub_blob.fcr = 1
sub_blob.rng = blob.rng * 2
blob.sub_layers += intra_blob(sub_blob, **kwargs)
spliced_layers = [
spliced_layers + sub_layers for spliced_layers, sub_layers in
zip_longest(spliced_layers, blob.sub_layers, fillvalue=[])
]
return spliced_layers
def intra_blob(
blob, **kwargs): # recursive input rng+ | angle cross-comp within blob
Ave = int(ave * blob.rdn)
AveB = int(aveB * blob.rdn)
if kwargs.get(
'render'
) is not None: # stop rendering sub-blobs when blob is too small
if blob.S < 100:
kwargs['render'] = False
spliced_layers = [] # to extend root_blob sub_layers
ext_dert__, ext_mask = extend_dert(blob)
if blob.fia: # comp_a -> P_blobs or comp_aga
dert__, mask = comp_a(
ext_dert__, Ave,
ext_mask) # -> ga sub_blobs -> P_blobs (comp_d, comp_P)
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
# cluster_derts_P eval, tentative, no cluster_derts_P yet:
if blob.G * (1 - blob.Ga /
(4.45 * blob.S)) - AveB > 0: # max_ga=4.45?
# G reduced by Ga value, base G is second deviation or specific borrow value
# flatten day and dax, not generalized for nested day and dax yet:
dert__ = list(dert__)
dert__ = (dert__[0], dert__[1], dert__[2], dert__[3],
dert__[4], dert__[5][0], dert__[5][1], dert__[6][0],
dert__[6][1], dert__[7], dert__[8])
crit__ = dert__[3] * (
1 - dert__[7] / 4.45
) - Ave # max_ga=4.45, record separately from g and ga?
# ga is not signed, thus additional eval, different Ave?
blob.fca = 0
sub_eval(blob, dert__, crit__, mask, **kwargs)
# comp_aga eval, inverse relative ga value, tentative, no comp_aga yet:
elif blob.G / (
1 - blob.Ga / (4.45 * blob.S)
) - AveB > 0: # max_ga=4.45, init G is 2nd deviation or borrow value
# G increased by Ga value, flatten day and dax?
crit__ = dert__[3] / (
1 - dert__[7] / 4.45
) - Ave # ~ eval per blob, record separately from g and ga?
# ga is not signed, thus additional eval, different Ave?
blob.fca = 1
sub_eval(blob, dert__, crit__, mask, **kwargs)
spliced_layers = [
spliced_layers + sub_layers for spliced_layers, sub_layers in
zip_longest(spliced_layers, blob.sub_layers, fillvalue=[])
]
else: # comp_r -> comp_r or comp_a
if blob.M > AveB:
dert__, mask = comp_r(ext_dert__, Ave, blob.fia, ext_mask)
crit__ = dert__[4] # signed inverse deviation of SAD
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
sub_eval(blob, dert__, crit__, mask, **kwargs)
spliced_layers = [
spliced_layers + sub_layers
for spliced_layers, sub_layers in zip_longest(
spliced_layers, blob.sub_layers, fillvalue=[])
]
elif blob.G > AveB:
dert__, mask = comp_a(ext_dert__, Ave, ext_mask) # -> m sub_blobs
crit__ = dert__[3] # signed deviation of g
if mask.shape[0] > 2 and mask.shape[
1] > 2 and False in mask: # min size in y and x, least one dert in dert__
sub_eval(blob, dert__, crit__, mask, **kwargs)
spliced_layers = [
spliced_layers + sub_layers
for spliced_layers, sub_layers in zip_longest(
spliced_layers, blob.sub_layers, fillvalue=[])
]
return spliced_layers
def intra_blob_root(
root_blob, render, verbose, fBa
): # recursive evaluation of cross-comp slice| range| angle per blob
# deep_blobs = [] # for visualization
spliced_layers = []
if fBa: blob_ = root_blob.asublayers[0]
else: blob_ = root_blob.rsublayers[0]
for blob in blob_: # fork-specific blobs, print('Processing blob number ' + str(bcount))
blob.prior_forks = root_blob.prior_forks.copy(
) # increment forking sequence: g -> r|a, a -> p
blob.root_dert__ = root_blob.dert__
blob_height = blob.box[1] - blob.box[0]
blob_width = blob.box[3] - blob.box[2]
if blob_height > 3 and blob_width > 3: # min blob dimensions: Ly, Lx
if root_blob.fBa:
# comp_slice fork in angle blobs
# add evaluate splice_blobs(root_blob), normally in frame_bblobs?
AveB = aveB * (
blob.rdn + 1
) # comp_slice is doubling the costs, likely higher, adjust per nsub_blobs?
if (AveB - blob.G) + (
blob.G - AveB *
pcoef) > 0: # val_comp_slice_blob = dev_G + inv_dev_Ga
blob.fBa = 0
blob.rdn = root_blob.rdn + 1
comp_slice_root(blob, verbose=True)
blob.prior_forks.extend('p')
if verbose:
print(
'\nslice_blob fork\n'
) # if render and blob.A < 100: deep_blobs.append(blob)
else:
ext_dert__, ext_mask__ = extend_dert(
blob) # dert__+= 1: cross-comp in larger kernels
'''
comp_r || comp_a, gap or overlap version:
if aveBa < 1: blobs of ~average G are processed by both forks
if aveBa > 1: blobs of ~average G are not processed:
'''
if blob.G < aveB * blob.rdn: # below-average G, eval for comp_r
# root values for sub_blobs:
blob.fBa = 0
blob.rng = root_blob.rng + 1
blob.rdn = root_blob.rdn + 1.5 # comp cost * fork rdn
# comp_r 4x4:
new_dert__, new_mask__ = comp_r(ext_dert__, blob.rng,
ext_mask__)
sign__ = ave * (blob.rdn +
1) - new_dert__[3] > 0 # m__ = ave - g__
blob.prior_forks.extend('r')
# if min Ly and Lx, dert__>=1: form, splice sub_blobs:
if new_mask__.shape[0] > 2 and new_mask__.shape[
1] > 2 and False in new_mask__:
cluster_fork_recursive(blob,
spliced_layers,
new_dert__,
sign__,
new_mask__,
verbose,
render,
fBa=0)
if blob.G > aveB * aveBa * blob.rdn: # above-average G, eval for comp_a
# root values for sub_blobs:
blob.fBa = 1
blob.rdn = root_blob.rdn + 1.5 # comp cost * fork rdn
# comp_a 2x2:
new_dert__, new_mask__ = comp_a(ext_dert__, ext_mask__)
Ave = ave * (blob.rdn + 1)
sign__ = (new_dert__[1] - Ave) + (
Ave * pcoef - new_dert__[2]
) > 0 # val_comp_slice_= dev_gr + inv_dev_ga
blob.prior_forks.extend('a')
# if min Ly and Lx, dert__>=1: form, splice sub_blobs:
if new_mask__.shape[0] > 2 and new_mask__.shape[
1] > 2 and False in new_mask__:
cluster_fork_recursive(blob,
spliced_layers,
new_dert__,
sign__,
new_mask__,
verbose,
render,
fBa=1)
'''
exclusive forks version:
vG = blob.G - ave_G # deviation of gradient, from ave per blob, combined max rdn = blob.rdn+1:
vvG = abs(vG) - ave_vG * blob.rdn # 2nd deviation of gradient, from fixed costs of if "new_dert__" loop below
# vvG = 0 maps to max G for comp_r if vG < 0, and to min G for comp_a if vG > 0:
if blob.sign: # sign of pixel-level g, which corresponds to sign of blob vG, so we don't need the later
if vvG > 0: # below-average G, eval for comp_r...
elif vvG > 0: # above-average G, eval for comp_a...
'''
if verbose:
print("\rFinished intra_blob") # print_deep_blob_forking(deep_blobs)
return spliced_layers