def layer_extract(self, func, silent=True, **kwargs):
if 'layer' in kwargs:
self.input = kwargs['layer']
else:
self.input = pyrat.data.active
query = pyrat.data.queryLayer(self.input)
if isinstance(query, list):
dshape = query[0]['shape']
else:
dshape = query['shape']
if self.vblock:
self.initBP(dshape[-1])
else:
self.initBP(dshape[-2])
if len(self.blocks) > 1 and self.nthreads > 1:
idx = [
self.blocks[i:i + self.nthreads]
for i in range(0, len(self.blocks), self.nthreads)
]
else:
idx = [[block] for block in self.blocks]
out = []
nb = 0
metain = pyrat.data.getAnnotation(layer=self.input)
if silent is False:
P = pyrat.tools.ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx:
meta = copy.deepcopy(metain)
inputs = []
for ix in bidx:
data = self.read_block(nb)
kwargs_copy = copy.deepcopy(kwargs)
kwargs_copy["args"] = data
kwargs_copy["meta"] = meta
if self.vblock:
kwargs_copy['block'] = (0, dshape[-2]) + tuple(
self.blocks[nb])
else:
kwargs_copy['block'] = tuple(
self.blocks[nb]) + (0, dshape[-1])
kwargs_copy['valid'] = tuple(self.valid[nb])
inputs.append((self, func.__name__, kwargs_copy))
nb += 1
if silent is False:
P.update(nb)
if self.nthreads > 1:
result = multimap(inputs)
else:
result = map(exec_out, inputs)
for res in result:
out.append(res[0])
if silent is False:
del P
return self.input
def layer_fromfunc(self, func, size=(1, 1), silent=True, **kwargs):
"""
Generates a new layer from the return of its method 'func', called with **kwargs (and possible args stored
in in the keyword 'args' as tuple). The size of the produced layer must be passed in the 'size'
keyword. Returns the name of the new layer(s)
"""
if self.vblock:
self.initBP(size[-1])
kwargs["size"] = (size[-2], self.blocksize)
else:
self.initBP(size[-2])
kwargs["size"] = (self.blocksize, size[-1])
if len(self.blocks) > 1 and self.nthreads > 1:
idx = [self.blocks[i:i + self.nthreads] for i in range(0, len(self.blocks), self.nthreads)]
else:
idx = [[block] for block in self.blocks]
kwargs["meta"] = {}
nb1 = 0 # input block number
nb2 = 0 # output block number
if silent is False:
P = pyrat.tools.ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx:
inputs = []
for ix in bidx:
kwargs_copy = copy.deepcopy(kwargs)
if self.vblock:
kwargs_copy['block'] = (0, size[-2]) + tuple(self.blocks[nb1])
else:
kwargs_copy['block'] = tuple(self.blocks[nb1]) + (0, size[-1])
kwargs_copy['valid'] = tuple(self.valid[nb1])
inputs.append((self, func.__name__, kwargs_copy))
nb1 += 1
if self.nthreads > 1:
result = multimap(inputs)
else:
result = map(exec_out, inputs)
for res in result:
if nb2 == 0:
if isinstance(res[0], list) or isinstance(res[0], tuple):
self.output = []
for n, re in enumerate(res[0]):
self.output.append(pyrat.data.addLayer(dtype=re.dtype, shape=size))
else:
self.output = pyrat.data.addLayer(dtype=res[0].dtype, shape=size)
self.save_block(res[0], nb2)
nb2 += 1
if silent is False:
P.update(nb2)
if silent is False:
del P
pyrat.data.setAnnotation(res[1], layer=self.output) # add meta data to output layer
return self.output
def run(self, *args, **kwargs):
# print("pyramid level", self.scale)
if self.scale == 0:
ilay = 'D'
olay = 'P/0'
ishp = [dim // 2 * 2 for dim in self.dshape]
oshp = [dim // 2 * 2 for dim in self.dshape]
else:
ilay = 'P/' + str(self.scale - 1)
olay = 'P/' + str(self.scale)
ishp = [dim // 2 * 2 for dim in self.dshape]
oshp = [dim // 2 for dim in self.dshape]
idat = self.hdfgroup[ilay]
if olay in self.hdfgroup and self.force is False:
self.dset.append(self.hdfgroup[olay])
self.scale += 1
self.dshape = oshp
self.progress.update(self.scale * 100)
self.run()
else:
odat = self.hdfgroup.require_dataset(olay,
self.lshape + tuple(oshp),
'float32')
self.dset.append(odat)
if min(oshp) > 1:
idx, ivalid, ibs = self.calc_blocks(ishp[-2],
pack=True,
blocksize=128)
odx, ovalid, obs = self.calc_blocks(oshp[-2],
pack=False,
blocksize=ibs //
(ishp[-2] // oshp[-2]))
nb = 0
for bidx in idx:
inputs = []
for ix in bidx:
data = idat[..., ix[0]:ix[1], 0:ishp[-1]]
inputs.append(
(subsample, (data, self.lshape + (obs, oshp[1]),
self.mode)))
result = multimap(inputs, mode='method')
# result = map(absrebin, inputs)
for res in result:
odat[...,
odx[nb][0] + ovalid[nb][0]:odx[nb][1], :] = res[
..., ovalid[nb][0]:ovalid[nb][1], :]
nb += 1
self.nblock += 1
self.progress.update(self.nblock)
self.scale += 1
self.dshape = oshp
self.run()
return self.dset
def layer_fromfunc(self, func, size=(1, 1), silent=True, **kwargs):
"""
Generates a new layer from the return of its method 'func', called with **kwargs (and possible args stored
in in the keyword 'args' as tuple). The size of the produced layer must be passed in the 'size'
keyword. Returns the name of the new layer(s)
"""
if self.vblock:
self.initBP(size[-1])
kwargs["size"] = (size[-2], self.blocksize)
else:
self.initBP(size[-2])
kwargs["size"] = (self.blocksize, size[-1])
if len(self.blocks) > 1 and self.nthreads > 1:
idx = [self.blocks[i:i + self.nthreads] for i in range(0, len(self.blocks), self.nthreads)]
else:
idx = [[block] for block in self.blocks]
kwargs["meta"] = {}
nb1 = 0 # input block number
nb2 = 0 # output block number
if silent is False:
P = ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx:
inputs = []
for ix in bidx:
kwargs_copy = copy.deepcopy(kwargs)
if self.vblock:
kwargs_copy['block'] = (0, size[-2]) + tuple(self.blocks[nb1])
else:
kwargs_copy['block'] = tuple(self.blocks[nb1]) + (0, size[-1])
kwargs_copy['valid'] = tuple(self.valid[nb1])
inputs.append((self, func.__name__, kwargs_copy))
nb1 += 1
if self.nthreads > 1:
result = multimap(inputs)
else:
result = map(exec_out, inputs)
for res in result:
if nb2 == 0:
if isinstance(res[0], list) or isinstance(res[0], tuple):
self.output = []
for n, re in enumerate(res[0]):
self.output.append(pyrat.data.addLayer(dtype=re.dtype, shape=size))
else:
self.output = pyrat.data.addLayer(dtype=res[0].dtype, shape=size)
self.save_block(res[0], nb2)
nb2 += 1
if silent is False:
P.update(nb2)
if silent is False:
del P
pyrat.data.setAnnotation(res[1], layer=self.output) # add meta data to output layer
return self.output
def layer_extract(self, func, silent=True, **kwargs):
if 'layer' in kwargs:
self.input = kwargs['layer']
else:
self.input = pyrat.data.active
query = pyrat.data.queryLayer(self.input)
if isinstance(query, list):
dshape = query[0]['shape']
else:
dshape = query['shape']
if self.vblock:
self.initBP(dshape[-1])
else:
self.initBP(dshape[-2])
if len(self.blocks) > 1 and self.nthreads > 1:
idx = [self.blocks[i:i + self.nthreads] for i in range(0, len(self.blocks), self.nthreads)]
else:
idx = [[block] for block in self.blocks]
out = []
nb = 0
metain = pyrat.data.getAnnotation(layer=self.input)
if silent is False:
P = pyrat.tools.ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx:
meta = copy.deepcopy(metain)
inputs = []
for ix in bidx:
data = self.read_block(nb)
kwargs_copy = copy.deepcopy(kwargs)
kwargs_copy["args"] = data
kwargs_copy["meta"] = meta
if self.vblock:
kwargs_copy['block'] = (0, dshape[-2]) + tuple(self.blocks[nb])
else:
kwargs_copy['block'] = tuple(self.blocks[nb]) + (0, dshape[-1])
kwargs_copy['valid'] = tuple(self.valid[nb])
inputs.append((self, func.__name__, kwargs_copy))
nb += 1
if silent is False:
P.update(nb)
if self.nthreads > 1:
result = multimap(inputs)
else:
result = map(exec_out, inputs)
for res in result:
out.append(res[0])
if silent is False:
del P
return self.input
def layer_process(self, func, silent=True, **kwargs):
"""
Generates a new layer from the return of its method 'func', called with **kwargs (and possible args stored
in in the keyword 'args' as tuple). The size of the produced layer must be passed in the 'size'
keyword. Returns the name of the new layer(s)
"""
if 'layer' in kwargs:
self.input = kwargs['layer']
else:
self.input = pyrat.data.active
if any([isinstance(foo, list) for foo in self.input]):
layshp = self.input
self.input = flattenlist(self.input)
nested = True
else:
nested = False
query = pyrat.data.queryLayer(self.input)
if isinstance(query, list):
dshape = query[0]['shape']
else:
dshape = query['shape']
if self.vblock: # init block processing
self.initBP(dshape[-1])
else:
self.initBP(dshape[-2])
if len(self.blocks) > 1 and self.nthreads > 1: # group chunks of blocks
idx = [self.blocks[i:i + self.nthreads] for i in range(0, len(self.blocks), self.nthreads)]
else:
idx = [[block] for block in self.blocks]
metain = pyrat.data.getAnnotation(layer=self.input)
nb1 = 0 # input block number
nb2 = 0 # output block number
if silent is False:
P = pyrat.tools.ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx: # loop over chunks of blocks
meta = copy.deepcopy(metain)
inputs = []
for ix in bidx: # loop over blocks in chunk
data = self.read_block(nb1)
if nested is True:
data = unflattenlist(data, layshp)
kwargs_copy = copy.deepcopy(kwargs)
kwargs_copy["args"] = data
kwargs_copy["meta"] = meta
if self.vblock:
kwargs_copy['block'] = (0, dshape[-2]) + tuple(self.blocks[nb1])
else:
kwargs_copy['block'] = tuple(self.blocks[nb1]) + (0, dshape[-1])
kwargs_copy['valid'] = tuple(self.valid[nb1])
inputs.append((self, func.__name__, kwargs_copy)) # accumulate inputs
nb1 += 1
if self.nthreads > 1:
result = multimap(inputs) # do the multiprocessing
else:
result = map(exec_out, inputs) # or avoid it...
for res in result: # loop over output blocks (in chunk)
metaout = res[1] # meta data (possibly modified)
if nb2 == 0: # first block -> generate new layer(s)
if isinstance(res[0], list) or isinstance(res[0], tuple):
self.output = []
for n, re in enumerate(res[0]):
lshape = re.shape[0:-2] # layer geometry
if self.vblock:
dshape = (re.shape[-2], dshape[-1])
else:
dshape = (dshape[-2], re.shape[-1])
if self.blockprocess is False: # no blockprocessing
lshape = () # -> entire image
dshape = re.shape
self.output.append(pyrat.data.addLayer(dtype=re.dtype, shape=lshape + dshape))
else:
lshape = res[0].shape[0:-2] # layer geometry
if self.vblock:
dshape = (res[0].shape[-2], dshape[-1])
else:
dshape = (dshape[-2], res[0].shape[-1])
if self.blockprocess is False: # no blockprocessing
lshape = () # -> entire image
dshape = res[0].shape
self.output = pyrat.data.addLayer(dtype=res[0].dtype, shape=lshape + dshape)
self.save_block(res[0], nb2)
nb2 += 1
if silent is False:
P.update(nb2)
if silent is False:
del P
pyrat.data.setAnnotation(metaout, layer=self.output) # add meta data to output layer
return self.output # return output layer
def layer_process(self, func, silent=True, **kwargs):
"""
Generates a new layer from the return of its method 'func', called with **kwargs (and possible args stored
in in the keyword 'args' as tuple). The size of the produced layer must be passed in the 'size'
keyword. Returns the name of the new layer(s)
"""
if 'layer' in kwargs:
self.input = kwargs['layer']
else:
self.input = pyrat.data.active
if any([isinstance(foo, list) for foo in self.input]):
layshp = self.input
self.input = flattenlist(self.input)
nested = True
else:
nested = False
query = pyrat.data.queryLayer(self.input)
if isinstance(query, list):
dshape = query[0]['shape']
else:
dshape = query['shape']
if self.vblock: # init block processing
self.initBP(dshape[-1])
else:
self.initBP(dshape[-2])
if len(self.blocks
) > 1 and self.nthreads > 1: # group chunks of blocks
idx = [
self.blocks[i:i + self.nthreads]
for i in range(0, len(self.blocks), self.nthreads)
]
else:
idx = [[block] for block in self.blocks]
metain = pyrat.data.getAnnotation(layer=self.input)
nb1 = 0 # input block number
nb2 = 0 # output block number
if silent is False:
P = pyrat.tools.ProgressBar(' ' + self.name, len(self.blocks))
P.update(0)
for bidx in idx: # loop over chunks of blocks
meta = copy.deepcopy(metain)
inputs = []
for ix in bidx: # loop over blocks in chunk
data = self.read_block(nb1)
if nested is True:
data = unflattenlist(data, layshp)
kwargs_copy = copy.deepcopy(kwargs)
kwargs_copy["args"] = data
kwargs_copy["meta"] = meta
if self.vblock:
kwargs_copy['block'] = (0, dshape[-2]) + tuple(
self.blocks[nb1])
else:
kwargs_copy['block'] = tuple(
self.blocks[nb1]) + (0, dshape[-1])
kwargs_copy['valid'] = tuple(self.valid[nb1])
inputs.append(
(self, func.__name__, kwargs_copy)) # accumulate inputs
nb1 += 1
if self.nthreads > 1:
result = multimap(inputs) # do the multiprocessing
else:
result = map(exec_out, inputs) # or avoid it...
for res in result: # loop over output blocks (in chunk)
metaout = res[1] # meta data (possibly modified)
if nb2 == 0: # first block -> generate new layer(s)
if isinstance(res[0], list) or isinstance(res[0], tuple):
self.output = []
for n, re in enumerate(res[0]):
lshape = re.shape[0:-2] # layer geometry
if self.vblock:
dshape = (re.shape[-2], dshape[-1])
else:
dshape = (dshape[-2], re.shape[-1])
if self.blockprocess is False: # no blockprocessing
lshape = () # -> entire image
dshape = re.shape
self.output.append(
pyrat.data.addLayer(dtype=re.dtype,
shape=lshape + dshape))
else:
lshape = res[0].shape[0:-2] # layer geometry
if self.vblock:
dshape = (res[0].shape[-2], dshape[-1])
else:
dshape = (dshape[-2], res[0].shape[-1])
if self.blockprocess is False: # no blockprocessing
lshape = () # -> entire image
dshape = res[0].shape
self.output = pyrat.data.addLayer(dtype=res[0].dtype,
shape=lshape +
dshape)
self.save_block(res[0], nb2)
nb2 += 1
if silent is False:
P.update(nb2)
if silent is False:
del P
if 'path' in metaout:
del metaout['path']
pyrat.data.setAnnotation(
metaout, layer=self.output) # add meta data to output layer
return self.output # return output layer