/usr/lib/python3/dist-packages/ginga/LayerImage.py is in python3-ginga 2.6.1-2.
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# LayerImage.py -- Abstraction of an generic layered image.
#
# Eric Jeschke (eric@naoj.org)
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import numpy
import time
from ginga import BaseImage
from ginga.misc import Bunch
class LayerImage(object):
"""Mixin class for BaseImage subclasses. Adds layers and alpha/rgb
compositing.
"""
def __init__(self):
self._layer = []
self.cnt = 0
self.compose_types = ('alpha', 'rgb')
self.compose = 'alpha'
def _insert_layer(self, idx, image, alpha=None, name=None):
if alpha is None:
alpha = 1.0
if name is None:
name = "layer%d" % (self.cnt)
self.cnt += 1
bnch = Bunch.Bunch(image=image, alpha=alpha, name=name)
self._layer.insert(idx, bnch)
def insert_layer(self, idx, image, alpha=None, name=None,
compose=True):
self._insert_layer(idx, image, alpha=alpha, name=name)
if compose:
self.compose_layers()
def set_layer(self, idx, image, alpha=None, name=None,
compose=True):
self.delete_layer(idx, compose=False)
self._insert_layer(idx, image, alpha=alpha, name=name)
if compose:
self.compose_layers()
def delete_layer(self, idx, compose=True):
self._layer.pop(idx)
if compose:
self.compose_layers()
def get_layer(self, idx):
return self._layer[idx]
def num_layers(self):
return len(self._layer)
def get_max_shape(self, entity='image'):
maxdim = -1
maxshape = ()
for layer in self._layer:
if entity == 'image':
shape = layer[entity].get_shape()
elif entity == 'alpha':
item = layer.alpha
# If alpha is an image, get the array
if isinstance(item, BaseImage.BaseImage):
item = layer.alpha.get_data()
shape = numpy.shape(item)
else:
raise BaseImage.ImageError("entity '%s' not in (image, alpha)" % (
entity))
if len(shape) > maxdim:
maxdim = len(shape)
maxshape = shape
return maxshape
## def alpha_combine(self, src, alpha, dst):
## return (src * alpha) + (dst * (1.0 - alpha))
def mono2color(self, data):
return numpy.dstack((data, data, data))
def alpha_multiply(self, alpha, data, shape=None):
"""(alpha) can be a scalar or an array.
"""
# alpha can be a scalar or an array
if shape is None:
shape = data.shape
if len(data.shape) == 2:
res = alpha * data
# If desired shape is monochrome then return a mono image
# otherwise broadcast to a grey color image.
if len(shape) == 2:
return res
# note: in timing tests, dstack was not as efficient here...
#data = numpy.dstack((res, res, res))
data = numpy.empty(shape)
data[:, :, 0] = res[:, :]
data[:, :, 1] = res[:, :]
data[:, :, 2] = res[:, :]
return data
else:
# note: in timing tests, dstack was not as efficient here...
#res = numpy.dstack((data[:, :, 0] * alpha,
# data[:, :, 1] * alpha,
# data[:, :, 2] * alpha))
res = numpy.empty(shape)
res[:, :, 0] = data[:, :, 0] * alpha
res[:, :, 1] = data[:, :, 1] * alpha
res[:, :, 2] = data[:, :, 2] * alpha
return res
def alpha_compose(self):
start_time = time.time()
shape = self.get_max_shape()
## ht, wd = shape[:2]
## # alpha can be a scalar or an array, prepare for the appropriate kind
## ashape = self.get_max_shape(entity='alpha')
## if len(ashape) == 0:
## alpha_used = 0.0
## else:
## alpha_used = numpy.zeros((ht, wd))
# result holds the result of the composition
result = numpy.zeros(shape)
cnt = 0
for layer in self._layer:
alpha = layer.alpha
if isinstance(alpha, BaseImage.BaseImage):
alpha = alpha.get_data()
#alpha = numpy.clip((1.0 - alpha_used) * alpha, 0.0, 1.0)
#mina = numpy.min(alpha)
#print "cnt=%d mina=%f" % (cnt, mina)
data = layer.image.get_data()
result += self.alpha_multiply(alpha, data, shape=shape)
## alpha_used += layer.alpha
#numpy.clip(alpha_used, 0.0, 1.0)
cnt += 1
self.set_data(result)
end_time = time.time()
self.logger.debug("alpha compose=%.4f sec" % (end_time - start_time))
# def rgb_compose(self):
# slices = []
# start_time = time.time()
# for i in range(len(self._layer)):
# layer = self.get_layer(i)
# data = self.alpha_multiply(layer.alpha, layer.image.get_data())
# slices.append(data)
# split_time = time.time()
# result = numpy.dstack(slices)
# end_time = time.time()
# self.set_data(result)
# print "rgb_compose alpha multiply=%.4f sec dstack=%.4f sec sec total=%.4f sec" % (
# split_time - start_time, end_time - split_time,
# end_time - start_time)
def rgb_compose(self):
#num = self.num_layers()
num = 3
layer = self.get_layer(0)
wd, ht = layer.image.get_size()
result = numpy.empty((ht, wd, num))
start_time = time.time()
for i in range(len(self._layer)):
layer = self.get_layer(i)
alpha = layer.alpha
if isinstance(alpha, BaseImage.BaseImage):
alpha = alpha.get_data()
data = self.alpha_multiply(alpha, layer.image.get_data())
result[:, :, i] = data[:, :]
end_time = time.time()
self.set_data(result)
self.logger.debug("rgb_compose total=%.4f sec" % (
end_time - start_time))
def rgb_decompose(self, image):
data = image.get_data()
shape = data.shape
if len(shape) == 2:
self._insert_layer(0, image)
else:
names = ("Red", "Green", "Blue")
alphas = (0.292, 0.594, 0.114)
for i in range(shape[2]):
imgslice = data[:, :, i]
#img = BaseImage.BaseImage(data_np=imgslice, logger=self.logger)
# Create the same type of image as we are decomposing
img = image.__class__(data_np=imgslice, logger=self.logger)
if i < 3:
name = names[i]
alpha = alphas[i]
else:
name = "layer%d" % i
alpha = 0.0
self._insert_layer(i, img, name=name, alpha=alpha)
self.compose_layers()
def set_compose_type(self, ctype):
assert ctype in self.compose_types, \
BaseImage.ImageError("Bad compose type '%s': must be one of %s" % (
ctype, str(self.compose_types)))
self.compose = ctype
self.compose_layers()
def set_alpha(self, lidx, val):
layer = self._layer[lidx]
layer.alpha = val
self.compose_layers()
def set_alphas(self, vals):
for lidx in range(len(vals)):
layer = self._layer[lidx]
layer.alpha = vals[lidx]
self.compose_layers()
def compose_layers(self):
if self.compose == 'rgb':
self.rgb_compose()
else:
self.alpha_compose()
#END
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