/usr/lib/python2.7/dist-packages/ginga/canvas/types/image.py is in python-ginga 2.6.1-2.
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# images.py -- classes for images drawn on ginga canvases.
#
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import numpy
from ginga.canvas.CanvasObject import (CanvasObjectBase, _bool, _color,
Point, MovePoint, ScalePoint,
register_canvas_types,
colors_plus_none)
from ginga.misc.ParamSet import Param
from ginga.misc import Bunch
from ginga import trcalc
from .mixins import OnePointMixin
class Image(OnePointMixin, CanvasObjectBase):
"""Draws an image on a ImageViewCanvas.
Parameters are:
x, y: 0-based coordinates of one corner in the data space
image: the image, which must be an RGBImage object
"""
@classmethod
def get_params_metadata(cls):
return [
## Param(name='coord', type=str, default='data',
## valid=['data'],
## description="Set type of coordinates"),
Param(name='x', type=float, default=0.0, argpos=0,
description="X coordinate of corner of object"),
Param(name='y', type=float, default=0.0, argpos=1,
description="Y coordinate of corner of object"),
## Param(name='image', type=?, argpos=2,
## description="Image to be displayed on canvas"),
Param(name='scale_x', type=float, default=1.0,
description="Scaling factor for X dimension of object"),
Param(name='scale_y', type=float, default=1.0,
description="Scaling factor for Y dimension of object"),
Param(name='interpolation', type=str, default='basic',
description="Interpolation method for scaling pixels"),
Param(name='linewidth', type=int, default=0,
min=0, max=20, widget='spinbutton', incr=1,
description="Width of outline"),
Param(name='linestyle', type=str, default='solid',
valid=['solid', 'dash'],
description="Style of outline (default: solid)"),
Param(name='color',
valid=colors_plus_none, type=_color, default='lightgreen',
description="Color of outline"),
Param(name='alpha', type=float, default=1.0,
min=0.0, max=1.0, widget='spinfloat', incr=0.05,
description="Opacity of outline"),
Param(name='showcap', type=_bool,
default=False, valid=[False, True],
description="Show caps for this object"),
## Param(name='flipy', type=_bool,
## default=True, valid=[False, True],
## description="Flip image in Y direction"),
Param(name='optimize', type=_bool,
default=True, valid=[False, True],
description="Optimize rendering for this object"),
]
def __init__(self, x, y, image, alpha=1.0, scale_x=1.0, scale_y=1.0,
interpolation='basic',
linewidth=0, linestyle='solid', color='lightgreen',
showcap=False, flipy=False, optimize=True,
**kwdargs):
self.kind = 'image'
CanvasObjectBase.__init__(self, x=x, y=y, image=image, alpha=alpha,
scale_x=scale_x, scale_y=scale_y,
interpolation=interpolation,
linewidth=linewidth, linestyle=linestyle,
color=color, showcap=showcap,
flipy=flipy, optimize=optimize,
**kwdargs)
OnePointMixin.__init__(self)
# The cache holds intermediate step results by viewer.
# Depending on value of `whence` they may not need to be recomputed.
self._cache = {}
self._zorder = 0
# images are not editable by default
self.editable = False
self.enable_callback('image-set')
def get_zorder(self):
return self._zorder
def set_zorder(self, zorder):
self._zorder = zorder
for viewer in self._cache:
viewer.reorder_layers()
viewer.redraw(whence=2)
def in_cache(self, viewer):
return viewer in self._cache
def get_cache(self, viewer):
if viewer in self._cache:
cache = self._cache[viewer]
else:
cache = self._reset_cache(Bunch.Bunch())
self._cache[viewer] = cache
return cache
def invalidate_cache(self, viewer):
cache = self.get_cache(viewer)
self._reset_cache(cache)
return cache
def draw(self, viewer):
"""General draw method for RGB image types.
Note that actual insertion of the image into the output is
handled in `draw_image()`
"""
cache = self.get_cache(viewer)
if not cache.drawn:
cache.drawn = True
viewer.redraw(whence=2)
cpoints = self.get_cpoints(viewer)
cr = viewer.renderer.setup_cr(self)
# draw optional border
if self.linewidth > 0:
cr.draw_polygon(cpoints)
if self.showcap:
self.draw_caps(cr, self.cap, cpoints)
def draw_image(self, viewer, dstarr, whence=0.0):
if self.image is None:
return
cache = self.get_cache(viewer)
#print("redraw whence=%f" % (whence))
dst_order = viewer.get_rgb_order()
image_order = self.image.get_order()
if (whence <= 0.0) or (cache.cutout is None) or (not self.optimize):
# get extent of our data coverage in the window
((x0, y0), (x1, y1), (x2, y2), (x3, y3)) = viewer.get_pan_rect()
xmin = int(min(x0, x1, x2, x3))
ymin = int(min(y0, y1, y2, y3))
xmax = int(numpy.ceil(max(x0, x1, x2, x3)))
ymax = int(numpy.ceil(max(y0, y1, y2, y3)))
# destination location in data_coords
#dst_x, dst_y = self.x, self.y + ht
dst_x, dst_y = self.crdmap.to_data(self.x, self.y)
a1, b1, a2, b2 = 0, 0, self.image.width, self.image.height
# calculate the cutout that we can make and scale to merge
# onto the final image--by only cutting out what is necessary
# this speeds scaling greatly at zoomed in sizes
dst_x, dst_y, a1, b1, a2, b2 = \
trcalc.calc_image_merge_clip(xmin, ymin, xmax, ymax,
dst_x, dst_y, a1, b1, a2, b2)
# is image completely off the screen?
if (a2 - a1 <= 0) or (b2 - b1 <= 0):
# no overlay needed
#print "no overlay needed"
return
# cutout and scale the piece appropriately by the viewer scale
scale_x, scale_y = viewer.get_scale_xy()
# scale additionally by our scale
_scale_x, _scale_y = scale_x * self.scale_x, scale_y * self.scale_y
res = self.image.get_scaled_cutout(a1, b1, a2, b2,
_scale_x, _scale_y,
#flipy=self.flipy,
method=self.interpolation)
# don't ask for an alpha channel from overlaid image if it
# doesn't have one
## if ('A' in dst_order) and not ('A' in image_order):
## dst_order = dst_order.replace('A', '')
## if dst_order != image_order:
## # reorder result to match desired rgb_order by backend
## cache.cutout = trcalc.reorder_image(dst_order, res.data,
## image_order)
## else:
## cache.cutout = res.data
cache.cutout = res.data
# calculate our offset from the pan position
pan_x, pan_y = viewer.get_pan()
pan_off = viewer.data_off
pan_x, pan_y = pan_x + pan_off, pan_y + pan_off
#print "pan x,y=%f,%f" % (pan_x, pan_y)
off_x, off_y = dst_x - pan_x, dst_y - pan_y
# scale offset
off_x *= scale_x
off_y *= scale_y
#print "off_x,y=%f,%f" % (off_x, off_y)
# dst position in the pre-transformed array should be calculated
# from the center of the array plus offsets
ht, wd, dp = dstarr.shape
cache.cvs_x = int(round(wd / 2.0 + off_x))
cache.cvs_y = int(round(ht / 2.0 + off_y))
# composite the image into the destination array at the
# calculated position
trcalc.overlay_image(dstarr, cache.cvs_x, cache.cvs_y, cache.cutout,
dst_order=dst_order, src_order=image_order,
alpha=self.alpha, flipy=False)
def _reset_cache(self, cache):
cache.setvals(cutout=None, drawn=False, cvs_x=0, cvs_y=0)
return cache
def reset_optimize(self):
for cache in self._cache.values():
self._reset_cache(cache)
def get_image(self):
return self.image
def set_image(self, image):
self.image = image
self.reset_optimize()
self.make_callback('image-set', image)
def get_scaled_wdht(self):
width = int(self.image.width * self.scale_x)
height = int(self.image.height * self.scale_y)
return (width, height)
def get_coords(self):
x1, y1 = self.crdmap.to_data(self.x, self.y)
wd, ht = self.get_scaled_wdht()
x2, y2 = x1 + wd, y1 + ht
return (x1, y1, x2, y2)
def get_center_pt(self):
wd, ht = self.get_scaled_wdht()
return (self.x + wd / 2.0, self.y + ht / 2.0)
def get_points(self):
x1, y1, x2, y2 = self.get_coords()
return [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
def contains(self, data_x, data_y):
x1, y1, x2, y2 = self.get_coords()
if ((x1 <= data_x < x2) and (y1 <= data_y < y2)):
return True
return False
def rotate(self, theta, xoff=0, yoff=0):
raise ValueError("Images cannot be rotated")
def setup_edit(self, detail):
detail.center_pos = self.get_center_pt()
detail.scale_x = self.scale_x
detail.scale_y = self.scale_y
def set_edit_point(self, i, pt, detail):
if i == 0:
x, y = pt
self.move_to(x, y)
elif i == 1:
scale_x, scale_y = self.calc_dual_scale_from_pt(pt, detail)
self.scale_x = detail.scale_x * scale_x
elif i == 2:
scale_x, scale_y = self.calc_dual_scale_from_pt(pt, detail)
self.scale_y = detail.scale_y * scale_y
elif i == 3:
scale_x, scale_y = self.calc_dual_scale_from_pt(pt, detail)
self.scale_x = detail.scale_x * scale_x
self.scale_y = detail.scale_y * scale_y
else:
raise ValueError("No point corresponding to index %d" % (i))
self.reset_optimize()
def get_edit_points(self, viewer):
x1, y1, x2, y2 = self.get_coords()
return [MovePoint(*self.get_center_pt()), # location
Point(x2, (y1 + y2) / 2.), # width scale
Point((x1 + x2) / 2., y2), # height scale
Point(x2, y2), # both scale
]
def scale_by(self, scale_x, scale_y):
self.scale_x *= scale_x
self.scale_y *= scale_y
self.reset_optimize()
def set_scale(self, scale_x, scale_y):
self.scale_x = scale_x
self.scale_y = scale_y
self.reset_optimize()
def set_origin(self, x, y):
self.x, self.y = x, y
self.reset_optimize()
class NormImage(Image):
"""Draws an image on a ImageViewCanvas.
Parameters are:
x, y: 0-based coordinates of one corner in the data space
image: the image, which must be an RGBImage object
"""
@classmethod
def get_params_metadata(cls):
return [
## Param(name='coord', type=str, default='data',
## valid=['data'],
## description="Set type of coordinates"),
Param(name='x', type=float, default=0.0, argpos=0,
description="X coordinate of corner of object"),
Param(name='y', type=float, default=0.0, argpos=1,
description="Y coordinate of corner of object"),
## Param(name='image', type=?, argpos=2,
## description="Image to be displayed on canvas"),
Param(name='scale_x', type=float, default=1.0,
description="Scaling factor for X dimension of object"),
Param(name='scale_y', type=float, default=1.0,
description="Scaling factor for Y dimension of object"),
Param(name='interpolation', type=str, default='basic',
description="Interpolation method for scaling pixels"),
Param(name='linewidth', type=int, default=0,
min=0, max=20, widget='spinbutton', incr=1,
description="Width of outline"),
Param(name='linestyle', type=str, default='solid',
valid=['solid', 'dash'],
description="Style of outline (default: solid)"),
Param(name='color',
valid=colors_plus_none, type=_color, default='lightgreen',
description="Color of outline"),
Param(name='alpha', type=float, default=1.0,
min=0.0, max=1.0, widget='spinfloat', incr=0.05,
description="Opacity of outline"),
Param(name='showcap', type=_bool,
default=False, valid=[False, True],
description="Show caps for this object"),
## Param(name='flipy', type=_bool,
## default=True, valid=[False, True],
## description="Flip image in Y direction"),
Param(name='optimize', type=_bool,
default=True, valid=[False, True],
description="Optimize rendering for this object"),
## Param(name='rgbmap', type=?,
## description="RGB mapper for the image"),
## Param(name='autocuts', type=?,
## description="Cuts manager for the image"),
]
def __init__(self, x, y, image, alpha=1.0, scale_x=1.0, scale_y=1.0,
interpolation='basic',
linewidth=0, linestyle='solid', color='lightgreen', showcap=False,
optimize=True, rgbmap=None, autocuts=None, **kwdargs):
self.kind = 'normimage'
super(NormImage, self).__init__(x=x, y=y, image=image, alpha=alpha,
scale_x=scale_x, scale_y=scale_y,
interpolation=interpolation,
linewidth=linewidth, linestyle=linestyle,
color=color,
showcap=showcap, optimize=optimize,
**kwdargs)
self.rgbmap = rgbmap
self.autocuts = autocuts
def draw_image(self, viewer, dstarr, whence=0.0):
if self.image is None:
return
#print("redraw whence=%f" % (whence))
cache = self.get_cache(viewer)
if (whence <= 0.0) or (cache.cutout is None) or (not self.optimize):
# get extent of our data coverage in the window
((x0, y0), (x1, y1), (x2, y2), (x3, y3)) = viewer.get_pan_rect()
xmin = int(min(x0, x1, x2, x3))
ymin = int(min(y0, y1, y2, y3))
xmax = int(numpy.ceil(max(x0, x1, x2, x3)))
ymax = int(numpy.ceil(max(y0, y1, y2, y3)))
# destination location in data_coords
dst_x, dst_y = self.crdmap.to_data(self.x, self.y)
a1, b1, a2, b2 = 0, 0, self.image.width, self.image.height
# calculate the cutout that we can make and scale to merge
# onto the final image--by only cutting out what is necessary
# this speeds scaling greatly at zoomed in sizes
dst_x, dst_y, a1, b1, a2, b2 = \
trcalc.calc_image_merge_clip(xmin, ymin, xmax, ymax,
dst_x, dst_y, a1, b1, a2, b2)
# is image completely off the screen?
if (a2 - a1 <= 0) or (b2 - b1 <= 0):
# no overlay needed
#print "no overlay needed"
return
# cutout and scale the piece appropriately by viewer scale
scale_x, scale_y = viewer.get_scale_xy()
# scale additionally by our scale
_scale_x, _scale_y = scale_x * self.scale_x, scale_y * self.scale_y
res = self.image.get_scaled_cutout(a1, b1, a2, b2,
_scale_x, _scale_y,
method=self.interpolation)
cache.cutout = res.data
# calculate our offset from the pan position
pan_x, pan_y = viewer.get_pan()
pan_off = viewer.data_off
pan_x, pan_y = pan_x + pan_off, pan_y + pan_off
#print "pan x,y=%f,%f" % (pan_x, pan_y)
off_x, off_y = dst_x - pan_x, dst_y - pan_y
# scale offset
off_x *= scale_x
off_y *= scale_y
#print "off_x,y=%f,%f" % (off_x, off_y)
# dst position in the pre-transformed array should be calculated
# from the center of the array plus offsets
ht, wd, dp = dstarr.shape
cache.cvs_x = int(round(wd / 2.0 + off_x))
cache.cvs_y = int(round(ht / 2.0 + off_y))
if self.rgbmap is not None:
rgbmap = self.rgbmap
else:
rgbmap = viewer.get_rgbmap()
if (whence <= 1.0) or (cache.prergb is None) or (not self.optimize):
# apply visual changes prior to color mapping (cut levels, etc)
vmax = rgbmap.get_hash_size() - 1
newdata = self.apply_visuals(viewer, cache.cutout, 0, vmax)
# result becomes an index array fed to the RGB mapper
if not numpy.issubdtype(newdata.dtype, numpy.dtype('uint')):
newdata = newdata.astype(numpy.uint)
idx = newdata
self.logger.debug("shape of index is %s" % (str(idx.shape)))
cache.prergb = idx
dst_order = viewer.get_rgb_order()
image_order = self.image.get_order()
get_order = dst_order
if ('A' in dst_order) and not ('A' in image_order):
get_order = dst_order.replace('A', '')
if (whence <= 2.5) or (cache.rgbarr is None) or (not self.optimize):
# get RGB mapped array
rgbobj = rgbmap.get_rgbarray(cache.prergb, order=dst_order,
image_order=image_order)
cache.rgbarr = rgbobj.get_array(get_order)
# composite the image into the destination array at the
# calculated position
trcalc.overlay_image(dstarr, cache.cvs_x, cache.cvs_y, cache.rgbarr,
dst_order=dst_order, src_order=get_order,
alpha=self.alpha, flipy=False)
def apply_visuals(self, viewer, data, vmin, vmax):
if self.autocuts is not None:
autocuts = self.autocuts
else:
autocuts = viewer.autocuts
# Apply cut levels
loval, hival = viewer.t_['cuts']
newdata = autocuts.cut_levels(data, loval, hival,
vmin=vmin, vmax=vmax)
return newdata
def _reset_cache(self, cache):
cache.setvals(cutout=None, prergb=None, rgbarr=None,
drawn=False, cvs_x=0, cvs_y=0)
return cache
def set_image(self, image):
self.image = image
self.reset_optimize()
self.make_callback('image-set', image)
def scale_by(self, scale_x, scale_y):
#print("scaling image")
self.scale_x *= scale_x
self.scale_y *= scale_y
self.reset_optimize()
#print("image scale_x=%f scale_y=%f" % (self.scale_x, self.scale_y))
# register our types
register_canvas_types(dict(image=Image, normimage=NormImage))
#END
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