def do_imread(*data, **kwargs):
""" Returns image file as array. """
# Check to see if the data given is either a file path or a file object
if isinstance(data[0], basestring) or isinstance(data[0], file):
return ImageData.fromfile(data[0])
else:
raise ValueError("do_imread takes a string filename")
def _load(self):
try:
image = ImageData.fromfile(self._load_file)
self.pd.set_data('imagedata', image._data)
self.plot.title = "YO DOGG: %s" % os.path.basename(self._load_file)
self.plot.request_redraw()
except Exception, exc:
print "YO DOGG: %s" % exc
def do_imread(*data, **kwargs):
""" Returns image file as array. """
# Check to see if the data given is either a file path or a file object
if isinstance(data[0], six.string_types) or isinstance(data[0], io.IOBase):
return ImageData.fromfile(data[0])
else:
raise ValueError("do_imread takes a string filename")
def _load(self):
# Load the image with the user supplied filename
image = ImageData.fromfile(self._load_file)
# Update the plot data. NB we must extract _data from the image
# for the time being, until ImageData is made more friendly
self.pd.set_data("imagedata", image._data)
# Set the title and redraw
self.plot.title = os.path.basename(self._load_file)
self.plot.request_redraw()
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255*ones((2,2,3),dtype='uint8'))
imgd = imgd._data
pd.set_data("image",imgd)
def _load(self):
# Load the image with the user supplied filename
image = ImageData.fromfile(self._load_file)
# Update the plot data. NB we must extract _data from the image
# for the time being, until ImageData is made more friendly
self.pd.set_data("imagedata", image._data)
# Set the title and redraw
self.plot.title = os.path.basename(self._load_file)
self.plot.request_redraw()
def set_pict(self):
pd = self.plot_data
if not pd:
return
try:
imgd = ImageData.fromfile(self.pict)._data[::-1]
except:
imgd = ImageData()
imgd.set_data(255 * ones((2, 2, 3), dtype='uint8'))
imgd = imgd._data
pd.set_data("image", imgd)
def image_from_renderer(renderer, orientation):
data = renderer.value
# Set bounding box size and origin to align rendered image with array
renderer.bounds = (data.get_width() + 1, data.get_height() + 1)
if orientation == 'v':
renderer.bounds = renderer.bounds[::-1]
renderer.position = 0, 0
with temp_image_file() as filename:
save_renderer_result(renderer, filename)
rendered_image = ImageData.fromfile(filename).data[TRIM_RENDERED]
return rendered_image
def empty_image():
""" Returns empty image plot """
from chaco.api import ImageData, GridDataSource, GridMapper, DataRange2D, ImagePlot
image_source = ImageData.fromfile(config.IMG_NOCOMPLEX_PATH)
w, h = image_source.get_width(), image_source.get_height()
index = GridDataSource(np.arange(w), np.arange(h))
index_mapper = GridMapper(
range=DataRange2D(low=(0, 0), high=(w - 1, h - 1)))
image_plot = ImagePlot(index=index,
value=image_source,
index_mapper=index_mapper,
origin='top left')
return image_plot
def __init__(self, **kw):
super(WorldMapPlot, self).__init__(**kw)
self._download_map_image()
image = ImageData.fromfile(self.image_path)
# For now, the locations are hardcoded, though this can be changed
# eassily to take command line args, read from a file, or by other
# means
austin_loc = (30.16, -97.44)
locations_x = numpy.array([austin_loc[1]])
locations_y = numpy.array([austin_loc[0]])
# transform each of the locations to the image data space, including
# moving the origin from bottom left to top left
locations_x = (locations_x + 180) * image.data.shape[1] / 360
locations_y = (locations_y * -1 + 90) * image.data.shape[0] / 180
# Create the plott data, adding the image and the locations
plot_data = ArrayPlotData()
plot_data.set_data("imagedata", image._data)
plot_data.set_data("locations_x", locations_x)
plot_data.set_data("locations_y", locations_y)
# Create the plot with the origin as top left, which matches
# how the image data is aligned
self.plot = Plot(plot_data, default_origin="top left")
self.plot.img_plot('imagedata')
# Plot the locations as a scatter plot to be overlayed on top
# of the map
loc_plot = self.plot.plot(('locations_x', 'locations_y'),
type='scatter',
size=3,
color='yellow',
marker='dot')[0]
loc_plot.x_mapper.range.high = image.data.shape[1]
loc_plot.x_mapper.range.low = 0
loc_plot.y_mapper.range.high = image.data.shape[0]
loc_plot.y_mapper.range.low = -0
# set up any tools, in this case just the zoom tool
zoom = ZoomTool(component=self.plot, tool_mode="box", always_on=False)
self.plot.overlays.append(zoom)
def empty_image():
""" Returns empty image plot """
from chaco.api import ImageData, GridDataSource, GridMapper, DataRange2D, ImagePlot
image_source = ImageData.fromfile(config.IMG_NOCOMPLEX_PATH)
w, h = image_source.get_width(), image_source.get_height()
index = GridDataSource(np.arange(w), np.arange(h))
index_mapper = GridMapper(range=DataRange2D(low=(0, 0),
high=(w-1, h-1)))
image_plot = ImagePlot(
index=index,
value=image_source,
index_mapper=index_mapper,
origin='top left'
)
return image_plot
def __init__(self, **kw):
super(WorldMapPlot, self).__init__(**kw)
self._download_map_image()
image = ImageData.fromfile(self.image_path)
# For now, the locations are hardcoded, though this can be changed
# eassily to take command line args, read from a file, or by other
# means
austin_loc = (30.16, -97.44)
locations_x = numpy.array([austin_loc[1]])
locations_y = numpy.array([austin_loc[0]])
# transform each of the locations to the image data space, including
# moving the origin from bottom left to top left
locations_x = (locations_x + 180) * image.data.shape[1]/360
locations_y = (locations_y*-1 + 90) * image.data.shape[0]/180
# Create the plott data, adding the image and the locations
plot_data = ArrayPlotData()
plot_data.set_data("imagedata", image._data)
plot_data.set_data("locations_x", locations_x)
plot_data.set_data("locations_y", locations_y)
# Create the plot with the origin as top left, which matches
# how the image data is aligned
self.plot = Plot(plot_data, default_origin="top left")
self.plot.img_plot('imagedata')
# Plot the locations as a scatter plot to be overlayed on top
# of the map
loc_plot = self.plot.plot(('locations_x', 'locations_y'),
type='scatter', size=3, color='yellow',
marker='dot')[0]
loc_plot.x_mapper.range.high = image.data.shape[1]
loc_plot.x_mapper.range.low = 0
loc_plot.y_mapper.range.high = image.data.shape[0]
loc_plot.y_mapper.range.low = -0
# set up any tools, in this case just the zoom tool
zoom = ZoomTool(component=self.plot, tool_mode="box", always_on=False)
self.plot.overlays.append(zoom)
def test_fromfile_png_rgba(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basi6a08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 4)
def _filename_changed(self, new):
image = ImageData.fromfile(new)
self.plot_image(image._data)
def test_fromfile_png_rgba(self):
# basic smoke test - assume that kiva.image does the right thing
path = os.path.join(data_dir, 'PngSuite', 'basi6a08.png')
data_source = ImageData.fromfile(path)
self.assertEqual(data_source.value_depth, 4)
def __init__(self):
self.plotData = ArrayPlotData()
self.backImg = Plot(self.plotData, default_origin="top left")
self.backImg.x_axis = None
self.backImg.y_axis = None
self.backImg.padding = (3, 3, 3, 3)
image = ImageData.fromfile("world.png")
self.plotData.set_data("imagedata", image._data)
self.backImg.img_plot("imagedata")
self.image = True
self.imageVisible = True
# self._refresh_fired()
# TMP
x = []
y = []
c = []
feed = parse(
"http://geofon.gfz-potsdam.de/eqinfo/list.php?datemin=&datemax=&latmin=&latmax=&lonmin=&lonmax=&magmin=5.&nmax=100&fmt=rss"
)
self.entries = feed["entries"]
clen = len(self.entries)
for i, entry in enumerate(self.entries):
edate, etime, elat, elon, edep, eunit, estatus = entry["summary"].split()
x.append(((float(elon) + 180) / 360) * 5400.0)
y.append(2700 - ((float(elat) + 90) / 180) * 2700.0)
c.append(clen - i)
self.plotData.set_data("X", x)
self.plotData.set_data("Y", y)
self.plotData.set_data("C", c)
self.backImg.plot(
("X", "Y", "C"),
type="cmap_scatter",
color_mapper=jet,
marker="circle",
index_sort="ascending",
color="orange",
marker_size=10,
bgcolor="white",
name="earthquakes",
)
my_plot = self.backImg.plots["earthquakes"][0]
my_plot.tools.append(ScatterInspector(my_plot, selection_mode="toggle", persistent_hover=False))
my_plot.overlays.append(
ScatterInspectorOverlay(
my_plot,
hover_color="transparent",
hover_marker_size=10,
hover_outline_color="purple",
hover_line_width=2,
selection_marker_size=8,
selection_color="lawngreen",
)
)
zoom = ZoomTool(component=my_plot, tool_mode="box", always_on=False)
my_plot.overlays.append(zoom)
pan = PanTool(my_plot)
my_plot.tools.append(pan)
self.index_datasource = my_plot.index
self.index_datasource.on_trait_change(self._metadata_handler, "metadata_changed")
self.map = Basemap()
