def test_rgb_tiles(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (3, 2000, 1000)
dtype = numpy.uint8
md = {
model.MD_DIMS: 'YXC',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.array(range(size[0] * size[1] * size[2])).reshape(
size[::-1]).astype(dtype)
print(arr.shape)
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 7, 3))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (1000, 2000, 3))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
tiles = getSubData(rdata.content[0], 0, (0, 0, 3, 1))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (512, 1024, 3))
numpy.testing.assert_almost_equal(merged_img.metadata[model.MD_POS],
(4.999512, 7.000244))
del rdata
os.remove(FILENAME)
def test_rgb_tiles(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (3, 2000, 1000)
dtype = numpy.uint8
md = {
model.MD_DIMS: 'YXC',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.array(range(size[0] * size[1] * size[2])).reshape(size[::-1]).astype(dtype)
print(arr.shape)
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 7, 3))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (1000, 2000, 3))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
tiles = getSubData(rdata.content[0], 0, (0, 0, 3, 1))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (512, 1024, 3))
numpy.testing.assert_almost_equal(merged_img.metadata[model.MD_POS], (4.999512, 7.000244))
del rdata
os.remove(FILENAME)
def projectAsRaw(self):
""" Project a raw image without converting to RGB
Handles tiles as well as regular DataArray's
"""
raw = self.stream.raw
if isinstance(raw[0], model.DataArrayShadow):
raw_tiles, _ = self._getTilesFromSelectedArea()
raw = img.mergeTiles(raw_tiles)
return raw
else:
return super(RGBSpatialProjection, self).projectAsRaw()
def _getMergedRawImage(self, z):
"""
Returns the merged image based on z and .rect, using the raw tiles (not projected)
z (int): Zoom level index
return (DataArray): The merged image
"""
# calculates the size of the merged image
width_zoomed = self._das.shape[1] / (2 ** z)
height_zoomed = self._das.shape[0] / (2 ** z)
# calculates the number of tiles on both axes
num_tiles_x = int(math.ceil(width_zoomed / self._das.tile_shape[1]))
num_tiles_y = int(math.ceil(height_zoomed/ self._das.tile_shape[0]))
tiles = []
for x in range(num_tiles_x):
tiles_column = []
for y in range(num_tiles_y):
tile = self._das.getTile(x, y, z)
tiles_column.append(tile)
tiles.append(tiles_column)
return img.mergeTiles(tiles)
def _getMergedRawImage(self, das, z):
"""
Returns the entire raw data of DataArrayShadow at a given zoom level
das (DataArrayShadow): shadow of the raw data
z (int): Zoom level index
return (DataArray): The merged image
"""
# calculates the size of the merged image
width_zoomed = das.shape[1] / (2 ** z)
height_zoomed = das.shape[0] / (2 ** z)
# calculates the number of tiles on both axes
num_tiles_x = int(math.ceil(width_zoomed / das.tile_shape[1]))
num_tiles_y = int(math.ceil(height_zoomed / das.tile_shape[0]))
tiles = []
for x in range(num_tiles_x):
tiles_column = []
for y in range(num_tiles_y):
tile = das.getTile(x, y, z)
tiles_column.append(tile)
tiles.append(tiles_column)
return img.mergeTiles(tiles)
def _getMergedRawImage(self, das, z):
"""
Returns the entire raw data of DataArrayShadow at a given zoom level
das (DataArrayShadow): shadow of the raw data
z (int): Zoom level index
return (DataArray): The merged image
"""
# calculates the size of the merged image
width_zoomed = das.shape[1] / (2 ** z)
height_zoomed = das.shape[0] / (2 ** z)
# calculates the number of tiles on both axes
num_tiles_x = int(math.ceil(width_zoomed / das.tile_shape[1]))
num_tiles_y = int(math.ceil(height_zoomed / das.tile_shape[0]))
tiles = []
for x in range(num_tiles_x):
tiles_column = []
for y in range(num_tiles_y):
tile = das.getTile(x, y, z)
tiles_column.append(tile)
tiles.append(tiles_column)
return img.mergeTiles(tiles)
def test_one_tile(self):
def getSubData(dast, zoom, rect):
x1, y1, x2, y2 = rect
tiles = []
for x in range(x1, x2 + 1):
tiles_column = []
for y in range(y1, y2 + 1):
tiles_column.append(dast.getTile(x, y, zoom))
tiles.append(tiles_column)
return tiles
FILENAME = u"test" + tiff.EXTENSIONS[0]
POS = (5.0, 7.0)
size = (250, 200)
md = {
model.MD_DIMS: 'YX',
model.MD_POS: POS,
model.MD_PIXEL_SIZE: (1e-6, 1e-6),
}
arr = numpy.arange(size[0] * size[1],
dtype=numpy.uint8).reshape(size[::-1])
data = model.DataArray(arr, metadata=md)
# export
tiff.export(FILENAME, data, pyramid=True)
rdata = tiff.open_data(FILENAME)
tiles = getSubData(rdata.content[0], 0, (0, 0, 0, 0))
merged_img = img.mergeTiles(tiles)
self.assertEqual(merged_img.shape, (200, 250))
self.assertEqual(merged_img.metadata[model.MD_POS], POS)
del rdata
os.remove(FILENAME)
def snapshot_viewport(self, tab, filepath, exporter, anim):
""" Save a snapshot of the raw image from the focused view to the
filesystem.
:param tab: (Tab) the current tab to save the snapshot from
:param filepath: (str) full path to the destination file
:param exporter: (func) exporter to use for writing the file
:param anim: (bool) if True will show an animation
When no dialog is shown, the name of the file will follow the scheme
`date`-`time`.tiff (e.g., 20120808-154812.tiff) and it will be saved
in the user's picture directory.
"""
try:
tab_data_model = tab.tab_data_model
# Take all the streams available
streams = tab_data_model.streams.value
if not streams:
logging.info("Failed to take snapshot, no stream in tab %s",
tab.name)
return
if anim:
self.start_snapshot_animation()
# get currently focused view
view = tab_data_model.focussedView.value
if not view:
try:
view = tab_data_model.views.value[0]
except IndexError:
view = None
# let's try to get a thumbnail
if not view or view.thumbnail.value is None:
thumbnail = None
else:
# need to convert from wx.Image to ndimage
thumbnail = img.wxImage2NDImage(view.thumbnail.value,
keep_alpha=False)
# add some basic info to the image
mpp = view.mpp.value
metadata = {
model.MD_POS: view.view_pos.value,
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_DESCRIPTION: "Composited image preview"
}
thumbnail = model.DataArray(thumbnail, metadata=metadata)
# for each stream seen in the viewport
raw_images = []
for s in streams:
data = s.raw
if isinstance(data, tuple): # 2D tuple = tiles
data = [mergeTiles(data)]
for d in data:
# add the stream name to the image
if not hasattr(d, "metadata"):
# Not a DataArray => let's try to convert it
try:
d = model.DataArray(d)
except Exception:
logging.warning(
"Raw data of stream %s doesn't seem to be DataArray",
s.name.value)
continue
if model.MD_DESCRIPTION not in d.metadata:
d.metadata[model.MD_DESCRIPTION] = s.name.value
raw_images.append(d)
popup.show_message(self._main_frame,
"Snapshot saved in %s" % (filepath, ),
timeout=3)
# record everything to a file
exporter.export(filepath, raw_images, thumbnail)
logging.info("Snapshot saved as file '%s'.", filepath)
except Exception:
logging.exception("Failed to save snapshot")
def snapshot_viewport(self, tab, filepath, exporter, anim):
""" Save a snapshot of the raw image from the focused view to the
filesystem.
:param tab: (Tab) the current tab to save the snapshot from
:param filepath: (str) full path to the destination file
:param exporter: (func) exporter to use for writing the file
:param anim: (bool) if True will show an animation
When no dialog is shown, the name of the file will follow the scheme
`date`-`time`.tiff (e.g., 20120808-154812.tiff) and it will be saved
in the user's picture directory.
"""
try:
tab_data_model = tab.tab_data_model
# Take all the streams available
streams = tab_data_model.streams.value
if not streams:
logging.info("Failed to take snapshot, no stream in tab %s",
tab.name)
return
if anim:
self.start_snapshot_animation()
# get currently focused view
view = tab_data_model.focussedView.value
if not view:
try:
view = tab_data_model.views.value[0]
except IndexError:
view = None
# let's try to get a thumbnail
if not view or view.thumbnail.value is None:
thumbnail = None
else:
# need to convert from wx.Image to ndimage
thumbnail = img.wxImage2NDImage(view.thumbnail.value,
keep_alpha=False)
# add some basic info to the image
mpp = view.mpp.value
metadata = {model.MD_POS: view.view_pos.value,
model.MD_PIXEL_SIZE: (mpp, mpp),
model.MD_DESCRIPTION: "Composited image preview"}
thumbnail = model.DataArray(thumbnail, metadata=metadata)
# for each stream seen in the viewport
raw_images = []
for s in streams:
data = s.raw
if isinstance(data, tuple): # 2D tuple = tiles
data = [mergeTiles(data)]
for d in data:
# add the stream name to the image
if not hasattr(d, "metadata"):
# Not a DataArray => let's try to convert it
try:
d = model.DataArray(d)
except Exception:
logging.warning("Raw data of stream %s doesn't seem to be DataArray", s.name.value)
continue
if model.MD_DESCRIPTION not in d.metadata:
d.metadata[model.MD_DESCRIPTION] = s.name.value
raw_images.append(d)
popup.show_message(self._main_frame,
"Snapshot saved in %s" % (filepath,),
timeout=3
)
# record everything to a file
exporter.export(filepath, raw_images, thumbnail)
logging.info("Snapshot saved as file '%s'.", filepath)
except Exception:
logging.exception("Failed to save snapshot")