def show_instrument(path: Path = Depends(imagepath_parameter)):
"""
Get image instrument info
"""
original = path.get_original()
check_representation_existence(original)
return InstrumentInfo.from_image(original)
def export_upload(
background: BackgroundTasks,
path: Path = Depends(imagepath_parameter),
):
"""
Export the upload representation of an image.
"""
image = path.get_original()
check_representation_existence(image)
upload_file = image.get_upload().resolve()
media_type = image.media_type
if upload_file.is_dir():
# if archive has been deleted
tmp_export = Path(f"/tmp/{unique_name_generator()}")
make_zip_archive(tmp_export, upload_file)
def cleanup(tmp):
tmp.unlink(missing_ok=True)
background.add_task(cleanup, tmp_export)
upload_file = tmp_export
media_type = "application/zip"
return FileResponse(upload_file, media_type=media_type, filename=path.name)
def show_channels(path: Path = Depends(imagepath_parameter)):
"""
Get image channel info
"""
original = path.get_original()
check_representation_existence(original)
return response_list(ChannelsInfo.from_image(original))
def show_normalized_pyramid(path: Path = Depends(imagepath_parameter)):
"""
Get image normalized pyramid
"""
original = path.get_original()
check_representation_existence(original)
return PyramidInfo.from_pyramid(original.normalized_pyramid)
def show_associated(path: Path = Depends(imagepath_parameter)):
"""
Get associated file info
"""
original = path.get_original()
check_representation_existence(original)
return response_list(AssociatedInfo.from_image(original))
def show_image(path: Path = Depends(imagepath_parameter)):
"""
Get standard image info
"""
original = path.get_original()
check_representation_existence(original)
return ImageInfo.from_image(original)
def _show_associated_image(
request: Request,
response: Response, # required for @cache # noqa
path: Path,
height,
width,
length,
associated_key,
headers,
config: Settings):
in_image = path.get_spatial()
check_representation_existence(in_image)
associated = getattr(in_image, f'associated_{associated_key.value}')
if not associated or not associated.exists:
raise NoAppropriateRepresentationProblem(path, associated_key)
out_format, mimetype = get_output_format(OutputExtension.NONE,
headers.accept,
VISUALISATION_MIMETYPES)
req_size = get_thumb_output_dimensions(associated, height, width, length)
out_size = safeguard_output_dimensions(headers.safe_mode,
config.output_size_limit, *req_size)
out_width, out_height = out_size
return AssociatedResponse(in_image, associated_key, out_width, out_height,
out_format).http_response(
mimetype,
extra_headers=add_image_size_limit_header(
dict(), *req_size, *out_size))
def show_plane_histogram(
z_slices: conint(ge=0),
timepoints: conint(ge=0),
path: Path = Depends(imagepath_parameter),
channels: Optional[List[conint(ge=0)]] = Query(
None, description="Only return histograms for these channels"
),
):
"""
Get histogram per plane.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
channels = ensure_list(channels)
z_slices = ensure_list(z_slices)
timepoints = ensure_list(timepoints)
channels = get_channel_indexes(in_image, channels)
z_slices = get_zslice_indexes(in_image, z_slices)
timepoints = get_timepoint_indexes(in_image, timepoints)
hist_info = []
htype = in_image.histogram_type()
for c, z, t in itertools.product(channels, z_slices, timepoints):
mini, maxi = in_image.plane_bounds(c, z, t)
hist_info.append(
PlaneHistogramInfo(
channel=c, z_slice=z, timepoint=t, type=htype,
color=in_image.channels[c].hex_color,
minimum=mini, maximum=maxi
)
)
return response_list(hist_info)
def show_channels_histogram_bounds(
path: Path = Depends(imagepath_parameter),
channels: Optional[List[conint(ge=0)]] = Query(
None, description="Only return histograms for these channels"
),
):
"""
Get histogram bounds per channel where all planes (Z,T) are merged.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
channels = ensure_list(channels)
channels = get_channel_indexes(in_image, channels)
hist_info = []
htype = in_image.histogram_type()
hist_filter = operator.itemgetter(*channels)
channels_bounds = hist_filter(in_image.channels_bounds())
if len(channels) == 1:
channels_bounds = [channels_bounds]
for channel, bounds in zip(channels, channels_bounds):
mini, maxi = bounds
hist_info.append(
ChannelHistogramInfo(
channel=channel, type=htype,
color=in_image.channels[channel].hex_color,
minimum=mini, maximum=maxi
)
)
return response_list(hist_info)
def show_metadata(path: Path = Depends(imagepath_parameter)):
"""
Get image metadata
"""
original = path.get_original()
check_representation_existence(original)
store = original.raw_metadata
return response_list([Metadata.from_metadata(md) for md in store.values()])
def show_metadata_annotations(path: Path = Depends(imagepath_parameter)):
"""
Get image annotation metadata
"""
original = path.get_original()
check_representation_existence(original)
return response_list([
MetadataAnnotation.from_metadata_annotation(a)
for a in original.annotations
])
async def _show_drawing(
request: Request,
response: Response, # required for @cache # noqa
path: Path,
annotations,
context_factor,
try_square,
point_cross,
point_envelope_length,
height,
width,
length,
zoom,
level,
channels,
z_slices,
timepoints,
min_intensities,
max_intensities,
filters,
gammas,
threshold,
log,
extension,
headers,
config,
colormaps=None,
c_reduction=ChannelReduction.ADD,
z_reduction=None,
t_reduction=None,
):
in_image = path.get_spatial()
check_representation_existence(in_image)
annots = parse_annotations(ensure_list(annotations),
ignore_fields=['fill_color'],
default={'stroke_width': 1},
point_envelope_length=point_envelope_length,
origin=headers.annot_origin,
im_height=in_image.height)
region = get_annotation_region(in_image, annots, context_factor,
try_square)
annot_style = dict(mode=AnnotationStyleMode.DRAWING,
point_cross=point_cross,
point_envelope_length=point_envelope_length)
return await _show_window(request, response, path, region, height, width,
length, zoom, level, channels, z_slices,
timepoints, min_intensities, max_intensities,
filters, gammas, threshold, 8, Colorspace.AUTO,
annots, annot_style, extension, headers, config,
colormaps, c_reduction, z_reduction, t_reduction)
def show_image_histogram_bounds(
path: Path = Depends(imagepath_parameter)
):
"""
Get histogram info for full image where all planes (C,Z,T) are merged.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
htype = in_image.histogram_type()
mini, maxi = in_image.image_bounds()
return HistogramInfo(type=htype, minimum=mini, maximum=maxi)
async def _show_crop(
request: Request,
response: Response,
path: Path,
annotations,
context_factor,
background_transparency,
height,
width,
length,
zoom,
level,
channels,
z_slices,
timepoints,
min_intensities,
max_intensities,
filters,
gammas,
threshold,
bits,
colorspace,
extension,
headers,
config,
colormaps=None,
c_reduction=ChannelReduction.ADD,
z_reduction=None,
t_reduction=None,
):
in_image = path.get_spatial()
check_representation_existence(in_image)
annots = parse_annotations(ensure_list(annotations),
ignore_fields=['stroke_width', 'stroke_color'],
default={'fill_color': WHITE},
origin=headers.annot_origin,
im_height=in_image.height)
region = get_annotation_region(in_image, annots, context_factor)
annot_style = dict(mode=AnnotationStyleMode.CROP,
background_transparency=background_transparency)
return await _show_window(request, response, path, region, height, width,
length, zoom, level, channels, z_slices,
timepoints, min_intensities, max_intensities,
filters, gammas, threshold, bits, colorspace,
annots, annot_style, extension, headers, config,
colormaps, c_reduction, z_reduction, t_reduction)
def show_info(path: Path = Depends(imagepath_parameter)):
"""
Get all image info
"""
original = path.get_original()
check_representation_existence(original)
data = dict()
data["image"] = ImageInfo.from_image(original)
data["instrument"] = InstrumentInfo.from_image(original)
data["associated"] = AssociatedInfo.from_image(original)
data["channels"] = ChannelsInfo.from_image(original)
data["representations"] = [
RepresentationInfo.from_path(rpr)
for rpr in original.get_representations()
]
return data
def show_image_histogram(
path: Path = Depends(imagepath_parameter),
hist_config: HistogramConfig = Depends()
):
"""
Get histogram for full image where all planes (C,Z,T) are merged.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
n_bins = parse_n_bins(hist_config.n_bins, len(in_image.value_range))
htype = in_image.histogram_type()
return Histogram(
type=htype,
**histogram_formatter(
in_image.image_histogram(), in_image.image_bounds(),
n_bins, hist_config.full_range
)
)
def _show_mask(
request: Request,
response: Response, # required for @cache # noqa
path: Path,
annotations,
context_factor,
height,
width,
length,
zoom,
level,
extension,
headers,
config):
in_image = path.get_spatial()
check_representation_existence(in_image)
annots = parse_annotations(ensure_list(annotations),
ignore_fields=['stroke_width', 'stroke_color'],
default={'fill_color': WHITE},
origin=headers.annot_origin,
im_height=in_image.height)
region = get_annotation_region(in_image, annots, context_factor)
out_format, mimetype = get_output_format(extension, headers.accept,
PROCESSING_MIMETYPES)
check_zoom_validity(in_image.pyramid, zoom)
check_level_validity(in_image.pyramid, level)
req_size = get_window_output_dimensions(in_image, region, height, width,
length, zoom, level)
out_size = safeguard_output_dimensions(headers.safe_mode,
config.output_size_limit, *req_size)
out_width, out_height = out_size
affine = annotation_crop_affine_matrix(annots.region, region, out_width,
out_height)
return MaskResponse(in_image, annots, affine, out_width, out_height, 8,
out_format).http_response(
mimetype,
extra_headers=add_image_size_limit_header(
dict(), *req_size, *out_size))
def show_plane_histogram(
z_slices: conint(ge=0),
timepoints: conint(ge=0),
path: Path = Depends(imagepath_parameter),
hist_config: HistogramConfig = Depends(),
channels: Optional[List[conint(ge=0)]] = Query(
None, description="Only return histograms for these channels"
),
):
"""
Get histogram per plane.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
channels = ensure_list(channels)
z_slices = ensure_list(z_slices)
timepoints = ensure_list(timepoints)
channels = get_channel_indexes(in_image, channels)
z_slices = get_zslice_indexes(in_image, z_slices)
timepoints = get_timepoint_indexes(in_image, timepoints)
histograms = []
n_bins = parse_n_bins(hist_config.n_bins, len(in_image.value_range))
htype = in_image.histogram_type()
for c, z, t in itertools.product(channels, z_slices, timepoints):
histograms.append(
PlaneHistogram(
channel=c, z_slice=z, timepoint=t, type=htype,
color=in_image.channels[c].hex_color,
**histogram_formatter(
in_image.plane_histogram(c, z, t),
in_image.plane_bounds(c, z, t),
n_bins, hist_config.full_range
)
)
)
return response_list(histograms)
def compute_histogram(
response: Response,
background: BackgroundTasks,
path: Path = Depends(imagepath_parameter),
# companion_file_id: Optional[int] = Body(None, description="Cytomine ID for the histogram")
sync: bool = True,
overwrite: bool = True
):
"""
Ask for histogram computation
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
hist_type = HistogramType.FAST # TODO: allow to build complete histograms
hist_path = in_image.processed_root() / Path(HISTOGRAM_STEM)
if sync:
build_histogram_file(in_image, hist_path, hist_type, overwrite)
response.status_code = status.HTTP_201_CREATED
else:
background.add_task(build_histogram_file, in_image, hist_path, hist_type, overwrite)
response.status_code = status.HTTP_202_ACCEPTED
def show_channels_histogram(
path: Path = Depends(imagepath_parameter),
hist_config: HistogramConfig = Depends(),
channels: Optional[List[conint(ge=0)]] = Query(
None, description="Only return histograms for these channels"
),
):
"""
Get histograms per channel where all planes (Z,T) are merged.
"""
in_image = path.get_spatial()
check_representation_existence(in_image)
channels = ensure_list(channels)
channels = get_channel_indexes(in_image, channels)
histograms = []
n_bins = parse_n_bins(hist_config.n_bins, len(in_image.value_range))
htype = in_image.histogram_type()
# hist_filter = operator.itemgetter(*channels)
# channels_bounds = hist_filter(in_image.channels_bounds())
# channels_histograms = hist_filter(in_image.channel_histogram()) TODO
for channel in channels:
histograms.append(
ChannelHistogram(
channel=channel, type=htype,
color=in_image.channels[channel].hex_color,
**histogram_formatter(
in_image.channel_histogram(channel),
in_image.channel_bounds(channel),
n_bins, hist_config.full_range
)
)
)
return response_list(histograms)
def _show_tile(
request: Request,
response: Response, # required for @cache # noqa
path: Path,
normalized: bool,
tile: dict,
channels,
z_slices,
timepoints,
min_intensities,
max_intensities,
filters,
gammas,
threshold,
log,
extension,
headers,
config,
colormaps=None,
c_reduction=ChannelReduction.ADD,
z_reduction=None,
t_reduction=None):
in_image = path.get_spatial()
check_representation_existence(in_image)
if not normalized or in_image.is_pyramid_normalized:
pyramid = in_image.pyramid
is_window = False
else:
pyramid = in_image.normalized_pyramid
is_window = True
if 'zoom' in tile:
reference_tier_index = tile['zoom']
tier_index_type = TierIndexType.ZOOM
else:
reference_tier_index = tile['level']
tier_index_type = TierIndexType.LEVEL
if 'ti' in tile:
check_tileindex_validity(pyramid, tile['ti'], reference_tier_index,
tier_index_type)
tile_region = pyramid.get_tier_at(
reference_tier_index, tier_index_type).get_ti_tile(tile['ti'])
else:
check_tilecoord_validity(pyramid, tile['tx'], tile['ty'],
reference_tier_index, tier_index_type)
tile_region = pyramid.get_tier_at(reference_tier_index,
tier_index_type).get_txty_tile(
tile['tx'], tile['ty'])
out_format, mimetype = get_output_format(extension, headers.accept,
VISUALISATION_MIMETYPES)
req_size = tile_region.width, tile_region.height
out_size = safeguard_output_dimensions(headers.safe_mode,
config.output_size_limit, *req_size)
out_width, out_height = out_size
channels = ensure_list(channels)
z_slices = ensure_list(z_slices)
timepoints = ensure_list(timepoints)
channels = get_channel_indexes(in_image, channels)
check_reduction_validity(channels, c_reduction, 'channels')
z_slices = get_zslice_indexes(in_image, z_slices)
check_reduction_validity(z_slices, z_reduction, 'z_slices')
timepoints = get_timepoint_indexes(in_image, timepoints)
check_reduction_validity(timepoints, t_reduction, 'timepoints')
min_intensities = ensure_list(min_intensities)
max_intensities = ensure_list(max_intensities)
colormaps = ensure_list(colormaps)
filters = ensure_list(filters)
gammas = ensure_list(gammas)
array_parameters = ('min_intensities', 'max_intensities', 'colormaps',
'gammas')
check_array_size_parameters(array_parameters,
locals(),
allowed=[0, 1, len(channels)],
nullable=False)
intensities = parse_intensity_bounds(in_image, channels, z_slices,
timepoints, min_intensities,
max_intensities)
min_intensities, max_intensities = intensities
colormaps = parse_colormap_ids(colormaps, ALL_COLORMAPS, channels,
in_image.channels)
array_parameters = ('filters', )
check_array_size_parameters(array_parameters,
locals(),
allowed=[0, 1],
nullable=False)
filters = parse_filter_ids(filters, FILTERS)
if is_window:
tile = WindowResponse(in_image, channels, z_slices, timepoints,
tile_region, out_format, out_width, out_height,
c_reduction, z_reduction, t_reduction, gammas,
filters, colormaps, min_intensities,
max_intensities, log, 8, threshold,
Colorspace.AUTO)
else:
tile = TileResponse(in_image, channels, z_slices, timepoints,
tile_region, out_format, out_width, out_height,
c_reduction, z_reduction, t_reduction, gammas,
filters, colormaps, min_intensities,
max_intensities, log, threshold)
return tile.http_response(mimetype,
extra_headers=add_image_size_limit_header(
dict(), *req_size, *out_size))
def _show_window(
request: Request, response: Response, # required for @cache # noqa
path: Path,
region: Union[Region, dict],
height, width, length, zoom, level,
channels, z_slices, timepoints,
min_intensities, max_intensities, filters, gammas, threshold,
bits, colorspace,
annotations: Union[ParsedAnnotations, dict, List[dict]],
annotation_style: dict,
extension,
headers,
config: Settings,
colormaps=None, c_reduction=ChannelReduction.ADD, z_reduction=None, t_reduction=None
):
in_image = path.get_spatial()
check_representation_existence(in_image)
if not isinstance(region, Region):
tier_index_type = region['tier_index_type']
reference_tier_index = region['reference_tier_index']
if reference_tier_index is None:
if tier_index_type == TierIndexType.LEVEL:
reference_tier_index = 0
else:
reference_tier_index = in_image.pyramid.max_zoom
if 'top' in region:
# Parse raw WindowRegion to Region
region = parse_region(
in_image, region['top'], region['left'],
region['width'], region['height'],
reference_tier_index, tier_index_type,
silent_oob=False
)
elif 'ti' in region:
# Parse raw WindowTileIndex region to Region
check_tileindex_validity(
in_image.pyramid, region['ti'],
reference_tier_index, tier_index_type
)
region = in_image.pyramid.get_tier_at(
reference_tier_index,
tier_index_type
).get_ti_tile(region['ti'])
elif ('tx', 'ty') in region:
# Parse raw WindowTileCoord region to Region
check_tilecoord_validity(
in_image.pyramid, region['tx'], region['ty'],
reference_tier_index, tier_index_type
)
region = in_image.pyramid.get_tier_at(
reference_tier_index,
tier_index_type
).get_txty_tile(region['tx'], region['ty'])
out_format, mimetype = get_output_format(extension, headers.accept, VISUALISATION_MIMETYPES)
check_zoom_validity(in_image.pyramid, zoom)
check_level_validity(in_image.pyramid, level)
req_size = get_window_output_dimensions(in_image, region, height, width, length, zoom, level)
out_size = safeguard_output_dimensions(headers.safe_mode, config.output_size_limit, *req_size)
out_width, out_height = out_size
channels = ensure_list(channels)
z_slices = ensure_list(z_slices)
timepoints = ensure_list(timepoints)
channels = get_channel_indexes(in_image, channels)
check_reduction_validity(channels, c_reduction, 'channels')
z_slices = get_zslice_indexes(in_image, z_slices)
check_reduction_validity(z_slices, z_reduction, 'z_slices')
timepoints = get_timepoint_indexes(in_image, timepoints)
check_reduction_validity(timepoints, t_reduction, 'timepoints')
min_intensities = ensure_list(min_intensities)
max_intensities = ensure_list(max_intensities)
colormaps = ensure_list(colormaps)
filters = ensure_list(filters)
gammas = ensure_list(gammas)
array_parameters = ('min_intensities', 'max_intensities', 'colormaps', 'gammas')
check_array_size_parameters(
array_parameters, locals(), allowed=[0, 1, len(channels)], nullable=False
)
intensities = parse_intensity_bounds(
in_image, channels, z_slices, timepoints, min_intensities, max_intensities
)
min_intensities, max_intensities = intensities
colormaps = parse_colormap_ids(colormaps, ALL_COLORMAPS, channels, in_image.channels)
array_parameters = ('filters',)
check_array_size_parameters(
array_parameters, locals(), allowed=[0, 1], nullable=False
)
filters = parse_filter_ids(filters, FILTERS)
out_bitdepth = parse_bitdepth(in_image, bits)
if annotations and annotation_style and not isinstance(annotations, ParsedAnnotations):
if annotation_style['mode'] == AnnotationStyleMode.DRAWING:
ignore_fields = ['fill_color']
default = {'stroke_color': RED, 'stroke_width': 1}
point_envelope_length = annotation_style['point_envelope_length']
else:
ignore_fields = ['stroke_width', 'stroke_color']
default = {'fill_color': WHITE}
point_envelope_length = None
annotations = parse_annotations(
ensure_list(annotations), ignore_fields,
default, point_envelope_length,
origin=headers.annot_origin, im_height=in_image.height
)
affine = None
if annotations:
affine = annotation_crop_affine_matrix(annotations.region, region, *out_size)
if annotations and annotation_style and \
annotation_style['mode'] == AnnotationStyleMode.MASK:
window = MaskResponse(
in_image,
annotations, affine,
out_width, out_height, out_bitdepth, out_format
)
else:
window = WindowResponse(
in_image, channels, z_slices, timepoints,
region, out_format, out_width, out_height,
c_reduction, z_reduction, t_reduction,
gammas, filters, colormaps,
min_intensities, max_intensities, False,
out_bitdepth, threshold, colorspace,
annotations, affine, annotation_style
)
return window.http_response(
mimetype,
extra_headers=add_image_size_limit_header(dict(), *req_size, *out_size)
)
def _show_resized(
request: Request, response: Response, # required for @cache # noqa
path: Path,
height, width, length, zoom, level,
channels, z_slices, timepoints,
min_intensities, max_intensities, filters, gammas, threshold,
bits, colorspace,
extension,
headers,
config: Settings,
colormaps=None, c_reduction=ChannelReduction.ADD, z_reduction=None, t_reduction=None
):
in_image = path.get_spatial()
check_representation_existence(in_image)
out_format, mimetype = get_output_format(extension, headers.accept, PROCESSING_MIMETYPES)
check_zoom_validity(in_image.pyramid, zoom)
check_level_validity(in_image.pyramid, level)
req_size = get_thumb_output_dimensions(in_image, height, width, length, zoom, level)
out_size = safeguard_output_dimensions(headers.safe_mode, config.output_size_limit, *req_size)
out_width, out_height = out_size
channels = ensure_list(channels)
z_slices = ensure_list(z_slices)
timepoints = ensure_list(timepoints)
channels = get_channel_indexes(in_image, channels)
check_reduction_validity(channels, c_reduction, 'channels')
z_slices = get_zslice_indexes(in_image, z_slices)
check_reduction_validity(z_slices, z_reduction, 'z_slices')
timepoints = get_timepoint_indexes(in_image, timepoints)
check_reduction_validity(timepoints, t_reduction, 'timepoints')
min_intensities = ensure_list(min_intensities)
max_intensities = ensure_list(max_intensities)
colormaps = ensure_list(colormaps)
filters = ensure_list(filters)
gammas = ensure_list(gammas)
array_parameters = ('min_intensities', 'max_intensities', 'colormaps', 'gammas')
check_array_size_parameters(
array_parameters, locals(), allowed=[0, 1, len(channels)], nullable=False
)
intensities = parse_intensity_bounds(
in_image, channels, z_slices, timepoints, min_intensities, max_intensities
)
min_intensities, max_intensities = intensities
colormaps = parse_colormap_ids(colormaps, ALL_COLORMAPS, channels, in_image.channels)
array_parameters = ('filters',)
check_array_size_parameters(
array_parameters, locals(), allowed=[0, 1], nullable=False
)
filters = parse_filter_ids(filters, FILTERS)
out_bitdepth = parse_bitdepth(in_image, bits)
return ResizedResponse(
in_image, channels, z_slices, timepoints,
out_format, out_width, out_height,
c_reduction, z_reduction, t_reduction,
gammas, filters, colormaps, min_intensities, max_intensities,
False, out_bitdepth, threshold, colorspace
).http_response(
mimetype,
extra_headers=add_image_size_limit_header(dict(), *req_size, *out_size)
)
