def writeN5(img, path, dataset_name, blockSize, gzip_compression_level=4, n_threads=0):
""" img: the RandomAccessibleInterval to store in N5 format.
path: the directory to store the N5 data.
dataset_name: the name of the img data.
blockSize: an array or list as long as dimensions has the img, specifying
how to chop up the img into pieces.
gzip_compression_level: defaults to 4, ranges from 0 (no compression) to 9 (maximum;
see java.util.zip.Deflater for details.).
n_threads: defaults to as many as CPU cores, for parallel writing. """
N5Utils.save(img, N5FSWriter(path, GsonBuilder()),
dataset_name, blockSize,
GzipCompression(gzip_compression_level) if gzip_compression_level > 0 else RawCompression(),
newFixedThreadPool(n_threads=n_threads, name="jython-n5writer"))
def readN5(path, dataset_name, show=None):
""" path: filepath to the folder with N5 data.
dataset_name: name of the dataset to use (there could be more than one).
show: defaults to None. "IJ" for virtual stack, "BDV" for BigDataViewer.
If "IJ", returns the RandomAccessibleInterval and the ImagePlus.
If "BDV", returns the RandomAccessibleInterval and the bdv instance. """
img = N5Utils.open(N5FSReader(path, GsonBuilder()), dataset_name)
if show:
if "IJ" == show:
return img, showStack(img, title=dataset_name)
elif "BDV" == show:
return img, showBDV(img, title=dataset_name)
return img
URL url = new URL (HOST + PATH + params);
List<RequestBody> objList = new ArrayList<>();
objList.add(new RequestBody(text));
String content = new Gson().toJson(objList);
translatedText = Post(url, content);
}
/*
* This function was not written by the team.
* Source: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/quickstart-java-translate
*/
public String prettify(String json_text) {
JsonParser parser = new JsonParser();
JsonElement json = parser.parse(json_text);
Gson gson = new GsonBuilder().setPrettyPrinting().create();
return gson.toJson(json);
}
/*
* Returns this part of json_text:
* "text":"[EVERYTHING IN HERE]","to":
*/
public String getTextFromJson (String json_text) {
return json_text.substring(json_text.indexOf("\"text\":") + 8, json_text.indexOf("\",\"to\":"));
}
/*
* Set the params global variable to be used in the next text translation.
* Unfortunately, some of these languages can't be used as of right now, because
* they're characters can't be handled by the matching algorithm.
dataset_name = "4D series"
if not os.path.exists(n5path):
# Create directory for storing the dataset in N5 format
os.mkdir(n5path)
# An array or list as long as dimensions has the img,
# specifying how to chop up the img into pieces.
blockSize = [128, 128, 128, 1] # each block is about 2 MB
# Compression: 0 means none. 4 is sensible. Can go up to 9.
# See java.util.zip.Deflater for details
gzip_compression_level = 4
# Threads: as many as CPU cores, for parallel writing
exe = Executors.newFixedThreadPool(
Runtime.getRuntime().availableProcessors())
N5Utils.save(
cachedCellImg, N5FSWriter(n5path, GsonBuilder()), dataset_name,
blockSize,
GzipCompression(gzip_compression_level)
if gzip_compression_level > 0 else RawCompression(), exe)
# The above waits until all jobs have run. Then:
exe.shutdown()
# Interestingly:
# KLB format: 11 stacks, 407 MB total
# N5 format with GZIP compression level 4: 688 files, 584 MB total.
# Open the N5 dataset
imgN5 = N5Utils.open(N5FSReader(n5path, GsonBuilder()), dataset_name)
# ... as a virtual stack