def test_ecotaxa(tmp_path):
archive_fn = tmp_path / "ecotaxa.zip"
print(archive_fn)
# Create an archive
with Pipeline() as p:
i = Unpack(range(10))
meta = Call(dict, i=i, foo="Sömé UTF-8 ſtríng…")
image = BinaryBlobs()
image_name = Format("image_{}.png", i)
EcotaxaWriter(archive_fn, (image_name, image), meta)
result = [o.to_dict(meta=meta, image=image) for o in p.transform_stream()]
# Read the archive
with Pipeline() as p:
image, meta = EcotaxaReader(archive_fn)
roundtrip_result = [
o.to_dict(meta=meta, image=image) for o in p.transform_stream()
]
for meta_field in ("i", "foo"):
assert [o["meta"][meta_field] for o in result
] == [o["meta"][meta_field] for o in roundtrip_result]
assert_equal([o["image"] for o in result],
[o["image"] for o in roundtrip_result])
def test_ecotaxa(tmp_path, ext):
archive_fn = tmp_path / ("ecotaxa" + ext)
print(archive_fn)
# Create an archive
with Pipeline() as p:
i = Unpack(range(10))
meta = Call(dict, i=i, foo="Sömé UTF-8 ſtríng…")
image = BinaryBlobs()
image_name = Format("image_{}.png", i)
EcotaxaWriter(
archive_fn,
(image_name, image),
meta,
object_meta={"foo": 0},
acq_meta={"foo": 1},
process_meta={"foo": 2},
sample_meta={"foo": 3},
)
result = [o.to_dict(meta=meta, image=image) for o in p.transform_stream()]
# Read the archive
with Pipeline() as p:
image, meta = EcotaxaReader(archive_fn)
roundtrip_result = [
o.to_dict(meta=meta, image=image) for o in p.transform_stream()
]
for meta_field in ("i", "foo"):
assert [o["meta"][meta_field] for o in result
] == [o["meta"][meta_field] for o in roundtrip_result]
for i, prefix in enumerate(("object_", "acq_", "process_", "sample_")):
assert [o["meta"][prefix + "foo"]
for o in result] == [i for _ in roundtrip_result]
assert_equal([o["image"] for o in result],
[o["image"] for o in roundtrip_result])
# Calculate features. The calculated features are added to the global_metadata.
# Returns a Variable representing a dict for every object in the stream.
meta = CalculateZooProcessFeatures(
regionprops, prefix="object_", meta=global_metadata
)
# Add object_id to the metadata dictionary
meta["object_id"] = object_id
# Generate object filenames
orig_fn = Format("{object_id}.jpg", object_id=object_id)
# Write objects to an EcoTaxa archive:
# roi image in original color, roi image in grayscale, metadata associated with each object
EcotaxaWriter(archive_fn, (orig_fn, roi_orig), meta)
# Progress bar for objects
TQDM(Format("Object {object_id}", object_id=object_id))
import datetime
BEGIN = datetime.datetime.now()
# Execute pipeline
p.run()
END = datetime.datetime.now()
print("MORPHOCUT :"+str(END-BEGIN))
image = ImageReader(path)
# Do some thresholding
mask = image < 128
# Find regions in the image
region = FindRegions(mask, image)
# Extract just the object
roi_image = region.intensity_image
# An object is identified by its label
roi_label = region.label
# Calculate a filename for the ROI image:
# "RUNNING_NUMBER-SOURCE_BASENAME-ROI_LABEL"
roi_name = Format(
"{:d}-{}-{:d}.jpg", running_number, source_basename, roi_label
)
meta = CalculateZooProcessFeatures(region, prefix="object_")
# End of parallel execution
# Store results
EcotaxaWriter("archive.zip", (roi_name, roi_image), meta)
# After the Pipeline was defined, it can be executed.
# A stream is created and transformed by the operations
# defined in the Pipeline.
p.run()
img = obj.image
img_gray = RGB2Gray(img, True)
mask = obj.mask
regionprops = ImageProperties(mask, img_gray)
object_meta = obj.data
object_id = Format("{lst_name}_{id}",
lst_name=obj.lst_name,
_kwargs=object_meta)
object_meta["id"] = object_id
object_meta = CalculateZooProcessFeatures(regionprops, object_meta)
EcotaxaWriter(
os.path.join(export_path, "export.zip"),
[
(Format("{object_id}.jpg", object_id=object_id), img),
(Format("{object_id}_gray.jpg",
object_id=object_id), img_gray),
(Format("{object_id}_mask.jpg", object_id=object_id), mask),
],
object_meta=object_meta,
)
TQDM(object_id)
p.run()
# Generate an object identifier
i = Enumerate()
object_id = Format("{name}_{i:d}", name=name, i=i)
# Calculate features. The calculated features are added to the global_metadata.
# Returns a Variable representing a dict for every object in the stream.
meta = CalculateZooProcessFeatures(regionprops,
prefix="object_",
meta=global_metadata)
# If CalculateZooProcessFeatures is not used, we need to copy global_metadata into the stream:
# meta = Call(lambda: global_metadata.copy())
# https://github.com/morphocut/morphocut/issues/51
# Add object_id to the metadata dictionary
meta["object_id"] = object_id
# Generate object filenames
orig_fn = Format("{object_id}.jpg", object_id=object_id)
gray_fn = Format("{object_id}-gray.jpg", object_id=object_id)
# Write objects to an EcoTaxa archive:
# roi image in original color, roi image in grayscale, metadata associated with each object
EcotaxaWriter(archive_fn, [(orig_fn, roi_orig), (gray_fn, roi_gray)],
meta)
# Progress bar for objects
TQDM(Format("Object {object_id}", object_id=object_id))
# Execute pipeline
p.run()
# Construct object ID
object_id = Format(
"{lst_name}_{id}", lst_name=obj.lst_name, _kwargs=object_meta
)
object_meta["id"] = object_id
# Calculate object properties (area, eccentricity, equivalent_diameter, mean_intensity, ...). See skimage.measure.regionprops.
regionprops = ImageProperties(mask, img_gray)
# Append object properties to metadata in a ZooProcess-like format
object_meta = CalculateZooProcessFeatures(regionprops, object_meta)
# Write each object to an EcoTaxa archive.
# Here, three different versions are written. Remove what you do not need.
EcotaxaWriter(
os.path.join(export_path, "export.zip"),
[
# The original RGB image
(Format("{object_id}.jpg", object_id=object_id), img),
# A graylevel version
(Format("{object_id}_gray.jpg", object_id=object_id), img_gray),
# The binary mask
(Format("{object_id}_mask.jpg", object_id=object_id), mask),
],
object_meta=object_meta,
)
# Display progress indicator for individual objects
TQDM(object_id)
p.run()