Segmentation
Init Segmentation
Each annotation init receives the coordinates for the specific type, label, and optional attributes. A binary mask should be exactly the same dimensions as the image item, with 0 for background and 1 for the annotation.
annotations_definition = dl.Segmentation(geo=mask, label=label)Create a Semantic Segmentation Annotation
# Get item from the platform
item = dataset.items.get(filepath='/your-image-file-path.jpg')
# Create a builder instance
builder = item.annotations.builder()
# Create semantic segmentation mask with label and attribute
mask = np.zeros(shape=(item.height, item.width), dtype=np.uint8)
# mark some part in the middle
mask[50:100, 200:250] = 1
# Add annotations of type segmentation
builder.add(annotation_definition=dl.Segmentation(geo=mask,
label='my-label'))
# Optional: Plot all of the annotations you created before uploading them to the platform
import matplotlib.pyplot as plt
plt.figure()
plt.imshow(builder.show())
for annotation in builder:
plt.figure()
plt.imshow(annotation.show())
plt.title(annotation.label)
# Upload semantic segmentation to the item
item.annotations.upload(builder)Convert Mask to Polygon
The Dataloop SDK includes a function to convert a semantic mask to a polygon annotation, which is often easier to edit and work with in the UI.
The following example filters for items with semantic mask annotations, and converts them into Polygon annotations.
filters = dl.Filters()
# set resource
filters.resource = 'items'
# add filter - only files
filters.add(field='type', values='file')
# add annotation filters - only items with 'binary' annotations
filters.add_join(field='type', values='binary')
# get results
pages = dataset.items.list(filters=filters)
# run over all items in page
for page in pages:
for item in page:
print('item=' + item.id)
annotations = item.annotations.list()
builder = item.annotations.builder()
# run over all annotation in item
for annotation in annotations:
# print(annotation)
if annotation.type == 'binary':
print("Found binary annotation - id:", annotation.id)
builder.add(dl.Polygon.from_segmentation(mask=annotation.annotation_definition.geo,
# binary mask of the annotation
label=annotation.label,
max_instances=None))
annotation.delete()
item.annotations.upload(annotations=builder)Convert Polygon to Mask
The Dataloop SDK also includes a function to convert a Polygon annotation into semantic mask annotation.
The following example filters for items with Polygon annotations, and converts them into semantic mask annotations.
This script uses module CV2, please make sure it is installed.
from PIL import Image
filters = dl.Filters()
# set resource
filters.resource = 'items'
# add filter - only files
filters.add(field='type', values='file')
# add annotation filters - only items with polygon annotations
filters.add_join(field='type', values='segment')
# get results
pages = dataset.items.list(filters=filters)
# run over all items in page
for page in pages:
for item in page:
print('item=' + item.id)
annotations = item.annotations.list()
item = dataset.items.get(item_id=item.id)
buffer = item.download(save_locally=False)
img = Image.open(buffer)
builder = item.annotations.builder()
# run over all annotation in item
for annotation in annotations:
# print(annotation)
if annotation.type == 'segment':
print("Found polygon annotation - id:", annotation.id)
builder.add(dl.Segmentation.from_polygon(geo=annotation.annotation_definition.geo,
# binary mask of the annotation
label=annotation.label,
shape=img.size[::-1] # (h,w)
))
annotation.delete()
item.annotations.upload(annotations=builder)Create Semantic Segmentation from Image Mask and Upload
The following script creates a semantic mask based on RGB colors of an image item and upload them to the Dataloop platform
Please notice that directory paths look different in OS and Linux and does not require "r" at the beginning
Make sure to use install OpenCV package to version 3.4.8.x with the script
pip install opencv-python == 3 .4.8.latest
from PIL import Image
import numpy as np
import dtlpy as dl
# Get project and dataset
project = dl.projects.get(project_name='project_name')
dataset = project.datasets.get(dataset_name='dataset_name')
# image filepath
image_filepath = r'C:/home/images/with_family.png'
# annotations filepath - RGB with color for each label
annotations_filepath = r'C:/home/masks/with_family.png'
# upload item to root directory
item = dataset.items.upload(local_path=image_filepath,
remote_path='/')
# read mask from file
mask = np.array(Image.open(annotations_filepath))
# get unique color (labels)
unique_colors = np.unique(mask.reshape(-1, mask.shape[2]), axis=0)
# init dataloop annotations builder
builder = item.annotations.builder()
# for each label - create a dataloop mask annotation
for i, color in enumerate(unique_colors):
print(color)
if i == 0:
# ignore background
continue
# get mask of same color
class_mask = np.all(color == mask, axis=2)
# add annotation to builder
builder.add(annotation_definition=dl.Segmentation(geo=class_mask,
label=str(i)))
# upload all annotations
item.annotations.upload(builder)