This module is a means for interfacing with projectkiwi.io using python.
Project description
Projectkiwi3 Client
This package adds a basic python interface for the projectkiwi api.
Installation
pip install projectkiwi3
Getting Started
Retrieve your projectkiwi API key here: https://projectkiwi.io/developer
import projectkiwi3
client = projectkiwi3.Client("YOUR_API_KEY")
# list all our projects
projects = client.getProjects()
Reading Annotations
project = projects[0]
# get current annotations
annotations = client.getAnnotations(project.id)
Adding Annotations
# get labels
newLabel = client.addLabel(project.id, "demo label", "rgb(3, 186, 252)")
# create new annotation
newAnnotation = client.addAnnotation(
project.id,
coordinates=[[-123.4, 56.789012]],
shape="Point",
labelId=newLabel.id
confidence=1.0
)
print(newAnnotation)
# id=553971 sub='google-oauth2|115859123295676188590' shape='Point' createdAt='2024-09-05T18:47:17.529Z' confidence=1.0 labelId=3 label=Label(id=3, name='demo label', color='rgb(3, 186, 252)', active=True, modifiedAt='2024-07-15T20:29:59.697Z') coordinates=[[-123.4, 56.789012]]
Working with labeling Queues
Labeling Queues allow us to break up larger labeling tasks in to bite size portions, easily digestible for both humans and GPUs.
import matplotlib.pyplot as plt
# list out all labeling queues in the project
labelingQueues = client.getLabelingQueues(project.id)
# let's just pick out the latest one
labelingQueue = labelingQueues[-1]
# find imagery layer to use - every geotiff you upload creates an imagery layer
imageryLayer = client.getAllImagery(project.id)[-1]
# Preview an image for a single task within the labeling queue
task = labelingQueue.labelingTasks[-1]
img = client.getImageForTask(imageryLayer, task.coordinates)
plt.imshow(img)
plt.show()
Project details
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