Last active
June 30, 2024 16:19
-
-
Save m-albert/85856b6f11450a24e208fd6428207681 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| <config lang="json"> | |
| { | |
| "name": "BioImage.IO Colab Annotator", | |
| "type": "web-python", | |
| "tags": [], | |
| "ui": "", | |
| "version": "0.1.0", | |
| "cover": "", | |
| "description": "Collaborative Annotator for BioImage.IO with Automated Segmentation", | |
| "icon": "extension", | |
| "inputs": null, | |
| "outputs": null, | |
| "api_version": "0.1.8", | |
| "env": "", | |
| "permissions": [], | |
| "requirements": ["kaibu-utils", "imjoy-rpc", "websockets", "ssl"], | |
| "dependencies": [] | |
| } | |
| </config> | |
| <script lang="python"> | |
| from imjoy_rpc import api | |
| from imjoy_rpc.hypha import connect_to_server | |
| class BioImageIOCrowdSourcing(): | |
| def __init__(self): | |
| self.image = None | |
| self.mask = None | |
| self.filename = None | |
| self.newname = None | |
| self.mask_id = None | |
| self.image_id = None | |
| self.annotation_layer = None | |
| async def setup(self): | |
| viewer = await api.createWindow(src="https://kaibu.org/#/app", fullscreen=True) | |
| await api.showMessage("Connecting to server....") | |
| server = await connect_to_server({"server_url": "https://ai.imjoy.io"}) | |
| self.biocolab = await server.get_service("biocolab") | |
| ## Define image reading and displaying function | |
| async def get_image(): | |
| if self.image is not None: | |
| await viewer.remove_layer({"id": self.image_id.id}) | |
| await viewer.remove_layer({"id": self.annotation_layer.id}) | |
| self.image, self.filename, self.newname = await self.biocolab.get_random_image() | |
| self.image_id = await viewer.view_image( | |
| self.image, | |
| name="image") | |
| # Add the little annotation functionality to the interface | |
| self.annotation_layer = await viewer.add_shapes( | |
| [], | |
| shape_type="polygon", | |
| draw_edge_color="magenta", | |
| name="annotation", | |
| ) | |
| ## Define image annotation printing into a mask with the name "mask" | |
| # async def create_mask(): | |
| # self.features = await self.annotation_layer.get_features() | |
| # if self.mask is not None: | |
| # await viewer.remove_layer({"id": self.mask_id.id}) | |
| # self.mask = features_to_mask(self.features, [self.image.shape[0], self.image.shape[1]]) | |
| # self.mask_id = await viewer.view_image( | |
| # self.mask, | |
| # name="mask" | |
| # ) | |
| # await api.showMessage("Mask created") | |
| async def save_annotation(): | |
| self.features = await self.annotation_layer.get_features() | |
| await self.biocolab.save_annotation(self.filename, self.newname, self.features, [self.image.shape[0], self.image.shape[1]]) | |
| await api.showMessage("Annotation Saved to " + self.filename) | |
| async def select_option(option): | |
| if option == "Get Image": | |
| await get_image() | |
| elif option == "Save Annotations": | |
| await save_annotation() | |
| await viewer.add_widget( | |
| { | |
| "_rintf": True, | |
| "name": "Control", | |
| "type": "control", | |
| "elements": [ | |
| { | |
| "type": "dropdown", | |
| "label": "Options", | |
| "options": ["Get Image", "Save Annotations"], | |
| "callback": select_option, | |
| } | |
| ], | |
| }) | |
| await api.showMessage("Redy to annotate!") | |
| api.export(BioImageIOCrowdSourcing()) | |
| </script> |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment