Supervisely SDK for Python¶
Python SDK Reference:
Public REST API Reference:
Guides:
Basics and Usage Examples:
- Project Structure
- Data Management
- Augmentations
- Supervisely Python SDK Tutorial #3: Augmentations
- Imports
- Required imports
- Load input data
- Define visualization helpers
- Original image and its annotation
- Horizontal flip (left-right)
- Vertical flip
- Random crop - height and width from range
- Random crop - fixed height and width
- Deterministic crop (relative units)
- Deterministic crop (absolute pixels)
- Resize
- Scale proportionally
- Rotate (with padding)
- Rotate (without padding)
- Random brightness
- Random contrast
- Random Gaussian noise
- Random per-color scale
- Blur
- Median Blur
- Gaussian Blur
- NN: deploy and inference on Supervisely via API
- Automate NN training and inference via API
- Inference modes: full image / sliding window / ROI / bboxes
- Data manipulation via API: copy / move / delete
- Users and labeling jobs API
- Imports
- Initialize API access with your credentials
- Print all roles that are available on private Supervisely instance
- List all registered users
- Get UserInfo by ID
- Get UserInfo by login
- Update user info
- Get User Membership (list all user teams with corresponding roles)
- Create new user
- Disable/Enable user
- Invite user to team
- List all team users with corresponding roles
- Change user role in team
- Remove user from team
- Labeling jobs
- Get all labeling jobs in a team
- Labeling Jobs Filtering (filters [created_by_id, assigned_to_id, project_id, dataset_id] can be used in various combinations)
- Get all labeling that were created by user ‘max’
- Get all labeling that were created by user ‘max’ and were assigned to labeler02
- Archive Labeling Job
- Get all active labeling jobs in a team
- Labeling Jobs Statuses
- Custom NN pilepile: (1) detect objects and (2) segment them with separate networks
- Imports
- Initialize API access with your credentials
- Initialize the API access object
- Script parameters
- Verify parameters
- Deploy both detection and segmentataion models (if not deployed before)
- Get models ouptut classes (+ check that models are deployed correctly)
- Apply detection model (sliding window)
- Apply segmentation model (sliding window)
- Postprocessing step: keep only ties that are on the cats
- Mission complete!
- Upload images via API
- Supervisely Tutorial #10
- Imports
- Initialize API access with your credentials
- Initialize the API access object
- Script parameters
- Verify parameters
- Find project or create the new one
- Get paths of all images in a directory
- Calculate hashes for all images paths
- Get hashes that are already on server
- Create dataset for new images
- Upload images to the created dataset
- Done!
- Custom Data Pipeline
- Supervisely Tutorial #11
- Imports
- Initialize API access with your credentials
- Initialize the API access object
- Script parameters
- Verify of initialize parameters
- Create new dataset for new batch of data
- Upload new incoming data
- Copy uploaded data to labeling projects
- Create labeling jobs
- Combine labeled cats and docs and copy dataset to the final project
- Done!
- Filter and combine images
- How to work with NN pixelwise probabilities
- Additional examples in our github