Cornea: Image Segmentation Skills
SUMMARY
Cornea is a module in the Telekinesis SDK containing skills for 2D image segmentation.
It provides segmentation capabilities using classical computer vision techniques and deep learning models, allowing developers to extract structured visual information from images for robotics applications.
When to Use Cornea?
Use Cornea for robotics applications that require pixel-level understanding of images, such as:
- Vision-guided pick-and-place pipelines
- Palletizing and bin organization
- Object isolation for manipulation and grasp planning
- Obstacle detection in camera-based navigation
- Scene understanding for Physical AI agents
What Does Cornea Provide?
Cornea includes a collection of modular skills for:
- Semantic and instance segmentation
- Classical image segmentation techniques
- Deep learning–based segmentation models
- Image preprocessing and postprocessing for segmentation pipelines
How to Use Cornea?
To use the skills from Cornea, simply use:
from telekinesis import corneaHere is a minimal example:
from telekinesis import cornea, datatypes
# ===================== Load Image ==========================================
image_url = "https://assets.telekinesis.ai/examples/v1/images/cylinder_on_conveyor.jpg"
image = datatypes.Image.from_url(url=image_url)
# ===================== Run Skill ==========================================
segmented_image = cornea.segment_image_using_rgb(
image=image, lower_bound=(0, 50, 50), upper_bound=(180, 255, 255)
)Overview of Skills
CorneaSegment Image Using RGB
Perform image segmentation based on RGB color ranges to identify objects by their specific color values.
CorneaSegment Image Using HSV
Perform robust color-based segmentation using HSV color space, separating hue, saturation, and value for consistent detection under varying lighting conditions.
CorneaSegment Image Using LAB
Perform perceptually uniform color-based segmentation using LAB color space, ideal for matching human color perception in image processing tasks.
CorneaSegment Image Using YCrCb
Perform YCrCb color space segmentation to detect objects based on chrominance, commonly used for skin detection and face detection.
CorneaSegment Image Using Flood Fill
Flood fill image segmentation for extracting connected regions based on a seed point and color tolerance for computer vision and image processing pipelines.
CorneaSegment Image Using Watershed
Perform marker-based watershed segmentation to separate touching or overlapping objects in an image.
CorneaSegment Image Using Focus Region
Focus region segmentation for identifying in-focus areas based on image sharpness for computer vision, depth estimation, and image processing pipelines.
CorneaSegment Image Using Otsu Threshold
Automatically segment an image with a single global threshold chosen by Otsu's method, ideal for clearly bimodal intensity histograms.
CorneaSegment Image Using Local Threshold
Segment images with non-uniform lighting or varying background intensity using a single-parameter, neighborhood-based local threshold.
CorneaSegment Image Using Yen Threshold
Automatically segment an image with a single global threshold chosen by Yen's entropy-based method, a parameter-free alternative to Otsu thresholding.
CorneaSegment Image Using Threshold
Perform manual global threshold segmentation with a chosen pixel value and one of five comparison modes to create a labeled segmentation mask.
CorneaSegment Image Using Adaptive Threshold
Adaptive per-pixel threshold segmentation for images with non-uniform lighting, shadows, and varying intensity, with control over the local-averaging method and comparison direction.
CorneaSegment Image Using Laplacian Threshold
Segment an image's edge-rich, textured regions from its smooth regions by thresholding the Laplacian (second-derivative) edge response.
CorneaSegment Image Using Felzenszwalb
Generate irregularly-shaped, boundary-following superpixels with Felzenszwalb's graph-based segmentation algorithm.
CorneaSegment Image Using SLIC Superpixel
Generate compact, near-uniform superpixels with the SLIC (Simple Linear Iterative Clustering) algorithm for predictable over-segmentation.
CorneaFilter Segments By Area
Remove segments smaller or larger than a given pixel-area range from an existing label map, discarding noise or implausibly large regions.
CorneaFilter Segments By Color
Remove segments from an existing label map whose mean pixel intensity falls outside a given range, keeping only bright or dark regions of interest.
CorneaFilter Segments By Mask
Keep only the segments of an existing label map that overlap a region-of-interest mask, discarding the rest.
CorneaSegment Image Using GrabCut
Perform foreground/background segmentation using GrabCut, an iterative graph cut algorithm for interactive object extraction and background removal.
cornea.segment_image_foreground_using_birefnet()CorneaSegment Image Using Foreground BiRefNet
Segment the salient foreground object from the background using BiRefNet, a deep-learning model that needs no bounding box or seed point.
CorneaSegment Image Using SAM
Segment objects inside bounding box prompts using Meta's Segment Anything Model (SAM), a deep-learning alternative to GrabCut for precise per-object masks.