Pupil: Image Processing Skills

SUMMARY

Pupil is a module in the Telekinesis SDK containing skills for image processing and low-level vision algorithms.

It provides a set of modular tools for preprocessing, filtering, and transforming images, enabling developers to build robust vision pipelines for robotics applications.

When to Use Pupil?

Use Pupil for robotics applications that require low-level image processing and preprocessing, such as:

• Enhancing camera input for vision-guided pick-and-place
• Preparing images for object detection or segmentation
• Noise reduction and filtering for robot navigation
• Real-time feature extraction for tracking or visual servoing
• Camera calibration and image rectification in robotics pipelines

What Does Pupil Provide?

Pupil includes a collection of modular skills for:

• Image filtering and enhancement
• Noise reduction and smoothing
• Geometric transformations and rectification
• Basic computer vision algorithms for preprocessing

How to Use Pupil?

To use the skills from Pupil, simply use:

from telekinesis import pupil

Here is a minimal example:

from telekinesis import pupil
from datatypes import datatypes, io

# Load an image
image = io.load_image(filepath=datatypes.String("path/to/image.png"))

# Apply Gaussian blur
blurred_image = pupil.filter_image_using_gaussian_blur(
    image=image,
    kernel_size=datatypes.Int(5),
    sigma_x=datatypes.Float(1.0)
)

# Save the result
io.save_image(image=blurred_image, filepath=datatypes.String("output.png"))

Overview of Skills

SDK Call	Description
enhance_image_using_clahe	Applies Contrast Limited Adaptive Histogram Equalization.
enhance_image_using_auto_gamma_correction	Applies gamma correction for brightness adjustment.
enhance_image_using_white_balance	Applies white balance correction to remove color casts.
filter_image_using_bilateral	Applies bilateral filtering for edge-preserving smoothing.
filter_image_using_blur	Applies simple box blur for fast smoothing.
filter_image_using_box	Applies normalized box filter with depth control.
filter_image_using_gaussian_blur	Applies Gaussian blur for smooth noise reduction.
filter_image_using_median_blur	Applies median filtering to remove salt-and-pepper noise.
filter_image_using_laplacian	Applies Laplacian operator for edge detection.
filter_image_using_sobel	Applies Sobel operator for gradient-based edge detection.
filter_image_using_scharr	Applies Scharr operator for improved gradient estimation.
filter_image_using_gabor	Applies Gabor filter for texture and oriented feature detection.
filter_image_using_frangi	Applies Frangi vesselness filter for detecting tubular structures.
filter_image_using_hessian	Applies Hessian-based vesselness filtering.
filter_image_using_sato	Applies Sato tubeness filter for ridge detection.
filter_image_using_meijering	Applies Meijering neuriteness filter for detecting thin structures.
filter_image_using_morphological_erode	Erodes bright regions to remove small features.
filter_image_using_morphological_dilate	Dilates bright regions to fill gaps and expand features.
filter_image_using_morphological_close	Closes holes by applying dilation followed by erosion.
filter_image_using_morphological_open	Opens regions by applying erosion followed by dilation.
filter_image_using_morphological_gradient	Computes morphological gradient for edge detection.
filter_image_using_morphological_tophat	Extracts small bright features using top-hat transform.
filter_image_using_morphological_blackhat	Extracts small dark features using black-hat transform.
transform_image_using_pyramid_downsampling	Downsamples image with anti-aliasing for pyramid construction.
transform_image_using_pyramid_upsampling	Upsamples image for pyramid reconstruction.
transform_mask_using_blob_thinning	Applies morphological thinning for skeletonization.