typed op• Data kinds: rgbimage → rgbimage
• Call: fullseye.apply(img, "tb_sensor_capture", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])
*The figure is the actual output on a synthetic 128×128 input. Left: input, right: output. Point clouds are drawn as a top-down scatter (brightness = z), 1-D series as a line plot, volumes as the maximum-intensity projection along z, videos as the middle frame, complex images as magnitude; return values that are not pictures are shown as the values themselves.*
Sweeping knob a (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_sensor_capture: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_sensor_capture: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_sensor_capture: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_sensor_capture: other inputs (docs site)
Radiance → real-sensor output (shot noise, read noise, saturation, quantisation).
Poisson with mean photon count = radiance · exposure_ms · gain_e_per_unit / 1000. Read-out noise (normal) is added, it is saturated at full_well_e, and quantised to bit_depth bits. Saturation is a clip and does not wrap around (blown-out highlights stay white).
The return value is an integer array in 0..2^bit_depth−1. Fixing seed makes it deterministic.
The optics op sensor_capture bridged into the 2-D evolution registry. The implementation is the same; only the calling convention is adapted to op(v, a, b). a sweeps exposure_ms (default 10) and b sweeps gain_e_per_unit (default 50000).
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
The program below has been verified to run (same input as the figure). In Studio's help this block becomes buttons that load and run it on the spot.
img_to_rgb 0.50 0.50 tb_sensor_capture 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op sensor_capture. This bridge op is the same implementation adapted to the fn(v, a, b) convention, so the behaviour carries over unchanged (only the call form differs).
• vision_layout_from_catalog — py -3.11 examples/vision_layout_from_catalog.py
rgbimage as input)identity · tb_wetness · tb_specular_diffuse_split · tb_specular_coefficient_map · tb_specular_free_transform · tb_rgb_to_quaternion
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal · tb_voxel_grid_downsample
*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.