typed op• Data kinds: video → video
• Call: fullseye.apply(img, "tb_temporal_bandpass", 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_temporal_bandpass: knob a sweep (docs site)
Sweeping knob b (0.1 / 0.5 / 0.9, the other knob at its default):
▸ tb_temporal_bandpass: knob b sweep (docs site)
Stages (the ops that come before → this op, left to right):
▸ tb_temporal_bandpass: stages (docs site)
On other images (synthetic scene / photo / coins. Top row: inputs, bottom row: their outputs. Knobs at default):
▸ tb_temporal_bandpass: other inputs (docs site)
Motion (GIF: frames / views / slices in turn. The still figure is the complete one; the GIF is supplementary):
Ideal temporal band-pass of every pixel's time series -> (T, H, W).
Each pixel is transformed along time, every DFT bin whose frequency lies outside [f_lo, f_hi] (in Hz, magnitude, DC always excluded) is zeroed, and the result is transformed back. Frequency-selective where videops.moving_average and videops.spatiotemporal_gaussian are low-pass; this is the filter isolating "what is happening at 4 Hz".
Exact for a component sitting on a bin: with T frames at fps, a sinusoid at k*fps/T Hz passes with gain 1 and everything else in the band passes untouched. Measured on a bin-centred 4 Hz unit sinusoid in a 64-frame 32 fps clip that also carries a DC offset of 0.5 and a 12 Hz component of amplitude 0.3, the recovered waveform matches the 4 Hz term alone to max|err| = 4.36e-15.
A brick-wall filter rings in time; that is the price of an exact pass-band and it is the same choice the 2012 Eulerian magnification paper makes. The output is zero-mean along time by construction.
Typed bridge of the motionmag op temporal_bandpass into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a picks the centre of the pass-band among the clip's own non-DC DFT bins (0 = the lowest bin, 1 = the Nyquist bin) and b its half-width (0 = one bin, 1 = half the bins on each side). The band edges sit half a bin outside the chosen bins (frame rate fixed at 32 fps), so the band holds at least one bin for any clip of two or more frames. (Until 2026-10-07 the band was a fixed 3-5 Hz, which holds no bin for a 12-frame clip, so every call was refused.)
• 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_video 0.50 0.50 tb_temporal_bandpass 0.50 0.50
▸ Load this pipeline · Load & run
The examples below call the underlying ledger op temporal_bandpass. 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).
• motion_magnification — py -3.11 examples/motion_magnification.py
• poc_beam_modal_video — py -3.11 examples/poc_beam_modal_video.py
• poc_motion_magnification — py -3.11 examples/poc_motion_magnification.py
video as input)identity · tb_temporal_band_power · tb_temporal_median_window · tb_moving_average_window · tb_background_subtraction_window · tb_frame_difference_causal · tb_exponential_background · tb_exponential_foreground
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.