aug_motion_blur — 2D `augmentation` op

• Data kinds: image → image

• Call: fullseye.apply(img, "aug_motion_blur", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Linear motion blur: convolution with a normalised LINE (box) kernel, the point-spread function of a constant-velocity camera/scene pan during the exposure. `a sets the streak length L = 3 + 20*a px (forced odd and clamped so the kernel never exceeds the image), b sets the streak angle b*180 degrees. Border handling is reflect` so the frame edge does not darken. Energy-preserving (kernel sums to 1).

Detailed usage guide

• gallery2d_color_artistic family guide

References (sample data, literature)

• 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 canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_color_artistic — py -3.11 examples/gallery2d_color_artistic.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`augmentation`)

aug_shot_noise · aug_read_noise · aug_fixed_pattern · aug_vignette · aug_chromatic · aug_rolling_shutter · aug_jpeg_blocks · aug_cutout

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

aug_motion_blur — 2D augmentation op