Plotting Kernels

image_processing.visualization provides two functions for inspecting a kernel bank, plus matching convenience methods on BaseKernel. Both work with any BaseKernel subclass (not just ElongatedMaskKernel), as long as its params is a dataclass.

---

1. All orientations in one grid

from image_processing import ElongatedMaskKernel, plot_kernel_grid

kernel = ElongatedMaskKernel()
plot_kernel_grid(kernel, save_path="kernel_grid.png")

Or as a method directly on the kernel:

kernel.plot_all(save_path="kernel_grid.png")

This renders every orientation in a cols-wide grid (default 6), each titled with its angle in degrees, and a shared colorbar. The figure’s suptitle also lists the kernel’s parameter values, read automatically from the dataclass fields of kernel.params.

Signature

plot_kernel_grid(
    kernel: BaseKernel,
    cols: int = 6,
    save_path: str | Path | None = None,
    show: bool = True,
) -> matplotlib.figure.Figure

---

2. A single orientation

from image_processing import plot_single_kernel

plot_single_kernel(kernel, index=0, save_path="kernel_single.png")

Or:

kernel.plot(index=0, save_path="kernel_single.png")

Renders one orientation at a larger size, titled with the kernel class name, angle, and spatial dimensions.

Signature

plot_single_kernel(
    kernel: BaseKernel,
    index: int = 0,
    save_path: str | Path | None = None,
    show: bool = True,
) -> matplotlib.figure.Figure

# Raises IndexError if `index` is out of range for the kernel bank.

---

3. Notes

• Both functions build the kernel bank if it isn’t cached yet (via kernel.kernels), then move it to CPU/NumPy for plotting — the original tensor stays on its original device.

• show=False skips plt.show(), useful for headless/CI runs or when you only want to save to disk.

• save_path accepts anything Matplotlib’s savefig accepts (str or pathlib.Path); the file format is inferred from the extension.

• Kernel weights use a diverging colormap (RdBu_r) centered at zero, so positive and negative weights are easy to tell apart at a glance.

4. Example

from image_processing import ElongatedMaskKernel, ElongatedMaskParams

params = ElongatedMaskParams(n_angles=18, kernel_half_size=30)
kernel = ElongatedMaskKernel(params, device="cpu")

kernel.plot_all(save_path="grid.png")      # overview of all 18 orientations
kernel.plot(index=3, save_path="k3.png")   # close-up of the 4th orientation