plot_utils.py

import copy

import matplotlib
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np


def plot_images(imgs, titles=None, cmaps="gray", dpi=100, size=6, pad=0.5):
    """Plot a set of images horizontally.
    Args:
        imgs: a list of NumPy or PyTorch images, RGB (H, W, 3) or mono (H, W).
        titles: a list of strings, as titles for each image.
        cmaps: colormaps for monochrome images.
    """
    n = len(imgs)
    if not isinstance(cmaps, (list, tuple)):
        cmaps = [cmaps] * n
    figsize = (size * n, size * 3 / 4) if size is not None else None
    fig, ax = plt.subplots(1, n, figsize=figsize, dpi=dpi)
    if n == 1:
        ax = [ax]
    for i in range(n):
        ax[i].imshow(imgs[i], cmap=plt.get_cmap(cmaps[i]))
        ax[i].get_yaxis().set_ticks([])
        ax[i].get_xaxis().set_ticks([])
        ax[i].set_axis_off()
        for spine in ax[i].spines.values():  # remove frame
            spine.set_visible(False)
        if titles:
            ax[i].set_title(titles[i])
    fig.tight_layout(pad=pad)

    return fig


def plot_lines(
    lines, fig, line_colors="orange", point_colors="cyan", ps=4, lw=2, indices=(0, 1)
):
    """Plot lines and endpoints for existing images.
    Args:
        lines: list of ndarrays of size (N, 2, 2).
        colors: string, or list of list of tuples (one for each keypoints).
        ps: size of the keypoints as float pixels.
        lw: line width as float pixels.
        indices: indices of the images to draw the matches on.
    """
    if not isinstance(line_colors, list):
        line_colors = [line_colors] * len(lines)
    if not isinstance(point_colors, list):
        point_colors = [point_colors] * len(lines)

    # fig = plt.gcf()
    ax = fig.axes
    assert len(ax) > max(indices)
    axes = [ax[i] for i in indices]
    fig.canvas.draw()

    # Plot the lines and junctions
    for a, l, lc, pc in zip(axes, lines, line_colors, point_colors):
        for i in range(len(l)):
            line = matplotlib.lines.Line2D(
                (l[i, 1, 1], l[i, 0, 1]),
                (l[i, 1, 0], l[i, 0, 0]),
                zorder=1,
                c=lc,
                linewidth=lw,
            )
            a.add_line(line)
        pts = l.reshape(-1, 2)
        a.scatter(pts[:, 1], pts[:, 0], c=pc, s=ps, linewidths=0, zorder=2)

    return fig


def plot_color_line_matches(lines, fig, lw=2, indices=(0, 1)):
    """Plot line matches for existing images with multiple colors.
    Args:
        lines: list of ndarrays of size (N, 2, 2).
        lw: line width as float pixels.
        indices: indices of the images to draw the matches on.
    """
    n_lines = len(lines[0])

    cmap = plt.get_cmap("nipy_spectral", lut=n_lines)
    colors = np.array([mcolors.rgb2hex(cmap(i)) for i in range(cmap.N)])

    np.random.shuffle(colors)

    ax = fig.axes
    assert len(ax) > max(indices)
    axes = [ax[i] for i in indices]
    fig.canvas.draw()

    # Plot the lines
    for a, l in zip(axes, lines):
        for i in range(len(l)):
            line = matplotlib.lines.Line2D(
                (l[i, 1, 1], l[i, 0, 1]),
                (l[i, 1, 0], l[i, 0, 0]),
                zorder=1,
                c=colors[i],
                linewidth=lw,
            )
            a.add_line(line)

    return fig