Matplotlib:以图像为注释的3D散点图

Question

我正在尝试为包含0到9位数字的数据集中的tSNE嵌入图像生成三维散点图.我还想用数据集中的图像注释这些点.

通过有关这个问题的现有资源去后,我发现,提到可以轻松完成二维散点图与matplotlib.offsetbox 这里.

关于3D注释的SO 还有一个问题,但仅限于文本.有谁知道如何用图像而不是文本进行注释？

谢谢 !

Answer 1

matplotlib.offsetbox无法在3D中运行.作为解决方法,可以使用覆盖3D绘图的2D轴并将图像注释放置在与3D轴中的位置对应的位置处的2D轴上.

要计算这些位置的坐标,可以参考如何将3d数据单元转换为使用matplotlib显示单位？.然后,可以使用那些显示坐标的逆变换来获得覆盖轴中的新坐标.

from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
import matplotlib.pyplot as plt
from matplotlib import offsetbox
import numpy as np

xs = [1,1.5,2,2]
ys = [1,2,3,1]
zs = [0,1,2,0]

c = ["b","r","g","gold"]

fig = plt.figure()
ax = fig.add_subplot(111, projection=Axes3D.name)

ax.scatter(xs, ys, zs, c=c, marker="o")

# Create a dummy axes to place annotations to
ax2 = fig.add_subplot(111,frame_on=False) 
ax2.axis("off")
ax2.axis([0,1,0,1])


def proj(X, ax1, ax2):
    """ From a 3D point in axes ax1, 
        calculate position in 2D in ax2 """
    x,y,z = X
    x2, y2, _ = proj3d.proj_transform(x,y,z, ax1.get_proj())
    return ax2.transData.inverted().transform(ax1.transData.transform((x2, y2)))

def image(ax,arr,xy):
    """ Place an image (arr) as annotation at position xy """
    im = offsetbox.OffsetImage(arr, zoom=2)
    im.image.axes = ax
    ab = offsetbox.AnnotationBbox(im, xy, xybox=(-30., 30.),
                        xycoords='data', boxcoords="offset points",
                        pad=0.3, arrowprops=dict(arrowstyle="->"))
    ax.add_artist(ab)


for s in zip(xs,ys,zs):
    x,y = proj(s, ax, ax2)
    image(ax2,np.random.rand(10,10),[x,y])

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()

以上解决方案是静态的.这意味着如果旋转或缩放绘图,注释将不再指向正确的位置.为了同步循环,可以连接到绘制事件并检查极限或视角是否已经改变并相应地更新注释坐标.(在2019年编辑:较新版本还需要将事件从顶部2D轴传递到底部3D轴;代码已更新)

from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
import matplotlib.pyplot as plt
from matplotlib import offsetbox
import numpy as np

xs = [1,1.5,2,2]
ys = [1,2,3,1]
zs = [0,1,2,0]
c = ["b","r","g","gold"]


fig = plt.figure()
ax = fig.add_subplot(111, projection=Axes3D.name)

ax.scatter(xs, ys, zs, c=c, marker="o")

# Create a dummy axes to place annotations to
ax2 = fig.add_subplot(111,frame_on=False) 
ax2.axis("off")
ax2.axis([0,1,0,1])

class ImageAnnotations3D():
    def __init__(self, xyz, imgs, ax3d,ax2d):
        self.xyz = xyz
        self.imgs = imgs
        self.ax3d = ax3d
        self.ax2d = ax2d
        self.annot = []
        for s,im in zip(self.xyz, self.imgs):
            x,y = self.proj(s)
            self.annot.append(self.image(im,[x,y]))
        self.lim = self.ax3d.get_w_lims()
        self.rot = self.ax3d.get_proj()
        self.cid = self.ax3d.figure.canvas.mpl_connect("draw_event",self.update)

        self.funcmap = {"button_press_event" : self.ax3d._button_press,
                        "motion_notify_event" : self.ax3d._on_move,
                        "button_release_event" : self.ax3d._button_release}

        self.cfs = [self.ax3d.figure.canvas.mpl_connect(kind, self.cb) \
                        for kind in self.funcmap.keys()]

    def cb(self, event):
        event.inaxes = self.ax3d
        self.funcmap[event.name](event)

    def proj(self, X):
        """ From a 3D point in axes ax1, 
            calculate position in 2D in ax2 """
        x,y,z = X
        x2, y2, _ = proj3d.proj_transform(x,y,z, self.ax3d.get_proj())
        tr = self.ax3d.transData.transform((x2, y2))
        return self.ax2d.transData.inverted().transform(tr)

    def image(self,arr,xy):
        """ Place an image (arr) as annotation at position xy """
        im = offsetbox.OffsetImage(arr, zoom=2)
        im.image.axes = ax
        ab = offsetbox.AnnotationBbox(im, xy, xybox=(-30., 30.),
                            xycoords='data', boxcoords="offset points",
                            pad=0.3, arrowprops=dict(arrowstyle="->"))
        self.ax2d.add_artist(ab)
        return ab

    def update(self,event):
        if np.any(self.ax3d.get_w_lims() != self.lim) or \
                        np.any(self.ax3d.get_proj() != self.rot):
            self.lim = self.ax3d.get_w_lims()
            self.rot = self.ax3d.get_proj()
            for s,ab in zip(self.xyz, self.annot):
                ab.xy = self.proj(s)


imgs = [np.random.rand(10,10) for i in range(len(xs))]
ia = ImageAnnotations3D(np.c_[xs,ys,zs],imgs,ax, ax2 )

ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()