問題描述

我正在嘗試為包含從 0 到 9 的數字的數據集中的圖像的 tSNE 嵌入生成 3D 散點圖.我還想用數據集中的圖像注釋這些點.

在瀏覽了與該問題相關的現有資源后，我發現使用 matplotlib.offsetbox 可以輕松完成 2D 散點圖，如上所述

上述解決方案是靜態的.這意味著如果繪圖被旋轉或縮放，注釋將不再指向正確的位置.為了同步注釋，可以連接到繪圖事件并檢查限制或視角是否發生了變化，并相應地更新注釋坐標.(2019 年較新版本還需要將事件從頂部 2D 軸傳遞到底部 3D 軸；代碼已更新)

從 mpl_toolkits.mplot3d 導入 Axes3D從 mpl_toolkits.mplot3d 導入 proj3d將 matplotlib.pyplot 導入為 plt從 matplotlib 導入偏移框將 numpy 導入為 npxs = [1,1.5,2,2]ys = [1,2,3,1]zs = [0,1,2,0]c = ["b","r","g","黃金"]無花果 = plt.figure()ax = fig.add_subplot(111, projection=Axes3D.name)ax.scatter(xs, ys, zs, c=c, marker="o")# 創建一個虛擬軸來放置注釋ax2 = fig.add_subplot(111,frame_on=False)ax2.axis("關閉")ax2.axis([0,1,0,1])類 ImageAnnotations3D():def __init__(self, xyz, imgs, ax3d,ax2d):自我.xyz = xyzself.imgs = imgsself.ax3d = ax3dself.ax2d = ax2dself.annot = []對于 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) 對于 self.funcmap.keys() 中的種類]def cb(自我，事件):event.inaxes = self.ax3dself.funcmap[event.name](事件)定義項目(自我，X):""" 從軸 ax1 中的一個 3D 點，在 ax2 """ 中計算二維位置x,y,z = Xx2, y2, _ = proj3d.proj_transform(x,y,z, self.ax3d.get_proj())tr = self.ax3d.transData.transform((x2, y2))返回 self.ax2d.transData.inverted().transform(tr)def 圖像(自我，arr，xy):""" 將圖像 (arr) 作為注釋放置在 xy 位置 """im = offsetbox.OffsetImage(arr, zoom=2)im.image.axes = 斧頭ab = offsetbox.AnnotationBbox(im, xy, xybox=(-30., 30.),xycoords='data', boxcoords="offset points",墊=0.3，箭頭道具=dict(箭頭樣式=->"))self.ax2d.add_artist(ab)返回ab定義更新(自我，事件):如果 np.any(self.ax3d.get_w_lims() != self.lim) 或 
p.any(self.ax3d.get_proj()！= self.rot):self.lim = self.ax3d.get_w_lims()self.rot = self.ax3d.get_proj()對于 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 標簽')ax.set_ylabel('Y 標簽')ax.set_zlabel('Z 標簽')plt.show()

I am trying to generate a 3D scatter plot for tSNE embeddings of images from a dataset containing digits from 0 to 9. I would also like to annotate the points with the images from the dataset.

After going through existing resources pertaining the issue, I found that it can be done easily for 2D scatter plot with matplotlib.offsetbox as mentioned here.

There is also a question on SO relating to 3D annotation but with text only. Does anyone know how to annotate with image instead of text ?

Thanks !

The matplotlib.offsetbox does not work in 3D. As a workaround one may use a 2D axes overlaying the 3D plot and place the image annotation to that 2D axes at the position which corresponds to the position in the 3D axes.

To calculate the coordinates of those positions, one may refer to How to transform 3d data units to display units with matplotlib?. Then one may use the inverse transform of those display coordinates to obtain the new coordinates in the overlay axes.

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()

The above solution is static. This means if the plot is rotated or zoomed, the annotations will not point to the correct locations any more. In order to synchronize the annoations, one may connect to the draw event and check if either the limits or the viewing angles have changed and update the annotation coordinates accordingly. (Edit in 2019: Newer versions also require to pass on the events from the top 2D axes to the bottom 3D axes; code updated)

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()

這篇關于Matplotlib:以圖像為注釋的 3D 散點圖的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！