英文:
More efficent way of plotting image on 3D plane in matplotlib than using a meshgrid
问题
有没有比这个更好的方法在matplotlib中绘制带有图像的3D平面：
xx, yy = np.meshgrid(np.linspace(0, 1, img_size[1]), np.linspace(0, 1, img_size[0]))zz = np.ones((img_size[0],img_size[1]))ax.plot_surface(xx, yy, zz, rstride=1, cstride=1, facecolors=img / 255, shade=False)
我不想创建与像素数量相同的面，因为这相当低效。是否有更好的方法？
这是我的绘图效果：
英文:
Is there a better way to plot a plane with image in 3D in matplotlib than this:
xx, yy = np.meshgrid(np.linspace(0, 1, img_size[1]), np.linspace(0, 1, img_size[0]))zz = np.ones((img_size[0],img_size[1]))ax.plot_surface(xx, yy, zz, rstride=1, cstride=1, facecolors=img / 255, shade=False)
I dont want to create a surface with as many faces as I have pixels, since that quite inefficient.
Is there a better way to do so?
This is what my plot looks like:
答案1
得分: 2
一个简单的一行方法是按如下方式进行子采样：img_smaller = img[::10, ::10, :]。这将使图像缩小10倍，但由于未应用插值，图像会看起来粗糙，如果尺寸不是10的倍数，则边缘可能会被剪切：
在降采样之前：
使用数组切片进行降采样：
另一种方法是在使用PIL加载图像后进行降采样。设置downsample=12，即12倍降采样：
PIL降采样：
from PIL import Image#加载图像，或者创建一个测试图像np.random.seed(0)img = Image.fromarray( np.random.randint(0, 255, size=(100, 100, 3), dtype=np.ubyte))img = Image.open('.. /image.png')#进行12倍子采样并应用插值downsample = 12img_small = img.resize((img.height // downsample, img.width // downsample),resample=Image.BICUBIC)h, w = img_small.height, img_small.widthimg_small_arr = np.asarray(img_small)#绘图ax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))zz = np.ones((h, w))ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)#facecolor=[0,0,0,0], linewidth=1) #可选添加'grid'效果
使用切片的一行方法：
#使用切片进行降采样img_small_arr = np.asarray(img)[::10, ::10, :]#新尺寸h, w, _ = img_small_arr.shape#其余绘图部分与之前相同ax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))zz = np.ones((h, w))ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)
英文:
A simple one-liner method is to subsample as follows: img_smaller = img[::10, ::10, :]. This will subsample it down 10x, but the image will look coarse as no interpolation was applied, and edges may look clipped if the dimensions aren't multiples of 10:
Before downsampling:
Downsampling using array slicing:
Another approach is to downsample the image after loading it using PIL. After setting downsample=12, i.e. 12x downsampling:
PIL downsampling:
from PIL import Image#Load image, or make a test imagenp.random.seed(0)img = Image.fromarray( np.random.randint(0, 255, size=(100, 100, 3), dtype=np.ubyte))img = Image.open('../image.png')#Subsample 12x and apply interpolationdownsample = 12img_small = img.resize((img.height // downsample, img.width // downsample),resample=Image.BICUBIC)h, w = img_small.height, img_small.widthimg_small_arr = np.asarray(img_small)#Plotax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))zz = np.ones((h, w))ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)#facecolor=[0,0,0,0], linewidth=1) #optionally add a 'grid' effect
One-liner method using slicing:
#Downsample using slicingimg_small_arr = np.asarray(img)[::10, ::10, :]#New dimensionsh, w, _ = img_small_arr.shape#Rest of the plotting, as beforeax = plt.figure(figsize=(3, 3)).add_subplot(projection='3d')xx, yy = np.meshgrid(np.linspace(0, 1, w), np.linspace(0, 1, h))zz = np.ones((h, w))ax.plot_surface(xx, yy, zz, facecolors=img_small_arr / 255, shade=False)
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