Is it possible to achieve a continuous color gradient with surface plot using matplotlib?-CodePudding

I am trying to visualise the pixel intensity of a photo by plotting a 3D graph. In the code below, lab is an image I want to analyse. The code will look at the pixel intensity of every pixels in the image and plot a graph, where the height denotes the pixel intensity.

Here is a portion of my code:

import cv2
import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

from skimage import io, color
import glob
from PIL import Image


plt.figure(dpi=1200)
    ax = plt.axes(projection='3d')
    y = range(lab.shape[0])
    x = range(lab.shape[1])
    X, Y = np.meshgrid(x, y)

    ax.view_init(elev=60., azim=60)

    thickness = ax.plot_surface(
        X,
        Y,
        lab[:, :, 0],  # change value here to adjust the height
        cmap=cm.coolwarm,
        antialiased=False)

    # Add a color bar which maps values to colors.
    fig.colorbar(thickness, shrink=0.5, aspect=5)

It outputs:

As you can see, the colour gradient is not continuous despite the graph having many fine details and slight fluctuations in height which is not represented by the color map. Is it possible to achieve a continuous color gradient with surface plot using matplotlib like the image below?

Thank you.

CodePudding user response：

You can use the colormap hsv to get the same result.

import cv2
import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm

from skimage import io, color
import glob
from PIL import Image

lab = cv2.imread('Lenna.png')
lab = cv2.cvtColor(lab, cv2.COLOR_BGR2LAB)

fig = plt.figure()
ax = plt.axes(projection='3d')
y = range(lab.shape[0])
x = range(lab.shape[1])
X, Y = np.meshgrid(x, y)

ax.view_init(elev=60., azim=60)

thickness = ax.plot_surface(
        X,
        Y,
        lab[:, :, 0],  # change value here to adjust the height
        cmap=plt.get_cmap('hsv'),
        antialiased=False)

# Add a color bar which maps values to colors.
fig.colorbar(thickness, shrink=0.5, aspect=5)

plt.show()

output :

Take a look at the documentation for more colormaps.