恐怕我来晚了一点，但我之前也有一个类似的问题。接受的答案(@ptomato)帮助了我，但我也想张贴这个，以防它对某人有用。

''' I wanted to create a heatmap resembling a football pitch which would show the different actions performed '''

import numpy as np
import matplotlib.pyplot as plt
import random

#fixing random state for reproducibility
np.random.seed(1234324)

fig = plt.figure(12)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

#Ratio of the pitch with respect to UEFA standards 
hmap= np.full((6, 10), 0)
#print(hmap)

xlist = np.random.uniform(low=0.0, high=100.0, size=(20))
ylist = np.random.uniform(low=0.0, high =100.0, size =(20))

#UEFA Pitch Standards are 105m x 68m
xlist = (xlist/100)*10.5
ylist = (ylist/100)*6.5

ax1.scatter(xlist,ylist)

#int of the co-ordinates to populate the array
xlist_int = xlist.astype (int)
ylist_int = ylist.astype (int)

#print(xlist_int, ylist_int)

for i, j in zip(xlist_int, ylist_int):
    #this populates the array according to the x,y co-ordinate values it encounters 
    hmap[j][i]= hmap[j][i] + 1   

#Reversing the rows is necessary 
hmap = hmap[::-1]

#print(hmap)
im = ax2.imshow(hmap)

这是结果

恐怕我来晚了一点，但我之前也有一个类似的问题。接受的答案(@ptomato)帮助了我，但我也想张贴这个，以防它对某人有用。

''' I wanted to create a heatmap resembling a football pitch which would show the different actions performed '''

import numpy as np
import matplotlib.pyplot as plt
import random

#fixing random state for reproducibility
np.random.seed(1234324)

fig = plt.figure(12)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)

#Ratio of the pitch with respect to UEFA standards 
hmap= np.full((6, 10), 0)
#print(hmap)

xlist = np.random.uniform(low=0.0, high=100.0, size=(20))
ylist = np.random.uniform(low=0.0, high =100.0, size =(20))

#UEFA Pitch Standards are 105m x 68m
xlist = (xlist/100)*10.5
ylist = (ylist/100)*6.5

ax1.scatter(xlist,ylist)

#int of the co-ordinates to populate the array
xlist_int = xlist.astype (int)
ylist_int = ylist.astype (int)

#print(xlist_int, ylist_int)

for i, j in zip(xlist_int, ylist_int):
    #this populates the array according to the x,y co-ordinate values it encounters 
    hmap[j][i]= hmap[j][i] + 1   

#Reversing the rows is necessary 
hmap = hmap[::-1]

#print(hmap)
im = ax2.imshow(hmap)

这是结果

如果您正在使用1.2.x

import numpy as np
import matplotlib.pyplot as plt

x = np.random.randn(100000)
y = np.random.randn(100000)
plt.hist2d(x,y,bins=100)
plt.show()

下面是我在100万个点集上做的一个，有3个类别(红色、绿色和蓝色)。如果您想尝试这个功能，这里有一个到存储库的链接。Github回购

histplot(
    X,
    Y,
    labels,
    bins=2000,
    range=((-3,3),(-3,3)),
    normalize_each_label=True,
    colors = [
        [1,0,0],
        [0,1,0],
        [0,0,1]],
    gain=50)

如果你不想要六边形，你可以使用numpy的histogram2d函数:

import numpy as np
import numpy.random
import matplotlib.pyplot as plt

# Generate some test data
x = np.random.randn(8873)
y = np.random.randn(8873)

heatmap, xedges, yedges = np.histogram2d(x, y, bins=50)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]

plt.clf()
plt.imshow(heatmap.T, extent=extent, origin='lower')
plt.show()

这是一个50x50的热图。如果你想要，比如说512x384，你可以在调用histogram2d时放入bins=(512,384)。

例子:

这些解决方案都不适用于我的应用程序，所以我想出了这个解决方案。本质上，我在每个点上都放置了一个二维高斯分布:

import cv2
import numpy as np
import matplotlib.pyplot as plt

def getGaussian2D(ksize, sigma, norm=True):
    oneD = cv2.getGaussianKernel(ksize=ksize, sigma=sigma)
    twoD = np.outer(oneD.T, oneD)
    return twoD / np.sum(twoD) if norm else twoD

def pt2heat(pts, shape, kernel=16, sigma=5):
    heat = np.zeros(shape)
    k = getGaussian2D(kernel, sigma)
    for y,x in pts:
        x, y = int(x), int(y)
        for i in range(-kernel//2, kernel//2):
            for j in range(-kernel//2, kernel//2):
                if 0 <= x+i < shape[0] and 0 <= y+j < shape[1]:
                    heat[x+i, y+j] = heat[x+i, y+j] + k[i+kernel//2, j+kernel//2]
    return heat


heat = pts2heat(pts, img.shape[:2])
plt.imshow(heat, cmap='heat')

以下是在相关图像上叠加的点，以及生成的热图: