如何在 matplotlib.pyplot 填充函数后识别其颜色的某些坐标（x，y）？

Question

有什么方法可以将某个坐标（x,y）输入到pyplot并输出其填充颜色作为回报？

例如。 (0,0) -> 红色 , (0.75,0) -> 蓝色 , (1,1) ->白色

import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(15,15))
x,y,x2,y2=[],[],[],[]
for i in np.linspace(0,2,300):
    x.append(np.cos(np.pi*i))
    y.append(np.sin(np.pi*i))  
    x2.append(0.5*np.cos(np.pi*i))
    y2.append(0.5*np.sin(np.pi*i))

plt.fill(x,y,'b')
plt.fill(x2,y2,'r')

Color Image

Answer 1

这是一种可能的方法。让我们从修改后的代码（基于您的）开始：

# Modified code
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry import Point, Polygon  # additional code

plt.figure(figsize=(15,15))
x,y,x2,y2=[],[],[],[]
for i in np.linspace(0,2,300):
    x.append(np.cos(np.pi*i))
    y.append(np.sin(np.pi*i))  
    x2.append(0.5*np.cos(np.pi*i))
    y2.append(0.5*np.sin(np.pi*i))

# grab the fill objects for use later
# .fill return: matplotlib.patches.Polygon object
blue_pgn = plt.fill(x,y,'b')  #Plot filled polygons
red_pgn = plt.fill(x2,y2,'r')

plt.show()

它产生与您的代码相同的情节，但也公开了 2 个有用的对象，blue_pgn 和 red_pgn 。

这是第二部分：

# Create geometries from objects in the plot
blue_geom = Polygon(blue_pgn[0].get_xy())
red_geom =  Polygon(red_pgn[0].get_xy())

# create a function
def from_xy_to_color(x,y):
    point_xy = Point(x,y)
    if red_geom.contains(point_xy):
        print("xy:",x,y,", color:","Red")
    elif blue_geom.contains(point_xy):
        print("xy:",x,y,", color:","Blue")
    else:
        print("xy:",x,y,", color:","White")

# test all the points
xys = [[0,0], [.75,0], [1,1]]
for xy in xys:
    #print(xy)
    from_xy_to_color(*xy)

这部分的输出：

xy: 0 0 , color: Red
xy: 0.75 0 , color: Blue
xy: 1 1 , color: White

Answer 2

from matplotlib.backends.backend_agg import FigureCanvasAgg
import numpy as np
import matplotlib.pyplot as plt


def circle_coords():
    t = np.linspace(0, 2, 300) * np.pi
    x = np.cos(t)
    y = np.sin(t)
    return x, y


def plot(fig):
    ax = fig.gca()
    x, y = circle_coords()
    ax.fill(x, y, 'b')
    ax.fill(x / 2, y / 2, 'r')


def capture():
    plt.Figure((4, 4), dpi=20)
    fig = plt.gcf()
    canvas = FigureCanvasAgg(fig)
    plot(fig)
    canvas.draw()
    r = canvas_to_numpy(canvas)
    plt.close()
    return r


def canvas_to_numpy(canvas):
    s, (width, height) = canvas.print_to_buffer()
    x = np.frombuffer(s, np.uint8)
    return x.reshape((height, width, 4))


def random_points(shape, n_points=4):
    height, width = shape
    x = np.random.uniform(0, width, size=n_points)
    y = np.random.uniform(0, height, size=n_points)
    return np.vstack([x, y]).T


def main():
    arr = capture()

    p = [[360, 274],
         [379, 48],
         [117, 216]]

    fig = plt.gcf()
    ax = fig.gca()

    np.set_printoptions(precision=2)
    print(p)
    for x, y in p:
        r, g, b, a = arr[y, x] / 255
        c = f"{r, g, b}"
        print(arr[y, x])
        ax.text(x, y, c)
        ax.scatter(x, y, c="yellow")
    plt.imshow(arr)
    plt.show()
    plt.close()


main()

见：Matplotlib figure to image as a numpy array