我有一个用 numpy 绘制径向渐变的代码。到目前为止它看起来像这样:
import numpy as np
import matplotlib.pyplot as plt
arr = np.zeros((256,256,3), dtype=np.uint8)
imgsize = arr.shape[:2]
innerColor = (0, 0, 0)
outerColor = (255, 255, 255)
for y in range(imgsize[1]):
for x in range(imgsize[0]):
#Find the distance to the center
distanceToCenter = np.sqrt((x - imgsize[0]//2) ** 2 + (y - imgsize[1]//2) ** 2)
#Make it on a scale from 0 to 1innerColor
distanceToCenter = distanceToCenter / (np.sqrt(2) * imgsize[0]/2)
#Calculate r, g, and b values
r = outerColor[0] * distanceToCenter + innerColor[0] * (1 - distanceToCenter)
g = outerColor[1] * distanceToCenter + innerColor[1] * (1 - distanceToCenter)
b = outerColor[2] * distanceToCenter + innerColor[2] * (1 - distanceToCenter)
# print r, g, b
arr[y, x] = (int(r), int(g), int(b))
plt.imshow(arr, cmap='gray')
plt.show()
最佳答案
您可以使用矢量化非常有效地计算距离,而无需 for 循环:
x_axis = np.linspace(-1, 1, 256)[:, None]
y_axis = np.linspace(-1, 1, 256)[None, :]
arr = np.sqrt(x_axis ** 2 + y_axis ** 2)
或者你可以使用网格:
x_axis = np.linspace(-1, 1, 256)
y_axis = np.linspace(-1, 1, 256)
xx, yy = np.meshgrid(x_axis, y_axis)
arr = np.sqrt(xx ** 2 + yy ** 2)
并再次使用广播在inner
和outer
颜色之间进行插值
inner = np.array([0, 0, 0])[None, None, :]
outer = np.array([1, 1, 1])[None, None, :]
arr /= arr.max()
arr = arr[:, :, None]
arr = arr * outer + (1 - arr) * inner
关于python - 使用 numpy 绘制圆形渐变,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/55159007/