使用numpy绘制圆形渐变

Question

我有一个用numpy绘制径向梯度的代码。到目前为止是这样的：

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()

有没有办法用numpy函数来优化代码并提高速度？ 之后应该是这样的：

Answer 1

由于对称性，实际上只需要计算图像256*256的四分之一，即64*64，然后将其逐块旋转90度，然后将它们组合起来。这样，总时间是计算256×256像素的1/4倍。在

以下是示例。在

import numpy as np
import matplotlib.pyplot as plt

##Just calculate 64*64
arr = np.zeros((64,64,3), 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 corner
        distanceToCenter = np.sqrt((x) ** 2 + (y - imgsize[1]) ** 2)

        #Make it on a scale from 0 to 1innerColor
        distanceToCenter = distanceToCenter / (np.sqrt(2) * imgsize[0])

        #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))
#rotate and combine
arr1=arr
arr2=arr[::-1,:,:]
arr3=arr[::-1,::-1,:]
arr4=arr[::,::-1,:]
arr5=np.vstack([arr1,arr2])
arr6=np.vstack([arr4,arr3])
arr7=np.hstack([arr6,arr5])
plt.imshow(arr7, cmap='gray')
plt.show()

Answer 2

您可以使用矢量化非常有效地计算距离，而不需要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)

也可以使用网格网格：

^{pr2}$

并再次使用广播在inner和outer颜色之间插值

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