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python - 将 numba 添加到 python 脚本

转载 作者:太空宇宙 更新时间:2023-11-03 20:20:51 25 4
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我尝试将 numba 应用于以下脚本以减少运行时间,但没有成功。关于如何做到这一点有什么建议吗?我正在spyder/anaconda 中使用python 3.7。

import timeit
start = timeit.default_timer()


from PIL import Image, ImageDraw # Importerer biblioteket


# Load image:
input_image = Image.open("beatles.jpg") # Loads an image from the hard drive
input_pixels = input_image.load()

# Box Blur kernel
box_kernel = [[1 / 9, 1 / 9, 1 / 9], [1 / 9, 1 / 9, 1 / 9], [1 / 9, 1 / 9, 1 / 9]]

# Select kernel here:
kernel = box_kernel

# Middle of the kernel
offset = len(kernel) // 2

# Creating the output image
output_image = Image.new("RGB", input_image.size)
draw = ImageDraw.Draw(output_image)


# Beregne konvolusjon mellom intensity (original bilde) og kernel
for x in range(offset, input_image.width - offset):
for y in range(offset, input_image.height - offset):
acc = [0, 0, 0]
for a in range(len(kernel)):
for b in range(len(kernel)):
xn = x + a - offset
yn = y + b - offset
pixel = input_pixels[xn, yn]
acc[0] += pixel[0] * kernel[a][b]
acc[1] += pixel[1] * kernel[a][b]
acc[2] += pixel[2] * kernel[a][b]

draw.point((x, y), (int(acc[0]), int(acc[1]), int(acc[2])))

output_image.save("Beatles box blur.png")

stop = timeit.default_timer()

print('Time: ', stop - start)

最佳答案

我在这里快速做了一些事情,希望它可以帮助您开始:

import time
import numpy as np

from numba import jit


def numba_off(offset, width, height, kernel):
for x in range(offset, width - offset):
for y in range(offset, height - offset):
acc = [0, 0, 0]
for a in range(len(kernel)):
for b in range(len(kernel)):
pixel = [1, 1, 1]
acc[0] += pixel[0] * kernel[a][b]
acc[1] += pixel[1] * kernel[a][b]
acc[2] += pixel[2] * kernel[a][b]


@jit(nopython=True)
def numba_on(offset, width, height, kernel):
for x in range(offset, width - offset):
for y in range(offset, height - offset):
acc = [0, 0, 0]
for a in range(len(kernel)):
for b in range(len(kernel)):
pixel = [1, 1, 1]
acc[0] += pixel[0] * kernel[a][b]
acc[1] += pixel[1] * kernel[a][b]
acc[2] += pixel[2] * kernel[a][b]


box_kernel = np.array([[1 / 9, 1 / 9, 1 / 9], [1 / 9, 1 / 9, 1 / 9], [1 / 9, 1 / 9, 1 / 9]])
kernel = box_kernel
offset = len(kernel) // 2

start = time.time()
numba_off(offset, 1000, 1000, kernel)
end = time.time()
print("Elapsed (without) = %s" % (end - start))

start = time.time()
numba_on(offset, 1000, 1000, kernel)
end = time.time()
print("Elapsed (with compilation) = %s" % (end - start))
start = time.time()
numba_on(offset, 1000, 1000, kernel)
end = time.time()
print("Elapsed (after compilation) = %s" % (end - start))

您应该能够在此基础上继续发展。阅读以下内容可能对您有好处:5 minute numba guide

关于python - 将 numba 添加到 python 脚本,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/58167886/

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