Python 官方文档:入门教程 => 点击学习
目录引言OpenCV和Pillow的优缺点对比读写图像读图像写图像缩放图像旋转图像引言 最近再做图像处理相关的操作的时间优化,用到了OpenCV和Pillow两个库,两个库各有优缺点
库 | 优点 | 缺点 |
---|---|---|
OpenCV | 由C和c++编写,跨平台,有着多个语言的实现,部署比较方便 | 对显示中文支持较差、python下常用函数不是太好看-_-! |
Pillow | 常用函数操作封装较好,对显示中文字体有着很好的支持 | 处理时间较慢 |
测试环境:
测试图像 :
读取图像的通道顺序区别:
获得图像shape区别:
(height, width, channel
(width, height)
示例代码:
import cv2
from PIL import Image
img_path = 'images/test_demo.png'
cv_img = cv2.imread(img_path)
height, width, channel = cv_img.shape
pillow_img = Image.open(img_path)
width, height = pillow_img.size
示例代码:
import cv2
from PIL import Image
import numpy as np
png_img_path = 'images/test_demo.png'
jpg_img_path = 'images/test_demo.jpg'
# 由jupyter notebook中魔法命令:%%timeit测得
# 169 ms ± 1.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
cv_img = cv2.imread(png_img_path)
# 52.9 ms ± 541 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
cv_img = cv2.imread(jpg_img_path)
# 300 ms ± 8.45 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
pillow_img = Image.open(png_img_path)
pillow_img = np.array(pillow_img)
# 47.4 ms ± 1.87 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
pillow_img = Image.open(jpg_img_path)
pillow_img = np.array(pillow_img)
小结:
示例代码:
save_png_path = 'output/result.png'
save_jpg_path = 'output/result.jpg'
cv_img = cv2.imread(png_img_path)
pillow_img = Image.open(png_img_path)
# 346 ms ± 11.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
cv2.imwrite(save_png_path, cv_img)
# 158 ms ± 4.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
cv2.imwrite(save_jpg_path, cv_img)
# 2.81 s ± 38.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
pillow_img.save(save_png_path)
# 51.3 ms ± 1.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
t = pillow_img.convert('RGB')
t.save(save_jpg_path)
小结:
示例代码:
png_img_path = 'images/test_demo.png'
resize_shape = (2048, 2048)
cv_img = cv2.imread(png_img_path)
pillow_img = Image.open(png_img_path)
# 6.93 ms ± 173 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
cv2.resize(cv_img, resize_shape, interpolation=cv2.INTER_CUBIC)
# 151 ms ± 2.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
pillow_img.resize(resize_shape, resample=Image.Resampling.BICUBIC)
小结: OpenCV速度完胜Pillow
示例代码:
angle = 38
# 23.6 ms ± 732 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
h, w = cv_img.shape[:2]
M = cv2.getRotationMatrix2D((w / 2, h / 2), angle, 1)
rot_img = cv2.warpAffine(cv_img, M, (w, h))
# 82.1 ms ± 2.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
rot_img_pillow = np.array(pillow_img.rotate(angle))
小结:OpenCV速度完胜Pillow
总结:
到此这篇关于Python中 OpenCV和Pillow处理图像操作及时间对比的文章就介绍到这了,更多相关python OpenCV处理图像 内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网!
--结束END--
本文标题: python中 OpenCV和Pillow处理图像操作及时间对比
本文链接: https://www.lsjlt.com/news/120625.html(转载时请注明来源链接)
有问题或投稿请发送至: 邮箱/279061341@qq.com QQ/279061341
下载Word文档到电脑,方便收藏和打印~
2024-03-01
2024-03-01
2024-03-01
2024-02-29
2024-02-29
2024-02-29
2024-02-29
2024-02-29
2024-02-29
2024-02-29
回答
回答
回答
回答
回答
回答
回答
回答
回答
回答
0