脚本专栏

Python list与NumPy array 区分详解
脚本专栏 2025/5/18 佚名
3 2

1. 数据类型 type()

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Yongqiang Cheng

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys

sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
current_directory = os.path.dirname(os.path.abspath(__file__))

import numpy as np
# import tensorflow as tf
import cv2
import time

print(16 * "++--")
print("current_directory:", current_directory)

PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In TensorFlow, channel is RGB. In OpenCV, channel is BGR.
print("Python list")
print("PIXEL_MEAN:", PIXEL_MEAN)
print("type(PIXEL_MEAN):", type(PIXEL_MEAN))
print("type(PIXEL_MEAN[0]):", type(PIXEL_MEAN[0]), "\n")

PIXEL_MEAN_array = np.array(PIXEL_MEAN)
print("NumPy array")
print("PIXEL_MEAN_array:", PIXEL_MEAN_array)
print("type(PIXEL_MEAN_array):", type(PIXEL_MEAN_array))
print("type(PIXEL_MEAN_array[0]):", type(PIXEL_MEAN_array[0]))
print("PIXEL_MEAN_array.dtype:", PIXEL_MEAN_array.dtype)


/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py --gpu=0
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
Python list
PIXEL_MEAN: [123.68, 116.779, 103.939]
type(PIXEL_MEAN): <type 'list'>
type(PIXEL_MEAN[0]): <type 'float'> 

NumPy array
PIXEL_MEAN_array: [123.68 116.779 103.939]
type(PIXEL_MEAN_array): <type 'numpy.ndarray'>
type(PIXEL_MEAN_array[0]): <type 'numpy.float64'>
PIXEL_MEAN_array.dtype: float64

Process finished with exit code 0

2. 数据融合 (data fusion)

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Yongqiang Cheng

from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import os
import sys

sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
current_directory = os.path.dirname(os.path.abspath(__file__))

import numpy as np
# import tensorflow as tf
import cv2
import time

print(16 * "++--")
print("current_directory:", current_directory)

PIXEL_MEAN = [123.68, 116.779, 103.939] # R, G, B. In TensorFlow, channel is RGB. In OpenCV, channel is BGR.
print("Python list")
print("PIXEL_MEAN:", PIXEL_MEAN)
print("type(PIXEL_MEAN):", type(PIXEL_MEAN))
print("type(PIXEL_MEAN[0]):", type(PIXEL_MEAN[0]), "\n")

PIXEL_MEAN_array = np.array(PIXEL_MEAN)
print("NumPy array")
print("PIXEL_MEAN_array:", PIXEL_MEAN_array)
print("type(PIXEL_MEAN_array):", type(PIXEL_MEAN_array))
print("type(PIXEL_MEAN_array[0]):", type(PIXEL_MEAN_array[0]))
print("PIXEL_MEAN_array.dtype:", PIXEL_MEAN_array.dtype, "\n")

image_array = np.array(
  [[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], [[21, 22, 23], [24, 25, 26], [27, 28, 29], [30, 31, 32]]])
print("image_array:", image_array)
print("type(image_array):", type(image_array))
print("type(image_array[0]):", type(image_array[0]))
print("image_array.dtype:", image_array.dtype, "\n")

image_array_fusion = image_array + np.array(PIXEL_MEAN)
print("image_array_fusion:", image_array_fusion)
print("type(image_array_fusion):", type(image_array_fusion))
print("type(image_array_fusion[0]):", type(image_array_fusion[0]))
print("image_array_fusion.dtype:", image_array_fusion.dtype)


/usr/bin/python2.7 /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py --gpu=0
++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--
current_directory: /home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow
Python list
PIXEL_MEAN: [123.68, 116.779, 103.939]
type(PIXEL_MEAN): <type 'list'>
type(PIXEL_MEAN[0]): <type 'float'> 

NumPy array
PIXEL_MEAN_array: [123.68 116.779 103.939]
type(PIXEL_MEAN_array): <type 'numpy.ndarray'>
type(PIXEL_MEAN_array[0]): <type 'numpy.float64'>
PIXEL_MEAN_array.dtype: float64 

image_array: [[[ 1 2 3]
 [ 4 5 6]
 [ 7 8 9]
 [10 11 12]]

 [[21 22 23]
 [24 25 26]
 [27 28 29]
 [30 31 32]]]
type(image_array): <type 'numpy.ndarray'>
type(image_array[0]): <type 'numpy.ndarray'>
image_array.dtype: int64 

image_array_fusion: [[[124.68 118.779 106.939]
 [127.68 121.779 109.939]
 [130.68 124.779 112.939]
 [133.68 127.779 115.939]]

 [[144.68 138.779 126.939]
 [147.68 141.779 129.939]
 [150.68 144.779 132.939]
 [153.68 147.779 135.939]]]
type(image_array_fusion): <type 'numpy.ndarray'>
type(image_array_fusion[0]): <type 'numpy.ndarray'>
image_array_fusion.dtype: float64

Process finished with exit code 0

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。

标签：
Python,list与NumPy,array区分,Python,list与NumPy,array
免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！ 如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com
白云城资源网 Copyright www.dyhadc.com

评论“Python list与NumPy array 区分详解”

暂无“Python list与NumPy array 区分详解”评论...
www.dyhadc.com 白云城资源网
129,905影音资源
244,626技术资源
111,817软件资源
578,645站长资源

《魔兽世界》大逃杀！60人新游玩模式《强袭风暴》3月21日上线

暴雪近日发布了《魔兽世界》10.2.6 更新内容，新游玩模式《强袭风暴》即将于3月21 日在亚服上线，届时玩家将前往阿拉希高地展开一场 60 人大逃杀对战。

艾泽拉斯的冒险者已经征服了艾泽拉斯的大地及遥远的彼岸。他们在对抗世界上最致命的敌人时展现出过人的手腕，并且成功阻止终结宇宙等级的威胁。当他们在为即将于《魔兽世界》资料片《地心之战》中来袭的萨拉塔斯势力做战斗准备时，他们还需要在熟悉的阿拉希高地面对一个全新的敌人──那就是彼此。在《巨龙崛起》10.2.6 更新的《强袭风暴》中，玩家将会进入一个全新的海盗主题大逃杀式限时活动，其中包含极高的风险和史诗级的奖励。

《强袭风暴》不是普通的战场，作为一个独立于主游戏之外的活动，玩家可以用大逃杀的风格来体验《魔兽世界》，不分职业、不分装备（除了你在赛局中捡到的），光是技巧和战略的强弱之分就能决定出谁才是能坚持到最后的赢家。本次活动将会开放单人和双人模式，玩家在加入海盗主题的预赛大厅区域前，可以从强袭风暴角色画面新增好友。游玩游戏将可以累计名望轨迹，《巨龙崛起》和《魔兽世界：巫妖王之怒 经典版》的玩家都可以获得奖励。

更新日志

友情链接

杰晶网络 DDR爱好者之家 南强小屋 黑松山资源网 白云城资源网 更多链接
白云城资源网 Copyright www.dyhadc.com