Python 练习册

时间：2023-03-08 22:00:02

01：将你的 QQ 头像（或者微博头像）右上角加上红色的数字，类似于微信未读信息数量那种提示效果

【图像处理】

类似于图中效果：

Python 练习册

py 2.7代码：

from PIL import Image, ImageDraw, ImageFont

def add_word(img):

    char_size = 30

    fillcolor = "#ff0000"

    draw = ImageDraw.Draw(img)

    my_font = ImageFont.truetype(r'C:\Windows\Fonts\SIMYOU.TTF', char_size)#从本地载入字体文件

    width, height = img.size

    draw.text((width - char_size,char_size-20), '', font=my_font, fill=fillcolor)

    img.save('result.jpg','JPEG')

    del draw

if __name__ == "__main__":

    img = Image.open('test.jpg')

    add_word(img)

更多：

draw.line((0, 0) + im.size, fill=128)  #画一道线

参考文档：

pillow 函数接口查询 官方文档

02：任一个英文的纯文本文件，统计其中的单词出现的个数【文本处理】

import re

def statis_words(article):

        re_pat = re.compile("\W",re.S)

        pre_article = re.sub(re_pat," ",article)

        re_pat2 = re.compile(" *",re.S)

        list_words = re_pat2.split(pre_article)

        dict_re = dict.fromkeys(list_words)

        for i in list_words:

            if not dict_re[i]:

                dict_re[i] = 0

            if i in list_words:

                dict_re[i]+=1

        for i in dict_re.iteritems():#打印

            print i

if __name__ == "__main__":

    file_path = "words.txt"

    article = ""

    with open(file_path) as f:

        for i in f.readlines():

            article += i

    statis_words(article.replace("\n",' '))

03：你有一个目录，装了很多照片，把它们的尺寸变成都不大于 iPhone5 分辨率的大小【图像处理】

import os

from PIL import Image

iPhone5_WIDTH = 1136

iPhone5_HEIGHT = 640

def resize_iPhone5_pic(path, new_path, width=iPhone5_WIDTH, height=iPhone5_HEIGHT):

    im = Image.open(path)

    w,h = im.size

    if w > width:

        h = width * h // w

        w = width

    if h > height:

        w = height * w // h

        h = height

    im_resized = im.resize((w,h), Image.ANTIALIAS)

    im_resized.save(new_path)

def walk_dir_and_resize(path):

    for root, dirs, files in os.walk(path):#递归path下所有目录

        for f_name in files:

            if f_name.lower().endswith('jpg'):

                path_dst = os.path.join(root,f_name)

                f_new_name = 'iPhone5_' + f_name

                resize_iPhone5_pic(path=path_dst, new_path=f_new_name)

if __name__ == '__main__':

    walk_dir_and_resize('./')#当前目录

核心函数  image.resize()

Image.resize(size, resample=0)

Returns a resized copy of this image.

Parameters:
  • size – The requested size in pixels, as a 2-tuple: (width, height).
  • resample – An optional resampling filter. This can be one of PIL.Image.NEAREST (use nearest neighbour), PIL.Image.BILINEAR (linear interpolation), PIL.Image.BICUBIC(cubic spline interpolation), or PIL.Image.LANCZOS (a high-quality downsampling filter). If omitted, or if the image has mode “1” or “P”, it is set PIL.Image.NEAREST.
Returns:

An Image object.

size: 图像宽度，长度

resample:

PIL.Image.NEAREST (use nearest neighbour)   最近邻插值法

PIL.Image.BILINEAR (linear interpolation),   双线性插值法

PIL.Image.BICUBIC(cubic spline interpolation), 双三次插值

or PIL.Image.LANCZOS (a high-quality downsampling filter)   Lanczos算法  采样放缩算法

缩小时 ANTIALIAS

更多图像处理请参考 opencv

reference：Image Module

04：你有一个目录，放了你一个月的日记，都是 txt，为了避免分词的问题，假设内容都是英文，请统计出你认为每篇日记最重要的词【字符串处理】【文件管理】

注：暂认为出现频率最多的为最重要的

#文件管理
#coding:utf-8

import os

import re

def analyse_article(article):

        re_pat = re.compile("(?=[\n\x21-\x7e]+)[^A-Za-z0-9]")#+|[{}【】。,;“‘”?]")#("^([\u4e00-\u9fa5]+|[a-zA-Z0-9]+)$")#("(?=[\x21-\x7e]+)[^A-Za-z0-9]+|["{}【】。,;’“‘”?"]")#("[\W\u4e00-\u9fa5] ",re.S)  \s  空格符

        pre_article = re.sub(re_pat," ",article)

        chinese_symbol = ["\xa1\xa3","\xa1\xb0","\xa1\xb1","\xa3\xac","\xa1\xbe","\xa1\xbf","\xa1\xb6","\xa1\xb7","\xa3\xba","\xa3\xbb"]#中文标点

        for i in chinese_symbol:

            pre_article = pre_article.replace(i," ")

        re_pat2 = re.compile(" *",re.S)

        list_words = re_pat2.split(pre_article)

        dict_re = dict.fromkeys(list_words)

        #print pre_article

        for i in list_words:

            if not dict_re[i]:

                dict_re[i] = 0

            if i in list_words:

                dict_re[i]+=1

        if dict_re.get(""):

            del dict_re[""]

        key_words = sorted(dict_re.items(),key = lambda e:e[1])[-1]

        return (key_words[0], key_words[1])

def walk_dir_and_analyse(path):

    text = ""

    key_words_list = []

    for root, dirs, files in os.walk(path):#递归path下所有目录

        for f_name in files:

            if f_name.lower().endswith('txt'):

                with open(os.path.join(root,f_name)) as f:

                    for i in f.readlines():

                        text += i

                    key_words_list.append(analyse_article(text))

    for i in key_words_list:

        print "\""+ i[0] + "\" for "+ str(i[1]) +" times"

if __name__ == "__main__":

    walk_dir_and_analyse("./")

输出

>python 4.py
"春眠不觉晓" for 2 times

05:敏感词文本文件 filtered_words.txt，当用户输入敏感词语，则用星号 * 替换，例如当用户输入「北京是个好城市」，则变成「**是个好城市」。

#[字符串处理]

#敏感词文本文件 filtered_words.txt，

#里面的内容为以下内容，当用户输入敏感词语时，

#python ' '中将自动加入结尾符号，要注意字串实际长度，包括读入txt文件时的字符串长度

#coding:utf-8

def words_filter(path,words_list):

    content = ""

    with open(path) as f:

        for i in f.readlines():

            for j in words_list:

                if j in i:

                    i = i.replace(j,"*"*(len(j)/(len('单')-1)))  #一个中文两个字节长度

            content += i

        return content

if __name__ == "__main__":

    word_path = "filtered_words.txt"

    path = "words.txt"

    words_list = []

    with open(word_path) as f:

        for i in f.readlines():

            words_list.append(i.replace("\n",""))

    print words_filter(path,words_list)

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