Python 练习册
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：
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), '1', 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: |
|
|---|---|
| Returns: |
An |
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)
