本文实例讲述了Python使用PIL库实现验证码图片的方法。分享给大家供大家参考,具体如下:
现在的网页中,为了防止机器人提交表单,图片验证码是很常见的应对手段之一。这里就不详细介绍了,相信大家都遇到过。
现在就给出用Python的PIL库实现验证码图片的代码。代码中有详细注释。
#!/usr/bin/env python #coding=utf-8 import random from PIL import Image, ImageDraw, ImageFont, ImageFilter _letter_cases = "abcdefghjkmnpqrstuvwxy" # 小写字母,去除可能干扰的i,l,o,z _upper_cases = _letter_cases.upper() # 大写字母 _numbers = ''.join(map(str, range(3, 10))) # 数字 init_chars = ''.join((_letter_cases, _upper_cases, _numbers)) def create_validate_code(size=(120, 30), chars=init_chars, img_type="GIF", mode="RGB", bg_color=(255, 255, 255), fg_color=(0, 0, 255), font_size=18, font_type="ae_AlArabiya.ttf", length=4, draw_lines=True, n_line=(1, 2), draw_points=True, point_chance = 2): ''' @todo: 生成验证码图片 @param size: 图片的大小,格式(宽,高),默认为(120, 30) @param chars: 允许的字符集合,格式字符串 @param img_type: 图片保存的格式,默认为GIF,可选的为GIF,JPEG,TIFF,PNG @param mode: 图片模式,默认为RGB @param bg_color: 背景颜色,默认为白色 @param fg_color: 前景色,验证码字符颜色,默认为蓝色#0000FF @param font_size: 验证码字体大小 @param font_type: 验证码字体,默认为 ae_AlArabiya.ttf @param length: 验证码字符个数 @param draw_lines: 是否划干扰线 @param n_lines: 干扰线的条数范围,格式元组,默认为(1, 2),只有draw_lines为True时有效 @param draw_points: 是否画干扰点 @param point_chance: 干扰点出现的概率,大小范围[0, 100] @return: [0]: PIL Image实例 @return: [1]: 验证码图片中的字符串 ''' width, height = size # 宽, 高 img = Image.new(mode, size, bg_color) # 创建图形 draw = ImageDraw.Draw(img) # 创建画笔 def get_chars(): '''生成给定长度的字符串,返回列表格式''' return random.sample(chars, length) def create_lines(): '''绘制干扰线''' line_num = random.randint(*n_line) # 干扰线条数 for i in range(line_num): # 起始点 begin = (random.randint(0, size[0]), random.randint(0, size[1])) #结束点 end = (random.randint(0, size[0]), random.randint(0, size[1])) draw.line([begin, end], fill=(0, 0, 0)) def create_points(): '''绘制干扰点''' chance = min(100, max(0, int(point_chance))) # 大小限制在[0, 100] for w in xrange(width): for h in xrange(height): tmp = random.randint(0, 100) if tmp > 100 - chance: draw.point((w, h), fill=(0, 0, 0)) def create_strs(): '''绘制验证码字符''' c_chars = get_chars() strs = ' %s ' % ' '.join(c_chars) # 每个字符前后以空格隔开 font = ImageFont.truetype(font_type, font_size) font_width, font_height = font.getsize(strs) draw.text(((width - font_width) / 3, (height - font_height) / 3), strs, font=font, fill=fg_color) return ''.join(c_chars) if draw_lines: create_lines() if draw_points: create_points() strs = create_strs() # 图形扭曲参数 params = [1 - float(random.randint(1, 2)) / 100, 0, 0, 0, 1 - float(random.randint(1, 10)) / 100, float(random.randint(1, 2)) / 500, 0.001, float(random.randint(1, 2)) / 500 ] img = img.transform(size, Image.PERSPECTIVE, params) # 创建扭曲 img = img.filter(ImageFilter.EDGE_ENHANCE_MORE) # 滤镜,边界加强(阈值更大) return img, strs if __name__ == "__main__": code_img = create_validate_code() code_img.save("validate.gif", "GIF")
最后结果返回一个元组,第一个返回值是Image类的实例,第二个参数是图片中的字符串(比较是否正确的作用)。
最后结果返回一个元组,第一个返回值是Image类的实例,第二个参数是图片中的字符串(比较是否正确的作用)。
需要提醒的是,如果在生成ImageFont.truetype实例的时候抛出IOError异常,有可能是运行代码的电脑没有包含指定的字体,需要下载安装。
生成的验证码图片效果:
这时候,细心的同学可能要问,如果每次生成验证码,都要先保存生成的图片,再显示到页面。这么做让人太不能接受了。这个时候,我们需要使用python内置的StringIO模块,它有着类似file对象的行为,但是它操作的是内存文件。于是,我们可以这么写代码:
try: import cStringIO as StringIO except ImportError: import StringIO mstream = StringIO.StringIO() img = create_validate_code()[0] img.save(mstream, "GIF")
这样,我们需要输出的图片的时候只要使用“mstream.getvalue()”即可。比如在Django里,我们首先定义这样的url:
from django.conf.urls.defaults import * urlpatterns = patterns('example.views', url(r'^validate/$', 'validate', name='validate'), )
在views中,我们把正确的字符串保存在session中,这样当用户提交表单的时候,就可以和session中的正确字符串进行比较。
from django.shortcuts import HttpResponse from validate import create_validate_code def validate(request): mstream = StringIO.StringIO() validate_code = create_validate_code() img = validate_code[0] img.save(mstream, "GIF") request.session['validate'] = validate_code[1] return HttpResponse(mstream.getvalue(), "image/gif")
希望本文所述对大家Python程序设计有所帮助。

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Dreamweaver Mac version
Visual web development tools

WebStorm Mac version
Useful JavaScript development tools

Zend Studio 13.0.1
Powerful PHP integrated development environment