对于提供上传的服务器,需要对上传的文件进行过滤。
本文为大家提供了python通过文件头判断文件类型的方法,避免不必要的麻烦。
分享代码如下
import struct # 支持文件类型 # 用16进制字符串的目的是可以知道文件头是多少字节 # 各种文件头的长度不一样,少半2字符,长则8字符 def typeList(): return { "52617221": EXT_RAR, "504B0304": EXT_ZIP} # 字节码转16进制字符串 def bytes2hex(bytes): num = len(bytes) hexstr = u"" for i in range(num): t = u"%x" % bytes[i] if len(t) % 2: hexstr += u"0" hexstr += t return hexstr.upper() # 获取文件类型 def filetype(filename): binfile = open(filename, 'rb') # 必需二制字读取 tl = typeList() ftype = 'unknown' for hcode in tl.keys(): numOfBytes = len(hcode) / 2 # 需要读多少字节 binfile.seek(0) # 每次读取都要回到文件头,不然会一直往后读取 hbytes = struct.unpack_from("B"*numOfBytes, binfile.read(numOfBytes)) # 一个 "B"表示一个字节 f_hcode = bytes2hex(hbytes) if f_hcode == hcode: ftype = tl[hcode] break binfile.close() return ftype if __name__ == '__main__': print filetype(Your-file-path)
常见文件格式的文件头
文件格式 文件头(十六进制)
JPEG (jpg) FFD8FF
PNG (png) 89504E47
GIF (gif) 47494638
TIFF (tif) 49492A00
Windows Bitmap (bmp) 424D
CAD (dwg) 41433130
Adobe Photoshop (psd) 38425053
Rich Text Format (rtf) 7B5C727466
XML (xml) 3C3F786D6C
HTML (html) 68746D6C3E
Email [thorough only] (eml) 44656C69766572792D646174653A
Outlook Express (dbx) CFAD12FEC5FD746F
Outlook (pst) 2142444E
MS Word/Excel (xls.or.doc) D0CF11E0
MS Access (mdb) 5374616E64617264204A
以上就是本文的全部内容,希望对大家的学习有所帮助。

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.


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