Why Python for Computer Vision?
In the early days of computer vision, most programs were developed in C/C++. As computer hardware became faster and more open-source platforms emerged, developers gained more flexibility in choosing the language for their computer vision projects. The efficiency and ease of writing code have become important factors when selecting a programming language. While performance is still a consideration, the convenience and speed of development have also played a major role.
Python has become a popular choice among developers due to its cross-platform compatibility, openness, ease of use, and rich ecosystem of libraries. A vast number of books on Python programming have been published globally, with over 5,712 titles listed on Amazon as of May 28, 2016. Additionally, courses such as "Getting Started with Python" are available on platforms like Coursera and Edx, including those taught by MIT professors. In China, numerous learning resources and online courses have also been introduced. Beyond just learning materials, Python comes with powerful modules for image processing, mathematical computation, and data mining, along with extensive online support.
This article focuses on the Windows platform and explains how to set up a Python-based computer vision development environment. It covers the basic methods for downloading, installing software, configuring the environment, and provides simple code examples. It's important to note that the installation process can vary depending on the system and software versions, making it challenging to achieve a one-step solution.
Downloading and Installing the Package
Common configurations for a Python-based computer vision environment include:
- **Python**: Python 2.7 or Python 3.x
- **Pip**: A package manager used to install third-party libraries easily.
- **NumPy**: A module for handling vectors and matrices in Python.
- **SciPy**: An advanced math library for scientific computing.
- **Matplotlib**: A visualization tool for plotting results.
- **PIL (Pillow)**: A library for basic image processing tasks.
- **LIBSVM**: An open-source machine learning library.
- **OpenCV**: A widely used open-source computer vision library with Python bindings, supporting Python 2.7.
**Choosing the Python Version**
Deciding between Python 2 and Python 3 can be confusing. Python 2 has broader support and fewer issues, but Python 3 represents the future with significant improvements. However, some third-party libraries may not yet fully support Python 3. Since Python 3 is not backward compatible with Python 2, it’s recommended for beginners to start with Python 2.7 and transition to Python 3 once more libraries are updated.
**Installing Python and Setting Environment Variables**
Download the version that matches your operating system and run the installer. For example, you might choose the default installation path: `C:\Python27`. After installation, add the Python directory to the system's PATH variable. This allows you to run Python directly from the command line. To do this, go to "Computer > Right-click > Properties > Advanced System Settings," then edit the PATH variable to include `;C:\Python27`.
**Installing pip**
Pip is a powerful package manager that simplifies the installation and management of third-party Python libraries. The latest version at the time was pip 8.1.2. To install pip, download the archive file, extract it, and navigate to the folder using the command prompt. Run the following command:
```
python setup.py install
```
After installation, add the `C:\Python27\Scripts` directory to the system PATH so that pip can be used from anywhere. Once configured, you can use commands like `pip install numpy` to install required libraries quickly.
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