Anaconda and Python instances installed on Windows

This article mainly introduces you to the installation of Anaconda and Python tutorial in detail, very good, with reference value, need to refer to the friend

When it comes to digital image processing programming, perhaps most people will think of MATLAB, but MATLAB has its own shortcomings:

1, not open source, the price is expensive

2, the software capacity is large. Generally more than 3G, high version even up to 5G or more.

3, can only do research, not easily converted into software.

Therefore, we use the Python scripting language here to do digital image processing.

To use Python, you must first install Python, which is typically 2.7 or more, and is easy to install, whether it's on a Windows system or a Linux system.

To use Python for various development and scientific calculations, you will also need to install the corresponding package. This is very similar to MATLAB, but Matlab is called the Toolbox, and Python is called a library or a package. Based on the Python scripting language development of Digital Image processing package, in fact, many, such as Pil,pillow, OpenCV, Scikit-image and so on.

Compared to these packages, PIL and pillow only provide the most basic digital image processing, the function is limited, OPENCV is actually a C + + library, just provides the Python interface, the update speed is very slow. Until now Python has grown to version 3.5, while OPENCV only supports Python version 2.7; Scikit-image is a scipy-based image processing package that handles images as a numpy array, just like Matlab, so We finally chose scikit-image for digital image processing.

One, the required installation package

Because Scikit-image is based on scipy, installing NumPy and scipy is sure. To display the picture, you also need to install the Matplotlib package, combined, the required packages are:

Python >= 2.6Numpy >= 1.6.1Cython >= 0.21Six >=1.4scipy >=0.9matplotlib >= 1.1.0NetworkX >= 1.8Pillow >= 1.7.8dask[array] >= 0.5.0

Comparison, installation is very cumbersome, especially scipy, on Windows Basic installation is not on.

But don't be afraid, we choose an integrated installation environment on the line, it is recommended Anaconda, it put the above required packages are integrated together, so we actually only need to install Anaconda software on the line, and nothing else to pretend.

Second, download and install Anaconda

First to www.continuum.io/downloads download Anaconda, now the version has python2.7 version and python3.5 version, download the corresponding version, the corresponding system anaconda, it is actually a sh script file, About 280M or so.

This series takes windows7+python3.5 as an example, so we download the version in the Red box:

Name is: Anaconda3-2.4.1-windows-x86_64.exe

is an executable exe file, the download is complete, the direct double-click can be installed.

At the time of installation, suppose we install the D packing directory, such as:

and select all two options to write the installation path to the environment variable.

Then wait for the installation to complete.

When the installation is complete, open a command prompt for Windows:

Input Conda list can query now which libraries are installed, commonly used NumPy, scipy is listed among them. If you still have what package is not installed on, you can run

Conda Install * * * for installation. (* * * for the name of the package required)

If a package version is not up-to-date, you can update it by running Conda update.

Three, simple test

Anaconda comes with an editor Spyder that we can use later to write code.

Spyder.exe placed in the installation directory scripts inside, as mine is D:/anaconda3/scripts/spyder.exe, directly double-click to run. We can right-click to send to the desktop shortcut, it is more convenient to run later.

We simply write a program to test the success of the installation, which is used to open a picture and display it. First prepare a picture, then open the Spyder, and write the following code:

From skimage import ioimg=io.imread (' d:/dog.jpg ') io.imshow (IMG)

Change the d:/dog.jpg to your picture location

Then click on the green triangle in the toolbar above to run, and finally show

If the "Ipython console" in the lower right corner shows a picture, the installation of our operating environment is successful.

We can select "Variable Explorer" in the upper right corner to view picture information, such as

We can save this program, note that the Python script file has a suffix called py.

Iv. Sub-modules of the Skimage package

The full name of the Skimage package is scikit-image scikit (Toolkit for SciPy), which expands the scipy.ndimage and provides more image processing capabilities. It was written by the Python language and developed and maintained by the SCIPY community. The Skimage package consists of many sub-modules, each of which provides different functions. The main sub-modules are listed below:

Sub-module name	Main implementation functions
Io	Read, save, and display pictures or videos
Data	Provide some test pictures and sample data
Color	Color Space Transformation
Filters	Image enhancement, edge detection, sorting filters, automatic thresholds, etc.
Draw	Operates on basic drawing on numpy arrays, including lines, rectangles, circles, and text
Transform	Geometric transformations or other transformations, such as rotation, stretching, and radon transformations
Morphology	Morphological operations, such as opening and closing operations, skeleton extraction, etc.
Exposure	Image intensity adjustment, such as brightness adjustment, histogram equalization, etc.
Feature	Feature detection and extraction, etc.
Measure	Measurement of image properties, such as similarity or contours
Segmentation	Image segmentation
Restoration	Image recovery
Util	General functions

When using some image manipulation functions, you need to import the corresponding sub-modules, if you need to import multiple sub-modules, separated by commas, such as:

From Skimage import Io,data,color