OpenCV

Tags:

• python

3 minute read

Gregor von Laszewski (laszewski@gmail.com)

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Learning Objectives

• Provide some simple calculations so we can test cloud services.
• Showcase some elementary OpenCV functions
• Show an environmental image analysis application using Secchi disks

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OpenCV (Open Source Computer Vision Library) is a library of thousands of algorithms for various applications in computer vision and machine learning. It has C++, C, Python, Java, and MATLAB interfaces and supports Windows, Linux, Android, and Mac OS. In this section, we will explain the basic features of this library, including the implementation of a simple example.

Overview

OpenCV has many functions for image and video processing. The pipeline starts with reading the images, low-level operations on pixel values, preprocessing e.g. denoising, and then multiple steps of higher-level operations which vary depending on the application. OpenCV covers the whole pipeline, especially providing a large set of library functions for high-level operations. A simpler library for image processing in Python is Scipy’s multi-dimensional image processing package (scipy.ndimage).

Installation

OpenCV for Python can be installed on Linux in multiple ways, namely PyPI(Python Package Index), Linux package manager (apt-get for Ubuntu), Conda package manager, and also building from source. You are recommended to use PyPI. Here’s the command that you need to run:

$ pip install opencv-python

This was tested on Ubuntu 16.04 with a fresh Python 3.6 virtual environment. In order to test, import the module in Python command line:

import cv2

If it does not raise an error, it is installed correctly. Otherwise, try to solve the error.

For installation on Windows, see:

• https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_setup/py_setup_in_windows/py_setup_in_windows.html#install-opencv-python-in-windows

Note that building from source can take a long time and may not be feasible for deploying to limited platforms such as Raspberry Pi.

A Simple Example

In this example, an image is loaded. A simple processing is performed, and the result is written to a new image.

Loading an image

%matplotlib inline
import cv2

img = cv2.imread('images/opencv/4.2.01.tiff')

The image was downloaded from USC standard database:

http://sipi.usc.edu/database/database.php?volume=misc&image=9

Displaying the image

The image is saved in a numpy array. Each pixel is represented with 3 values (R,G,B). This provides you with access to manipulate the image at the level of single pixels. You can display the image using imshow function as well as Matplotlib’s imshow function.

You can display the image using imshow function:

cv2.imshow('Original',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

or you can use Matplotlib. If you have not installed Matplotlib before, install it using:

$ pip install matplotlib

Now you can use:

import matplotlib.pyplot as plt
plt.imshow(img)

which results in Figure 1

Figure 1: Image display

Scaling and Rotation

Scaling (resizing) the image relative to different axis

res = cv2.resize(img,
                 None,
                 fx=1.2,
                 fy=0.7,
                 interpolation=cv2.INTER_CUBIC)
plt.imshow(res)

which results in Figure 2

Figure 2: Scaling and rotation

Rotation of the image for an angle of t

rows,cols,_ = img.shape
t = 45
M = cv2.getRotationMatrix2D((cols/2,rows/2),t,1)
dst = cv2.warpAffine(img,M,(cols,rows))

plt.imshow(dst)

which results in Figure 3

Figure 3: image

Gray-scaling

img2 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(img2, cmap='gray')

which results in +Figure 4

Figure 4: Gray sacling

Image Thresholding

ret,thresh =    cv2.threshold(img2,127,255,cv2.THRESH_BINARY)
plt.subplot(1,2,1), plt.imshow(img2, cmap='gray')
plt.subplot(1,2,2), plt.imshow(thresh, cmap='gray')

which results in Figure 5

Figure 5: Image Thresholding

Edge Detection

Edge detection using Canny edge detection algorithm

edges = cv2.Canny(img2,100,200)

plt.subplot(121),plt.imshow(img2,cmap = 'gray')
plt.subplot(122),plt.imshow(edges,cmap = 'gray')

which results in Figure 6

Figure 6: Edge detection

Additional Features

OpenCV has implementations of many machine learning techniques such as KMeans and Support Vector Machines can be put into use with only a few lines of code. It also has functions especially for video analysis, feature detection, object recognition, and many more. You can find out more about them on their website

OpenCV(https://docs.opencv.org/3.0-beta/index.html was initially developed for C++ and still has a focus on that language, but it is still one of the most valuable image processing libraries in Python.