Computer Vision in Python

Mahotas

Python Computer Vision Library

Mahotas is a library of fast computer vision algorithms (all implemented in C++ for speed) operating over numpy arrays.

Python versions 2.7, 3.4+, are supported.

Notable algorithms:

• watershed
• convex points calculations.
• hit & miss, thinning.
• Zernike & Haralick, LBP, and TAS features.
• Speeded-Up Robust Features (SURF), a form of local features.
• thresholding.
• convolution.
• Sobel edge detection.
• spline interpolation
• SLIC super pixels.

Mahotas currently has over 100 functions for image processing and computer vision and it keeps growing.

The release schedule is roughly one release a month and each release brings new functionality and improved performance. The interface is very stable, though, and code written using a version of mahotas from years back will work just fine in the current version, except it will be faster (some interfaces are deprecated and will be removed after a few years, but in the meanwhile, you only get a warning). In a few unfortunate cases, there was a bug in the old code and your results will change for the better.

Please cite the mahotas paper (see details below under Citation) if you use it in a publication.

Examples

This is a simple example (using an example file that is shipped with mahotas) of calling watershed using above threshold regions as a seed (we use Otsu to define threshold).

# import using ``mh`` abbreviation which is common:
import mahotas as mh

# Load one of the demo images
im = mh.demos.load('nuclear')

# Automatically compute a threshold
T_otsu = mh.thresholding.otsu(im)

# Label the thresholded image (thresholding is done with numpy operations
seeds,nr_regions = mh.label(im > T_otsu)

# Call seeded watershed to expand the threshold
labeled = mh.cwatershed(im.max() - im, seeds)

Here is a very simple example of using mahotas.distance (which computes a distance map):

import pylab as p
import numpy as np
import mahotas as mh

f = np.ones((256,256), bool)
f[200:,240:] = False
f[128:144,32:48] = False
# f is basically True with the exception of two islands: one in the lower-right
# corner, another, middle-left

dmap = mh.distance(f)
p.imshow(dmap)
p.show()

(This is under mahotas/demos/distance.py.)

How to invoke thresholding functions:

import mahotas as mh
import numpy as np
from pylab import imshow, gray, show, subplot
from os import path

# Load photo of mahotas' author in greyscale
photo = mh.demos.load('luispedro', as_grey=True)

# Convert to integer values (using numpy operations)
photo = photo.astype(np.uint8)

# Compute Otsu threshold
T_otsu = mh.otsu(photo)
thresholded_otsu = (photo > T_otsu)

# Compute Riddler-Calvard threshold
T_rc = mh.rc(photo)
thresholded_rc = (photo > T_rc)

# Now call pylab functions to display the image
gray()
subplot(2,1,1)
imshow(thresholded_otsu)
subplot(2,1,2)
imshow(thresholded_rc)
show()

As you can see, we rely on numpy/matplotlib for many operations.

Install

If you are using conda, you can install mahotas from conda-forge using the following commands:

conda config --add channels conda-forge
conda install mahotas

Compilation from source

You will need python (naturally), numpy, and a C++ compiler. Then you should be able to use:

pip install mahotas

You can test your installation by running:

python -c "import mahotas as mh; mh.test()"

If you run into issues, the manual has more extensive documentation on mahotas installation, including how to find pre-built for several platforms.

Citation

If you use mahotas on a published publication, please cite:

Luis Pedro Coelho Mahotas: Open source software for scriptable computer vision in Journal of Open Research Software, vol 1, 2013. [DOI]

In Bibtex format:

@article{mahotas, author = {Luis Pedro Coelho}, title = {Mahotas: Open source software for scriptable computer vision}, journal = {Journal of Open Research Software}, year = {2013}, doi = {http://dx.doi.org/10.5334/jors.ac}, month = {July}, volume = {1} }

You can access this information using the mahotas.citation() function.

Development

Development happens on github (http://github.com/luispedro/mahotas).

You can set the DEBUG environment variable before compilation to get a debug version:

export DEBUG=1
python setup.py test

You can set it to the value 2 to get extra checks:

export DEBUG=2
python setup.py test

Be careful not to use this in production unless you are chasing a bug. Debug level 2 is very slow as it adds many runtime checks.

The Makefile that is shipped with the source of mahotas can be useful too. make debug will create a debug build. make fast will create a non-debug build (you need to make clean in between). make test will run the test suite.

Links & Contacts

Documentation: https://mahotas.readthedocs.io/

Issue Tracker: github mahotas issues

Mailing List: Use the pythonvision mailing list for questions, bug submissions, etc. Or ask on stackoverflow (tag mahotas)

Main Author & Maintainer: Luis Pedro Coelho (follow on twitter or github).

Mahotas also includes code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib], Christoph Gohlke, as well as others.

Presentation about mahotas for bioimage informatics

For more general discussion of computer vision in Python, the pythonvision mailing list is a much better venue and generates a public discussion log for others in the future. You can use it for mahotas or general computer vision in Python questions.

Recent Changes

Version 1.4.12 (Oct 14 2021)

• Update to newer NumPy
• Build wheels for Python 3.9 & 3.10

Version 1.4.11 (Aug 16 2020)

• Convert tests to pytest
• Fix testing for PyPy

Version 1.4.10 (Jun 11 2020)

• Build wheels automatically (PR #114 by nathanhillyer)

Version 1.4.9 (Nov 12 2019)

• Fix FreeImage detection (issue #108)

Version 1.4.8 (Oct 11 2019)

• Fix co-occurrence matrix computation (patch by @databaaz)

Version 1.4.7 (Jul 10 2019)

• Fix compilation on Windows

Version 1.4.6 (Jul 10 2019)

• Make watershed work for >2³¹ voxels (issue #102)
• Remove milk from demos
• Improve performance by avoid unnecessary array copies in cwatershed(), majority_filter(), and color conversions
• Fix bug in interpolation

Version 1.4.5 (Oct 20 2018)

• Upgrade code to newer NumPy API (issue #95)

Version 1.4.4 (Nov 5 2017)

• Fix bug in Bernsen thresholding (issue #84)

Version 1.4.3 (Oct 3 2016)

• Fix distribution (add missing README.md file)

Version 1.4.2 (Oct 2 2016)

• Fix resize\_to return exactly the requested size
• Fix hard crash when computing texture on arrays with negative values (issue #72)
• Added distance argument to haralick features (pull request #76, by Guillaume Lemaitre)

Version 1.4.1 (Dec 20 2015)

• Add filter\_labeled function
• Fix tests on 32 bit platforms and older versions of numpy

Version 1.4.0 (July 8 2015)

• Added mahotas-features.py script
• Add short argument to citation() function
• Add max_iter argument to thin() function
• Fixed labeled.bbox when there is no background (issue #61, reported by Daniel Haehn)
• bbox now allows dimensions greater than 2 (including when using the as_slice and border arguments)
• Extended croptobbox for dimensions greater than 2
• Added use_x_minus_y_variance option to haralick features
• Add function lbp_names

Version 1.3.0 (April 28 2015)

• Improve memory handling in freeimage.write_multipage
• Fix moments parameter swap
• Add labeled.bbox function
• Add return_mean and return_mean_ptp arguments to haralick function
• Add difference of Gaussians filter (by Jianyu Wang)
• Add Laplacian filter (by Jianyu Wang)
• Fix crash in median_filter when mismatched arguments are passed
• Fix gaussian_filter1d for ndim > 2

Version 1.2.4 (December 23 2014)

• Add PIL based IO

Version 1.2.3 (November 8 2014)

• Export mean_filter at top level
• Fix to Zernike moments computation (reported by Sergey Demurin)
• Fix compilation in platforms without npy_float128 (patch by Gabi Davar)

Version 1.2.2 (October 19 2014)

• Add minlength argument to labeled_sum
• Generalize regmax/regmin to work with floating point images
• Allow floating point inputs to cwatershed()
• Correctly check for float16 & float128 inputs
• Make sobel into a pure function (i.e., do not normalize its input)
• Fix sobel filtering

Version 1.2.1 (July 21 2014)

• Explicitly set numpy.include_dirs() in setup.py [patch by Andrew Stromnov]

Version 1.2 (July 17 2014)

• Export locmax|locmin at the mahotas namespace level
• Break away ellipse_axes from eccentricity code as it can be useful on its own
• Add find() function
• Add mean_filter() function
• Fix cwatershed() overflow possibility
• Make labeled functions more flexible in accepting more types
• Fix crash in close_holes() with nD images (for n > 2)
• Remove matplotlibwrap
• Use standard setuptools for building (instead of numpy.distutils)
• Add overlay() function

Version 1.1.1 (July 4 2014)

• Fix crash in close_holes() with nD images (for n > 2)

1.1.0 (February 12 2014)

• Better error checking
• Fix interpolation of integer images using order 1
• Add resize_to & resize_rgb_to
• Add coveralls coverage
• Fix SLIC superpixels connectivity
• Add remove_regions_where function
• Fix hard crash in convolution
• Fix axis handling in convolve1d
• Add normalization to moments calculation

See the ChangeLog for older version.License

Author: Luispedro 
Source Code: https://github.com/luispedro/mahotas 
License: View license

#python #cplusplus 

What is GEEK

Buddha Community

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Python Tricks Every Developer Should Know

Python is awesome, it’s one of the easiest languages with simple and intuitive syntax but wait, have you ever thought that there might ways to write your python code simpler?

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

Let’s get started

Swapping value in Python

Instead of creating a temporary variable to hold the value of the one while swapping, you can do this instead

>>> FirstName = "kalebu"
>>> LastName = "Jordan"
>>> FirstName, LastName = LastName, FirstName 
>>> print(FirstName, LastName)
('Jordan', 'kalebu')

#python #python-programming #python3 #python-tutorials #learn-python #python-tips #python-skills #python-development

How to Remove all Duplicate Files on your Drive via Python

Today you’re going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates.

Intro

In many situations you may find yourself having duplicates files on your disk and but when it comes to tracking and checking them manually it can tedious.

Heres a solution

Instead of tracking throughout your disk to see if there is a duplicate, you can automate the process using coding, by writing a program to recursively track through the disk and remove all the found duplicates and that’s what this article is about.

But How do we do it?

If we were to read the whole file and then compare it to the rest of the files recursively through the given directory it will take a very long time, then how do we do it?

The answer is hashing, with hashing can generate a given string of letters and numbers which act as the identity of a given file and if we find any other file with the same identity we gonna delete it.

There’s a variety of hashing algorithms out there such as

• md5
• sha1
• sha224, sha256, sha384 and sha512

#python-programming #python-tutorials #learn-python #python-project #python3 #python #python-skills #python-tips

The Best Project to Start in Computer Vision with Python

GrabCut — A Google Colab NoteBook implementation for Image Matting (background removal)

Follow the article along with the complete code implementation on GitHub. Open the notebook in Google Colab, import your image(s), and run the cells!Originally published on louisbouchard.ai, read it 2 days before on my blog!

Image matting is an extremely interesting task where the goal is to find any object of interest, or human, in a picture and remove its background. This task is hard to achieve due to its complexity, finding the person, people, or objects with the perfect contour. This post reviews an exciting technique using basic computer vision algorithms to achieve this task. The GrabCut algorithm. It is swift but not very precise for complex objects like humans or animals. Nonetheless, it can be handy in specific contexts and is a perfect applied first project to start in computer vision and python! As mentioned above, the implementation uses Google Colab, thus having no requirements or setup needed, making it an exciting project to duplicate for learning.

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How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…

You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.

1. init

class AnyClass:
    def __init__():
        print("Init called on its own")
obj = AnyClass()

The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.

The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.

Init called on its own

2. add

Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,

class AnyClass:
    def __init__(self, var):
        self.some_var = var
    def __add__(self, other_obj):
        print("Calling the add method")
        return self.some_var + other_obj.some_var
obj1 = AnyClass(5)
obj2 = AnyClass(6)
obj1 + obj2

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