Machine Learning Tutorials. Utilizing both a sliding window and an image pyramid we are able to detect objects in images at various scales and locations. To learn more about the role sliding windows play in object classification and image classification, read on.
For each of these windows, we would normally take the window region and apply an image classifier to determine if the window has an object that interests us — in this case, a face. Combined with image pyramids we can create image classifiers that can recognize objects at varying scales and locations in the image. These techniques, while simple, play an absolutely critical role in object detection and image classification.
But first ensure that you have OpenCV and imutils installed:.Lecture 18.2 — Application Example Photo OCR - Sliding Windows — [ Machine Learning - Andrew Ng ]
Remember the helpers. The second argument is the stepSize. Normally, we would not want to loop over each and every pixel of the image i. On Lines we import our necessary packages. Lines handle parsing our command line arguments. From there, Line 14 loads our image off disk and Line 15 defines our window width and height to be pixels, respectfully.
To see our image pyramid and sliding window in action, open up a terminal and execute the following command:. And again, if we had an image classifier ready to go, we could take each of these windows and classify the contents of the window.
Once again, we can see that the sliding window is slid across the image at each level of the pyramid. High levels of the pyramid and thus smaller layers have fewer windows that need to be examined.
In this blog post, we learned all about sliding windows and their application to object detection and image classification. By combining a sliding window with an image pyramid we are able to localize and detect objects in images at multiple scales and locations.
Sliding window in Python
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Default Python iteration can be considered a special case, where the window length is 1. I'm currently using the following code. Does anyone have a more Pythonic, less verbose, or more efficient method for doing this? There's one in an old version of the Python docs with itertools examples :.
The one from the docs is a little more succinct and uses itertools to greater effect I imagine. This seems tailor-made for a collections. However, even if you use a list you shouldn't be slicing twice; instead, you should probably just pop 0 from the list and append the new item. In my tests it handily beats everything else posted here most of the time, though pillmuncher's tee version beats it for large iterables and small windows. On larger windows, the deque pulls ahead again in raw speed.
Access to individual items in the deque may be faster or slower than with lists or tuples. Items near the beginning are faster, or items near the end if you use a negative index. I was able to recover some speed by using negative indexes for the last five terms in the addition, but tee was still a little faster.
Overall I would estimate that either one is plenty fast for most uses and if you need a little more performance, profile and pick the one that works best. Here's a generalization that adds support for stepfillvalue parameters:.
It yields in chunks size items at a time rolling step positions per iteration padding each chunk with fillvalue if necessary. For an example of use case for the step parameter, see Processing a large. Since the current python docs don't have "window" in the itertool examples i.
Basically, we create a series of sliced iterators, each with a starting point one spot further forward. Then, we zip these together. Note, this function returns a generator it is not directly a generator itself. Much like the appending-element and advancing-iterator versions above, the performance i. I like this one because it is a two-liner it could be a one-liner, but I prefer naming concepts.
It turns out that the above code is wrong. It works if the parameter passed to iterable is a sequence but not if it is an iterator. If it is an iterator, the same iterator is shared but not tee'd among the islice calls and this breaks things badly. Also, one more version for the books. Instead of copying an iterator and then advancing copies many times, this version makes pairwise copies of each iterator as we move the starting position forward.
Just to show how you can combine itertools recipesI'm extending the pairwise recipe as directly as possible back into the window recipe using the consume recipe:. The window recipe is the same as for pairwiseit just replaces the single element "consume" on the second tee -ed iterator with progressively increasing consumes on n - 1 iterators. Using consume instead of wrapping each iterator in islice is marginally faster for sufficiently large iterables since you only pay the islice wrapping overhead during the consume phase, not during the process of extracting each window-ed value so it's bounded by nnot the number of items in iterable.
Performance-wise, compared to some other solutions, this is pretty good and better than any of the other solutions I tested as it scales. Tested on Python 3. Same as consumebut inlining else case of consume to avoid function call and n is None test to reduce runtime, particularly for small inputs where the setup overhead is a meaningful part of the work:. Side-note: A variant on pairwise that uses tee with the default argument of 2 repeatedly to make nested tee objects, so any given iterator is only advanced once, not independently consumed an increasing number of times, similar to MrDrFenner's answer is similar to non-inlined consume and slower than the inlined consume on all tests, so I've omitted it those results for brevity.
As you can see, if you don't care about the possibility of the caller needing to store results, my optimized version of kindall's solution wins most of the time, except in the "large iterable, small window size case" where inlined consume wins ; it degrades quickly as the iterable size increases, while not degrading at all as the window size increases every other solution degrades more slowly for iterable size increases, but also degrades for window size increases.
It can even be adapted for the "need tuples" case by wrapping in map tuple,Size of the moving window. This is the number of observations used for calculating the statistic.
Each window will be a fixed size. If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period.
Simple Sliding Window Iterator in Python
This is only valid for datetimelike indexes. Minimum number of observations in window required to have a value otherwise result is NA. Provide a window type. If Noneall points are evenly weighted. See the notes below for further information.
Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window. Remaining cases not implemented for fixed windows. By default, the result is set to the right edge of the window. To learn more about different window types see scipy.
Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. Home What's New in 1. DataFrame pandas. T pandas. Parameters window int, offset, or BaseIndexer subclass Size of the moving window. Returns a Window or Rolling sub-classed for the particular operation See also expanding Provides expanding transformations.
Timestamp ' 'Timestamp ' ' ].A common task encountered in bioinformatics is the need to process a sequence bit-by-bit, sometimes with overlapping regions. I have provided an example of a very simple; easy to extend; and stand-alone python iterator that returns a single defined window of any python string object per iteration to allow simple, intuitive handling of sliding window tasks. The code is simple to understand, and does not depend on other packages.
Consider the task of scanning a sequence of length l for a pattern representing a transcription factor binding site TFBS or other feature-of-interest. This type of process is called a sliding window.
It has an l of If the feature that we are interested in has been described to usually be 5 bp long, we would define our k window size to be 5. I have diagrammed the result of the sliding window procedure for our hypothetical sequence below. This is a perfect job for a python iterator, and the code that I am providing here is basically that technically it returns a generator.
Its a simple Python function that when initiated with a sequence, a window size, and a step size returns an object that behaves as if it is a list that emits each defined sequence chunk when called in a for loop. This way you can use something like the following code to access each chunk and do with it what you will.
This is very similar to the technique we used to parse FastQ files but uses the yield statement to convert a simple function into a generator factory thanks to Jake and gasstationwithoutpumps for suggesting this trick that had slipped by me! This and other python code lives in a gitHub repository. I am not claiming that this code is perfect or even better than average. It works well for me and I think it may be useful to others, but as with all code you simply copy off of a webpage: you use it at your own risk.
That said, I am always eager to learn better python. So if any other master python-monks feel like commenting on where I could improve, I am always happy to hear about it! Are you trying for backward compatibility with earlier versions of python? I had the same immediate response. Class-based iterators are fine if you have a data structure which needs an iterator added to it, but if you are just iterating over an existing object like a string, a generator function is simpler and clearer.
That is probably my old C habits kicking in, though, as the increase in speed would be tiny and the loss of clarity and hence chance of error could be large. One interesting change would be to make this work for any iterable, not just strings. The yielded value would be a list that sliced the input iterable. Once again: thanks for the input. Code and repo updated.
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It only takes a minute to sign up. This is accelerometer data where the data frame columns are labeled "Time stamp""Time skipped""x""y""z"and "label" with the index set to "Time stamp". Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Sliding window time series data with Python Pandas data frame Ask Question. Asked 2 years, 9 months ago. Active 2 years, 9 months ago. Viewed 13k times. The sampling rate is around Hz.
How should I create a sliding window in this case? Active Oldest Votes. Brian Spiering Brian Spiering 7, 12 12 silver badges 39 39 bronze badges. Pandas might automagically do that for you. I would be explicit about datetime casting. It is tricky. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.
Sliding Window Protocol
It only takes a minute to sign up. I'm creating a small library of Python utilities, and I'd like feedback on a function which allows iterating over an arbitrary iterable in a sliding-window fashion.
Looks pretty good to me. You are taking advantage of the standard library, are following the relevant style guides and you even have tests, good job! Personally, I am more in favor of using import fromat least for well-known standard library functions and maybe for utility functions that are used everywhere throughout a project.
This way you can more often fit the code on one line easily, making it more readable.
One small improvement is that your initial slice can be one larger, the argument behaves just like a range or slice argument and is exclusive of the end i. However, this way you need to deal with the last element in a special way, so YMMV:.
This also reveals one behavior which you should at least be aware of which seems to be the case, since you have a test for it. In any case, the chosen behavior should be documented in the docstring. There are a couple of ways to do this. I thought using deque like the original code would be slow, but it is actually faster than most I tried.
Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Sliding window iteration in Python Ask Question. Asked 22 days ago. Active 21 days ago. Viewed times. Relevant parts of iteration. Are there any potential performance problems? Am I reinventing a wheel and something like this already exists? I'll welcome any other feedback too, of course. Angew is no longer proud of SO Angew is no longer proud of SO 4 4 silver badges 16 16 bronze badges.
See rolling or more-itertools on PyPI. Active Oldest Votes. Graipher Graipher I actually consider "swallow data if too short" a feature, as my original motivation for this utility was computing differences of adjacent elements etc.
On the other hand, I can imagine applications where yielding the shorter one would be preferable. Whether you add it or not, the default behaviour should be documented in the docstring. RootTwo RootTwo 4, 8 8 silver badges 19 19 bronze badges.Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree. This Lib looks exactly to what I was looking for, but the lack of documentation makes it difficult to get started.
Also Spring Boot integration will be quite handy. Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. My use case is that I have a numpy array and want to apply a rolling standard deviation window on one of the columns and put it in another column of this numpy array.
Identify vehicles in a video from a front-facing camera on a car using computer vision and machine learning classification techniques. Add a description, image, and links to the sliding-windows topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the sliding-windows topic, visit your repo's landing page and select "manage topics.
Learn more. Skip to content. Here are 72 public repositories matching this topic Language: All Filter by language. Sort options. Star 1. Code Issues Pull requests. Updated Oct 31, Jupyter Notebook. Star Open some docs required. Read more. Updated Feb 1, Python. Open Performance Comparisons. Hi, thanks for the good effort you have put in this project already. May I ask for an enhancement of the documentation?