We use it to count the number of cars passing through a toll booth.
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In the second post in this series I’ll show you how to update the code to work with your Raspberry Pi and camera board - and how to extend your home surveillance system to capture any detected motion and upload it to your personal Dropbox.Īnd maybe at the end of all this we can catch James red handed… A little bit about background subtractionīackground subtraction is critical in many computer vision applications. This example will work with both pre-recorded videos and live streams from your webcam however, we’ll be developing this system on our laptops/desktops. The remainder of this article will detail how to build a basic motion detection and tracking system for home surveillance using computer vision techniques. This is the first post in a two part series on building a motion detection and tracking system for home surveillance. Looking for the source code to this post? Jump Right To The Downloads Section A 2-part series on motion detection I mounted a Raspberry Pi to the top of my kitchen cabinets to automatically detect if he tried to pull that beer stealing shit again: He is my only (ex-)friend who drinks IPAs. In reality, I didn’t really see him drink the beer as my face was buried in my laptop, fingers floating above the keyboard, feverishly pounding out tutorials and articles. And after calling it quits for the night, all I wanted was to do relax and watch my all-time favorite movie, Jurassic Park, while sipping an ice cold Finestkind IPA from Smuttynose, a brewery I have become quite fond of as of late.īut that son of a bitch James had come over last night and drank my last beer. My brain was fried, practically leaking out my ears like half cooked scrambled eggs. You see, I had just spent over 12 hours writing content for the upcoming PyImageSearch Gurus course. But I muttered them to myself in an exasperated sigh of disgust as I closed the door to my refrigerator. These are words a man should never, ever have to say. This is a primary reason that Python is slow compared to C for operations on numerical data.That son of a bitch. The dynamic typing means that there are a lot more steps involved with any operation.
set c->PyObject_HEAD->typecode to integer result of this is result, and is an integer. Set b->PyObject_HEAD->typecode to integer Set a->PyObject_HEAD->typecode to integer
The sequence of events looks roughly like this: Python Addition ¶ Finally it must create and initialize a new Python object to hold the return value. So the interpreter must inspect PyObject_HEAD for each variable to find the type information, and then call the appropriate summation routine for the two types. Here the interpreter knows only that 1 and 2 are objects, but not what type of object they are. So whether you're a novice or experienced programmer, I hope you'll learn something from the following exploration. Along the way, we'll take a look at using Python's standard library to introspect the goings-on of CPython itself. So I decided I would write this post, and dive into the details that I usually gloss over. The jargon does little to enlighten people about what's actually going on "under the hood", so to speak.
And then I talk about how to get around this by using NumPy, SciPy, and related tools for vectorization of operations and calling into compiled code, and go on from there.īut I realized something recently: despite the relative accuracy of the above statements, the words "dynamically-typed-interpreted-buffers-vectorization-compiled" probably mean very little to somebody attending an intro programming seminar. When I teach courses on Python for scientific computing, I make this point very early in the course, and tell the students why: it boils down to Python being a dynamically typed, interpreted language, where values are stored not in dense buffers but in scattered objects. We've all heard it before: Python is slow.
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