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Over the past year the PyImageSearch blog has had a lot of popular blog posts. Using chiliad-means clustering to detect the dominant colors in an image was (and still is) hugely popular. One of my personal favorites, building a kick-ass mobile document scanner has been the virtually popular PyImageSearch article for months. And the showtime (large) tutorial I always wrote, Hobbits and Histograms, an article on edifice a simple image search engine, still gets a lot of hits today.
But by far , the most pop postal service on the PyImageSearch blog is my tutorial on installing OpenCV and Python on your Raspberry Pi 2 and B+. It'southward really, actually awesome to see the love you and the PyImageSearch readers have for the Raspberry Pi community — and I plan to continue writing more articles about OpenCV + the Raspberry Pi in the futurity.
Anyway, later on I published the Raspberry Pi + OpenCV installation tutorial, many of the comments asked that I go along on and discuss how to access the Raspberry Pi camera using Python and OpenCV.
In this tutorial we'll be using picamera, which provides a pure Python interface to the camera module. And best of all, I'll be showing you how to use picamera to capture images in OpenCV format.
Read on to discover out how…
Of import: Exist certain to follow one of my Raspberry Pi OpenCV installation guides prior to following the steps in this tutorial.
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Jump Right To The Downloads Department OpenCV and Python versions:
This instance will run on Python two.seven/Python 3.4+ and OpenCV ii.4.X/OpenCV 3.0+.
Footstep i: What practice I need?
To go started, you'll need a Raspberry Pi camera board module.
I got my 5MP Raspberry Pi camera board module from Amazon for under $30, with shipping. It's difficult to believe that the photographic camera board module is most equally expensive as the Raspberry Pi itself — but information technology simply goes to show how much hardware has progressed over the by 5 years. I also picked up a camera housing to keep the camera rubber, because why not?
Assuming yous already have your camera module, y'all'll need to install it. Installation is very uncomplicated and instead of creating my ain tutorial on installing the camera lath, I'll only refer you lot to the official Raspberry Pi camera installation guide:
Assuming your photographic camera lath and properly installed and setup, it should look something like this:
Footstep two: Enable your camera module.
At present that you accept your Raspberry Pi camera module installed, yous need to enable it. Open upwardly a concluding and execute the following command:
$ sudo raspi-config
This will bring up a screen that looks similar this:
Use your arrow keys to gyre down to Option 5: Enable camera, striking your enter central to enable the photographic camera, and then arrow down to the Finish button and hit enter again. Lastly, you'll need to reboot your Raspberry Pi for the configuration to take touch on.
Step 3: Examination out the photographic camera module.
Earlier we dive into the code, permit's run a quick sanity check to ensure that our Raspberry Pi camera is working properly.
Note: Trust me, you'll want to run this sanity check before you start working with the lawmaking. It's always good to ensure that your camera is working prior to diving into OpenCV code, otherwise you could easily waste product time wondering when your lawmaking isn't working correctly when it'southward simply the camera module itself that is causing you problems.
Anyhow, to run my sanity check I connected my Raspberry Pi to my Goggle box and positioned information technology such that it was pointing at my couch:
And from at that place, I opened upwards a concluding and executed the following control:
$ raspistill -o output.jpg
This command activates your Raspberry Pi photographic camera module, displays a preview of the epitome, then afterward a few seconds, snaps a motion picture, and saves it to your current working directory every bit output.jpg .
Here's an example of me taking a photograph of my Idiot box monitor (so I could document the process for this tutorial) as the Raspberry Pi snaps a photo of me:
And here's what output.jpg looks like:
Clearly my Raspberry Pi photographic camera module is working correctly! At present nosotros can motility on to the some more exciting stuff.
Step 4: Installing picamera.
So at this point nosotros know that our Raspberry Pi photographic camera is working properly. But how do nosotros interface with the Raspberry Pi photographic camera module using Python?
The reply is the picamera module.
Remember from the previous tutorial how we utilized virtualenv and virtualenvwrapper to cleanly install and segment our Python packages from the the organisation Python and packages?
Well, we're going to do the same thing here.
Before installing picamera , be sure to actuate our cv virtual environment:
$ workon cv
Note: If you are installing the the picamera module system broad, you lot can skip the previous commands. However, if you lot are following along from the previous tutorial, you'll want to make sure yous are in the cv virtual surround earlier continuing to the adjacent command.
And from there, we can install picamera by utilizing pip:
$ pip install "picamera[array]"
Important: Notice how I specified picamera[array] and not but picamera .
Why is this so important?
While the standard picamera module provides methods to interface with the camera, nosotros need the (optional) array sub-module and then that we can utilize OpenCV. Think, when using Python bindings, OpenCV represents images every bit NumPy arrays — and the assortment sub-module allows united states of america to obtain NumPy arrays from the Raspberry Pi photographic camera module.
Bold that your install finished without error, you lot now accept the picamera module (with NumPy array support) installed.
Step 5: Accessing a single image of your Raspberry Pi using Python and OpenCV.
Alright, now nosotros can finally kickoff writing some code!
Open upward a new file, proper noun it test_image.py , and insert the following code:
# import the necessary packages from picamera.assortment import PiRGBArray from picamera import PiCamera import time import cv2 # initialize the photographic camera and grab a reference to the raw camera capture camera = PiCamera() rawCapture = PiRGBArray(camera) # allow the camera to warmup time.sleep(0.1) # grab an image from the camera camera.capture(rawCapture, format="bgr") image = rawCapture.array # display the prototype on screen and await for a keypress cv2.imshow("Image", image) cv2.waitKey(0) We'll first past importing our necessary packages on Lines ii-5.
From there, we initialize our PiCamera object on Line 8 and catch a reference to the raw capture component on Line 9. This rawCapture object is especially useful since information technology (i) gives u.s.a. directly admission to the photographic camera stream and (2) avoids the expensive compression to JPEG format, which we would so take to accept and decode to OpenCV format anyhow. I highly recommend that you use PiRGBArray whenever you demand to admission the Raspberry Pi camera — the performance gains are well worth information technology.
From in that location, we sleep for a 10th of a second on Line 12 — this allows the camera sensor to warm up.
Finally, we grab the actual photo from the rawCapture object on Line 15 where we have special care to ensure our image is in BGR format rather than RGB. OpenCV represents images as NumPy arrays in BGR order rather than RGB — this little nuisance is subtle, simply very important to think as it tin can atomic number 82 to some confusing bugs in your code down the line.
Finally, we display our image to screen on Lines 19 and 20.
To execute this case, open up a terminal, navigate to your test_image.py file, and issue the post-obit control:
$ python test_image.py
If all goes as expected you should accept an image displayed on your screen:
Annotation: I decided to add this section of the blog mail service after I had finished upwards the balance of the article, so I did not take my camera setup facing the couch (I was really playing with some custom abode surveillance software I was working on). Lamentable for whatever defoliation, just rest bodacious, everything will work as advertised provided you have followed the instructions in the article!
Step 6: Accessing the video stream of your Raspberry Pi using Python and OpenCV.
Alright, so we've learned how to catch a unmarried paradigm from the Raspberry Pi camera. But what about a video stream?
Yous might judge that nosotros are going to utilise the cv2.VideoCapture function here — but I actually recommend against this. Getting cv2.VideoCapture to play nice with your Raspberry Pi is not a nice feel (you'll need to install extra drivers) and something you should by and large avoid.
And also, why would nosotros employ the cv2.VideoCapture role when we can easily admission the raw video stream using the picamera module?
Let's go ahead and take a look on how we can admission the video stream. Open up up a new file, proper noun it test_video.py , and insert the following code:
# import the necessary packages from picamera.array import PiRGBArray from picamera import PiCamera import fourth dimension import cv2 # initialize the camera and catch a reference to the raw camera capture camera = PiCamera() camera.resolution = (640, 480) camera.framerate = 32 rawCapture = PiRGBArray(photographic camera, size=(640, 480)) # permit the camera to warmup time.sleep(0.one) # capture frames from the camera for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=Truthful): # grab the raw NumPy array representing the prototype, then initialize the timestamp # and occupied/unoccupied text image = frame.array # bear witness the frame cv2.imshow("Frame", image) key = cv2.waitKey(1) & 0xFF # clear the stream in grooming for the side by side frame rawCapture.truncate(0) # if the `q` fundamental was pressed, suspension from the loop if key == ord("q"): interruption This example starts off similarly to the previous one. We commencement off past importing our necessary packages on Lines 2-v.
And from there we construct our photographic camera object on Line 8 which allows us to interface with the Raspberry Pi camera. All the same, we also accept the fourth dimension to set up the resolution of our camera (640 x 480 pixels) on Line 9 and the frame rate (i.e. frames per 2d, or merely FPS) on Line 10. We also initialize our PiRGBArray object on Line eleven, but we also take care to specify the same resolution as on Line 9.
Accessing the actual video stream is handled on Line 17 by making a call to the capture_continuous method of our camera object.
This method returns a frame from the video stream. The frame then has an assortment property, which corresponds to the frame in NumPy assortment format — all the hard work is done for us on Lines 17 and 20!
We and so take the frame of the video and display on screen on Lines 23 and 24.
An important line to pay attending to is Line 27: You lot must articulate the current frame before you move on to the next 1!
If y'all fail to clear the frame, your Python script will throw an error — so exist sure to pay close attention to this when implementing your ain applications!
Finally, if the user presses the q cardinal, we intermission course the loop and exit the plan.
To execute our script, merely open a terminal (making sure you are in the cv virtual environment, of course) and issue the post-obit command:
$ python test_video.py
Below follows an example of me executing the above command:
As you can see, the Raspberry Pi camera's video stream is beingness read past OpenCV then displayed on screen! Furthermore, the Raspberry Pi camera shows no lag when accessing frames at 32 FPS. Granted, nosotros are non doing whatsoever processing on the individual frames, but every bit I'll show in hereafter blog posts, the Pi 2 can easily keep up 24-32 FPS even when processing each frame.
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Summary
This commodity extended our previous tutorial on installing OpenCV and Python on your Raspberry Pi 2 and B+ and covered how to access the Raspberry Pi photographic camera module using Python and OpenCV.
We reviewed two methods to access the photographic camera. The first method allowed usa to access a single photo. And the 2nd method allowed us to access the raw video stream from the Raspberry Pi photographic camera module.
In reality, there are many ways to admission the Raspberry Pi camera module, as the picamera documentation details. However, the methods detailed in this blog postal service are used because (one) they are easily uniform with OpenCV and (ii) they are quite speedy. There are certainly more than one mode to skin this cat, but if you intend on using OpenCV + Python, I would advise using the code in this article as "boilerplate" for your ain applications.
In hereafter blog posts nosotros'll take these examples and use information technology to build computer vision systems toobserve motion in videos and recognize faces in images.
Be sure to sign up for the PyImageSearch Newsletter to receive updates when new Raspberry Pi and computer vision posts become live, yous definitely don't desire to miss them!
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Source: https://pyimagesearch.com/2015/03/30/accessing-the-raspberry-pi-camera-with-opencv-and-python/
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