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- Thonny se cv2 virtualenv how to#
- Thonny se cv2 virtualenv install#
- Thonny se cv2 virtualenv code#
- Thonny se cv2 virtualenv tv#
Thonny se cv2 virtualenv install#
Remember from the previous tutorial how we utilized virtualenv and virtualenvwrapper to cleanly install and segment our Python packages from the the system Python and packages? But how do we interface with the Raspberry Pi camera module using Python? So at this point we know that our Raspberry Pi camera is working properly.
Thonny se cv2 virtualenv tv#
Here’s an example of me taking a photo of my TV monitor (so I could document the process for this tutorial) as the Raspberry Pi snaps a photo of me: Figure 4: Sweet, the Raspberry Pi camera module is working!Īnd here’s what output.jpg looks like: Figure 5: The image captured using the raspi-still command.Ĭlearly my Raspberry Pi camera module is working correctly! Now we can move on to the some more exciting stuff.
![thonny se cv2 virtualenv thonny se cv2 virtualenv](https://i1.wp.com/www.dobitaobyte.com.br/wp-content/uploads/2021/04/micropython-ssd1306.png)
This command activates your Raspberry Pi camera module, displays a preview of the image, and then after a few seconds, snaps a picture, and saves it to your current working directory as output.jpg.
Thonny se cv2 virtualenv code#
It’s always good to ensure that your camera is working prior to diving into OpenCV code, otherwise you could easily waste time wondering when your code isn’t working correctly when it’s simply the camera module itself that is causing you problems.Īnyway, to run my sanity check I connected my Raspberry Pi to my TV and positioned it such that it was pointing at my couch: Figure 3: Example setup of my Raspberry Pi 2 and camera.Īnd from there, I opened up a terminal and executed the following command: $ raspistill -o output.jpg Note: Trust me, you’ll want to run this sanity check before you start working with the code. Step 3: Test out the camera module.īefore we dive into the code, let’s run a quick sanity check to ensure that our Raspberry Pi camera is working properly. Lastly, you’ll need to reboot your Raspberry Pi for the configuration to take affect. Use your arrow keys to scroll down to Option 5: Enable camera, hit your enter key to enable the camera, and then arrow down to the Finish button and hit enter again. This will bring up a screen that looks like this: Figure 2: Enabling the Raspberry Pi camera module using the raspi-config command. Open up a terminal and execute the following command: $ sudo raspi-config Now that you have your Raspberry Pi camera module installed, you need to enable it. IMPORTANT: Be sure to follow one of my Raspberry Pi OpenCV installation guides prior to following the steps in this tutorial.Īssuming your camera board and properly installed and setup, it should look something like this: Figure 1: Installing the Raspberry Pi camera board.
Thonny se cv2 virtualenv how to#
And best of all, I’ll be showing you how to use picamera to capture images in OpenCV format. In this tutorial we’ll be using picamera, which provides a pure Python interface to the camera module. It’s really, really 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 future.Īnyway, after I published the Raspberry Pi + OpenCV installation tutorial, many of the comments asked that I continue on and discuss how to access the Raspberry Pi camera using Python and OpenCV. And the first (big) tutorial I ever wrote, Hobbits and Histograms, an article on building a simple image search engine, still gets a lot of hits today.īut by far, the most popular post on the PyImageSearch blog is my tutorial on installing OpenCV and Python on your Raspberry Pi 2 and B+. One of my personal favorites, building a kick-ass mobile document scanner has been the most popular PyImageSearch article for months. Using k-means clustering to find the dominant colors in an image was (and still is) hugely popular.
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Over the past year the PyImageSearch blog has had a lot of popular blog posts. Click here to download the source code to this post