![]() We initially used this class in the OpenCV panorama stitching tutorial. We then have our panorama.py file which defines the Stitcher class used to stitch images together. For a thorough review of the basic motion detector, be sure to read last week’s post. This class hasn’t changed at all, so we won’t be reviewing the implementation in this post. We then have the basicmotiondetector.py implementation from last week’s post on accessing multiple cameras with Python and OpenCV. Project structureīefore we get started, let’s look at our project structure: |- pyimagesearchĪs you can see, we have defined a pyimagesearch module for organizational purposes. The goal is to take frames captured from both my video streams, stitch them together, and then perform motion detection in the panorama image.Ĭonstructing a panorama, rather than using multiple cameras and performing motion detection independently in each stream ensures that I don’t have any “blind spots” in my field of view. The setup is pointing towards my front door, kitchen, and hallway, giving me a full view of what’s going on inside my apartment: Figure 3: Getting ready for real-time panorama construction. Here is another angle looking up at the setup: Figure 2: Placing my setup on top of a bookcase so it has a good viewing angle of my apartment. Again, if you decide to use your laptop/desktop system, you can simply hook-up multiple webcams to your machine - the same concepts discussed in this post still apply.īelow you can see my setup: Figure 1: My the Raspberry Pi 2 + USB webcam + Pi camera module setup. I’ll also be using my Logitech C920 webcam (that is plug-and-play compatible with the Raspberry Pi) along with the Raspberry Pi camera module. I simply went with the Pi 2 for it’s small form factor and ease of maneuvering in space constrained places. Hardware setupįor this project, I’ll be using my Raspberry Pi 2, although you could certainly use your laptop or desktop system instead. ![]()
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