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The choice of image size will depend on the camera used all cameras support VGA size (640 x 480 pixels), more modern versions the high-definition standards of 720p (1280 x 720) or 1080p (1920 x 1080). If image is not None and queue.qsize() < 2: # Grab images from the camera (separate thread)Ĭap = cv2.VideoCapture(cam_num-1 + CAP_API)Ĭap.set(cv2.CAP_PROP_FRAME_WIDTH, IMG_SIZE)Ĭap.set(cv2.CAP_PROP_FRAME_HEIGHT, IMG_SIZE) This is done using the ‘text_update’ signal that was defined above: It is convenient to redirect the ‘print’ output to the text box, rather than appearing on the Python console. Similarly, there is a redundant QLabel below the displays, which isn’t currently used, but is handy for displaying static text below the images. This is intentional, since much of my OpenCV experimentation requires additional displays to show the image processing in action this can easily be done by creating more ImageWidgets, and adding them to the ‘displays’ layout. There is a horizontal box layout called ‘displays’, that seems to be unnecessary as it only has one display widget in it. Self.vlayout = QVBoxLayout() # Window layout PyQt main windowīeing compatible with PyQt version 4 and 5 requires some boilerplate code to handle the way some functions have been moved between libraries:
#QIMAGE ONE FOR PC DRIVER#
With regard to cameras, all the USB Webcams I’ve tried have worked fine on Windows without needing to have any extra driver software installed they also work on the Raspberry Pi, as well as the standard Pi camera with the ribbon-cable interface. The previous ‘stretch’ distribution has python-opencv version 2.4, which is a bit too old: my code isn’t compatible with it.
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Sudo apt install python3-opencv python3-pyqt5Īt the time of writing, the most recent version of Raspbian Linux is ‘buster’, and that has OpenCV 3.2, which is quite usable. Fortunately there is a simpler option, if you don’t mind using versions that are a few years old, namely to load the binary image from the standard repository, e.g. Installing on a Raspberry Pi is potentially a lot more complicated it is generally recommended to install from source, and for opencv-python, this is a bit convoluted. for Python 3: py -3 -m pip install numpy opencv-python PyQt5 If pip isn’t available, you should be able to run the module from the command line by invoking Python, e.g. On Windows, the OpenCV and PyQt5 libraries can be installed using pip: pip install numpy opencv-python PyQt5 OpenCV is an incredibly powerful image-processing tool, but it can be difficult to know where to start – how do you grab an image from a camera, and display it in a user-friendly GUI? This post describes such an application, that runs unmodified on a PC or Raspberry Pi, Windows or Linux, Python 2.7 or 3.x, and PyQt v4 or v5.