NSAViewer, desktop Photobooth
Take photos of yourself, with a terrible camera.

Heads up! You've already completed this tutorial.

This app isn't actually a direct line from your webcam to the NSA, it's a demo of using the webcam/camera support in Qt. The name is a nod to the paranoia (or is it...) of being watched through your webcam by government spooks.

With this app you can use your laptop's built-in camera to view yourself, and take photobooth-style snapshots. The app uses Qt's built in webcam methods to provide support for multiple cameras if you have them.

Originally the plan was to make the app (openly) uplod snapshots of all to a remote server to complete the idea, but this is the internet, and nobody wants to see that.

Qt Camera Interface

Camera support in Qt5 is accessible via QtMultimedia with multimedia-specific widgets available via QtMultimediaWidgets. The first step to using cameras is to get a list of currently available cameras on the system, via QCameraInfo.availableCameras(). This returns a list of QCameraInfo objects, which provide various bits of information about each camera, including a unique ID.

If we have no cameras available we just quit out, ungracefully. If a camera is available we setup the QCameraViewfinder to provide a live updating viewfinder view from the active camera. We select the first camera in our list to use as a 'default' — on a laptop this is usually the built in camera.

python
class MainWindow(QMainWindow):

    def __init__(self, *args, **kwargs):
        super(MainWindow, self).__init__(*args, **kwargs)

        self.available_cameras = QCameraInfo.availableCameras()
        if not self.available_cameras:
            pass #quit

        self.viewfinder = QCameraViewfinder()
        self.viewfinder.show()
        self.setCentralWidget(self.viewfinder)

        # Set the default camera.
        self.select_camera(0)

This is all that is required to stream the active camera live to the viewfinder widget.

Camera Selection

The toolbar allows a user to select the active camera, take snapshot photos and to select the output folder for these photos. Each QAction is connected to a custom slot to handle the specific behaviour.

The camera selection list is pre-filled with the user-friendly name of each camera, via QCameraInfo.description(). The index of the selected camera in the combobox matches its position in the list.

python
camera_toolbar = QToolBar("Camera")
camera_toolbar.setIconSize(QSize(14, 14))
self.addToolBar(camera_toolbar)

photo_action = QAction(QIcon(os.path.join('images', 'camera-black.png')), "Take photo...", self)
photo_action.setStatusTip("Take photo of current view")
photo_action.triggered.connect(self.take_photo)
camera_toolbar.addAction(photo_action)

change_folder_action = QAction(QIcon(os.path.join('images', 'blue-folder-horizontal-open.png')), "Change save location...", self)
change_folder_action.setStatusTip("Change folder where photos are saved.")
change_folder_action.triggered.connect(self.change_folder)
camera_toolbar.addAction(change_folder_action)

camera_selector = QComboBox()
camera_selector.addItems([c.description() for c in self.available_cameras])
camera_selector.currentIndexChanged.connect( self.select_camera )

camera_toolbar.addWidget(camera_selector)

The camera select method accepts a single parameter i, which is the index of a camera in our prefilled self.available_cameras list. This is a QCameraInfo object, which can be passed to QCamera to create a new camera object.

Once the camera object is created, we set it to use our existing viewfinder widget (central widget). The capture mode is set to QCamera.CaptureStillImage and then the camera must be started with .start().

Capture of images from a camera is handled by QCameraImageCapture, which we setup by passing in our previously created camera object. The .imageCaptured signal is triggered every time (after) an image is captured, so we can connect to it to show a status update — the snapshotting is done seperately.

def select_camera(self, i): self.camera = QCamera(self.available_cameras[i]) self.camera.setViewfinder(self.viewfinder) self.camera.setCaptureMode(QCamera.CaptureStillImage) self.camera.error.connect(lambda: self.alert(self.camera.errorString())) self.camera.start()

python
self.capture = QCameraImageCapture(self.camera)
self.capture.error.connect(lambda i, e, s: self.alert(s))
self.capture.imageCaptured.connect(lambda d, i: self.status.showMessage("Image %04d captured" % self.save_seq))

self.current_camera_name = self.available_cameras[i].description()
self.save_seq = 0
Over 10,000 developers have bought Create GUI Applications with Python & Qt!
Create GUI Applications with Python & Qt5
Take a look

Downloadable ebook (PDF, ePub) & Complete Source code

Also available from Leanpub and Amazon Paperback

[[ discount.discount_pc ]]% OFF for the next [[ discount.duration ]] [[discount.description ]] with the code [[ discount.coupon_code ]]

Purchasing Power Parity

Developers in [[ country ]] get [[ discount.discount_pc ]]% OFF on all books & courses with code [[ discount.coupon_code ]]

Taking a photo

Taking a camera snapshot is handled in our custom take_photo slot using the QCameraImageCapture object created when initialising the camera. The .capture() method accepts a filename, which we create using our selected save path and a full-name timestamp. The file is stamped with a current time, plus the current camera and a sequence number to avoid conflicts. Snapshots are saved in JPEG format.

python
def take_photo(self):
    timestamp = time.strftime("%d-%b-%Y-%H_%M_%S")
    self.capture.capture(os.path.join(self.save_path, "%s-%04d-%s.jpg" % (
        self.current_camera_name,
        self.save_seq,
        timestamp
    )))
    self.save_seq += 1

The sequence number is incremented after the snapshot is taken.

Well done, you've finished this tutorial! Mark As Complete
[[ user.completed.length ]] completed [[ user.streak+1 ]] day streak

NSAViewer, desktop Photobooth was written by Martin Fitzpatrick .

Martin Fitzpatrick has been developing Python/Qt apps for 8 years. Building desktop applications to make data-analysis tools more user-friendly, Python was the obvious choice. Starting with Tk, later moving to wxWidgets and finally adopting PyQt.