Capturing Frames¶

EasyVision.vision submodule conttains various capturing adapters.

Frame structure¶

Each capturing adapter captures an image from some external source and returns a Frame object. Frame consists of a timestamp, frame index, images - collection of Image objects and a processing_mask. Frame is implemented as namedtuple object with several additional helper methods. Please refer to EasyVision.vision submodule documentation for more details.

Frame supports fromdict, todict methods as well as tobytes, frombytes and tobuffer, frombuffer which are used for multiprocessing and PyroServer.

Image structure¶

An Image consists of source, image, mask, original, features and feature_type fields and is implemented as namedtuple object.

source is a reference to the capture/processor object, that generated this image.

image is usually a numpy array containing the actual captured image.

mask is a generated or captured mask, that should be applied to the image before e.g. feature extraction.

original is an original image. Usually this one is not being used, so it is None.

features and feature_type is generated by FeatureExtraction/BlobExtraction processing object.

Image supports fromdict, todict methods as well as tobytes, frombytes and tobuffer, frombuffer which are used for multiprocessing and PyroServer.

Capturing from a list of images¶

It is useful to be able to process a list of images in offline processing scenarious. That is possible with ImagesReader class:

images = [...]
with ImagesReader(images) as vision:
    for frame in vision:
        # do something with the frame

It is also possible to provide a list of masks:

images = [...]
masks = [...]
with ImagesReader(images, mask_paths=masks) as vision:
    for frame in vision:
        # do something with the frame

Capturing from video file or camera¶

Alternatively one can read frames from a video file like so:

with VideoCapture('path/to/video/file.mp4') as vision:
    for frame in vision:
        # do something with the frame

Or perform online capturing from e.g. a webcam:

with VideoCapture(0) as vision:
    for frame in vision:
        # do something with the frame

Capturing from capturing server¶

For some really computationally heavy processing it is useful to split the processing stack into several parts, that run on separate machines. For that purpose there is a Pyro4 Server and PyroCapture. The capturing protocol is already familiar:

with PyroCapture('RemoteProcessingStack') as vision:
    for frame in vision:
        # do something with the frame

However in order to use PyroCapture class, a Pyro4 NameServer should already be running and RemoteProcessingStack should be registered. You can start Pyro4 Name Server like this:

# pyro4-ns

Then you should start EasyVision server on the remote machine like this:

# python -m EasyVision.bin.server RemoteProcessingStack remote_processing_stack.json

For more information on how to configure Pyro4 you can refer to Pyro4 documentation. The format of remote_processing_stack.json file is described on ProcessorStackBuilder documentation page.

Writing your own adaptors¶

Just subclass from EasyVision.vision.base.VisionBase and implement all abstract methods.