OpenCV

OpenCV is a popular framework for image and video processing. On this tutorial, we show how OpenPifPaf can integrate with a workflow from OpenCV. OpenPifPaf also comes with a video tool for processing videos from files or usb cameras that is based on OpenCV, openpifpaf.video.

Source

The cv2.VideoCapture class supports an enourmous amount of sources (files, cameras, rtmp, etc, …) and abstracts the details away. Here, we will just pass in a single image.

capture = cv2.VideoCapture('coco/000000081988.jpg')
_, image = capture.read()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

with openpifpaf.show.Canvas.image(image) as ax:
    pass

Prediction

Now that we have the image, we can use the openpifpaf.Predictor and then visualize its predicted annotations with an AnnotationPainter:

predictor = openpifpaf.Predictor(checkpoint='shufflenetv2k16')
predictions, gt_anns, meta = predictor.numpy_image(image)

annotation_painter = openpifpaf.show.AnnotationPainter()
with openpifpaf.show.Canvas.image(image) as ax:
    annotation_painter.annotations(ax, predictions)

This example is intentionally left to be quite basic. If you are interested to accelerate this process with a GPU or you have many images that should be pre-loaded in parallel, please have a look at the Prediction API or use the openpifpaf.video command line tool.