Notebook

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, {ref}openpifpaf.video <cli-help-video>.

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import cv2
import openpifpaf

%matplotlib inline
openpifpaf.show.Canvas.show = True
openpifpaf.show.Canvas.image_min_dpi = 200

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.

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

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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:

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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 {doc}Prediction API <predict_api> or use the {ref}openpifpaf.video <cli-help-video> command line tool.