Google Pixel 2's portrait mode is made open source

Smart Android And Trik-Commenting on Andorid indeed never endless, because smart devices this one is often updated every certain amount of time. So that the market can always be garapnya menerinya with pleasure. And it is not denied if this device has become the lifestyle of each society. To not wonder if the 6th business information and many are turning to mobail smartphone. With Android which thoroughly dominated the mobile industry, choosing the best Android smartphone is almost identical to choose the best smartphone, period. But while Android phones have few real opponents on other platforms, internal competition is intense.

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There’s no denying that the Google Pixel 2 has a really great camera. Although the camera UI does not offer manual controls, Google’s HDR+ algorithm is really good at exposing most scenes very evenly. It can even do this fairly well in lower-lit conditions.

HDR+ aside, Google made a Research Blog post about the Google Camera’s portrait mode. The Google Pixel’s portrait mode only needs a single camera lens, unlike many other OEMs that require a second camera to map out depth to synthesize the bokeh (blur) effect.

We hope that publicly sharing our system with the community will make it easier for other groups in academia and industry to reproduce and further improve upon state-of-art systems, train models on new datasets, and envision new applications for this technology.

Google’s camera uses “semantic image segmentation” to make this happen. It is able to map

out which pixels pertain to the subject and which ones belong to the background. This technology has been released as open source by Google so that any phone maker can implement the technology in its own smartphones or app developers to make include it into their own apps.

The semantic image segmentation model can also be used to label pixels as anything like road, person, dog, and sky, so portrait mode is only one of the possibilities of the model. Check out the blog post in the source link to get a better idea of how DeepLab works to accurately predict what is actually in the image.

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