This is an example of the fully-fledged effect: Our effect is merely blending/bluring it, making it a bit jarring, but we can mask it by lowering the change in camera angle. The "3D Photo" effect is, in fact, a rather popular research area, and thorough implementation would require additional techniques, including inpainting - to predict the "missing" bits of image when we change camera angles. const IMG_WIDTH = 300 const IMG_HEIGHT = 381 const SCALE_FACTOR = 25 const app = new PIXI. We can tie the x, y parameters of the filter to a mousemove event or even Gyroscope callback so to create the effect of the camera following the user's interactions. It also accepts (optionally) a depth map to create even more convincing 3D effect. PixiJS - out of the box, offers various filters, one of which is the DisplacementFilter which can be used to easily create parallax-like effect. It probably would not work as well for different objects, say, paintings or animals. In this case, this model would work best for indoor furniture scenes, as those are abundant in its training dataset. As such, it would work better for certain objects and scenes compared to others. Side note: Given that the model is UNet-based - the intuition here is the model does not actually "estimate" the depth of the image as how humans would, but rather it does so by identifying the different objects and draw regions on the image. output_path + '/' +output_file_name + '_' +args. Res = trans_topil (baseline_output ) # Invert black and white # Run the image through the model forward()īaseline_output = model (img_tensor ). from PIL import Image, ImageOps, ImageEnhance Resize ( ( 256, 256 ), interpolation =PIL. # Remove center crop to take in the entire photo
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