Getting that player in the early 2nd would be fine, while getting him at would almost be a steal.
Finally, you project that layered representation back onto 2D from two different positions -- one for the left eye, and one for the right eye.This gives you the two different images you needed.EDIT The second video you linked to describes the is that it doesn't discriminate between the foreground and background. Merge(b, g, zeros, None, right) # # cv Rect is ( x, y, width, height ) and it MUST be a tuple, not a list # cv.) Learning 3-D Scene Structure from a Single Still Image, Ashutosh Saxena, Min Sun, Andrew Y. Ng, In ICCV workshop on 3D Representation for Recognition (3d RR-07), 2007.
Guessing depth from single image
In the case of the piano, everything is more or less foreground, so the approach works. Create Image(size, im.depth, im.n Channels) right = cv. Basically, the first link described pretty much what I did. I'm not sure about the actual displace filter implementation.The algorithm the video describes is: import cv SHIFT=8 if __name__ == '__main__': import sys _, fname = im = cv. Create Image(size, im.depth, im.n Channels) anaglyph = cv. The second link isn't really all that relevant -- it's not a stereo image, it just achieves a pseudo-3D effect.Create Image((width - SHIFT, height), im.depth, im.n Channels) # # This would be easier if we had COI support for cv. # Open CV uses BGR order (even if input image is greyscale): # red goes on the left, cyan on the right: # # b = cv. If this is what you want, then it's fairly simple to implement, just do exactly as that kid was saying in the video. I might watch the third video later -- it's a bit long, and it's late here.(best paper) [ps, pdf, ppt] 3-D Reconstruction from Sparse Views using Monocular Vision, Ashutosh Saxena, Min Sun, Andrew Y. 3-D Depth Reconstruction from a Single Still Image, Ashutosh Saxena, Sung H.