Hi Chris, the short version is no, there is no function in scikit-image that can do what the Stanford paper does. However, you can find some useful building blocks in scikit-image and scikit-learn, that you can put together to build your own version of the algorithm (from what I figured out after a quick reading of http://www.cs.cornell.edu/~asaxena/reconstruction3d/saxena_iccv_3drr07_learning3d.pdf)
- Felzenswalb superpixels are available in skimage.segmentation (http://scikit-image.org/docs/dev/auto_examples/segmentation/plot_segmentations.html) - Local features can be computed using several functions of scikit-image, such as color transforms (color.ycbcr) and edge filters (filters.sobel_h, filters.sobel_v, ...) - A logistic regression algorithm is available in scikit-learn (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) The trickiest part to code would be the MAP inference on the MRF, where I guess you would have to code it yourself (I think). So, it is feasible to assemble these different blocks, but it's not a small project either. As for other libraries, I'm afraid nothing comes to my mind. OpenCV does have a depth estimation function, but only from stereo images. Hope this helps, Emma On Wed, Aug 31, 2016 at 09:30:20PM -0400, Chris Spencer wrote: > Essentially, what's described here: > http://www.cs.cornell.edu/~asaxena/learningdepth/ > On Wed, Aug 31, 2016 at 9:29 PM, Chris Spencer <chriss...@gmail.com> wrote: > Given a JPG image of a scene, convert that into a depth map, where each > pixel is associated with a discrete distance estimate. > On Wed, Aug 31, 2016 at 7:12 PM, Stefan van der Walt > <stef...@berkeley.edu> > wrote: > Hi Chris > On Sun, Aug 28, 2016, at 17:31, Chris Spencer wrote: > > Is there any algorithm in scikit-image that I could use to train a > predictor that could estimate the distance at each pixel in an image? > > I've gone through all the examples, and none of them seemed to > directly apply. The image segmentation example seemed to be the > closest, but that didn't use supervised learning, so I wasn't sure how > I could adapt it's code. > Could you explain what you mean by "estimate the distance"? > Stéfan -- You received this message because you are subscribed to the Google Groups "scikit-image" group. To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscr...@googlegroups.com. To post to this group, send an email to scikit-image@googlegroups.com. To view this discussion on the web, visit https://groups.google.com/d/msgid/scikit-image/20160901072903.GA2668326%40phare.normalesup.org. For more options, visit https://groups.google.com/d/optout.