Thanks, that's what I was looking for. On Thu, Sep 1, 2016 at 3:29 AM, Emmanuelle Gouillart < emmanuelle.gouill...@nsup.org> wrote:
> 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 a topic in the > Google Groups "scikit-image" group. > To unsubscribe from this topic, visit https://groups.google.com/d/ > topic/scikit-image/qvGm0zOv2rc/unsubscribe. > To unsubscribe from this group and all its topics, 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. > -- 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/CANe40g%2BCFmmPeABvjszxAKfgYjsxhs_Vd8eUQBKe1asesOhR8w%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.