Open a set of images as two sequences.

I am looking for a command line solution to this problem.

I have a set of images, Set_A, that has been modified and written out as Set_B. I would like to open Set_A and Set_B from the command line in to compare the differences between each image in Set_A to its counterpart in Set_B.

Set_A and Set_B can be the following.

  1. Can be comprised of one or more numbered sequences.
  2. Can be comprised of single unnumbered images.
  3. Images in a set may reside in different source folders.

In my mind I was hoping for syntax that looked something like:

>: rv (image1.png,image2.dpx,imageA.dpx) (new_image1.png,new_image2.dpx,new_imageA.dpx) 

1 条评论

  • 0
    Alan Trombla

    Hi Sean,

    I'm sorry to say I don't think there's any way to do that from the command line.  To put this in terms of RV Session structure, you'd like to make a Source for each element of Set_A and Set_B, then make one Sequence node (Sequence_A) with all the Set_A Sources as inputs and one (Sequence_B) with all Set_B Sources as inputs, then add a Layout (for side-by-side comparisons) or a Stack (for A/B switching or "wiping") with inputs Sequence_A and Sequence_B.

    Unfortunately a graph this complicated can't be assembled with just the command line.  We call this feature the "constructive command line" and it's something we'd love to have, but it's a big job and has never made it to the top of the list.

    If you have some Python expertise, I'd suggest either:

    1. Generate an RV Session file as a pre-process, with the graph structure as described above.  This could be done with the supplied rvSession.py module without any scripting "inside" RV.
    2. Write an RV Mode with a custom command-line trigger to take the incoming Sources, assume they are in two groups, and construct your desired graph on start-up.  So then your command line would look like:
    rv image1.png image2.dpx imageA.dpx new_image1.png new_image2.dpx new_imageA.dpx -flags myModeTrigger

    We'd be happy to help with either approach.