jedypod

# Dockerfile
FROM centos

LABEL maintainer="JED"

ENV HOME /root
WORKDIR $HOME

RUN yum update -y && yum clean all

jedypod

set cut_paste_input [stack 0]
push $cut_paste_input
Group {
 name CornerPinOffset
 tile_color 0x9073adff
 addUserKnob {20 PinControl}
 addUserKnob {26 CornerPinControls l "" +STARTLINE T "<font size=5><b>Corner Pin Data</b></font>"}
 addUserKnob {20 CornerPinData l "" +STARTLINE n 1}
 addUserKnob {41 to1 T cpMaster.to1}
 addUserKnob {41 enable1 -STARTLINE T cpMaster.enable1}

jedypod

set cut_paste_input [stack 0]
push $cut_paste_input
Group {
 name ExposureBlend
 label "\[value brackets] Brackets"
 addUserKnob {20 ExposureBlend_tab l ExposureBlend}
 addUserKnob {7 time_th l "time threshold" t "threshold duration in seconds between exposure bracket sets" R 1 10}
 time_th 2
 addUserKnob {1 capture_time l "capture time" t "Enter the metadata key for the exposure capture time metadata key in your source images"}
 capture_time exr/Exif:DateTimeOriginal

jedypod

Nuke ffmpeg Render

Writeffmpeg is a python script and a node that allows you to render from Nuke directly into ffmpeg. Instead of a 2 step process where you render from Nuke to a temporary image sequence that you then transcode using ffmpeg, this solution writes to a single uint16 cache tiff file and this data gets piped into ffmpeg.

I've included a little to help demo how it works.

Files

ffmpeg_render.py Writeffmpeg.nk

Dependencies

jedypod

set cut_paste_input [stack 0]
push $cut_paste_input
Group {
 name FLog2Lin
 label "\[value operation]"
 selected true
 xpos -40
 ypos -952
 addUserKnob {20 FLog2Lin}
 addUserKnob {4 operation M {log2lin lin2log}}

jedypod

import OpenImageIO as oiio
from OpenImageIO import ImageInput, ImageOutput
from OpenImageIO import ImageBuf, ImageSpec, ImageBufAlgo


import os, sys
import shlex, subprocess
import glob

#--------------------------------------------

jedypod

import nuke, math
def get_closest_node(node):
    # Return the closest node to node
    distances = {}
    for n in nuke.allNodes():
        if n.name() == node.name():
            continue
        distance = math.sqrt( 
            math.pow( (node.xpos() - n.xpos()), 2 ) + math.pow( (node.ypos() - n.ypos()), 2 )
        )

jedypod

globals:
  # Path to the ocio config to use
  ocioconfig: /path/to/config.ocio
  # Default ocio profile to use if not specified on the commandline
  ocio_default_transform: grade

  ###############################################
  ## Reformatting and Cropping
  ###############################################
  # If debug is true, no encoding will be done and there will be debug prints

jedypod

import OpenImageIO as oiio
from OpenImageIO import ImageInput, ImageOutput
from OpenImageIO import ImageBuf, ImageSpec, ImageBufAlgo


import os, sys
import shlex, subprocess
import glob

#--------------------------------------------

jedypod

Group {
 name AutoExposure
 addUserKnob {20 User}
 addUserKnob {41 wb T ColorMatch.wb}
 addUserKnob {41 dst_color l dst T ColorMatch.dst_color}
 addUserKnob {22 calc T "n = nuke.thisNode()\nnuke.root().begin()\nn.begin()\nct = nuke.toNode('CURVETOOL')\nct\['intensitydata'].clearAnimated()\nt = nuke.frame()\nnuke.execute(ct, t, t)" +STARTLINE}
 addUserKnob {26 ""}
 addUserKnob {41 intensitydata T CURVETOOL.intensitydata}
 addUserKnob {41 sample T ColorSampler.sample}
 addUserKnob {26 ""}