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## 01_NaiveDisplayTransform_Demo.mp4

      
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## SummedAreaTable.cpp
/* Create a summed area table in one direction, based on an input image.
    In order to create a 2-dimensional summed area table,
    you need two copies of the node, the 2nd with col=true

    Currently there are issues with precision for higher resolution images...
*/
kernel SummedAreaTable : ImageComputationKernel<eComponentWise> {
  Image<eRead, eAccessRandom, eEdgeConstant> src;
  Image<eReadWrite, eAccessRandom> dst;

## BoxBlur.cpp
/* Perform a horizontal or vertical Box Blur
    Can perform multiple iterations in order to approximate a Gaussian Blur.
*/
kernel BoxBlur : ImageComputationKernel<eComponentWise> {
  Image<eRead, eAccessRandom, eEdgeClamped> src;
  Image<eReadWrite, eAccessRandom, eEdgeClamped> dst;

   param:
    float2 size; // blur size
    int iterations; // number of iterations to perform

## Distort.cpp
kernel Distort : ImageComputationKernel<ePixelWise> {
  Image<eRead, eAccessRandom, eEdgeClamped> in;
  Image<eRead, eAccessPoint, eEdgeClamped> uv;
  Image<eWrite, eAccessPoint> out;

  param:
    bool stmap;
    bool enable_blur;
    float blur_size;
    int blur_samples;

## Transform.cpp
/*
Nuke blinkscript implementation of a simple image Transform operator.
Demonstrates pixel filter interpolation algorithms.

The following pixel filters are implemented:
0 - Blackman-Harris : Similar to cubic, but better performance in high frequencies
1 - Lanczos4 : 2x2 lanczos windowed sinc function
2 - Lanczos6 : 3x3 lanczos windowed sinc function
3 - Cubic : (Bicubic interpolation) - a=0.0, b=0.0
4 - Mitchell : (Bicubic interpolation) - a=1/3, b=1/3

## opusencdir
#!/usr/bin/python3

import os, sys, re, shutil
import logging

import threading
import concurrent.futures
import multiprocessing

import argparse, shlex

## menu.py
def viewer_shotcuts_alt():
    # Alt+1, Alt+2 etc to connect node to viewer
    for n in range(9):
        def connect_viewer(n=n):
            selection = nuke.selectedNodes()
            nuke.connectViewer(n, nuke.selectedNode())
            [node.setSelected(False) for node in nuke.selectedNodes()]
            [node.setSelected(True) for node in selection]

        nuke.menu("Node Graph").addMenu("ViewerThing").addCommand(

## nuke_viewer_shortcut_intercept.py
"""Test of finding Nuke's viewer widget, and intercepting the hardwired "c" shortcut and rewiring it to view the RGB channel
"""

from PySide import QtGui, QtCore


def findviewer():
    stack = QtGui.QApplication.topLevelWidgets()
    viewers = []
    while stack:

## custom_ui_docked.py
import PySide.QtCore as QtCore
import PySide.QtGui as QtGui
from nukescripts import panels

class PanelTest(QtGui.QWidget):
    def __init__(self, parent=None):
        QtGui.QWidget.__init__(self, parent)
        self.setLayout(QtGui.QVBoxLayout())
        self.myTable    = QtGui.QTableWidget()
        self.myTable.header = ['Date', 'Files', 'Size', 'Path' ]

## GamutCompression_blinkscript.cpp
kernel GamutCompression : ImageComputationKernel<ePixelWise> {
  Image<eRead, eAccessPoint, eEdgeClamped> src;
  Image<eWrite> dst;

  param:
    float threshold;
    float cyan;
    float magenta;
    float yellow;
    int method;
	/* Create a summed area table in one direction, based on an input image.
	In order to create a 2-dimensional summed area table,
	you need two copies of the node, the 2nd with col=true

	Currently there are issues with precision for higher resolution images...
	*/
	kernel SummedAreaTable : ImageComputationKernel<eComponentWise> {
	Image<eRead, eAccessRandom, eEdgeConstant> src;
	Image<eReadWrite, eAccessRandom> dst;
	/* Perform a horizontal or vertical Box Blur
	Can perform multiple iterations in order to approximate a Gaussian Blur.
	*/
	kernel BoxBlur : ImageComputationKernel<eComponentWise> {
	Image<eRead, eAccessRandom, eEdgeClamped> src;
	Image<eReadWrite, eAccessRandom, eEdgeClamped> dst;

	param:
	float2 size; // blur size
	int iterations; // number of iterations to perform
	kernel Distort : ImageComputationKernel<ePixelWise> {
	Image<eRead, eAccessRandom, eEdgeClamped> in;
	Image<eRead, eAccessPoint, eEdgeClamped> uv;
	Image<eWrite, eAccessPoint> out;

	param:
	bool stmap;
	bool enable_blur;
	float blur_size;
	int blur_samples;
	/*
	Nuke blinkscript implementation of a simple image Transform operator.
	Demonstrates pixel filter interpolation algorithms.

	The following pixel filters are implemented:
	0 - Blackman-Harris : Similar to cubic, but better performance in high frequencies
	1 - Lanczos4 : 2x2 lanczos windowed sinc function
	2 - Lanczos6 : 3x3 lanczos windowed sinc function
	3 - Cubic : (Bicubic interpolation) - a=0.0, b=0.0
	4 - Mitchell : (Bicubic interpolation) - a=1/3, b=1/3
	#!/usr/bin/python3

	import os, sys, re, shutil
	import logging

	import threading
	import concurrent.futures
	import multiprocessing

	import argparse, shlex
	"""Test of finding Nuke's viewer widget, and intercepting the hardwired "c" shortcut and rewiring it to view the RGB channel
	"""

	from PySide import QtGui, QtCore


	def findviewer():
	stack = QtGui.QApplication.topLevelWidgets()
	viewers = []
	while stack:
	import PySide.QtCore as QtCore
	import PySide.QtGui as QtGui
	from nukescripts import panels

	class PanelTest(QtGui.QWidget):
	def __init__(self, parent=None):
	QtGui.QWidget.__init__(self, parent)
	self.setLayout(QtGui.QVBoxLayout())
	self.myTable = QtGui.QTableWidget()
	self.myTable.header = ['Date', 'Files', 'Size', 'Path' ]
	kernel GamutCompression : ImageComputationKernel<ePixelWise> {
	Image<eRead, eAccessPoint, eEdgeClamped> src;
	Image<eWrite> dst;

	param:
	float threshold;
	float cyan;
	float magenta;
	float yellow;
	int method;