{"Document":{"ObjectName":"Scenario::ScenarioDocumentModel","id":1,"BaseScenario":{"ObjectName":"Scenario::BaseScenario","id":0,"Constraint":{"ObjectName":"Scenario::IntervalModel","id":0,"Metadata":{"ScriptingName":"shader-texture-feedback","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Inlet":{"uuid":"a1574bb0-cbd4-4c7d-9417-0c25cfd1187b","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"Audio In","Exposed":"audio in"},"Outlet":{"uuid":"a1d97535-18ac-444a-8417-0cbc1692d897","ObjectName":"Outlet","id":0,"Hidden":false,"Custom":"Audio Out","Exposed":"audio out","Address":"audio:/out/main","GainInlet":{"uuid":"9a13fb32-269a-47bf-99a9-930188c1f19c","ObjectName":"Inlet","id":10000,"Hidden":false,"Custom":"Gain","Exposed":"gain","Value":{},"Init":{},"Domain":{"Float":{"Min":0.0,"Max":1.0}}},"PanInlet":{"uuid":"9a13fb32-269a-47bf-99a9-930188c1f19c","ObjectName":"Inlet","id":10001,"Hidden":false,"Custom":"Pan","Exposed":"pan","Value":{},"Init":{},"Domain":{}},"Gain":1.0,"Pan":[1.0,1.0],"Propagate":true},"Processes":[{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":5,"Metadata":{"ScriptingName":"Bloom","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[814.7348001125233,-134.68324160939116],"Size":[157.0,160.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n  \"DESCRIPTION\": \"Multi-pass bloom/glow: extract bright pixels, blur horizontally and vertically with a 13-tap Gaussian kernel, then add the glow to the original image. Works on premultiplied colour, so the glow also spreads over a transparent background.\",\n  \"CREDIT\": \"ossia score\",\n  \"ISFVSN\": \"2\",\n  \"CATEGORIES\": [\n    \"Post-Process\"\n  ],\n  \"ALPHA\": \"premultiplied\",\n  \"INPUTS\": [\n    {\n      \"NAME\": \"inputImage\",\n      \"TYPE\": \"image\"\n    },\n    {\n      \"NAME\": \"threshold\",\n      \"LABEL\": \"Brightness threshold (luminance)\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.8,\n      \"MIN\": 0.0,\n      \"MAX\": 2.0\n    },\n    {\n      \"NAME\": \"intensity\",\n      \"LABEL\": \"Glow intensity\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 1.0,\n      \"MIN\": 0.0,\n      \"MAX\": 5.0\n    },\n    {\n      \"NAME\": \"blurSize\",\n      \"LABEL\": \"Glow spread (pixels between taps)\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 5.0,\n      \"MIN\": 1.0,\n      \"MAX\": 20.0\n    },\n    {\n      \"NAME\": \"softThreshold\",\n      \"LABEL\": \"Threshold softness (knee)\",\n      \"TYPE\": \"float\",\n      \"DEFAULT\": 0.5,\n      \"MIN\": 0.0,\n      \"MAX\": 1.0\n    }\n  ],\n  \"PASSES\": [\n    { \"TARGET\": \"bright\", \"FLOAT\": true },\n    { \"TARGET\": \"blurH\", \"FLOAT\": true },\n    { \"TARGET\": \"blurV\", \"FLOAT\": true },\n    {}\n  ]\n}*/\n\n// 13-tap Gaussian kernel weights (sigma = 3.0, normalized so center + 2*sides = 1)\nconst float weight[7] = float[7](\n  0.13703,  // offset 0\n  0.12960,  // offset +-1\n  0.10969,  // offset +-2\n  0.08310,  // offset +-3\n  0.05634,  // offset +-4\n  0.03418,  // offset +-5\n  0.01854   // offset +-6\n);\n\n// 13-tap offsets: 0, +-1, +-2, +-3, +-4, +-5, +-6\nconst float offset[7] = float[7](0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0);\n\nvoid main()\n{\n  vec2 uv = isf_FragNormCoord;\n\n  // --- Pass 0: Bright extraction ---\n  if(PASSINDEX == 0)\n  {\n    // Threshold on the straight colour, extract the premultiplied texel.\n    vec4 color = IMG_NORM_PIXEL(inputImage, uv);\n    float brightness = dot(color.rgb, vec3(0.2126, 0.7152, 0.0722));\n\n    // Soft knee: smoothly ramp from 0 at (threshold - knee) to 1 at (threshold + knee)\n    float knee = threshold * softThreshold;\n    float soft = brightness - threshold + knee;\n    soft = clamp(soft / (2.0 * knee + 0.0001), 0.0, 1.0);\n    soft = soft * soft;\n\n    float contrib = max(soft, step(threshold, brightness));\n    isf_FragColor = IMG_NORM_PIXEL_PREMULTIPLIED(inputImage, uv) * contrib;\n  }\n  // --- Pass 1: Horizontal Gaussian blur of bright pass ---\n  else if(PASSINDEX == 1)\n  {\n    vec2 texelSize = vec2(1.0) / RENDERSIZE;\n    float spread = blurSize * texelSize.x;\n\n    vec4 result = IMG_NORM_PIXEL(bright, uv) * weight[0];\n    for(int i = 1; i < 7; i++)\n    {\n      vec2 off = vec2(offset[i] * spread, 0.0);\n      result += IMG_NORM_PIXEL(bright, uv + off) * weight[i];\n      result += IMG_NORM_PIXEL(bright, uv - off) * weight[i];\n    }\n    isf_FragColor = result;\n  }\n  // --- Pass 2: Vertical Gaussian blur of horizontal result ---\n  else if(PASSINDEX == 2)\n  {\n    vec2 texelSize = vec2(1.0) / RENDERSIZE;\n    float spread = blurSize * texelSize.y;\n\n    vec4 result = IMG_NORM_PIXEL(blurH, uv) * weight[0];\n    for(int i = 1; i < 7; i++)\n    {\n      vec2 off = vec2(0.0, offset[i] * spread);\n      result += IMG_NORM_PIXEL(blurH, uv + off) * weight[i];\n      result += IMG_NORM_PIXEL(blurH, uv - off) * weight[i];\n    }\n    isf_FragColor = result;\n  }\n  // --- Pass 3: Composite original + bloom ---\n  else\n  {\n    // Premultiplied: the glow's light adds to the colour, its coverage to\n    // the alpha, so a halo over a transparent background keeps its strength.\n    vec4 original = IMG_NORM_PIXEL_PREMULTIPLIED(inputImage, uv);\n    vec4 bloom = IMG_NORM_PIXEL(blurV, uv);\n    isf_FragColor = vec4(original.rgb + bloom.rgb * intensity,\n                         min(original.a + bloom.a * intensity, 1.0));\n  }\n}\n","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"inputImage","Exposed":"inputimage","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"threshold","Exposed":"threshold","Value":{"Float":0.0},"Init":{"Float":0.800000011920929},"Domain":{"Float":{"Min":0.0,"Max":2.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":2,"Hidden":true,"Custom":"intensity","Exposed":"intensity","Value":{"Float":5.0},"Init":{"Float":1.0},"Domain":{"Float":{"Min":0.0,"Max":5.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":3,"Hidden":true,"Custom":"blurSize","Exposed":"blursize","Value":{"Float":11.424702644348145},"Init":{"Float":5.0},"Domain":{"Float":{"Min":1.0,"Max":20.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":4,"Hidden":true,"Custom":"softThreshold","Exposed":"softthreshold","Value":{"Float":0.07987630367279053},"Init":{"Float":0.5},"Domain":{"Float":{"Min":0.0,"Max":1.0}}}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out","Address":"Window:/"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":4,"Metadata":{"ScriptingName":"Twirl","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[454.2844986132223,-66.72905655373776],"Size":[180.0,126.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n    \"CATEGORIES\": [\n        \"Distortion Effect\"\n    ],\n    \"CREDIT\": \"by VIDVOX\",\n    \"INPUTS\": [\n        {\n            \"NAME\": \"inputImage\",\n            \"TYPE\": \"image\"\n        },\n        {\n            \"DEFAULT\": 5,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"radius\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0,\n            \"MAX\": 10,\n            \"MIN\": -10,\n            \"NAME\": \"amount\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": [\n                0.5,\n                0.5\n            ],\n            \"MAX\": [\n                1,\n                1\n            ],\n            \"MIN\": [\n                0,\n                0\n            ],\n            \"NAME\": \"center\",\n            \"TYPE\": \"point2D\"\n        }\n    ],\n    \"ISFVSN\": \"2\"\n}\n*/\n\n\nconst float pi = 3.14159265359;\n\n\nvoid main (void)\n{\n\tvec2 uv = vec2(isf_FragNormCoord[0],isf_FragNormCoord[1]);\n\tvec2 texSize = RENDERSIZE;\n\tvec2 tc = uv * texSize;\n\tfloat radius_sized = radius * max(RENDERSIZE.x,RENDERSIZE.y);\n\ttc -= (center * RENDERSIZE);\n\tfloat dist = length(tc);\n\tif (dist < radius_sized) \t{\n\t\tfloat percent = (radius_sized - dist) / radius_sized;\n\t\tfloat theta = percent * percent * amount * 2.0 * pi;\n\t\tfloat s = sin(theta);\n\t\tfloat c = cos(theta);\n\t\ttc = vec2(dot(tc, vec2(c, -s)), dot(tc, vec2(s, c)));\n\t}\n\ttc += (center * RENDERSIZE);\n\tvec2 loc = tc / texSize;\n\tvec4 color = IMG_NORM_PIXEL(inputImage, loc);\n\n\tif ((loc.x < 0.0)||(loc.y < 0.0)||(loc.x > 1.0)||(loc.y > 1.0))\t{\n\t\tgl_FragColor = vec4(0.0);\n\t}\n\telse\t{\n\t\tgl_FragColor = color;\n\t}\n}","Inlets":[{"uuid":"5ac86198-2d03-4830-9e41-a6d529922d29","ObjectName":"Inlet","id":0,"Hidden":false,"Custom":"inputImage","Exposed":"inputimage","Format":0,"FormatSet":false,"Filter":2,"AddressMode":1,"MipmapMode":0},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":1,"Hidden":true,"Custom":"radius","Exposed":"radius","Value":{"Float":0.7502221465110779},"Init":{"Float":1.0},"Domain":{"Float":{"Min":0.0,"Max":1.0}}},{"uuid":"af2b4fc3-aecb-4c15-a5aa-1c573a239925","ObjectName":"Inlet","id":2,"Hidden":true,"Custom":"amount","Exposed":"amount","Value":{"Float":-4.115699291229248},"Init":{"Float":0.0},"Domain":{"Float":{"Min":-10.0,"Max":10.0}}},{"uuid":"0adbbdda-fda4-451e-91cc-1da731bde9d5","ObjectName":"Inlet","id":3,"Hidden":true,"Custom":"center","Exposed":"center","Value":{"Vec2f":[0.5,0.5]},"Init":{"Vec2f":[0.5,0.5]},"Domain":{"Vec2f":{"Min":[0.0,0.0],"Max":[1.0,1.0],"Values":[[],[]]}},"Integral":false}],"Outlets":[{"uuid":"f1c71046-b754-49a5-8e66-d01374773dfc","ObjectName":"Outlet","id":1,"Hidden":false,"Custom":"Texture Out","Exposed":"texture out"}]},{"uuid":"74ca45ff-92c9-44a0-8f1a-754dea05ee1b","ObjectName":"gfxProcess","id":3,"Metadata":{"ScriptingName":"VHS Glitch","Comment":"","Color":"Transparent1","Label":"","Touched":true},"Duration":10584000000,"Height":300.0,"StartOffset":0,"LoopDuration":10584000000,"Pos":[-83.23464333373889,-67.43181494434656],"Size":[526.0,252.0],"Loops":false,"FoldMode":0,"Vertex":"","Fragment":"/*{\n    \"CATEGORIES\": [\n        \"Glitch\",\n        \"Retro\"\n    ],\n    \"CREDIT\": \"David Lublin, original by Staffan Widegarn Åhlvik\",\n    \"DESCRIPTION\": \"VHS Glitch Style\",\n    \"INPUTS\": [\n        {\n            \"NAME\": \"inputImage\",\n            \"TYPE\": \"image\"\n        },\n        {\n            \"DEFAULT\": 1,\n            \"NAME\": \"autoScan\",\n            \"TYPE\": \"bool\"\n        },\n        {\n            \"DEFAULT\": 0.5,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"xScanline\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0.5,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"xScanline2\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"yScanline\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0.5,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"xScanlineSize\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0.25,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"xScanlineSize2\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0.25,\n            \"MAX\": 1,\n            \"MIN\": -1,\n            \"NAME\": \"yScanlineAmount\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0,\n            \"MAX\": 3,\n            \"MIN\": 0,\n            \"NAME\": \"grainLevel\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 1,\n            \"NAME\": \"scanFollow\",\n            \"TYPE\": \"bool\"\n        },\n        {\n            \"DEFAULT\": 1,\n            \"MAX\": 10,\n            \"MIN\": 0,\n            \"NAME\": \"analogDistort\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 1,\n            \"MAX\": 3,\n            \"MIN\": 0,\n            \"NAME\": \"bleedAmount\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 0.5,\n            \"MAX\": 1,\n            \"MIN\": 0,\n            \"NAME\": \"bleedDistort\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": 1,\n            \"MAX\": 2,\n            \"MIN\": 0,\n            \"NAME\": \"bleedRange\",\n            \"TYPE\": \"float\"\n        },\n        {\n            \"DEFAULT\": [\n                0.8,\n                0,\n                0.4,\n                1\n            ],\n            \"NAME\": \"colorBleedL\",\n            \"TYPE\": \"color\"\n        },\n        {\n            \"DEFAULT\": [\n                0,\n                0.5,\n                0.75,\n                1\n            ],\n            \"NAME\": \"colorBleedC\",\n            \"TYPE\": \"color\"\n        },\n        {\n            \"DEFAULT\": [\n                0.8,\n                0,\n                0.4,\n                1\n            ],\n            \"NAME\": \"colorBleedR\",\n            \"TYPE\": \"color\"\n        }\n    ],\n    \"ISFVSN\": \"2\"\n}\n*/\n\n\n\n\n//\tBased on https://github.com/staffantan/unity-vhsglitch\n//\tConverted by David Lublin / VIDVOX\n\n\nconst float tau = 6.28318530718;\n\n\nfloat rand(vec3 co){\n\treturn abs(mod(sin( dot(co.xyz ,vec3(12.9898,78.233,45.5432) )) * 43758.5453, 1.0));\n}\n\nvoid main()\t{\n\tfloat\tactualXLine = (!autoScan) ? xScanline : mod(xScanline + ((1.0+sin(0.34*TIME))/2.0 + (1.0+sin(TIME))/3.0 + (1.0+cos(2.1*TIME))/3.0 + (1.0+cos(0.027*TIME))/2.0)/3.5,1.0);\n\tfloat\tactualXLineWidth = (!autoScan) ? xScanlineSize : xScanlineSize + ((1.0+sin(1.2*TIME))/2.0 + (1.0+cos(3.91*TIME))/3.0 + (1.0+cos(0.014*TIME))/2.0)/3.5;\n\tvec2\tloc = isf_FragNormCoord;\n\tvec4\tvhs = IMG_NORM_PIXEL(inputImage, loc);\n\tfloat\tdx = 1.0+actualXLineWidth/25.0-abs(distance(loc.y, actualXLine));\n\tfloat\tdx2 = 1.0+xScanlineSize2/10.0-abs(distance(loc.y, xScanline2));\n\tfloat\tdy = (1.0-abs(distance(loc.y, yScanline)));\n\tif (autoScan)\n\t\tdy = (1.0-abs(distance(loc.y, mod(yScanline+TIME,1.0))));\n\t\n\tdy = (dy > 0.5) ? 2.0 * dy : 2.0 * (1.0 - dy);\n\t\n\tfloat\trX = (scanFollow) ? rand(vec3(dy,actualXLine,analogDistort)) : rand(vec3(dy,bleedAmount,analogDistort));\n\tfloat\txTime = (actualXLine > 0.5) ? 2.0 * actualXLine : 2.0 * (1.0 - actualXLine);\n\t\n\tloc.x += yScanlineAmount * dy * 0.025 + analogDistort * rX/(RENDERSIZE.x/2.0);\n\t\n\tif(dx2 > 1.0 - xScanlineSize2 / 10.0)\t{\n\t\tfloat\trX2 = (dy * rand(vec3(dy,dx2,dx+TIME)) + dx2) / 4.0;\n\t\tfloat\tdistortAmount = analogDistort * (sin(rX * tau / dx2) + cos(rX * tau * 0.78 / dx2)) / 10.0;\n\t\t//loc.y = xScanline2;\n\t\t//loc.x += (1.0 + distortAmount * sin(tau * (loc.x) / rX2 ) - 1.0) / 15.0;\n\t\tloc.x += (1.0 + distortAmount * sin(tau * (loc.x) / rX2 ) - 1.0) / 15.0;\n\t}\n\tif(dx > 1.0 - actualXLineWidth / 25.0)\n\t\tloc.y = actualXLine;\n\n\tloc.x = mod(loc.x,1.0);\n\tloc.y = mod(loc.y,1.0);\n\t\n\tvec4\tc = IMG_NORM_PIXEL(inputImage, loc);\n\tfloat\tx = (loc.x*320.0)/320.0;\n\tfloat\ty = (loc.y*240.0)/240.0;\n\tfloat\tbleed = 0.0;\n\t\n\tif (scanFollow)\n\t\tc -= rand(vec3(x, y, xTime)) * xTime / (5.0-grainLevel);\n\telse\n\t\tc -= rand(vec3(x, y, bleedAmount)) * (bleedAmount/20.0) / (5.0-grainLevel);\n\t\n\tif (bleedAmount > 0.0)\t{\n\t\tIMG_NORM_PIXEL(inputImage, loc + vec2(0.01, 0)).r;\n\t\tbleed += IMG_NORM_PIXEL(inputImage, loc + bleedRange * vec2(0.02, 0)).r;\n\t\tbleed += IMG_NORM_PIXEL(inputImage, loc + bleedRange * vec2(0.01, 0.01)).r;\n\t\tbleed += IMG_NORM_PIXEL(inputImage, loc + bleedRange * vec2(-0.02, 0.02)).r;\n\t\tbleed += IMG_NORM_PIXEL(inputImage, loc + bleedRange * vec2(0.0, -0.03)).r;\n\t\tbleed /= 6.0;\n\t\tbleed *= bleedAmount;\n\t}\n\n\tif (bleed > 0.1){\n\t\tfloat\tbleedFreq = 1.0;\n\t\tfloat\tbleedX = 0.0;\n\t\tif (autoScan)\n\t\t\tbleedX = x + bleedDistort * (yScanlineAmount + (1.5 + cos(TIME / 13.0 + tau*(bleedDistort+(1.0-loc.y))))/2.0) * sin((TIME / 9.0 + bleedDistort) * tau + loc.y * loc.y * tau * bleedFreq) / 8.0;\n\t\telse\n\t\t\tbleedX = x + (yScanlineAmount + (1.0 + sin(tau*(bleedDistort+loc.y)))/2.0) * sin(bleedDistort * tau + loc.y * loc.y * tau * bleedFreq) / 10.0;\n\t\tvec4\tcolorBleed = (bleedX < 0.5) ? mix(colorBleedL, colorBleedC, 2.0 * bleedX) : mix(colorBleedR, colorBleedC, 2.0 - 2.0 * bleedX);\n\t\tif (scanFollow)\n\t\t\tc += bleed * max(xScanlineSize,xTime) * colorBleed;\n\t\telse\n\t\t\tc += bleed * colorBleed;\n\t}\n\tgl_FragColor = 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","AutomaticThumbnail":true}],"Version":5,"Commit":"5bbabca73a6a56659aeedfb7129e6de35e65ba7c","Tag":"3.8.2"}