From 47397444b30b0f461b1633297a68300179586fda Mon Sep 17 00:00:00 2001
From: skal <pascal.massimino@gmail.com>
Date: Tue, 10 Feb 2026 08:01:25 +0100
Subject: feat: Add CNN post-processing effect with modular WGSL architecture
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Implements multi-layer convolutional neural network shader for stylized
post-processing of 3D rendered scenes:

**Core Components:**
- CNNEffect: C++ effect class with single-layer rendering (expandable to multi-pass)
- Modular WGSL snippets: cnn_activation, cnn_conv3x3/5x5/7x7, cnn_weights_generated
- Placeholder identity-like weights for initial testing (to be replaced by trained weights)

**Architecture:**
- Flexible kernel sizes (3×3, 5×5, 7×7) via separate snippet files
- ShaderComposer integration (#include resolution)
- Residual connections (input + processed output)
- Supports parallel convolutions (design ready, single conv implemented)

**Size Impact:**
- ~3-4 KB shader code (snippets + main shader)
- ~2-4 KB weights (depends on network architecture when trained)
- Total: ~5-8 KB (acceptable for 64k demo)

**Testing:**
- CNNEffect added to test_demo_effects.cc
- 36/36 tests passing (100%)

**Next Steps:**
- Training script (scripts/train_cnn.py) to generate real weights
- Multi-layer rendering with ping-pong textures
- Weight quantization for size optimization

handoff(Claude): CNN effect foundation complete, ready for training integration
---
 workspaces/main/shaders/cnn/cnn_conv5x5.wgsl | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 workspaces/main/shaders/cnn/cnn_conv5x5.wgsl

(limited to 'workspaces/main/shaders/cnn/cnn_conv5x5.wgsl')

diff --git a/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl b/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl
new file mode 100644
index 0000000..3d4a03a
--- /dev/null
+++ b/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl
@@ -0,0 +1,26 @@
+// 5x5 convolution with 25 samples
+// Applies mat4 weights per sample
+
+fn cnn_conv5x5(
+  tex: texture_2d<f32>,
+  samp: sampler,
+  uv: vec2<f32>,
+  resolution: vec2<f32>,
+  weights: array<mat4x4<f32>, 25>,
+  bias: vec4<f32>
+) -> vec4<f32> {
+  let step = 1.0 / resolution;
+  var sum = bias;
+  var idx = 0;
+
+  for (var dy = -2; dy <= 2; dy++) {
+    for (var dx = -2; dx <= 2; dx++) {
+      let offset = vec2<f32>(f32(dx), f32(dy)) * step;
+      let sample = textureSample(tex, samp, uv + offset);
+      sum += weights[idx] * sample;
+      idx++;
+    }
+  }
+
+  return sum;
+}
-- 
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