feat(cnn_v3): gen_sample tool + 7 simple training samples

- pack_photo_sample.py: --target now required (no albedo fallback)
- gen_sample.py: bash wrapper with positional args (input target output_dir)
- input/photo7.jpg: copy of photo2 (second style target)
- target_1: photo2_1_out→photo2_out, photo2_2_out→photo7_out
- dataset/simple/sample_001..007: 7 packed photo/target pairs

handoff(Gemini): training data ready; next step is train_cnn_v3.py run

1 files changed, 19 insertions, 13 deletions

diff --git a/cnn_v3/docs/HOW_TO_CNN.md b/cnn_v3/docs/HOW_TO_CNN.md
index f325a38..6214b20 100644
--- a/cnn_v3/docs/HOW_TO_CNN.md
+++ b/cnn_v3/docs/HOW_TO_CNN.md
@@ -79,14 +79,16 @@ Each sample is a directory containing 7 PNG files. The dataloader discovers samp
 The network handles this correctly because channel-dropout training (§2e) teaches it
 to work with or without geometry data.
 
-**Step 1 — Pack the photo:**
+**Step 1 — Pack an input/target pair with `gen_sample`:**
+
 ```bash
 cd cnn_v3/training
-python3 pack_photo_sample.py \
-    --photo /path/to/photo.png \
-    --output dataset/simple/sample_001/
+./gen_sample.sh /path/to/photo.png /path/to/stylized.png dataset/simple/sample_001/
 ```
 
+`gen_sample.sh <input> <target> <output_dir>` is the recommended one-shot wrapper.
+It calls `pack_photo_sample.py` with both `--photo` and `--target` in a single step.
+
 **What gets written:**
 
 | File | Content | Notes |
@@ -97,26 +99,29 @@ python3 pack_photo_sample.py \
 | `matid.png` | All zeros uint8 | No material IDs |
 | `shadow.png` | 255 everywhere uint8 | Assume fully lit |
 | `transp.png` | 1 − alpha uint8 | 0 = opaque |
-| `target.png` | Copy of photo RGBA | **Placeholder — must be replaced** |
+| `target.png` | Stylized target RGBA | Ground truth for training |
 
-**Step 2 — Provide a styled target:**
+**Step 2 — Verify the target:**
 
-`target.png` defaults to the input photo (identity style). You must replace it with
-your stylized ground truth before training:
+The network learns the mapping `albedo → target`. If you pass the same image as both
+input and target, the network learns identity (useful as sanity check, not for real
+training). Confirm `target.png` looks correct before running training.
 
+**Alternative — pack without a target yet:**
 ```bash
+python3 pack_photo_sample.py \
+    --photo /path/to/photo.png \
+    --output dataset/simple/sample_001/
+# target.png defaults to a copy of the input; replace it before training:
 cp my_stylized_version.png dataset/simple/sample_001/target.png
 ```
 
-The network learns the mapping `albedo → target`. If target = albedo, the network
-learns identity (useful as sanity check, not for real training).
-
 **Batch packing:**
 ```bash
 for f in photos/*.png; do
     name=$(basename "${f%.png}")
-    python3 pack_photo_sample.py --photo "$f" \
-        --output dataset/simple/sample_${name}/
+    ./gen_sample.sh "$f" "targets/${name}_styled.png" \
+        dataset/simple/sample_${name}/
 done
 ```
 
@@ -723,6 +728,7 @@ all geometric channels (normal, depth, depth_grad, mat_id, prev) = 0.
 
 | File | Purpose |
 |------|---------|
+| `cnn_v3/training/gen_sample.sh` | One-shot wrapper: pack input+target pair into sample directory |
 | `cnn_v3/training/blender_export.py` | Configure Blender Cycles passes, render multi-layer EXR |
 | `cnn_v3/training/pack_blender_sample.py` | EXR → sample PNG directory (7 files) |
 | `cnn_v3/training/pack_photo_sample.py` | Photo → zeroed-geometry sample directory |