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name: neural-style-tf description: "Use the legacy TensorFlow neural-style-tf script for artistic image and video style transfer, command planning, runtime checks, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

neural-style-tf

Use this repo skill when the task involves Cameron Smith's neural-style-tf implementation of Gatys-style neural style transfer: constructing neural_style.py commands, preparing TensorFlow 1.x runtime checks, choosing image/video flags, preserving original colors, using segmentation masks, or diagnosing VGG/OpenCV/ffmpeg/legacy TensorFlow failures.

Do not use this skill for modern TensorFlow 2/Keras style-transfer APIs, PyTorch neural style examples, Stable Diffusion/Diffusers generation, or general image augmentation unless the user specifically names neural-style-tf or its script-style VGG-19 workflow.

First checks

1. Confirm the target checkout or working directory has a neural_style.py script. This repository is not an installable package with console entry points; it is operated by running the script.
2. Read references/repo-provenance.md when deciding whether this skill is current for a checkout or should be refreshed.
3. Read references/runtime-and-installation.md before installing dependencies, downloading VGG-19 weights, or choosing CPU/GPU. For a disposable CPU-compatible check environment, use a private Python 3.7 runtime with TensorFlow 1.15, OpenCV, SciPy, NumPy, and protobuf<3.21.
4. Run scripts/check_runtime.py from the candidate runtime to verify TensorFlow v1 APIs, OpenCV/SciPy/NumPy imports, optional VGG weights, and optional ffmpeg availability.
5. Use scripts/inspect_cli_defaults.py when a user asks for source-verified defaults without importing TensorFlow.

Route by task

User intent	Read next	Why
Build a still-image style-transfer command from content/style paths, choose output names, or replace the interactive shell wrapper.	sub-skills/image-stylization/SKILL.md	Owns base python neural_style.py image command construction, output layout, CPU/GPU choice, VGG path, and basic optimizer/image-size controls.
Blend multiple styles, use style masks/segmentation, preserve original colors, adjust layers/losses, or tune initialization/pooling/Adam.	sub-skills/advanced-controls/SKILL.md	Owns expert flags and validation for count-sensitive advanced parameters.
Plan video stylization, frame extraction, optical flow, temporal consistency, frame formats, or video assembly.	sub-skills/video-stylization/SKILL.md	Owns video/frame pipeline planning and optical-flow file expectations.
Explain every neural_style.py argument by group.	references/cli-reference.md	Compact source-verified CLI catalog with defaults and caveats.
Diagnose installation, TensorFlow 2 incompatibility, protobuf descriptor errors, missing VGG, bad image reads, GPU fallback, or ffmpeg/deepflow issues.	references/troubleshooting.md	Cross-cutting failure map before drilling into sub-skill-specific troubleshooting.

Minimal command pattern

A basic still-image command has this shape:

python neural_style.py \
  --content_img lion.jpg --content_img_dir ./image_input \
  --style_imgs kandinsky.jpg --style_imgs_dir ./styles \
  --model_weights imagenet-vgg-verydeep-19.mat \
  --device /cpu:0 --img_output_dir ./image_output --img_name result

Prefer the bundled image command builder for user-supplied file paths:

python sub-skills/image-stylization/scripts/build_image_command.py \
  --script neural_style.py \
  --content ./image_input/lion.jpg \
  --style ./styles/kandinsky.jpg \
  --device /cpu:0 --max-size 64 --max-iterations 1 --optimizer adam

The builder prints a command and runs nothing unless --run is explicitly supplied.

Runtime constraints to preserve in answers

• neural_style.py uses TensorFlow 1.x symbols such as tf.Session and tf.contrib.opt.ScipyOptimizerInterface; TensorFlow 2.x without compatibility changes is not a drop-in replacement.
• Full renders require external VGG-19 MatConvNet weights named or passed as imagenet-vgg-verydeep-19.mat.
• The script defaults to --device /gpu:0, but CPU is accepted with --device /cpu:0; video is normally impractical without a compatible GPU.
• OpenCV reads images in BGR order; bad paths surface as No such file from the source check_image(...) path.
• Source CLI facts in this skill were verified by static parser inspection and CLI help under a TensorFlow 1.x CPU-compatible inspection runtime; full image/video renders are optional and model-weight dependent.

Router metadata

Managed repo-skill import uses references/repo-routing-metadata.json. Do not hand-edit live router Markdown; use the verified import helper only when a user later asks to import this skill.