# Image To Svg > Convert raster images (photos, paintings, illustrations, line art) into SVG vector reproductions. Use when the user uploads an image and asks to reproduce, vectorize, trace, or convert it to SVG. Also use when asked to decompose an image into shapes, create an SVG version of a picture, or faithfully reproduce artwork as vector graphics. Handles graphic/line-art inputs (Kandinsky, architectural dr… - **Type:** Skill - **Install:** `agentstack add skill-oaustegard-claude-skills-image-to-svg` - **Verified:** Pending review - **Seller:** [oaustegard](https://agentstack.voostack.com/s/oaustegard) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [oaustegard](https://github.com/oaustegard) - **Source:** https://github.com/oaustegard/claude-skills/tree/main/image-to-svg - **Website:** https://austegard.com/ai-tools/claude-skill-releases.html ## Install ```sh agentstack add skill-oaustegard-claude-skills-image-to-svg ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Image to SVG Reproduction Convert raster images into faithful SVG reproductions using data-driven color quantization and contour extraction. **Never hand-draw shapes from visual interpretation** — always extract geometry from the actual pixel data. ## Core Principle **Trust the data, not your imagination.** Claude's visual interpretation of images is unreliable for precise color matching, shape positioning, and spatial relationships. Every shape, color, and position must come from computational analysis of the source pixels. ## Quick Start ```bash pip install opencv-python-headless scikit-image scipy scikit-learn --break-system-packages -q apt-get install -y librsvg2-bin -qq ``` ```python import sys sys.path.insert(0, '/mnt/skills/user/image-to-svg/scripts') from pipeline import image_to_svg svg, flow = image_to_svg("source.jpg", mode="painting") with open("output.svg", "w") as f: f.write(svg) flow.summary() # timing + status per step ``` ## Mode Selection **Look at the image** and ask: "Does this have smooth gradients or hard edges?" Gradients → higher K. Hard edges → lower K. | Mode | K | Best for | Dark shape gating | |------|---|----------|-------------------| | `"graphic"` | 28 | Logos, icons, Kandinsky, flat design | Loose (keeps thin lines) | | `"illustration"` | 40 | Comics, editorial, digital art | Moderate | | `"painting"` | 56 | Renaissance, Impressionist, watercolor | Standard | | `"photo"` | 64 | Portraits, landscapes, still life | Standard (prevents woodcut artifacts) | Default is `"painting"`. When uncertain, start there. **Tradeoffs**: K=64 produces ~2300 shapes (~1.2MB SVG) vs K=28's ~1000 shapes (~550KB). Processing time roughly doubles with K. The quality gain in tonal gradation is substantial for photos but wasted on graphic art. All mode defaults (K, dark_lum, compactness_min, etc.) can be overridden via `**kwargs`: ```python svg, flow = image_to_svg("source.jpg", mode="graphic", K=12, min_area=20) ``` ## Compositional Pipeline (Line Art) For images dominated by lines, strokes, and geometric shapes (Kandinsky, architectural drawings, technical illustrations, comic ink work), the standard fill-only pipeline produces jagged filled polygons instead of clean strokes. The compositional pipeline solves this with two passes: **Pass 1 — Line Extraction**: Isolate thin features via morphological erosion → skeletonize to 1px centerlines → Hough line detection → merge collinear fragments → measure stroke width → sample color. Emits SVG `` elements with `stroke-width`. **Pass 2 — Fill Extraction**: Suppress line regions from image (replace with local background estimate via median blur) → run standard K-means quantization on the cleaned image → contour extraction → `` fills. **Composition**: Fills render behind strokes in layered `` groups. ```python # Auto-detect: classifies input and routes automatically svg, flow = image_to_svg("kandinsky.jpg", mode="graphic") # Force compositional pipeline svg, flow = image_to_svg("technical_drawing.png", mode="graphic", pipeline="compositional") # Force fill-only (previous default behavior) svg, flow = image_to_svg("photo.jpg", mode="painting", pipeline="fill") ``` **Pipeline selection** (`pipeline` parameter): | Value | Behavior | |-------|----------| | `"auto"` (default) | Classify input via edge density + luminance bimodality + Hough line count. Route to compositional for graphic art, fill-only for photos. | | `"fill"` | Force fill-only pipeline. Use for photos, paintings, or when compositional produces unwanted results. | | `"compositional"` | Force two-pass pipeline. Use for line art, technical drawings, or ink work where you know lines are present. | **Auto-classification heuristics**: An image is classified as graphic when it has high edge density (>5% edge pixels) combined with bimodal luminance distribution (>0.35 bimodality coefficient), or high straight-line density (>3 Hough lines per 10k pixels). **SVG output structure** (compositional): ```xml ``` **Stroke width control**: Measured perpendicular to each detected line, then scaled by 0.65x and capped at 4.5 SVG units. This prevents thick features from rendering as bloated strokes while keeping thin lines crisp. **Current limitation — straight lines only**: Hough transform detects straight segments. Curved strokes (arcs, spirals) are not yet extracted as strokes — they fall through to the fill pass. Future work: `cv2.fitEllipse` or spline fitting on skeleton branches. ## Palette Remapping (Warhol Effects) Separate structure from color: K-means finds regions, palette remapping assigns bold colors. This produces screen-print / pop art effects. ```python # Named preset svg, flow = image_to_svg("photo.jpg", mode="graphic", K=4, palette="pop") # Custom hex list (darkest → lightest mapping order) svg, flow = image_to_svg("photo.jpg", mode="graphic", K=8, palette=["#000", "#dc143c", "#ff69b4", "#ffd700", "#32cd32", "#00bfff", "#ff8c00", "#f5f5f5"]) # Override background separately svg, flow = image_to_svg("photo.jpg", mode="graphic", K=4, palette="ocean", bg_color="#000000") ``` **Built-in presets**: `bw`, `mono3`, `mono4`, `pop`, `pop2`, `neon`, `warhol4`, `warhol6`, `warhol8`, `sunset`, `ocean` **How it works**: Unique shape colors are sorted by luminance. Palette entries are mapped proportionally — `palette[0]` replaces the darkest cluster, `palette[-1]` replaces the lightest. Background defaults to the lightest palette entry unless `bg_color` is set. Palette length doesn't need to match K exactly; colors are binned proportionally. **Portraits**: Use K=16-24 even with bold palettes. Facial features (glasses, beard, brow) need tonal range that low K eliminates. A good rule of thumb: palette length ≈ K/3 for clean luminance binning. At K=8 with a 4-color palette, a face becomes an undifferentiated blob. **Contrast preprocessing warning**: External contrast boosting (contrast-stretch, sigmoidal-contrast) can confuse background detection. The pipeline's edge-contact heuristic assumes untouched luminance distributions — aggressive tone-mapping pushes subject tones into background-adjacent bins, causing misclassification (e.g., dark jacket regions classified as background and mapped to the lightest palette color). If you see subject regions tearing to the background color, try without preprocessing first. The pipeline's own bilateral blur + optional kuwahara/oilpaint handles tonal separation. ### Background Detection Override (`bg_clusters`) Control which clusters are treated as background: ```python # Auto-detect (default) — edge-contact heuristic svg, flow = image_to_svg("photo.jpg", mode="illustration", K=20, palette="warhol6") # Disable — no clusters removed, no background rect color override svg, flow = image_to_svg("photo.jpg", mode="illustration", K=20, palette="warhol6", bg_clusters=0) # Force specific cluster indices (from quantize step's sorted_clusters output) svg, flow = image_to_svg("photo.jpg", mode="illustration", K=20, palette="warhol6", bg_clusters=[2, 5]) ``` Use `bg_clusters=0` when palette remapping already controls all colors explicitly and background detection is getting in the way. Use `bg_clusters=[list]` when you know which clusters are background but the heuristic misidentifies them. ### Portrait Pop-Art Recipe (Warhol Style) ```python # Key: enough K for facial features, palette length ~K/3, modest smoothing # Do NOT apply contrast preprocessing — it breaks background detection. results = image_to_svg_batch("portrait.jpg", [ {"name": "hot", "mode": "illustration", "K": 20, "smooth": "kuwahara:6", "palette": ["#000", "#D4145A", "#FF6B9D", "#FF85C0", "#FFD700", "#FFEF82", "#FFF8DC"]}, {"name": "cool", "mode": "illustration", "K": 20, "smooth": "kuwahara:6", "palette": ["#0D0035", "#4A00E0", "#7B68EE", "#00D4FF", "#7FFFD4", "#B0FFE0", "#E0FFFF"]}, {"name": "earth", "mode": "illustration", "K": 20, "smooth": "kuwahara:6", "palette": ["#1a0a00", "#8B4513", "#CD853F", "#DEB887", "#F5DEB3", "#FAEBD7", "#FFF8DC"]}, {"name": "neon", "mode": "illustration", "K": 20, "smooth": "kuwahara:6", "palette": ["#0d0d0d", "#ff00ff", "#00ff00", "#ffff00", "#00ffff", "#ff69b4", "#f5f5f5"]}, ], svg_width=700) ``` Why this works: K=20 preserves enough tonal clusters for facial structure (glasses, beard, brow ridge). 7-color palettes give ~K/3 luminance bins — enough variation to separate features without muddying. `kuwahara:6` smooths texture without dissolving edges (`:12` erases glasses). Raw source → pipeline smoothing only; no external contrast manipulation. ## ImageMagick Preprocessing (smooth) Reduce shape count and SVG file size by 20-45% using ImageMagick edge-preserving filters before quantization. Requires ImageMagick on PATH (pre-installed on Claude.ai containers). ```python # Oilpaint: bold, painterly smoothing (default strength=8) svg, flow = image_to_svg("photo.jpg", mode="photo", smooth="oilpaint") # Stronger smoothing = fewer shapes, more stylized svg, flow = image_to_svg("photo.jpg", mode="illustration", K=32, smooth="oilpaint:12") # Kuwahara: subtler, preserves more structure (default strength=5) svg, flow = image_to_svg("photo.jpg", mode="painting", smooth="kuwahara:7") # Works with batch API too results = image_to_svg_batch("photo.jpg", [ {"name": "raw", "mode": "photo"}, {"name": "smooth", "mode": "photo", "smooth": "oilpaint"}, {"name": "stylized", "mode": "illustration", "K": 32, "smooth": "oilpaint:12", "palette": "pop"}, ]) ``` **Available filters**: `oilpaint` (ImageMagick `-paint`), `kuwahara` (ImageMagick `-kuwahara`). Append `:N` for custom strength. **How it works**: The IM filter runs before the pipeline's bilateral+Gaussian blur. Both are edge-preserving smoothers at different scales — IM handles coarse texture, bilateral handles fine detail. The result is cleaner K-means regions with fewer fragmented shapes. **Measured impact** (1206×1597 photo, K=32): | smooth | Shapes | SVG size | Reduction | |--------|--------|----------|-----------| | none | 3381 | 1868KB | — | | oilpaint (8) | 2385 | 1329KB | -29% | | oilpaint:12 | 1842 | 1065KB | -43% | | kuwahara (5) | 2719 | 1453KB | -22% | | kuwahara:7 | 2000 | 1152KB | -38% | ## Pipeline Architecture Uses the [flowing](/mnt/skills/user/flowing/SKILL.md) DAG runner. Steps with independent inputs run in parallel. ### Fill-only pipeline (`pipeline="fill"`) ``` preprocess → quantize → ┬─ detect_background ─┬─ extract_contours → assemble_svg └─ edge_map ─┘ ``` ### Compositional pipeline (`pipeline="compositional"`) ``` classify_input ──→ extract_lines ──→ suppress_line_regions ──→ [fill pipeline on cleaned image] │ │ └──────────── lines ───────────────────→ assemble_compositional ←── fills ``` Steps (fill-only): 1. **preprocess** — Bilateral + Gaussian blur (edge-preserving texture removal) 2. **quantize** — K-means color quantization at chosen K 3. **detect_background** — Identifies background clusters by edge contact (parallel with edge_map) 4. **edge_map** — Sobel edge detection via `cv2.Sobel` (parallel with detect_background) 5. **extract_contours** — Per-cluster contour extraction with dark territory awareness and woodcut prevention (d=1 dilation; stroke handles gaps) 6. **assemble_svg** — Z-ordered painter's algorithm assembly with stroke=fill gap coverage Additional steps (compositional): 1. **classify_input** — Edge density + bimodality + Hough line count analysis 2. **extract_lines** — Morphological thin-feature isolation → skeletonize → Hough → merge collinear → measure stroke width → sample color 3. **suppress_line_regions** — Replace line pixels with median-blur background estimate 4. **assemble_compositional** — Layer fills behind strokes in grouped SVG ### Resume on failure ```python svg, flow = image_to_svg("source.jpg", mode="photo") # If extract_contours failed: flow.override(extract_contours, corrected_shapes) flow.resume() # quantize, detect_background, edge_map stay cached ``` ## Batch API Generate multiple variants from one image, sharing computation across runs with the same K: ```python from pipeline import image_to_svg_batch results = image_to_svg_batch("photo.jpg", [ {"name": "photo", "mode": "photo"}, {"name": "warhol", "mode": "graphic", "K": 12, "palette": "warhol4"}, {"name": "neon", "mode": "graphic", "K": 12, "palette": "neon"}, {"name": "sunset", "mode": "graphic", "K": 12, "palette": "sunset"}, {"name": "bw", "mode": "graphic", "K": 16, "palette": "bw"}, ], svg_width=1400) for name, svg in results.items(): with open(f"{name}.svg", "w") as f: f.write(svg) ``` Variants sharing the same K run the pipeline (preprocess → quantize → edge_map → extract_contours) **once**, then fan out at assembly for palette remapping. This guarantees structural identity across palette variants (same shapes, same paths) and saves ~20-60s per shared K group. **Verification still applies in batch mode.** The turnkey feel of batch processing makes it easy to skip the side-by-side comparison — don't. Render at least one variant per K group and verify before delivering. Background detection failures and palette mapping issues are invisible without rendering. ## Verification Protocol **After EVERY run, render and visually compare side-by-side.** This is non-negotiable. ```python import subprocess from PIL import Image subprocess.run(['rsvg-convert', '-w', '1400', 'output.svg', '-o', 'output.png']) orig = Image.open('source.jpg') rendered = Image.open('output.png') target_h = 800 orig_r = orig.resize((int(orig.width * target_h / orig.height), target_h)) rend_r = rendered.resize((int(rendered.width * target_h / rendered.height), target_h)) gap = 20 comp = Image.new('RGB', (orig_r.width + rend_r.width + gap, target_h), (255,255,255)) comp.paste(orig_r, (0, 0)) comp.paste(rend_r, (orig_r.width + gap, 0)) comp.save('comparison.png') # LOOK AT comparison.png BEFORE claiming success ``` ## Manual Post-Processing ### Handling Subtle Color Differences When two regions have similar luminance but different hue/saturation, K-means in RGB space merges them. Use **HSV multispectral analysis**: ```python hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV) h_ch, s_ch, v_ch = hsv[:,:,0], hsv[:,:,1], hsv[:,:,2] # Separate gray (low saturation) from red (high saturation) at similar brightness red_mask = ((h_ch 168)) & (s_ch > 120) & (v_ch > 80) gray_mask = (s_ch 40) & (v_ch ` element gets `stroke="{fill}" stroke-width="{gap_stroke}" stroke-linejoin="round"`. This bleeds each shape outward with its own fill color, covering inter-cluster gaps with the locally correct color. **Auto-scaling**: `gap_stroke` is computed as `max(1.0, round(svg_width / source_width))`. A 500px source at `svg_width=1000` gets `gap_stroke=2`; a 1000px source gets `gap_stroke=1`. This prevents the "snow" artifact where high-shape-count SVGs show visible light halos at zoom-out from excessive stroke bleed. Override explicitly: `image_to_svg("img.jpg", gap_stroke=3)`. **Why stroke beats dilation for gaps**: Dilation operates on binary masks *before* contour simplification — it blurs detail. Stroke operates on final polygons *after* `approxPolyDP` — it catches all gaps including those introduced by simplification. Pure vector, no file size penalty beyond attribute bytes (~12%). **Background fallback**: When `detect_background` finds no clusters, the bg rect uses `#000000` (black) instead of white. Black reads as shadow; white reads as absence. **Dilation** is reduced to `iterations=1` — just enough for morphological noise cleanup. Gap coverage is fully handled by stroke. ## Anti-Patterns 1. **Never hand-draw shapes** from visual interpretation. Use CV extraction. 2. **Never claim a fix works without r … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [oaustegard](https://github.com/oaustegard) - **Source:** [oaustegard/claude-skills](https://github.com/oaustegard/claude-skills) - **License:** MIT - **Homepage:** https://austegard.com/ai-tools/claude-skill-releases.html Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** yes - **Shell / process execution:** yes - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: flagged — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-oaustegard-claude-skills-image-to-svg - Seller: https://agentstack.voostack.com/s/oaustegard - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.