Jewelry Image Parser

name jewelry-image-parser description Deterministic rules for converting 2D jewelry images into structured parametric JSON for CAD generation. PURPOSE This skill defines how 2D jewelry images are interpreted and converted into structured parametric data. The objective is: Structured extraction Deterministic behavior Stable JSON output No artistic guessing Output must strictly follow the jewelry parametric schema. INPUT ASSUMPTIONS Input: Single jewelry item Front-facing or slight perspective Clean background preferred Ring, pendant, or earring If multiple objects are detected: → Select the most central object. If background noise exists: → Ignore background regions. OUTPUT FORMAT (MANDATORY) { "ring_radius": float, "band_width": float, "band_thickness": float, "center_stone": { "type": string, "diameter": float, "height": float, "prongs": int }, "setting_type": "prong" | "bezel", "side_stones": [ { "type": string, "diameter": float, "count": int } ], "confidence_score": float } No extra fields allowed. If uncertain → use fallback defaults. RING DETECTION RULES Detect circular or elliptical band region. Estimate inner and outer boundary. Compute band_width as: outer_radius - inner_radius Estimate ring_radius as: average(inner_diameter / 2) If ellipse due to perspective: → Correct using vertical axis scaling. If band is partially occluded: → Infer missing arc using symmetry assumption. CENTER STONE IDENTIFICATION LOGIC Detect largest gemstone region inside ring boundary. Must lie on central vertical axis. Shape classification rules: Circular → "round" Elongated symmetric → "oval" Rectangular step-cut → "emerald" Teardrop → "pear" If ambiguous: → Default to "round" Diameter estimation: Use bounding box width Normalize relative to band width Height estimation: Set as 60% of diameter (default) Prong estimation: Count visible prongs If unclear: Default = 4 SETTING TYPE DETECTION If thin metal claws around stone: → setting_type = "prong" If continuous metal rim surrounding stone: → setting_type = "bezel" If unclear: → Default = "prong" SIDE STONE DETECTION RULES Detect repeated small circular regions along band. Must be symmetrically placed. Count total visible stones. Estimate diameter as average bounding width. If only one side visible: → Mirror count. If detection confidence < threshold: → side_stones = [] SYMMETRY ASSUMPTION RULE Rings are symmetric across vertical center axis. If asymmetry detected: → Mirror dominant side. → Ignore minor perspective distortions. Never generate asymmetrical rings unless confidence > 0.9. SCALE NORMALIZATION RULE If real-world scale cannot be inferred: Default: ring_radius = 9 mm band_width = 2.2 mm band_thickness = 1.8 mm All other dimensions scaled proportionally. Never leave values undefined. CONFIDENCE SCORE LOGIC Compute confidence_score as weighted average of: Band detection certainty Center stone detection certainty Setting classification certainty Symmetry validation Range: 0.0 – 1.0 If confidence_score < 0.6: → Use conservative defaults → Reduce complexity → Avoid generating side stones FALLBACK DEFAULTS If center stone not confidently detected: { "type": "round", "diameter": 6.0, "height": 3.6, "prongs": 4 } If band unclear: { "ring_radius": 9.0, "band_width": 2.2, "band_thickness": 1.8 } System must never return null geometry. PROHIBITED BEHAVIOR Do not hallucinate complex filigree. Do not infer engraving. Do not guess gemstone color. Do not estimate metal type from image. Do not generate decorative elements unless clearly visible. This parser extracts structure only. PRIORITY ORDER Structural correctness Symmetry Manufacturability compatibility Visual approximation If trade-off required: → Favor structural stability. OBJECTIVE The image parser is not an artistic interpreter. It is a structural extraction system. It must: Produce consistent JSON Avoid overfitting to visual noise Enable reliable downstream parametric generation