{ "openapi": "3.1.0", "info": { "title": "Supervision Draw", "description": "Draw detections onto an image with Roboflow supervision's own annotators, over HTTP and MCP. Send a base64 image and detections (boxes in xyxy pixels, optional class_id, confidence, label, polygon) and get back the annotated image plus per-class counts. Annotators: box, round_box, box_corner, circle, dot, ellipse, triangle, label, color, mask, polygon, halo, background_overlay, blur, pixelate, percentage_bar, chained in order. A hosted alternative to installing Python and supervision just to render detection results.", "version": "1.0.0" }, "servers": [ { "url": "https://supervision-draw.saastemly.com" } ], "paths": { "/v1/annotate": { "post": { "operationId": "annotate", "summary": "Annotate an image with detections using supervision's annotators (box, round_box, box_corner, circle, dot, ellipse, triangle, label, color, mask, polygon, halo, background_overlay, blur, pixelate, percentage_bar), applied in order.", "description": "Annotate an image with detections using supervision's annotators (box, round_box, box_corner, circle, dot, ellipse, triangle, label, color, mask, polygon, halo, background_overlay, blur, pixelate, percentage_bar), applied in order. Input: base64 image up to 4 megapixels, up to 500 detections with xyxy pixel boxes and optional class_id, confidence, ASCII label, polygon. Returns the annotated image (base64 PNG or JPEG), size and counts per class.\n\nPrice: $0.003 per call (x402, or prepaid credits with an API key).", "x-price-usd": 0.003, "responses": { "200": { "description": "The result", "content": { "application/json": { "example": { "image": "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"mimeType": "image/jpeg", "width": 240, "height": 160, "detections": 3, "counts": { "total": 3, "byClass": { "car": 1, "person": 1, "ball": 1 } }, "annotators": [ "box", "label" ] } } } }, "400": { "description": "The request is not valid (nothing is charged)" }, "402": { "description": "Payment required: the x402 terms are in the PAYMENT-REQUIRED header; or send an API key" } }, "security": [ {}, { "apiKey": [] } ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "type": "object", "properties": { "image": { "type": "string", "description": "Base64 of a PNG, JPEG, BMP or WebP file (a data: URI also works), up to 4 million pixels and about 2 MB" }, "detections": { "type": "array", "description": "Up to 500 detections, drawn in this order", "items": { "type": "object", "properties": { "xyxy": { "type": "array", "items": { "type": "number" }, "description": "Box [x1, y1, x2, y2] in pixels from the top-left corner; may extend past the image" }, "class_id": { "type": "integer", "description": "Class number, 0 or more; picks the colour and the name from class_names" }, "confidence": { "type": "number", "description": "0 to 1; shown in the label unless label is given" }, "label": { "type": "string", "description": "Label text, printable ASCII, up to 64 characters (replaces the generated one)" }, "tracker_id": { "type": "integer", "description": "Tracker id, used when color_lookup is track" }, "polygon": { "type": "array", "items": { "type": "array", "items": { "type": "number" } }, "description": "Outline [[x, y], ...] (3 to 512 points) that mask, polygon and halo draw; required on every detection when one of them is used" } }, "required": [ "xyxy" ] } }, "annotators": { "type": "array", "description": "Annotators applied in order, each a type name or an object {\"type\": ..., options}. Default [box, label]. Put blur, pixelate, color, mask or background_overlay before box and label. Types: box, round_box, box_corner, circle, dot, ellipse, triangle, label, color, mask, polygon, halo, background_overlay, blur, pixelate, percentage_bar. Options use supervision's names: color, color_lookup, thickness, opacity, roundness, corner_length, radius, position, start_angle, end_angle, base, height, width, kernel_size, pixel_size, outline_thickness, outline_color, border_color, border_thickness, force_box, text_color, text_scale, text_thickness, text_padding, text_position, border_radius, smart_position. color: A hex colour like \"#ff8800\" or a name: black, blue, green, grey, red, roboflow, white, yellow (default: supervision's class palette). color_lookup: What picks the colour: \"class\" (default when every detection has a class_id), \"index\" (position in the list) or \"track\" (tracker_id). position and text_position: Anchor point: center, center_left, center_right, top_center, top_left, top_right, bottom_left, bottom_center, bottom_right, center_of_mass", "items": { "type": "object", "properties": { "type": { "type": "string", "description": "Annotator type" } }, "required": [ "type" ] } }, "class_names": { "type": "array", "items": { "type": "string" }, "description": "Names indexed by class_id, used in generated labels and in counts.byClass (printable ASCII)" }, "format": { "type": "string", "enum": [ "png", "jpeg" ], "description": "Output format (default png)" }, "quality": { "type": "integer", "description": "JPEG quality 1-100 (default 90)" } }, "required": [ "image", "detections" ] }, "example": { "image": "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", "detections": [ { "xyxy": [ 30, 60, 90, 120 ], "class_id": 0, "confidence": 0.94 }, { "xyxy": [ 140, 40, 180, 115 ], "class_id": 1, "confidence": 0.88 }, { "xyxy": [ 190, 90, 225, 125 ], "class_id": 2, "confidence": 0.71 } ], "class_names": [ "car", "person", "ball" ], "annotators": [ { "type": "box", "thickness": 2 }, { "type": "label", "text_scale": 0.4, "text_padding": 4 } ], "format": "jpeg", "quality": 85 } } } } } } }, "components": { "securitySchemes": { "apiKey": { "type": "http", "scheme": "bearer", "description": "An API key with prepaid credits, from https://supervision-draw.saastemly.com" } } }, "x-mcp-server": "https://supervision-draw.saastemly.com/mcp" }