com.moltlinestudio/vision

Tools

• bbox_convert

  Convert bounding boxes between COCO, Pascal VOC and YOLO. FREE. The three formats disagree on everything: COCO is [x, y, width, height], VOC is [x1, y1, x2, y2], YOLO is [cx, cy, w, h] normalised to the image. Getting this wrong produces boxes that look plausible and quietly ruin every metric. Typical input {"boxes": [[10, 20, 100, 50]], "from_format": "coco", "to_format": "yolo", "image_width": 640, "image_height": 480} returns {"boxes": [[0.0938, 0.0938, 0.1562, 0.1042]], "converted": 1, "rejected": []}. Use whenever a dataset and a model disagree about format. Not for scoring predictions (detection_metrics) and not for removing overlaps (nms). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "boxes must contain at least one box"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• colour_check

  Check a colour pair against the WCAG contrast thresholds. FREE. Uses the WCAG 2 relative-luminance formula, so the number matches what an accessibility audit will report. Typical input {"foreground": "#767676", "background": "#ffffff"} returns {"contrast_ratio": 4.54, "AA": true, "AAA": false, "required": {"AA": 4.5, "AAA": 7.0}, "verdict": "Passes AA for normal text, fails AAA."}. Use when generating or auditing an interface. Not for converting colours between spaces and not for palettes. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "foreground must be a hex colour like #767676"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• detection_metrics

  Score detections against ground truth and show the working. PREMIUM (license). Greedy matching at the IoU threshold, highest-confidence prediction first, each ground-truth box matched at most once - the standard protocol. Reports per-class precision, recall and F1, and average precision by the all-points interpolation used by Pascal VOC 2010 onward. Typical input {"predictions": [{"box": [0,0,10,10], "label": "cat", "score": 0.9}], "ground_truth": [{"box": [1,1,11,11], "label": "cat"}]} returns {"overall": {"tp": 1, "fp": 0, "fn": 0, "precision": 1.0, "recall": 1.0, "f1": 1.0}, "per_class": {...}, "mAP": 1.0}. Use to compare two models on the same held-out set. Not for cleaning up a single model's overlapping output first - run nms before this. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "ground_truth must contain at least one box"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• image_probe

  Read an image's format and pixel size from its header alone. FREE. Dimensions live in the first few dozen bytes of PNG, JPEG, GIF, BMP and WebP, so a base64 prefix is enough - you do not need to send the whole file, and nothing is decoded. Typical input {"data_base64": "iVBORw0KG..."} returns {"format": "png", "width": 1920, "height": 1080, "aspect_ratio": 1.7778, "aspect_label": "16:9", "megapixels": 2.07, "orientation": "landscape", "bytes_inspected": 512}. Use to find out what you are dealing with before planning a resize. Not for pixel content - nothing here reads pixels - and not for EXIF. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "data_base64 must not be empty"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• nms

  Remove duplicate detections of the same object. PREMIUM (license). Greedy non-maximum suppression: keep the highest-scoring box, drop everything overlapping it above the threshold, repeat. Ties break on the earlier index, so the result is deterministic rather than dependent on sort stability. Typical input {"boxes": [[0,0,10,10],[1,1,11,11],[50,50,60,60]], "scores": [0.9, 0.8, 0.7]} returns {"keep": [0, 2], "suppressed": [{"index": 1, "by": 0, "iou": 0.6807}], "kept": 2}. Use after a detector that emits overlapping boxes. Not for scoring against ground truth (detection_metrics) and not for format changes (bbox_convert). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "boxes and scores must be the same length"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• resize_plan

  Work out the exact scale, padding and crop for a model input size. FREE. Returns the numbers you need to transform boxes alongside the image, which is the step that usually gets skipped. Typical input {"width": 1920, "height": 1080, "target": "yolo_640"} returns {"scale": 0.3333, "resized": [640, 360], "pad": {"left": 0, "top": 140, "right": 0, "bottom": 140}, "box_transform": "x_new = x * 0.3333 + 0; y_new = y * 0.3333 + 140"}. Use before feeding an image to a fixed-input model. Not for finding out the image's size in the first place - that is image_probe. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "unknown target <value>; use one of <value> or set"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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