Vision Analyze
Analyse images with Apple Vision for saliency, classification, horizon, aesthetics, and feature print.
What it does
Vision Analyze runs an image through Apple's on-device Vision engine and reports a saliency heatmap of where attention lands, scene and object classification labels, horizon angle, an aesthetic quality score, and a perceptual feature vector for comparing images. Each analysis is its own toggle, so only what is switched on is computed.
It has no visual output of its own, feeding numbers, labels and a heatmap image forward instead.
When to use it
Which part of an image draws the eye: turn on Saliency and read
saliency_mapAutomatic labels for what is in a shot: turn on Classify and read
labelsandtop_labelA tilted horizon detected for auto-levelling: turn on Horizon
A rough quality score: turn on Aesthetics
Two images compared for similarity: turn on Feature Print and compare the two
featuresvectors downstream
| Parameter | Type | Default | What it does |
|---|---|---|---|
image | Raster | — | Image to analyze |
saliency | Boolean | true | Enable saliency heatmap |
saliency_mode | String | "attention" | Saliency mode: attention (where eyes look) or objectness (distinct objects). One of: attention, objectness. |
classify | Boolean | true | Enable scene/object classification |
max_labels | Number | 5 | Maximum number of classification labels to return |
horizon | Boolean | false | Enable horizon angle detection |
aesthetics | Boolean | false | Enable aesthetic quality scoring |
feature_print | Boolean | false | Enable perceptual feature vector (for image similarity) |
enabled | Boolean | true | Enable analysis |
Gotchas
Every analysis is opt-in. Saliency and Classify default on; Horizon, Aesthetics and Feature Print default off. Check that a toggle is enabled when its output looks empty.
Saliency Mode chooses between "attention", where eyes tend to look, and "objectness", distinct object regions. The two can produce quite different heatmaps for the same image.
featuresis only meaningful when comparing two images. A single feature vector on its own carries nothing; the distance between two vectors is the signal.
This node is built on Apple's Vision framework and runs on macOS only. It produces no results on other platforms.
Worked example
Add a Vision Analyze node and wire an image into Image.
Leave Saliency and Classify on. Look at
saliency_mapfor the attention heatmap andtop_labelfor the leading classification.Turn on Horizon for the tilt angle for auto-levelling, or Aesthetics for a quality score.
Turn on Feature Print on two Vision Analyze nodes fed by different images, then compare their
featuresoutputs downstream to measure similarity.
See also
Object Detect · Vision Pose · Vision Text · Rasters (images)