Inspection

Inspection shows how the input image is processed for each module, where detected objects are displayed for each image, providing detailed information such as: rectangle coordinates, ID, image size, confidence percentage, etc.

The Context (application variable), is displayed in JSON format for each module. The data will be available in Inspection after you activate a model for any module within the Flow.

Module-independent data

Image Dimension (Pixels)

{ "globalData": null, "image": { "type": "<numpy>", "shape": [ 837, 1305, 3 ] }

 

Anomaly Detector - JSON Data

Detected Rectangle Data - XY Coordinates, Dimension[px], Area[px], ID, Color, Class Name

"detectedRectangles": [ { "x": 1086, "y": 510, "width": 3, "height": 3, "area": 2, "id": 1603111484990003, "classNames": [ { "color": "#ff00ff", "color_bgr": [ 255, 0, 255 ], "id": 1603111688057, "label": "Scratch" } ....

Heatmap Data - Dimension, Color, ID & Class Name

"heatmaps": [ [ { "type": "<numpy>", "shape": [ 768, 1024, 1 ] }, { "color": "#ff0000", "color_bgr": [ 0, 0, 255 ], "id": 1, "label": "Defect" } ] ....

Classifier - JSON Data

Detected Rectangle Data - Class Names, ID & Confidence % (Accuracy)

Accuracy * 100 = Accuracy %

Detected Rectangle for Product A has 99.99% confidence

Surface Detection - JSON Data

Detected Rectangle Data - XY Coordinates, Dimension, Area, ID, Color, Class Name

Heatmap Data - Dimension, Color, ID & Class Name

Detector - JSON Data

Detected Rectangle Data - Coordinates, Dimension, ID, Class Name, Confidence Percentage

Accuracy * 100 = Accuracy %

OCR - JSON Data

Detected Rectangle Info - ID, Dimension, OCR Text, Confidence Percentage

Measure - JSON Data

Lines - Name, Start & end coordinates, Angle, Measured length