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/validade a model for any module within the Flow.

Anomaly of Surface - JSON Data

Image Dimension (Pixels)

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

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

Image Dimension [px]

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

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

Acurracy * 100 = Accuracy %

Detected Rectangle for Product A has 99.99% confidence

"detectedRectangles": [
    {
      "classNames": [
        {
          "label": "Product A",
          "id": 1579785458561,
          "accuracy": 0.9999566078186035
        },
        {
          "label": "Product B",
          "id": 1563879945497,
          "accuracy": 0.000023665264961891808
        },
        {
          "label": "Product C",
          "id": 1579785458074,
          "accuracy": 0.00001757865356921684
        },
        {
          "label": "Missing",
          "id": 1579785459058,
          "accuracy": 0.0000020917841538903303
        },
        {
          "label": "Product D",
          "id": 1563879965635,
          "accuracy": 2.7898661159042604e-8
        }
      ]

Surface Detection - JSON Data

Image Dimension (Pixels)

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

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

"detectedRectangles": [
    {
      "x": 35,
      "y": 681,
      "width": 30,
      "height": 33,
      "area": 718.5,
      "id": 1603111484990000,
      "classNames": [
        {
          "color": "#ff0000",
          "color_bgr": [
            0,
            0,
            255
          ],
          "id": 1,
          "label": "Defect"
        }

Heatmap Data - Dimension, Color, ID & Class Name

"heatmaps": [
    [
      {
        "type": "<numpy>",
        "shape": [
          837,
          1305,
          1
        ]
      },
      {
        "color": "#ff0000",
        "color_bgr": [
          0,
          0,
          255
        ],
        "id": 1,
        "label": "Defect"
      }

Detector - JSON Data

Image Dimension (Pixels)

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

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

Acurracy * 100 = Accuracy %

Detected Rectangle for ''id=…1000'' has 99.18% confidence

  "detectedRectangles": [
    {
      "x": 456,
      "y": 361,
      "width": 165,
      "height": 163,
      "id": 1604385708721000,
      "confidence": 0.9918909072875977,
      "classNames": [
        {
          "id": 1604385716945,
          "label": "Screw02",
          "confidence": 0.9918909072875977
        }
      ]
    },
  ...

OCR - JSON Data

Image Dimension (Pixels)

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

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

Acurracy * 100 = Accuracy %

Detected Rectangle for ''text=Today'' has 99.71% confidence

"ocr": [
    {
      "id": 1615382086440,
      "width": 109,
      "height": 38,
      "x": 169.11210023365456,
      "y": 420.09002285870446,
      "text": "Today",
      "confidence": 0.997153103351593
      ...