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 /wiki/spaces/KB3141/pages/533529551 (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
"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
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
Detected Rectangle Data - Coordinates, Dimension, ID, Class Name, Confidence Percentage
Accuracy * 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
Detected Rectangle Info - ID, Dimension, OCR Text, Confidence Percentage
Accuracy * 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
...
Measure - JSON Data
Lines - Name, Start & End coordinates, Angle, Measured length
"lines": [
{
"id": 1651502661344,
"label": "Line1",
"start": {
"x": 165,
"y": 479
},
"end": {
"x": 158,
"y": 4
},
"angle": 1.5560605513728232,
"length": 475
},
{
"id": 1651502665897,
"label": "Line2",
"start": {
"x": 436,
"y": 479
},
"end": {
"x": 436,
"y": 4
},
"angle": 1.5707963267948966,
"length": 475
}
]