Inspection shows how the input image is processed throughout the whole flow shown by the graph. For each module, the graph shows its result on the image and processing time. The overall processing time for the whole flow is displayed below the graph.
The Context (application variable), is displayed in JSON format for each image. It shows basic information about the image, results of enabled models and other information like inputs from Operator View etc. Detected objects are displayed for each image, providing detailed information such as: rectangle coordinates, classname, confidence percentage, etc. (depending on the modules used).
Anomaly Detector - 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 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
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
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
Image Dimension (Pixels)
{
"globalData": null,
"image": {
"type": "<numpy>",
"shape": [
837,
1305,
3
]
}
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
For each line you can see its id, name, coordinates of its start and end, its angle (in radians) and measured length in current image (in pixels).
"lines": [
{
"id": 1660737984849,
"label": "Line1",
"start": {
"x": 296,
"y": 98
},
"end": {
"x": 295,
"y": 14
},
"angle": 1.5588921272365868,
"length": 84
},
{
"id": 1660737998736,
"label": "Line2",
"start": {
"x": 958,
"y": 55
},
"end": {
"x": 2,
"y": 53
},
"angle": 0.0020920471571390623,
"length": 956
}
]
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