Inspection shows how the input image is processed for each module, where detected objects are displayed for individual imageseach image, providing detailed information such as: rectangles rectangle coordinates, ID, image size, defect defection confidence percentage, etc.
The Context (application variable application), 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)
| Code Block |
|---|
|
{
"globalData": null,
"image": {
"type": "<numpy>",
"shape": [
837,
1305,
3
]
}, |
Detected
...
Rectangle Data - XY Coordinates,
...
Dimension[px], Area[px], ID, Color, Class Name
| Code Block |
|---|
|
"detectedRectangles": [
{
"x": 1086,
"y": 510,
"width": 3,
"height": 3,
"area": 2, |
...
| Code Block |
|---|
"
"id": 1603111484990003,
"classNames": [
{
"color": "#ff00ff",
"color_bgr": [
255,
0,
255
],
"id": 1603111688057,
"label": "Scratch"
}
.... |
Heatmap Data - Dimension, Color, ID & Class Name
| Code Block |
|---|
|
"heatmaps": [
[
{
"type": "<numpy>",
"shape": [
768,
1024,
1
]
},
{
"color": "#ff0000",
"color_bgr": [
0,
0,
255
],
"id": 1,
"label": "Defect"
}
]
.... |
Classifier - JSON Data
Image Dimension [px]
| Code Block |
|---|
|
{
"globalData": null,
"image": {
"type": "<numpy>",
"shape": [
837,
1305,
3
]
} |
Detected Retangle Data - Class Names, ID & Confidence % (Accuracy)
| Info |
|---|
Acurracy * 100 = Accuracy % |
| Info |
|---|
Detected Rectangle for Product A has 99.99% confidence |
| Code Block |
|---|
|
"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)
| Code Block |
|---|
|
{
"globalData": null,
"image": {
"type": "<numpy>",
"shape": [
837,
1305,
3
]
} |
Detected Rectangle Data - XY Coordinates, Dimension, Area, ID, Color, Class Name
| Code Block |
|---|
|
"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
| Code Block |
|---|
|
"heatmaps": [
[
{
"type": "<numpy>",
"shape": [
837,
1305,
1
]
},
{
"color": "#ff0000",
"color_bgr": [
0,
0,
255
],
"id": 1,
"label": "Defect"
} |
Detector - JSON Data
Image Dimension (Pixels)
| Code Block |
|---|
|
{
"globalData": null,
"image": {
"type": "<numpy>",
"shape": [
837,
1305,
3
]
} |
Detected Rectangle Data - Coordinates, Dimension, ID, Class Name, Confidence Percentage
| Info |
|---|
Acurracy * 100 = Accuracy % |
| Info |
|---|
Detected Rectangle for ''id=…1000'' has 99.18% confidence |
| Code Block |
|---|
|
"detectedRectangles": [
{
"x": 1086456,
"y": 510361,
"width": 3165,
"height": 3163,
"areaid": 21604385708721000,
"idconfidence": 16031114849900030.9918909072875977,
"classNames": [
{
"id": 1604385716945,
"colorlabel": "#ff00ffScrew02",
"color_bgrconfidence": [
0.9918909072875977
}
]
255},
... |
OCR - JSON Data
Image Dimension (Pixels)
| Code Block |
|---|
|
{
"globalData": null,
"image": {
0, "type": "<numpy>",
"shape": [
837,
1305,
255 3
]
} |
Detected Rectangle Info - ID, Dimension, OCR Text, Confidence Percentage
| Info |
|---|
Acurracy * 100 = Accuracy % |
| Info |
|---|
Detected Rectangle for ''text=Today'' has 99.71% confidence |
| Code Block |
|---|
|
"ocr": [
{
"id": 1615382086440,
"width": 109,
"height": 1603111688057, 38,
"x": 169.11210023365456,
"y": 420.09002285870446,
"labeltext": "ScratchToday",
"confidence": 0.997153103351593
} ... |