API reference
The API for CodeProject.AI is divided into categories Image, Vision, Text, and Status with each category further broken into sub-topics.
This document will continually change and be updated to reflect the latest server version and installed analysis modules
Computer Audition
Sound Classifier
Classify sound files based on the UrbanSound8K dataset.
Platforms
All
Parameters
-
sound (File): The HTTP file object (WAV sound file) to be analyzed.
-
min_confidence (Float): The minimum confidence level for successful classification. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"label": (Text) // The classification label of the sound.
"confidence": (Float) // The confidence in the classification in the range of 0.0 to 1.0.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('sound', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/sound/classify';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("label: " + data.label)
console.log("confidence: " + data.confidence.toFixed(2))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Computer Vision
License Plate Reader
Detects and reads the characters in license plates detected within an image
Platforms
All, !Windows-Arm64
Parameters
- upload (File): The image to ALPR.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/vision/alpr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
License Plate Reader, Legacy route
Detects the characters in license plates detected within an image
Platforms
All, !Windows-Arm64
Parameters
- upload (File): The image to ALPR.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/image/alpr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
License Plate Reader RKNN
Detects the characters in license plates detected within an image
Platforms
Orangepi, Radxarock
Parameters
- upload (File): The image to ALPR.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/vision/alpr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
License Plate Reader RKNN, legacy route
Detects the characters in license plates detected within an image
Platforms
Orangepi, Radxarock
Parameters
- upload (File): The image to ALPR.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/image/alpr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector (Coral.AI)
Detects multiple objects in an image.
The object detection module uses YOLO (You Only Look Once) to locate and classify the objects the models have been trained on. At this point there are 80 different types of objects that can be detected:
- person
- bicycle, car, motorbike, aeroplane, bus, train, truck, boat
- traffic light, fire hydrant, stop sign, parking meter, bench
- cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe
- backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket
- bottle, wine glass, cup, fork, knife, spoon, bowl
- banana, apple, sandwich, orange, broccoli, carrot, hot dog, pizza, donut, cake
- chair, sofa, pottedplant, bed, diningtable, toilet, tvmonitor, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush
Platforms
All
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (Coral.AI)
Detects objects based on YOLO PyTorch models. Models are stored in the /ObjectDetectionCoral/assets directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's file
Platforms
All
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (Coral.AI)
Returns a list of models available.
Platforms
All
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detection (YOLOv5 .NET)
The object detection module uses ML.NET and YOLO (You Only Look Once) to locate and classify the objects the models have been trained on. At this point there are 80 different types of objects that can be detected.
Platforms
All, !Windows-Arm64
Parameters
-
image (File): The image to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (YOLOv5 .NET)
Detects objects based on YOLO PyTorch models. Models are stored as .pt files in the /ObjectDetectionYOLOv5Net/assets directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's .pt file
The object detection modules generally use YOLO (You Only Look Once) to locate and classify the objects the models have been trained on. The custom models included by default are
- ipcam-animal - bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
- ipcam-dark - Bicycle, Bus, Car, Cat, Dog, Motorcycle, Person
- ipcam-general - person, vehicle, plus objects in ipcam-dark
- ipcam-combined - person, bicycle, car, motorcycle, bus, truck, bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
Platforms
All, !Windows-Arm64
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (YOLOv5 .NET)
Returns a list of models available.
Platforms
All, !Windows-Arm64
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector (YOLOv5 3.1)
Detects multiple objects of 80 types in an image.
Platforms
All, !Macos-Arm64
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (YOLOv5 3.1)
Detects objects based on YOLO PyTorch models. Models are stored as .pt files in the /ObjectDetectionYOLOv5-3.1/custom-models directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's .pt file
The object detection modules generally use YOLO (You Only Look Once) to locate and classify the objects the models have been trained on. The custom models included by default are
- ipcam-animal - bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
- ipcam-dark - Bicycle, Bus, Car, Cat, Dog, Motorcycle, Person
- ipcam-general - person, vehicle, plus objects in ipcam-dark
- ipcam-combined - person, bicycle, car, motorcycle, bus, truck, bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
Platforms
All, !Macos-Arm64
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (YOLOv5 3.1)
Returns a list of models available.
Platforms
All, !Macos-Arm64
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector (YOLOv5 6.2)
Detects multiple objects, of 80 different possible types, in an image.
Platforms
All, !Raspberrypi, !Jetson
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (YOLOv5 6.2)
Detects objects based on YOLO PyTorch models. Models are stored as .pt files in the /ObjectDetectionYOLOv5-6.2/assets directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's .pt file
The object detection modules generally use YOLO (You Only Look Once) to locate and classify the objects the models have been trained on. The custom models included by default are
- ipcam-animal - bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
- ipcam-dark - Bicycle, Bus, Car, Cat, Dog, Motorcycle, Person
- ipcam-general - person, vehicle, plus objects in ipcam-dark
- ipcam-combined - person, bicycle, car, motorcycle, bus, truck, bird, cat, dog, horse, sheep, cow, bear, deer, rabbit, raccoon, fox, skunk, squirrel, pig
Platforms
All, !Raspberrypi, !Jetson
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (YOLOv5 6.2)
Returns a list of models available.
Platforms
All, !Raspberrypi, !Jetson
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector (YOLOv5 RKNN)
Detects multiple objects in an image.
Platforms
Orangepi, Radxarock
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.3. Optional. Defaults to 0.30
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.30);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (YOLOv5 RKNN)
Detects objects based on YOLO PyTorch models. Models are stored as .rknn files in the /ObjectDetectionYoloRKNN/custom-models directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's .rknn file
Platforms
Orangepi, Radxarock
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (YOLOv5 RKNN)
Returns a list of models available.
Platforms
Orangepi, Radxarock
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector (YOLOv8)
Detects multiple objects, of 80 different possible types, in an image.
Platforms
All
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/detection';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Custom Object Detector (YOLOv8)
Detects objects based on YOLO PyTorch models. Models are stored as .pt files in the /ObjectDetectionYOLOv8/assets directory, and to make a call to a specific model use /vision/custom/model-name, where 'model-name' is the name of the model's .pt file
Platforms
All
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4.
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, mask, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.0);
var url = 'http://localhost:32168/v1/vision/custom';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Segmentation (YOLOv8)
Segment multiple objects in an image.
Platforms
All
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Default 0.4. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"count": (Integer) // The number of objects found.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/segmentation';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + (nothing returned))
console.log("count: " + data.count)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Object Detector List Custom Models (YOLOv8)
Returns a list of models available.
Platforms
All
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"models": (String) // An array of strings containing the names of the models installed.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/custom/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("models: " + data.models)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Optical Character Recognition
Detects the text within an image
Platforms
All, !Windows-Arm64
Parameters
- upload (File): The image to OCR.
Response
JSON
{
"success": (Boolean) // True if successful.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/vision/ocr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Optical Character Recognition, Legacy route
Detects the text within an image
Platforms
All, !Windows-Arm64
Parameters
- upload (File): The image to OCR.
Response
JSON
{
"success": (Boolean) // True if successful.
"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('upload', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/image/ocr';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("predictions: " + (nothing returned))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Scene Classifier
Classifies the scene in an image. It can recognise 365 different scenes.
Platforms
All, !Jetson
Parameters
- image (File): The HTTP file object (image) to be analyzed.
Response
JSON
{
"success": (Boolean) // True if successful.
"label": (Text) // The classification of the scene such as 'conference_room'.
"confidence": (Float) // The confidence in the classification in the range of 0.0 to 1.0.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/vision/scene';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("label: " + data.label)
console.log("confidence: " + data.confidence.toFixed(2))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Face Recognition
Face Detection
Detects faces in an image and returns the coordinates of the faces.
Platforms
All, !Jetson
Parameters
-
image (File): The HTTP File Object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"message": (String) // A summary of the inference operation.
"error": (String) // (Optional) An description of the error if success was false.
"predictions": (Object) // An array of objects with the x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/face';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("message: " + data.message)
console.log("error: " + data.error)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Face Comparison
Compares two faces in two images and returns a value indicating how similar the faces are.
Platforms
All, !Jetson
Parameters
-
image1 (File): First HTTP File Object (image) to be analyzed.
-
image2 (File): Second HTTP File Object (image) to be analyzed.
Response
JSON
{
"success": (Boolean) // True if successful.
"similarity": (Float) // How similar the two images are, in the range of 0.0 to 1.0.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image1', fileChooser.files[0]);
formData.append('image2', fileChooser.files[1]);
var url = 'http://localhost:32168/v1/vision/face/match';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("similarity: " + data.similarity.toFixed(2))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
List Registered Faces
Lists the users that have images registered in the Face Recognition database.
Platforms
All, !Jetson
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"faces": (Object) // An array of the userid strings for users with registered images.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/vision/face/list';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("faces: " + JSON.stringify(data.faces))
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Register Face
Registers one or more images for a user for recognition. This trains the face recognition model and allows the face recognition to report back a userId based on an image you supply that may or may not contain that user's face.
Platforms
All, !Jetson
Parameters
-
imageN (File): The one or more HTTP File Objects (images) to be registered.
-
userid (Text): The identifying string for the user.
Response
JSON
{
"success": (Boolean) // True if successful.
"Message": (Text) // face added
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('imageN', fileChooser.files[0]);
formData.append("userid", '');
var url = 'http://localhost:32168/v1/vision/face/register';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("Message: " + data.Message)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Delete Registered Face
Removes a userid and images from the Face Registration database.
Platforms
All, !Jetson
Parameters
- userid (Text): The identifying string for the user.
Response
JSON
{
"success": (Boolean) // True if successful.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("userid", '');
var url = 'http://localhost:32168/v1/vision/face/delete';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Face Recognition
Recognizes all faces in an image and returns the userId and coordinates of each face in the image. If a new (unregistered) face is detected then no userid for that face will be returned.
Platforms
All, !Jetson
Parameters
-
image (File): The HTTP file object (image) to be analyzed.
-
min_confidence (Float): The minimum confidence level for an object will be detected. In the range 0.0 to 1.0. Optional. Defaults to 0.4
Response
JSON
{
"success": (Boolean) // True if successful.
"predictions": (Object) // An array of objects with the userid, x_max, x_min, max, y_min, label and confidence.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("min_confidence", 0.4);
var url = 'http://localhost:32168/v1/vision/face/recognize';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("predictions: " + JSON.stringify(data.predictions))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Generative AI
LlamaChat
Uses the Llama LLM to answer simple wiki-based questions.
Platforms
All, !Windows-Arm64, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
-
prompt (Text): The prompt to generate text from
-
system_prompt (Text): The description of the assistant
-
max_tokens (Integer): The maximum number of tokens to generate
-
temperature (Float): The temperature to use for sampling
Response
JSON
{
"success": (Boolean) // True if successful.
"reply": (Text) // The reply from the model.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("prompt", '');
formData.append("system_prompt", '');
formData.append("max_tokens", 0);
formData.append("temperature", 0.0);
var url = 'http://localhost:32168/v1/text/chat';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("reply: " + data.reply)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Text to Image Stable Diffusion
Creates an image based on the input prompt
Platforms
Windows, macOS, Linux
Parameters
-
prompt (Text): The prompt to be used by Stable Diffusion to create the image.
-
negative_prompt (Text): The negative prompt to be used by Stable Diffusion to create the image.
-
num_images (Integer): The number of images to generate. Defaults to 1. Optional. Defaults to 1
-
num_steps (Integer): The number of inference steps to run. Optional. Defaults to 20
-
seed (Integer): The seed to use for the diffusion random number generator. Defaults to a random seed.
-
width (Integer): The width of the image to be created. Defaults to 512. Must be a multiple of 8. Optional. Defaults to 512
-
height (Integer): The height of the image to be created. Must be a multiple of 8. Optional. Defaults to 512
-
guidance_scale (Float): How well the process aligns with the prompt for image generation (0.0 - 20.0). Defaults to 7.0. Optional. Defaults to 7.0
Response
JSON
{
"success": (Boolean) // True if successful.
"images": (File[]) // An array of 1-4 images generated.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("prompt", '');
formData.append("negative_prompt", '');
formData.append("num_images", 1);
formData.append("num_steps", 20);
formData.append("seed", 0);
formData.append("width", 512);
formData.append("height", 512);
formData.append("guidance_scale", 7.0);
var url = 'http://localhost:32168/v1/text2image/create';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("images: " + (nothing returned))
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Image Processing
Background Remover
Removes the background from behind the main subjects in images.
Platforms
All, !Linux, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
-
image (File): The image to have its background removed.
-
use_alphamatting (Boolean): Whether or not to use alpha matting. Optional. Defaults to false
Response
JSON
{
"success": (Boolean) // True if successful.
"imageBase64": (Base64ImageData) // The base64 encoded image that has had its background removed.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("use_alphamatting", false);
var url = 'http://localhost:32168/v1/image/removebackground';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
// Assume we have an IMG tag named img1
img1.src = "data:image/png;base64," + data.imageBase64;
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Cartoonizer
Convert a photo into an anime style cartoon.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
-
image (File): The image to be converted.
-
model (String): Name of the model to use
Response
JSON
{
"success": (Boolean) // True if successful.
"imageBase64": (Base64ImageData) // The base64 encoded image.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("model", );
var url = 'http://localhost:32168/v1/image/cartoonize';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
// Assume we have an IMG tag named img1
img1.src = "data:image/png;base64," + data.imageBase64;
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Portrait Filter
Blurs the background behind the main subjects in an image.
Platforms
Windows, Windows-Arm64
Parameters
-
image (File): The image to be filtered.
-
strength (Float): How much to blur the background (0.0 - 1.0). Optional. Defaults to 0.5
Response
JSON
{
"success": (Boolean) // True if successful.
"filtered_image": (Base64ImageData) // The base64 encoded image that has had its background blurred.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
formData.append("strength", 0.5);
var url = 'http://localhost:32168/v1/image/portraitfilter';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
// Assume we have an IMG tag named img1
img1.src = "data:image/png;base64," + data.filtered_image;
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Super Resolution
Increases the resolution of an image using AI to ensure no bluriness is introduced.
Platforms
All
Parameters
- image (File): The image to have its resolution increased.
Response
JSON
{
"success": (Boolean) // True if successful.
"imageBase64": (Base64ImageData) // The base64 encoded image that has had its resolution increased.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
// Assume we have a HTML INPUT type=file control with ID=fileChooser
var formData = new FormData();
formData.append('image', fileChooser.files[0]);
var url = 'http://localhost:32168/v1/image/superresolution';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
// Assume we have an IMG tag named img1
img1.src = "data:image/png;base64," + data.imageBase64;
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Natural Language
Sentiment Analysis
Determines if the supplied text has a positive or negative sentiment
Platforms
Windows, macOS
Parameters
- text (Text): The text to be analyzed.
Response
JSON
{
"success": (Boolean) // True if successful.
"is_positive": (Boolean) // Whether the input text had a positive sentiment.
"positive_probability": (Float) // The probability the input text has a positive sentiment.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and text manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("text", '');
var url = 'http://localhost:32168/v1/text/sentiment';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("is_positive: " + data.is_positive)
console.log("positive_probability: " + data.positive_probability.toFixed(2))
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Text Summary
Summarizes some content by selecting a number of sentences that are most representative of the content.
Platforms
All
Parameters
-
text (Text): The text to be summarized
-
num_sentences (Integer): The number of sentences to produce.
Response
JSON
{
"success": (Boolean) // True if successful.
"summary": (Text) // The summarized text.
"inferenceMs": (Integer) // The time (ms) to perform the AI inference.
"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("text", '');
formData.append("num_sentences", 0);
var url = 'http://localhost:32168/v1/text/summarize';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("summary: " + data.summary)
console.log("inferenceMs: " + data.inferenceMs)
console.log("processMs: " + data.processMs)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Training
Create Custom Dataset
Create a custom dataset from the Open Images repository.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
-
dataset_name (String): The name of the dataset.
-
classes (String): A comma delimited list of classes to include in the dataset.
-
num_images (Integer): The max number of images to include for each class. Default 100. Optional. Defaults to 100
Response
JSON
{
"success": (Boolean) // True if creating a dataset started.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("dataset_name", null);
formData.append("classes", null);
formData.append("num_images", 100);
var url = 'http://localhost:32168/v1/train/create_dataset';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Train Custom Model (YOLOv5 6.2)
Create a custom model from a custom dataset.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
-
name (String): The name of the model.
-
dataset (String): The name of the dataset.
-
num_epochs (Integer): The epoch to train the model. Default 100. Optional. Defaults to 100
-
device (String): None or 'cpu' or 0 or '0' or '0,1,2,3'. Default: ''
-
batch (Integer): The batch size. Default: 8 Optional. Defaults to 8
-
freeze (Integer): The layers to freeze, 0-None, 10-Backbone, 24-All Optional. Defaults to 0
-
hyp (Integer): Hyper-Parameters: 0-finetune (VOC), 1-scratch low, 2-scratch medium, 3-scratch high Optional. Defaults to 0
Response
JSON
{
"success": (Boolean) // True if training started.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("name", null);
formData.append("dataset", null);
formData.append("num_epochs", 100);
formData.append("device", );
formData.append("batch", 8);
formData.append("freeze", 0);
formData.append("hyp", 0);
var url = 'http://localhost:32168/v1/train/train_model';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Resume Training Model
Resume training of a model.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
- model_name (String): The name of the model.
Response
JSON
{
"success": (Boolean) // True if successful.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("model_name", null);
var url = 'http://localhost:32168/v1/train/resume_training';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Get Model information (YOLOv5 6.2)
Gets info about the model.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
- model_name (String): The name of the model.
Response
JSON
{
"success": (Boolean) // True if successful.
"model_name": (String) // The name of the model.
"complete": (Boolean) // True if the training was completed, can restart if not.
"training_dir": (String) // The training directory containing the custom model file and the training results.
"model_path": (String) // The path to best the custom model file.
"results_graph_path": (String) // The path the results.png file if it exists.
"results_csv_path": (String) // The path the results.csv file if it exists.
"pr_curve_path": (String) // The path PR_curve.png file if it exists.
"results_graph_image": (Base64ImageData) // The base64 encoded image of the result graphs.
"pr_curve_image": (Base64ImageData) // The base64 encoded image of the PR Curve graph.
"results_csv_file": (Base64ImageData) // The base64 encoded data for the results.csv file.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("model_name", null);
var url = 'http://localhost:32168/v1/train/model_info';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("model_name: " + data.model_name)
console.log("complete: " + data.complete)
console.log("training_dir: " + data.training_dir)
console.log("model_path: " + data.model_path)
console.log("results_graph_path: " + data.results_graph_path)
console.log("results_csv_path: " + data.results_csv_path)
console.log("pr_curve_path: " + data.pr_curve_path)
// Assume we have an IMG tag named img1
img1.src = "data:image/png;base64," + data.results_graph_image;
// Assume we have an IMG tag named img2
img2.src = "data:image/png;base64," + data.pr_curve_image;
// Assume we have an IMG tag named img3
img3.src = "data:image/png;base64," + data.results_csv_file;
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Get Dataset information (YOLOv5 6.2)
Gets info about the dataset.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
- dataset_name (String): The name of the dataset.
Response
JSON
{
"success": (Boolean) // True if successful.
"complete": (Boolean) // True if the training was completed, can restart if not.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var formData = new FormData();
formData.append("dataset_name", null);
var url = 'http://localhost:32168/v1/train/dataset_info';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("complete: " + data.complete)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Get Available Classes)
Gets list of classes available from which to create datasets.
Platforms
All, !Raspberrypi, !Orangepi, !Radxarock, !Jetson
Parameters
(None)
Response
JSON
{
"success": (Boolean) // True if successful.
"moduleId": (String) // The Id of the module that processed this request.
"moduleName": (String) // The name of the module that processed this request.
"command": (String) // The command that was sent as part of this request. Can be detect, list, status.
"statusData": (Object) // [Optional] An object containing (if available) the current module status data.
"inferenceDevice": (String) // The name of the device handling the inference. eg CPU, GPU, TPU
"analysisRoundTripMs": (Integer) // The time (ms) for the round trip to the analysis module and back.
"processedBy": (String) // The hostname of the server that processed this request.
"timestampUTC": (String) // The timestamp (UTC) of the response.
}
Example
JavaScript
var url = 'http://localhost:32168/v1/train/list_classes';
fetch(url, { method: "POST", body: formData})
.then(response => {
if (response.ok) {
response.json().then(data => {
console.log("success: " + data.success)
console.log("moduleId: " + data.moduleId)
console.log("moduleName: " + data.moduleName)
console.log("command: " + data.command)
console.log("statusData: " + JSON.stringify(data.statusData))
console.log("inferenceDevice: " + data.inferenceDevice)
console.log("analysisRoundTripMs: " + data.analysisRoundTripMs)
console.log("processedBy: " + data.processedBy)
console.log("timestampUTC: " + data.timestampUTC)
})
}
});
.catch (error => {
console.log('Unable to complete API call: ' + error);
});
Settings
Change a setting
Sets the value of a setting for the given module.Platforms
All
Parameters
- name - the name of a setting. Please refer to Module Settings for the modules and global settings that can be changed.
- value - The new value of the setting.
Response
Note that when this API is called, the server will restart the given module automatically, and the changes will be persisted across server and module restarts.
List settings
Gets all the settings for the given module.Platforms
All
Response
The response will be a Json object with the general 'success' property, as well
as two collections: environmentVariables are the environment variables passed
to the module's code, and settings which define how the module will be started.
JSON
{
"success": true,
"environmentVariables": {
"CPAI_APPROOTPATH": "C:\\Program Files\\CodeProject\\AI",
"CPAI_PORT": "32168",
"APPDIR": "%CURRENT_MODULE_PATH%",
"CPAI_HALF_PRECISION": "enable",
"CPAI_MODULE_SUPPORT_GPU": "False",
"CUSTOM_MODELS_DIR": "%CURRENT_MODULE_PATH%\\custom-models",
"MODELS_DIR": "%CURRENT_MODULE_PATH%\\assets",
"MODEL_SIZE": "medium",
"USE_CUDA": "False",
"YOLOV5_AUTOINSTALL": "false",
"YOLOV5_VERBOSE": "false"
},
"settings": {
"autostart": true,
"supportGPU": true,
"logVerbosity": null,
"halfPrecision": "enable",
"parallelism": 0,
"postStartPauseSecs": 1
}
}
Status
Server Logs
Gets up to 20 log entries, starting from id =
Platforms
Windows, Linux, macOS, macOS-Arm, Docker
Parameters
- lastid - the ID of the last entry that was retrieved, in order to send only new log entries
- count - The number of entries to return
Response
JSON
{
"id": Integer, The id of the log entry
"timestamp": A datetime value. The timestamp as UTC time of the log entry
"entry": Text. The text of the entry itself
}
Server Ping
A server ping. Just so you can easily tell if it's alive
PlatformsWindows, Linux, macOS, macOS-Arm, Docker
Response
If all is good.
Server Version
Returns the current version of the server
PlatformsWindows, Linux, macOS, macOS-Arm, Docker
Response
JSON
{
"success": true,
"version": {
"major": 2,
"minor": 2,
"patch": 4,
"preRelease": "Beta",
"securityUpdate": false,
"build": 0,
"file": "CodeProject.AI.Server-2.2.4.zip",
"releaseNotes": "Features and improvements"
},
"message": "2.2.4-Beta"
}
Server Update Available
A note on whether an update is available
PlatformsWindows, Linux, macOS, macOS-Arm, Docker
Response
JSON
{
"success" : true/false,
"message" : "An update to version X is available" / "You have the latest",
"version" : <version object>, // [Deprecated] The latest available version
"current" : <version object>, // The current installed version
"latest" : <version object>, // The latest available version
"updateAvailable" : true/false
};
Where version object is