Nodeflux Cloud Client Library for Python

Installation

pip install nodeflux-cloud

Usage

Set the environment variable NODEFLUX_ACCESS_KEY and NODEFLUX_SECRET_KEY to your keys.

from nodeflux.cloud.clients import ImageAnalyticClient
from nodeflux.cloud.requests import ImageAnalyticRequest, AnalyticTypes

client = cloud.ImageAnalyticClient()

with open("some-image.jpg", "rb") as image_file:
    image_content = image_file.read()

requests = [
    ImageAnalyticRequest(
        image=image_content,
        analytics=[
            AnalyticTypes.FACE_DETECTION,
            AnalyticTypes.FACE_DEMOGRAPHY,
        ]
    )
]

response = client.batch_image_analytic(requests)
print(response)

API Reference

class ImageAnalyticClient(transport=None)

Service that performs Nodeflux Cloud image analytics.

batch_image_analytic

Run analytics to a batch of images.

class ImageAnalyticRequest(image: bytes, analytics: List[AnalyticTypes])

Individual image request to be analyzed by Nodeflux Cloud.

class AnalyticTypes

Enums of analytic types supported by Nodeflux Cloud.

class BatchmageAnalyticResponse

Response from Nodeflux Cloud image analytic request.

face_detections: List[FaceDetection]

If present, face detection analytic has completed successfully.

face_demographics: List[FaceDemography]

If present, face demography analytic has completed successfully.

face_recognitions: List[FaceRecognition]

If present, face recognition analytics has completed successfully.

vehicle_detections: List[VehicleDetection]

If present, vehicle detection analytics has completed successfully.

license_plate_recognitions: List[LicensePlateRecognition]

If present, license plate recognition analytics has completed successfully.

class FaceDetection

bounding_box: BoundingBox

Bounding box around the detected face.

confidence: float

Confidence of the face detection.

class FaceDemography

gender: Gender

Detected gender from a face.

gender_confidence: float

Confidence of gender detection

age_range: AgeRange

The estimated age range.

class FaceRecognition

candidates: List[FaceCandidate]

List of candidates that matches the requested face. If candidates is set, the face recognition analytic has been successful.

class FaceCandidate

id: int64

Unique id of the recognized face.

confidence: float

Confidence of the recognition.