Deployment - Python - Kinetix ML

Install the Library

Kinetix ML should be installed as a node module using the command below.

pip install KMLPipePy

Initialization

First start by importing the module.

from KMLPipePy import KMLPipeline

Create the pipeline object using your project name, version, and API Key.

pipe = KMLPipeline("[Your Project Name]", 1, "[Your API Key]")

Initialize the pipeline.

pipe.initialize()

Execution

To execute the pipeline you need to run the execute method with the required input data.

pipe.execute([]) # the [] should be replaced with your list of inputs

Webcam Inference

The pipeline will need a data source for each of its inputs. A common input is image data for nodes such as PoseDetection2D. For these nodes you can use OpenCV.

from KMLPipePy import KMLPipeline
from KMLPipePy.types import Canvas
import cv2
import time

pipe = KMLPipeline("[Project Name]", 1, "[API Key]")
pipe.initialize() # initialize the pipeline

out = Canvas() # initialize output drawing canvas
cam = cv2.VideoCapture(0) # start OpenCV webcam capture

while True:
  res, image = cam.read() # read image from webcam

  if image is not None and image.any():
    out.set_image(image)
    t0 = time.time()
    outputs = pipe.execute([image, out]) # execute pipeline
    print(outputs)
    t1 = time.time()
    print(f"{1/(t1-t0)} fps")

    if out.show(1):
      break

cam.release()

​Install the Library

​Initialization

​Execution

​Webcam Inference

Install the Library

Initialization

Execution

Webcam Inference