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You can now use the best Computer Vision algorithms on your own images/videos in a few lines of code :
You can install Ikomia API through the PIP package management system.
Our API is compatible with Python 3.7 / 3.8 / 3.9 .
pip install ikomia
Join the Ikomia community and access our hub of state-of-the-art algorithms.
import ikomia
import os
# Easy and unsafe authentication | Only for personal use
os.environ['IKOMIA_USER'] = "your_username"
os.environ['IKOMIA_PWD'] = "your_password"
ikomia.authenticate()
With just a few lines of code.
from ikomia.utils import ik
from ikomia.dataprocess import workflow
import cv2
# Create your worflow
wf = workflow.create("My workflow")
# Add algorithms to your workflow
yolo_id, yolo = wf.add_task(ik.infer_yolo_v5)
canny_id, canny = wf.add_task(ik.ocv_canny)
# Connect your algorithms
wf.connect_tasks(wf.getRootID(), yolo_id)
wf.connect_tasks(yolo_id, canny_id)
# Run
wf.run_on(path="path/to/image")
# YOLO output image with bounding boxes
img_detect = wf.get_image_with_graphics(yolo_id)
# Canny output image
img_final = wf.get_image(canny_id, index=0)
cv2.imshow("Detection", img_detect)
cv2.imshow("Result", img_final)
# Export your workflow for reuse
wf.save("/path/to/my_workflow_name.json")
in our Ikomia HUB, selected and tested
for easy sharing with the community
with more than 15 years of experience
in our library of models, selected and tested
for easy sharing with the community
with more than 15 years experience
Notebook
Description
Simple workflow
How to make a simple workflow
Neural Style transfer
How to run Neural Style transfer on your images
YOLO v7
How to train and run YOLO v7 on your datasets
Detectron2 Object Detection
How to use Detectron2 Object Detection
MMPose
How to use MMPose
Coming soon
Learn and create your first Ikomia Python app with our API. Enjoy the Ikomia tools.
Ikomia API and all our models are open source. Visit our GitHub repo for more information.
