pip install ikomia
About
3.0.0
Apache-2.0
Inference for pose estimation models from mmpose
Inference for pose estimation models from mmpose.
🚀 Use with Ikomia API
1. Install Ikomia API
We strongly recommend using a virtual environment. If you're not sure where to start, we offer a tutorial here.
2. Create your workflow
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
wf = Workflow()
algo = wf.add_task(name = 'infer_mmlab_pose_estimation', auto_connect=True)
wf.run_on(url="https://cdn.nba.com/teams/legacy/www.nba.com/bulls/sites/bulls/files/jordan_vs_indiana.jpg")
display(algo.get_image_with_graphics())
☀️ Use with Ikomia Studio
Ikomia Studio offers a friendly UI with the same features as the API.
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If you haven't started using Ikomia Studio yet, download and install it from this page.
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For additional guidance on getting started with Ikomia Studio, check out this blog post.
📝 Set algorithm parameters
- config_file (str): Path to the .py config file.
- model_weight_file (str): Path or URL to model weights file .pth. Optional if config_file come from method get_model_zoo (see below for more information).
- conf_thres (float) default '0.5': Threshold of Non Maximum Suppression. It will retain Object Keypoint Similarity overlap when inferior to ‘conf_thres’, [0,1].
- conf_kp_thres (float) default '0.3': Threshold of the keypoint visibility. It will calculate Object Keypoint Similarity based on those keypoints whose visibility higher than ‘conf_kp_thres’, [0,1].
- detector: object detector, ‘Person’, ‘Hand’, Face’.
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_mmlab_pose_estimation", auto_connect=True)
algo.set_parameters({
"config_file": "configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_vipnas-mbv3_8xb64-210e_coco-256x192.py",
"conf_thres": "0.5",
"conf_kp_thres": "0.3",
"detector": "Person",
})
# Run on your image
wf.run_on(url="https://cdn.nba.com/teams/legacy/www.nba.com/bulls/sites/bulls/files/jordan_vs_indiana.jpg")
display(algo.get_image_with_graphics())
You can get the full list of available config_file by running this code snippet:
from ikomia.dataprocess.workflow import Workflow
from ikomia.utils.displayIO import display
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_mmlab_pose_estimation", auto_connect=True)
# Get pretrained models
model_zoo = algo.get_model_zoo()
# Print possibilities
for parameters in model_zoo:
print(parameters)
🔍 Explore algorithm outputs
Every algorithm produces specific outputs, yet they can be explored them the same way using the Ikomia API. For a more in-depth understanding of managing algorithm outputs, please refer to the documentation.
from ikomia.dataprocess.workflow import Workflow
# Init your workflow
wf = Workflow()
# Add algorithm
algo = wf.add_task(name="infer_mmlab_pose_estimation", auto_connect=True)
# Run on your image
wf.run_on(url="https://cdn.nba.com/teams/legacy/www.nba.com/bulls/sites/bulls/files/jordan_vs_indiana.jpg")
# Iterate over outputs
for output in algo.get_outputs():
# Print information
print(output)
# Export it to JSON
output.to_json()
Developer
Ikomia
License
Apache License 2.0
A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
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