infer_yolop_v2
About
Panoptic driving Perception using YoloPv2
Run YOLOP_v2 for Panoptic driving Perception. This model detects traffic object detection, drivable area segmentation and lane line detection.
🚀 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.
pip install ikomia
2. Create your workflow
from ikomia.dataprocess.workflow import Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolop_v2", auto_connect=True)# Run on your imagewf.run_on(url="https://www.cnet.com/a/img/resize/4797a22dd672697529df19c2658364a85e0f9eb4/hub/2023/02/14/9406d927-a754-4fa9-8251-8b1ccd010d5a/ring-car-cam-2023-02-14-14h09m20s720.png?auto=webp&width=1920")# Inpect your resultdisplay(algo.get_image_with_graphics())display(algo.get_output(0).get_overlay_mask())
☀️ 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
- input_size (int) - default '640': Size of the input image.
- conf_thres (float) default '0.2': Box threshold for the prediction [0,1].
- iou_thres (float) - default '0.45': Intersection over Union, degree of overlap between two boxes [0,1].
- object (bool) - default 'True': Detect vehicles.
- road_lane (bool) - default 'True': Detect road and line.
Parameters should be in strings format when added to the dictionary.
from ikomia.dataprocess.workflow import Workflowfrom ikomia.utils.displayIO import display# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolop_v2", auto_connect=True)algo.set_parameters({"input_size": "640","conf_thres": "0.2","iou_thres": "0.45","object": "True","road_lane": "True"})# Run on your imagewf.run_on(url="https://www.cnet.com/a/img/resize/4797a22dd672697529df19c2658364a85e0f9eb4/hub/2023/02/14/9406d927-a754-4fa9-8251-8b1ccd010d5a/ring-car-cam-2023-02-14-14h09m20s720.png?auto=webp&width=1920")# Inpect your resultdisplay(algo.get_image_with_graphics())display(algo.get_output(0).get_overlay_mask())
🔍 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.
import ikomiafrom ikomia.dataprocess.workflow import Workflow# Init your workflowwf = Workflow()# Add algorithmalgo = wf.add_task(name="infer_yolop_v2", auto_connect=True)# Run on your imagewf.run_on(url="https://www.cnet.com/a/img/resize/4797a22dd672697529df19c2658364a85e0f9eb4/hub/2023/02/14/9406d927-a754-4fa9-8251-8b1ccd010d5a/ring-car-cam-2023-02-14-14h09m20s720.png?auto=webp&width=1920")# Iterate over outputsfor output in algo.get_outputs():# Print informationprint(output)# Export it to JSONoutput.to_json()
Developer
Ikomia
License
MIT License
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