infer_yolop_v2

infer_yolop_v2

About

1.2.0
MIT

Panoptic driving Perception using YoloPv2

Task: Object detectionTask: Instance segmentationTask: Semantic segmentation
YOLOPv2
infer
panoptic
driving
traffic
object detection
segmentation

Run YOLOP_v2 for Panoptic driving Perception. This model detects traffic object detection, drivable area segmentation and lane line detection.

Road object 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 Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolop_v2", auto_connect=True)

# Run on your image  
wf.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 result
display(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.

  • If you haven't started using Ikomia Studio yet, download and install it from this page.

  • 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 Workflow
from ikomia.utils.displayIO import display

# Init your workflow
wf = Workflow()

# Add algorithm
algo = 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 image  
wf.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 result
display(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 ikomia
from ikomia.dataprocess.workflow import Workflow

# Init your workflow
wf = Workflow()

# Add algorithm
algo = wf.add_task(name="infer_yolop_v2", auto_connect=True)

# Run on your image  
wf.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 outputs
for output in algo.get_outputs():
    # Print information
    print(output)
    # Export it to JSON
    output.to_json()

Developer

  • Ikomia
    Ikomia

License

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