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update app.py with description and add description.html file
#1
by
hasibzunair
- opened
- app.py +5 -2
- description.html +24 -0
app.py
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@@ -1,6 +1,6 @@
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import gradio as gr
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import cv2
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import
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import os
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from PIL import Image
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from ultralytics import YOLO
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@@ -8,7 +8,8 @@ from ultralytics import YOLO
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################## MODEL ##################
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model = YOLO('best.pt')
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title = "RSUD20K"
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################## IMAGE ##################
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demo_image = gr.Interface(
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fn=show_preds_image,
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title=title,
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inputs= inputs_image,
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outputs= outputs_image,
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examples= [os.path.join(Image_directory, fname) for fname in os.listdir(Image_directory) if fname.endswith(".jpg")],
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demo_video = gr.Interface(
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fn=show_preds_video,
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title=title,
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inputs= inputs_video,
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outputs= outputs_video,
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examples= [os.path.join(Video_directory, fname) for fname in os.listdir(Video_directory) if fname.endswith(".mp4")],
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import gradio as gr
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import cv2
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import codecs
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import os
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from PIL import Image
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from ultralytics import YOLO
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################## MODEL ##################
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model = YOLO('best.pt')
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title = "RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving"
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description = codecs.open("description.html", "r", "utf-8").read()
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################## IMAGE ##################
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demo_image = gr.Interface(
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fn=show_preds_image,
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title=title,
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description=description,
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inputs= inputs_image,
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outputs= outputs_image,
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examples= [os.path.join(Image_directory, fname) for fname in os.listdir(Image_directory) if fname.endswith(".jpg")],
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demo_video = gr.Interface(
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fn=show_preds_video,
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title=title,
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description=description,
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inputs= inputs_video,
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outputs= outputs_video,
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examples= [os.path.join(Video_directory, fname) for fname in os.listdir(Video_directory) if fname.endswith(".mp4")],
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description.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>Title</title>
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</head>
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<body>
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Try this demo for an object detector trained on <a href="https://github.com/hasibzunair/RSUD20K">RSUD20K</a>,
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introduced in our paper <a href="https://arxiv.org/abs/2401.07322">RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving</a>.
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</br>
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RSUD20K is a new object detection dataset for road scene understanding,
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comprised of over 20K high-resolution images from the driving perspective
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on Bangladesh roads, and includes 130K bounding box annotations for
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13 objects.
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</br>
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You can use this demo to get the a bounding box and class label predictions of objects present in your images or videos. To use it, simply
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upload an image or video of your choice and hit submit. You will get one or more bounding boxes and names of objects present
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in your images or videos from this list:
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(person, rickshaw, rickshaw van, auto rickshaw, truck, pickup truck, private car, motorcycle, bicycle, bus, micro bus, covered van, human hauler)
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</br>
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<a href="https://www.kaggle.com/datasets/hasibzunair/rsud20k-bangladesh-road-scene-understanding">Dataset Page</a>
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</br>
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</body>
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</html>
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