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import gradio as gr |
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import cv2 |
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import requests |
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import os |
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from ultralytics import YOLO |
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path = ['./data/0068.jpg', './data/0210.jpg', './data/IMG_7078.jpg', './data/IMG_7103.jpg', './data/IMG_7705.jpg'] |
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model_path = './best.pt' |
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model = YOLO(model_path) |
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def detect_cheerios(image_path): |
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results = model(image_path) |
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image = results[0].plot() [:,:,::-1] |
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return image |
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iface = gr.Interface( |
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fn=detect_cheerios, |
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inputs=gr.components.Image(type="filepath", label="Input Image"), |
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outputs=gr.Image(), |
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title="Cheerios detector", |
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description='<p>This model is trained to detect one Cheerios box in an indoor setting, and it is trained using synthetic data from the Duality.ai simulation software: FalconEditor. <a href="https://falcon.duality.ai/secure/documentation?learnWelcome=true&sidebarMode=learn" target="_blank">Try FalconEditor today</a>, and see if you can train a more robust model that functions in a larger variety of domains!</p> In a world where data regulations are starting to limit AI useage, FalconEditor offers a way to obtain large, regulation-passing datasets easily and quickly. Dive into synthetic data by creating a FREE learner account at <a href="https://falcon.duality.ai/secure/documentation?learnWelcome=true&sidebarMode=learn" target="_blank">falcon.duality.ai</a>. Follow along with tutorials as we walk you through how to assemble a scenario and collect data for AI training. Used by companies like P&G, KEF Robotics, and AWS, this powerful software is now available for non-commercial use. <p><a href="https://falcon.duality.ai/secure/documentation?learnWelcome=true&sidebarMode=learn" target="_blank">Sign up now, and start creating!</a></p>', |
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examples= path, |
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) |
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iface.launch() |
