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README.md
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---
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title: Yolo
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emoji:
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colorFrom:
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colorTo: green
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sdk: gradio
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sdk_version: 4.39.0
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app_file: app.py
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pinned: false
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license: cc-by-4.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Yolo Ifmt
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emoji: 📈
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colorFrom: gray
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colorTo: green
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sdk: gradio
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sdk_version: 4.39.0
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app_file: app.py
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pinned: false
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license: cc-by-4.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import cv2
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from ultralytics import YOLOv10
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from moviepy.editor import *
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model = YOLOv10("best.pt")
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def predict_image(img):
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results = model.predict(
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source=img,
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# conf=conf_threshold,
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# iou=iou_threshold,
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# show_labels=True,
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# show_conf=True,
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)
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im_rgb = cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)
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return im_rgb if results else None
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def predict_video(video):
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results = model.predict(source=video, save_dir='./')
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# print(results)
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images_list = []
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for r in results:
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im_rgb = cv2.cvtColor(r.plot(), cv2.COLOR_BGR2RGB)
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images_list.append(im_rgb)
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clip = ImageSequenceClip(images_list, fps=15)
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clip.ipython_display(width = 360)
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return '__temp__.mp4' if results else None
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Image = gr.Interface(fn=predict_image,
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inputs=gr.Image(type="pil", label="Upload Image"),
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outputs=gr.Image(type="pil", label="Result"),
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description="Upload images for YOLOv10 object detection.",
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)
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Video = gr.Interface(fn=predict_video,
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inputs=gr.Video(),
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outputs=gr.Video(),
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description="Upload Video for YOLOv10 object detection.",
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)
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demo = gr.TabbedInterface(
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[Image, Video],
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["Image", "Video"],
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title="Ultralytics Gradio YOLOv10",
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)
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demo.launch(share=True)
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best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:5609255604b716b245c8281dfac6d9af494bb1108d8103b4079bd575c1b9f7e7
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size 5766244
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requirements.txt
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Binary file (234 Bytes). View file
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