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AlshimaaGamalAlsaied
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Parent(s):
56d1b77
commit
Browse files- app.py +74 -0
- img1.png +0 -0
- img2.png +0 -0
- img3.png +0 -0
- requirements.txt +4 -0
- yolo5_epoch100 +1 -0
app.py
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import gradio as gr
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#import torch
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import yolov5
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import subprocess
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import tempfile
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import time
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from pathlib import Path
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import uuid
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import cv2
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import gradio as gr
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# Images
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#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
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#torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
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def image_fn(
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image: gr.inputs.Image = None,
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model_path: gr.inputs.Dropdown = None,
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image_size: gr.inputs.Slider = 640,
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conf_threshold: gr.inputs.Slider = 0.25,
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iou_threshold: gr.inputs.Slider = 0.45,
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):
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"""
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YOLOv7 inference function
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Args:
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image: Input image
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model_path: Path to the model
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image_size: Image size
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conf_threshold: Confidence threshold
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iou_threshold: IOU threshold
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Returns:
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Rendered image
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"""
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model = yolov5.load(model_path, device="cpu", hf_model=True, trace=False)
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model.conf = conf_threshold
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model.iou = iou_threshold
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results = model([image], size=image_size)
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return results.render()[0]
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image_interface = gr.Interface(
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fn=image_fn,
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inputs=[
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gr.inputs.Image(type="pil", label="Input Image"),
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gr.inputs.Dropdown(
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choices=[
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"alshimaa/yolo5_epoch100",
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#"kadirnar/yolov7-v0.1",
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],
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default="alshimaa/yolo5_epoch100",
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label="Model",
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)
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#gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
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#gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
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#gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
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],
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outputs=gr.outputs.Image(type="filepath", label="Output Image"),
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title="Obiect detection model p",
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examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]],
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cache_examples=True,
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theme='huggingface',
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)
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if __name__ == "__main__":
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gr.TabbedInterface(
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[image_interface],
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["Detect Images"],
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).launch()
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img1.png
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img2.png
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img3.png
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requirements.txt
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gradio
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torch
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yolov5detect
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HfApi
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yolo5_epoch100
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Subproject commit c2212ddb924a66157b32a3af4f71b35c3d4c23fb
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