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Duplicate from kadirnar/yolov8

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Co-authored-by: Kadir Nar <kadirnar@users.noreply.huggingface.co>

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  1. .gitattributes +34 -0
  2. README.md +16 -0
  3. app.py +84 -0
  4. requirements.txt +5 -0
.gitattributes ADDED
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README.md ADDED
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+ ---
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+ title: Yolov8
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+ emoji: πŸŒ…
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+ colorFrom: blue
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+ colorTo: yellow
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+ sdk: gradio
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+ sdk_version: 3.16.1
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+ app_file: app.py
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+ pinned: false
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+ license: gpl-3.0
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+ tags:
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+ - making-demos
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+ duplicated_from: kadirnar/yolov8
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ import gradio as gr
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+ import torch
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+ from sahi.prediction import ObjectPrediction
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+ from sahi.utils.cv import visualize_object_predictions, read_image
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+ from ultralyticsplus import YOLO
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+
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+ # Images
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+ torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
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+ torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.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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+
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+ def yolov8_inference(
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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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+ YOLOv8 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 = YOLO(model_path)
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+ model.conf = conf_threshold
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+ model.iou = iou_threshold
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+ results = model.predict(image, imgsz=image_size, return_outputs=True)
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+ object_prediction_list = []
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+ for _, image_results in enumerate(results):
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+ if len(image_results)!=0:
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+ image_predictions_in_xyxy_format = image_results['det']
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+ for pred in image_predictions_in_xyxy_format:
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+ x1, y1, x2, y2 = (
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+ int(pred[0]),
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+ int(pred[1]),
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+ int(pred[2]),
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+ int(pred[3]),
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+ )
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+ bbox = [x1, y1, x2, y2]
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+ score = pred[4]
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+ category_name = model.model.names[int(pred[5])]
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+ category_id = pred[5]
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+ object_prediction = ObjectPrediction(
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+ bbox=bbox,
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+ category_id=int(category_id),
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+ score=score,
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+ category_name=category_name,
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+ )
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+ object_prediction_list.append(object_prediction)
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+
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+ image = read_image(image)
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+ output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
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+ return output_image['image']
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+
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+
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+ inputs = [
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+ gr.inputs.Image(type="filepath", label="Input Image"),
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+ gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"],
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+ default="kadirnar/yolov8m-v8.0", label="Model"),
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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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+
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+ outputs = gr.outputs.Image(type="filepath", label="Output Image")
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+ title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
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+
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+ examples = [['highway.jpg', 'kadirnar/yolov8m-v8.0', 640, 0.25, 0.45], ['highway1.jpg', 'kadirnar/yolov8l-v8.0', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'kadirnar/yolov8x-v8.0', 1280, 0.25, 0.45]]
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+ demo_app = gr.Interface(
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+ fn=yolov8_inference,
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+ inputs=inputs,
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+ outputs=outputs,
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+ title=title,
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+ examples=examples,
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+ cache_examples=True,
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+ theme='huggingface',
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+ )
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+ demo_app.launch(debug=True, enable_queue=True)
requirements.txt ADDED
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+ opencv_python
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+ sahi
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+ torch
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+ ultralytics==8.0.4
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+ ultralyticsplus==0.0.3