Duplicate from kadirnar/yolov8

Co-authored-by: Kadir Nar <[email protected]>

.gitattributes

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README.md

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+---
+title: Yolov8
+emoji: 💩
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.16.1
+app_file: app.py
+pinned: false
+license: gpl-3.0
+duplicated_from: kadirnar/yolov8
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import torch
+from ultralytics import YOLO
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions
+import cv2
+from utils import attempt_download_from_hub
+
+# Images
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
+torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+
+def yolov8_inference(
+    image: gr.inputs.Image = None,
+    model_path: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45,
+):
+    """
+    YOLOv8 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    hf_model_path = attempt_download_from_hub(model_path)
+    model = YOLO(hf_model_path)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    prediction = model.predict(image, imgsz=image_size)
+    object_prediction_list = []
+    for _, image_predictions_in_xyxy_format in enumerate(prediction):
+        for pred in image_predictions_in_xyxy_format.cpu().detach().numpy():
+            x1, y1, x2, y2 = (
+                int(pred[0]),
+                int(pred[1]),
+                int(pred[2]),
+                int(pred[3]),
+            )
+            bbox = [x1, y1, x2, y2]
+            score = pred[4]
+            category_name = model.model.names[int(pred[5])]
+            category_id = pred[5]
+            object_prediction = ObjectPrediction(
+                bbox=bbox,
+                category_id=int(category_id),
+                score=score,
+                category_name=category_name,
+            )
+            object_prediction_list.append(object_prediction)
+
+    image = cv2.imread(image)
+    save_path = 'output.jpg'
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    output_image = cv2.imwrite(save_path, output_image["image"])
+    return save_path
+        
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"], label="Model"),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
+
+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]]
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    live=True,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+opencv_python
+sahi
+torch
+ultralytics

utils.py

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+def attempt_download_from_hub(repo_id, hf_token=None):
+    # https://github.com/fcakyon/yolov5-pip/blob/main/yolov5/utils/downloads.py
+    from huggingface_hub import hf_hub_download, list_repo_files
+    from huggingface_hub.utils._errors import RepositoryNotFoundError
+    from huggingface_hub.utils._validators import HFValidationError
+    try:
+        repo_files = list_repo_files(repo_id=repo_id, repo_type='model', token=hf_token)
+        model_file = [f for f in repo_files if f.endswith('.pt')][0]
+        file = hf_hub_download(
+            repo_id=repo_id,
+            filename=model_file,
+            repo_type='model',
+            token=hf_token,
+        )
+        return file
+    except (RepositoryNotFoundError, HFValidationError):
+        return None