Duplicate from fcakyon/yolov8-segmentation

Co-authored-by: Fatih <[email protected]>

.gitattributes

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README.md

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+---
+title: YOLOv8 Segmentation
+emoji: 🔥
+colorFrom: black
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.16.2
+app_file: app.py
+pinned: true
+license: gpl-3.0
+duplicated_from: fcakyon/yolov8-segmentation
+---

app.py

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+import gradio as gr
+import sahi
+import torch
+from ultralyticsplus import YOLO, render_model_output
+
+# Images
+sahi.utils.file.download_from_url(
+    "https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg",
+    "highway.jpg",
+)
+sahi.utils.file.download_from_url(
+    "https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg",
+    "small-vehicles1.jpeg",
+)
+sahi.utils.file.download_from_url(
+    "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/zidane.jpg",
+    "zidane.jpg",
+)
+
+
+model_names = [
+    "yolov8n-seg.pt",
+    "yolov8s-seg.pt",
+    "yolov8m-seg.pt",
+    "yolov8l-seg.pt",
+    "yolov8x-seg.pt",
+]
+
+current_model_name = "yolov8m-seg.pt"
+model = YOLO(current_model_name)
+
+
+def yolov8_inference(
+    image: gr.inputs.Image = None,
+    model_name: 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_name: Name of the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    global model
+    global current_model_name
+    if model_name != current_model_name:
+        model = YOLO(model_name)
+        current_model_name = model_name
+    model.overrides["conf"] = conf_threshold
+    model.overrides["iou"] = iou_threshold
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    renders = []
+    for image_results in model.predict(image, imgsz=image_size, return_outputs=True):
+        render = render_model_output(
+            model=model, image=image, model_output=image_results
+        )
+        renders.append(render)
+
+    return renders[0]
+
+
+inputs = [
+    gr.Image(type="filepath", label="Input Image"),
+    gr.Dropdown(
+        model_names,
+        value=current_model_name,
+        label="Model type",
+    ),
+    gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
+    gr.Slider(
+        minimum=0.0, maximum=1.0, value=0.25, step=0.05, label="Confidence Threshold"
+    ),
+    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
+]
+
+outputs = gr.Image(type="filepath", label="Output Image")
+title = "Ultralytics YOLOv8 Segmentation Demo"
+
+examples = [
+    ["zidane.jpg", "yolov8m-seg.pt", 640, 0.6, 0.45],
+    ["highway.jpg", "yolov8m-seg.pt", 640, 0.25, 0.45],
+    ["small-vehicles1.jpeg", "yolov8m-seg.pt", 640, 0.25, 0.45],
+]
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    theme="default",
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+sahi
+torch
+ultralytics==8.0.6
+ultralyticsplus==0.0.9