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106 lines
4.1 KiB
106 lines
4.1 KiB
--- |
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comments: true |
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description: Learn to use Gradio and Ultralytics YOLOv8 for interactive object detection. Upload images and adjust detection parameters in real-time. |
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keywords: Gradio, Ultralytics YOLOv8, object detection, interactive AI, Python |
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--- |
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# Interactive Object Detection: Gradio & Ultralytics YOLOv8 🚀 |
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## Introduction to Interactive Object Detection |
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This Gradio interface provides an easy and interactive way to perform object detection using the [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) model. Users can upload images and adjust parameters like confidence threshold and intersection-over-union (IoU) threshold to get real-time detection results. |
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## Why Use Gradio for Object Detection? |
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* **User-Friendly Interface:** Gradio offers a straightforward platform for users to upload images and visualize detection results without any coding requirement. |
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* **Real-Time Adjustments:** Parameters such as confidence and IoU thresholds can be adjusted on the fly, allowing for immediate feedback and optimization of detection results. |
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* **Broad Accessibility:** The Gradio web interface can be accessed by anyone, making it an excellent tool for demonstrations, educational purposes, and quick experiments. |
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<p align="center"> |
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<img width="800" alt="Gradio example screenshot" src="https://github.com/RizwanMunawar/ultralytics/assets/26833433/52ee3cd2-ac59-4c27-9084-0fd05c6c33be"> |
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</p> |
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## How to Install the Gradio |
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```bash |
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pip install gradio |
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``` |
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## How to Use the Interface |
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1. **Upload Image:** Click on 'Upload Image' to choose an image file for object detection. |
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2. **Adjust Parameters:** |
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* **Confidence Threshold:** Slider to set the minimum confidence level for detecting objects. |
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* **IoU Threshold:** Slider to set the IoU threshold for distinguishing different objects. |
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3. **View Results:** The processed image with detected objects and their labels will be displayed. |
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## Example Use Cases |
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* **Sample Image 1:** Bus detection with default thresholds. |
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* **Sample Image 2:** Detection on a sports image with default thresholds. |
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## Usage Example |
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This section provides the Python code used to create the Gradio interface with the Ultralytics YOLOv8 model. Supports classification tasks, detection tasks, segmentation tasks, and key point tasks. |
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```python |
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import PIL.Image as Image |
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import gradio as gr |
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from ultralytics import ASSETS, YOLO |
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model = YOLO("yolov8n.pt") |
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def predict_image(img, conf_threshold, iou_threshold): |
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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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imgsz=640, |
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) |
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for r in results: |
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im_array = r.plot() |
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im = Image.fromarray(im_array[..., ::-1]) |
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return im |
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iface = gr.Interface( |
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fn=predict_image, |
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inputs=[ |
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gr.Image(type="pil", label="Upload Image"), |
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gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"), |
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gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold") |
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], |
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outputs=gr.Image(type="pil", label="Result"), |
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title="Ultralytics Gradio", |
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description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.", |
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examples=[ |
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[ASSETS / "bus.jpg", 0.25, 0.45], |
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[ASSETS / "zidane.jpg", 0.25, 0.45], |
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] |
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) |
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if __name__ == '__main__': |
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iface.launch() |
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``` |
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## Parameters Explanation |
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| Parameter Name | Type | Description | |
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|------------------|---------|----------------------------------------------------------| |
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| `img` | `Image` | The image on which object detection will be performed. | |
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| `conf_threshold` | `float` | Confidence threshold for detecting objects. | |
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| `iou_threshold` | `float` | Intersection-over-union threshold for object separation. | |
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### Gradio Interface Components |
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| Component | Description | |
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|--------------|------------------------------------------| |
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| Image Input | To upload the image for detection. | |
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| Sliders | To adjust confidence and IoU thresholds. | |
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| Image Output | To display the detection results. |
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