Add application file

EyesCareEyeDetectModel.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:90365011becfe117f9f8c8e54884b99022b69301d95ccd0452cc9afd10c24bb7
+size 51594821

app.py

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+import gradio as gr
+import torch
+from ultralyticsplus import YOLO, render_result
+
+
+torch.hub.download_url_to_file(
+    'https://en.wikipedia.org/wiki/Human_eye#/media/File:Human_eye,_anterior_view.jpg', 'one.jpg')
+torch.hub.download_url_to_file(
+    'https://glaucoma.org/wp-content/uploads/2024/01/amazing-eye-close-up_900-585x540.jpg', 'two.jpg')
+torch.hub.download_url_to_file(
+    'https://www.nvisioncenters.com/wp-content/uploads//woman-blue-eye-close-up-352x235.jpg', 'three.jpg')
+
+
+def yoloV8_func(image: gr.inputs.Image = None,
+                image_size: gr.inputs.Slider = 640,
+                conf_threshold: gr.inputs.Slider = 0.70,
+                iou_threshold: gr.inputs.Slider = 0.50):
+    """This function performs YOLOv8 object detection on the given image.
+
+    Args:
+        image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
+        image_size (gr.inputs.Slider, optional): Desired image size for the model. Defaults to 640.
+        conf_threshold (gr.inputs.Slider, optional): Confidence threshold for object detection. Defaults to 0.4.
+        iou_threshold (gr.inputs.Slider, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
+    """
+    # Load the YOLOv8 model from the 'best.pt' checkpoint
+    model_path = "EyesCareEyeDetectModel.pt"
+    model = YOLO(model_path)
+
+    # Perform object detection on the input image using the YOLOv8 model
+    results = model.predict(image,
+                            conf=conf_threshold,
+                            iou=iou_threshold,
+                            imgsz=image_size)
+
+    # Print the detected objects' information (class, coordinates, and probability)
+    box = results[0].boxes
+    print("Object type:", box.cls)
+    print("Coordinates:", box.xyxy)
+    print("Probability:", box.conf)
+
+    # Render the output image with bounding boxes around detected objects
+    render = render_result(model=model, image=image, result=results[0])
+    return render
+
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    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 = "YOLOv9 101: Custom Object Detection on Eye"
+
+
+examples = [['one.jpg', 640, 0.5, 0.7],
+            ['two.jpg', 800, 0.5, 0.6],
+            ['three.jpg', 900, 0.5, 0.8]]
+
+yolo_app = gr.Interface(
+    fn=yoloV8_func,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
+
+# Launch the Gradio interface in debug mode with queue enabled
+yolo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+gradio==3.39.0
+torch
+ultralyticsplus==0.0.28