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README.dataset.txt

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+# Shelf-objects-annotated-owais > 2023-01-16 4:18pm
+https://universe.roboflow.com/supplychainshelfobjects-uxuzo/shelf-objects-annotated-owais
+
+Provided by a Roboflow user
+License: CC BY 4.0
+
+This dataset contains 48 classes annotated object for objects placed on shelfs of store.

app.py

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+import gradio as gr
+import torch
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
+from ultralyticsplus import YOLO
+
+# 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
+    """
+    model = YOLO(model_path)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    object_prediction_list = []
+    for _, image_results in enumerate(results):
+        if len(image_results)!=0:
+            image_predictions_in_xyxy_format = image_results['det']
+            for pred in image_predictions_in_xyxy_format:
+                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 = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    return output_image['image']
+        
+
+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"], 
+                       default="kadirnar/yolov8m-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,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)

requirements.txt

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+opencv_python
+sahi
+torch
+ultralytics==8.0.4
+ultralyticsplus==0.0.3