Update app.py

app.py

@@ -1,83 +1,19 @@
+from PIL import Image
+from ultralytics import YOLO
 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')
+# Cargar un modelo YOLOv8n preentrenado
+model = YOLO('best.pt')
 
-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)
+def detect_objects(image: Image.Image):
+    # Realizar la inferencia
+    results = model(image)
+    
+    # Obtener y mostrar los resultados
+    for r in results:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+        return im  # retornar la imagen con los objetos detectados
 
-    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", "ultralyticsplus/yolov8s", "Snearec/detectorMalezasYolo8"], 
-                       default="Snearec/detectorMalezasYolo8", 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"
-
-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)
+# Crear la interfaz de Gradio
+gr.Interface(fn=detect_objects, inputs="image", outputs="image").launch()