Update app.py

app.py

@@ -1,74 +1,53 @@
 import gradio as gr
-
 from ultralytics import YOLO
+from PIL import Image
 
-# Load the YOLOv8 model from the 'best.pt' checkpoint
-model_path = "new_data_improved_object_detector.pt"
-
-model = YOLO(model_path)  
 
-import torch
-
-#from ultralyticsplus import render_result
-from render import custom_render_result
-def yoloV8_func(image: gr.Image = None,
-                image_size: int = 640,
-                conf_threshold: float = 0.4,
-                iou_threshold: float = 0.5):
-    
-    """This function performs YOLOv8 object detection on the given image.
 
-    Args:
-        image (gr.Image, optional): Input image to detect objects on. Defaults to None.
-        image_size (int, optional): Desired image size for the model. Defaults to 640.
-        conf_threshold (float, optional): Confidence threshold for object detection. Defaults to 0.4.
-        iou_threshold (float, optional): Intersection over Union threshold for object detection. Defaults to 0.50.
-    """
-   
-    # model = torch.hub.load('ultralytics/yolov8', 'custom', path='/content/best.pt', force_reload=True, trust_repo=True)
+model_path = 'new_data_improved_object_detector.pt'
 
-    # Perform object detection on the input image using the YOLOv8 model
-    results = model.predict(image,
-                            conf=conf_threshold,
-                            iou=iou_threshold,
-                            imgsz=image_size)
+model = YOLO(model_path)
 
-    # 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 = custom_render_result(model=model, image=image, result=results[0])
-    return render
+def predict_image(img, conf_threshold, iou_threshold):
+    """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds."""
+    # Convert the input image to grayscale
+    img = img.convert('L')
 
+    results = model.predict(
+        source=img,
+        conf =0.4,
+        iou=0.6,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
 
-inputs = [
-    gr.Image(type="filepath", label="Input Image"),
-    gr.Slider(minimum=320, maximum=1280, step=32, label="Image Size", value=640),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label="IOU Threshold"),
-]
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
 
-outputs = gr.Image(type="filepath", label="Output Image")
+    return im
 
-title = "YOLOv8 101: Custom Object Detection on meter"
 
-examples = [
+iface = gr.Interface(
+    
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Ultralytics Gradio",
+    description="Upload images for inference. The Ultralytics YOLOv8n model is used by default.",
+    examples = [
             ['1.jpg'],
             ['2.jpg'],
             ['3.jpg']
       ]
-
-yolo_app = gr.Interface(
-    fn=yoloV8_func,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-    examples=examples,
-    cache_examples=False,
 )
 
+
 # Launch the Gradio interface in debug mode with queue enabled
-yolo_app.launch(debug=True, share=True).queue()
+yolo_app.launch(debug=True, share=False).queue()