update threshold values

app.py

@@ -1,79 +1,79 @@
-import gradio as gr
-from huggingface_hub import hf_hub_download
-import yolov9
-
-
-# Load the model
-model_path = r'./model/V2_best.pt'
-model = yolov9.load(model_path)
-
-def yolov9_inference(img_path, conf_threshold, iou_threshold):
-    """   
-    :param conf_threshold: Confidence threshold for NMS.
-    :param iou_threshold: IoU threshold for NMS.
-    :param img_path: Path to the image file.
-    :param size: Optional, input size for inference.
-    :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
-    """
-    global model
-    # Set model parameters
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    
-    # Perform inference
-    results = model(img_path, size=640)
-
-    # Optionally, show detection bounding boxes on image
-    output = results.render()
-    
-    return output[0]
-
-
-def app():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                img_path = gr.Image(type="filepath", label="Image")
-                conf_threshold = gr.Slider(
-                    label="Confidence Threshold",
-                    minimum=0.1,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.4,
-                )
-                iou_threshold = gr.Slider(
-                    label="IoU Threshold",
-                    minimum=0.1,
-                    maximum=1.0,
-                    step=0.1,
-                    value=0.5,
-                )
-                yolov9_infer = gr.Button(value="Prediction")
-
-            with gr.Column():
-                output_numpy = gr.Image(type="numpy",label="Output")
-
-        yolov9_infer.click(
-            fn=yolov9_inference,
-            inputs=[
-                img_path,
-                conf_threshold,
-                iou_threshold,
-            ],
-            outputs=[output_numpy],
-        )
-        
-
-gradio_app = gr.Blocks()
-with gradio_app:
-    gr.HTML(
-        """
-    <h1 style='text-align: center'>
-    Traffic Signs Detection - Case Study
-    </h1>
-    """)
-    with gr.Row():
-        with gr.Column():
-            app()
-
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import yolov9
+
+
+# Load the model
+model_path = r'./model/V2_best.pt'
+model = yolov9.load(model_path)
+
+def yolov9_inference(img_path, conf_threshold=0.4, iou_threshold=0.5):
+    """   
+    :param conf_threshold: Confidence threshold for NMS.
+    :param iou_threshold: IoU threshold for NMS.
+    :param img_path: Path to the image file.
+    :param size: Optional, input size for inference.
+    :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
+    """
+    global model
+    # Set model parameters
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    
+    # Perform inference
+    results = model(img_path, size=640)
+
+    # Optionally, show detection bounding boxes on image
+    output = results.render()
+    
+    return output[0]
+
+
+def app():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                img_path = gr.Image(type="filepath", label="Image")
+                # conf_threshold = gr.Slider(
+                #     label="Confidence Threshold",
+                #     minimum=0.1,
+                #     maximum=1.0,
+                #     step=0.1,
+                #     value=0.4,
+                # )
+                # iou_threshold = gr.Slider(
+                #     label="IoU Threshold",
+                #     minimum=0.1,
+                #     maximum=1.0,
+                #     step=0.1,
+                #     value=0.5,
+                # )
+                yolov9_infer = gr.Button(value="Prediction")
+
+            with gr.Column():
+                output_numpy = gr.Image(type="numpy",label="Output")
+
+        yolov9_infer.click(
+            fn=yolov9_inference,
+            inputs=[
+                img_path,
+                conf_threshold,
+                iou_threshold,
+            ],
+            outputs=[output_numpy],
+        )
+        
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    Traffic Signs Detection - Case Study
+    </h1>
+    """)
+    with gr.Row():
+        with gr.Column():
+            app()
+
 gradio_app.launch(debug=True)