Update app.py

app.py

@@ -19,7 +19,7 @@ image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_
              ['test_images/ee106392e56837366e79.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45]]
 
 # Load YOLO model
-model = YOLO('linhcuem/chamdiem_yolov8_ver10')
+# model = YOLO('linhcuem/chamdiem_yolov8_ver10')
 
 ###################################################
 def yolov8_img_inference(
@@ -30,36 +30,45 @@ def yolov8_img_inference(
     iou_threshold: gr.inputs.Slider = 0.45,
 ):
     model = YOLO(model_path)
-    model.conf = conf_threshold
-    model.iou = iou_threshold
-    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    # model.conf = conf_threshold
+    # model.iou = iou_threshold
+    model.overrides['conf'] = conf_threshold
+    model.overrides['iou'] = iou_threshold
+    model.overrides['agnostic_nms'] = False
+    model.overrides['max_det'] = 1000
+    image = read_image
+    results = model.predict(image)
+    results = render_result(model=model, image=image, result=results[0])
+    
+    
+    # results = model.predict(image, imgsz=image_size, return_outputs=True)
     # results = model.predict(image)
-    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)
+    # 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']
+    # image = read_image(image)
+    # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    # return output_image['image']
     # render = render_result(model=model, image=image, result=results[0])
     
 
@@ -87,9 +96,9 @@ interface_image = gr.Interface(
     theme='huggingface'
 )
 
-gr.TabbedInterface(
-    [interface_image],
-    tab_names=['Image inference']
-).queue().launch()
+# gr.TabbedInterface(
+#     [interface_image],
+#     tab_names=['Image inference']
+# ).queue().launch()
 
 interface_image.launch(debug=True, enable_queue=True)