Update app.py

app.py

@@ -32,7 +32,7 @@ image_path = [['test_images/2a998cfb0901db5f8210.jpg','cham_diem_yolov8', 640, 0
 # model = YOLO('linhcuem/chamdiemgianhang_yolov8_ver21')
 # model = YOLO('linhcuem/cham_diem_yolov8_ver20')
 # model_ids = ['linhcuem/checker_TB_yolov8_ver1', 'linhcuem/cham_diem_yolov8', 'linhcuem/chamdiemgianhang_yolov8_300epochs', 'linhcuem/cham_diem_yolov8_ver20', 'linhcuem/chamdiemgianhang_yolov8_ver21']
-# # model = YOLO('linhcuem/checker_TB_yolov8_ver1')
+model = YOLO('linhcuem/checker_TB_yolov8_ver1')
 # current_model_id = model_ids[-1]
 # model = YOLO(current_model_id)
 # model = YOLO(model_path)
@@ -46,7 +46,7 @@ def yolov8_img_inference(
     iou_threshold: gr.inputs.Slider = 0.45,
 ):
     # model = YOLO(model_path)
-    model = YOLO(model_path)
+
     model.conf = conf_threshold
     model.iou = iou_threshold
     # model.overrides['conf'] = conf_threshold
@@ -78,43 +78,43 @@ def yolov8_img_inference(
     return render, {names[k]: v for k, v in present_objects.items()}
 
 
-# def yolov8_vid_inference(video_path):
-#     cap = cv2.VideoCapture(video_path)
-#     while cap.isOpened():
-#         success, frame = cap.read()
-
-#         if success:
-#             frame_copy = frame.copy()
-#             outputs = model.predict(source=frame)
-#             results = outputs[0].cpu().numpy()
-#             for i, det in enumerate(results.boxes.xyxy):
-#                 cv2.rectangle(
-#                     frame_copy,
-#                     (int(det[0]), int(det[1])),
-#                     (int(det[2]), int(det[3])),
-#                     color=(0, 0, 255),
-#                     thickness=2,
-#                     lineType=cv2.LINE_AA
-#                 )
-#             yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
+def yolov8_vid_inference(video_path):
+    cap = cv2.VideoCapture(video_path)
+    while cap.isOpened():
+        success, frame = cap.read()
+
+        if success:
+            frame_copy = frame.copy()
+            outputs = model.predict(source=frame)
+            results = outputs[0].cpu().numpy()
+            for i, det in enumerate(results.boxes.xyxy):
+                cv2.rectangle(
+                    frame_copy,
+                    (int(det[0]), int(det[1])),
+                    (int(det[2]), int(det[3])),
+                    color=(0, 0, 255),
+                    thickness=2,
+                    lineType=cv2.LINE_AA
+                )
+            yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB)
     
 
-# inputs_vid = [
-#     gr.components.Video(type="filepath", label="Input Video"),
-# ]
+inputs_vid = [
+    gr.components.Video(type="filepath", label="Input Video"),
+]
 
-# outputs_vid = [
-#     gr.components.Image(type="numpy", label="Output Image"),
-#               ]
+outputs_vid = [
+    gr.components.Image(type="numpy", label="Output Image"),
+              ]
 
-# interface_vid = gr.Interface(
-#     fn=yolov8_vid_inference,
-#     inputs = inputs_vid,
-#     outputs = outputs_vid,
-#     title = "Detect Thiên Việt productions",
-#     cache_examples = False,
+interface_vid = gr.Interface(
+    fn=yolov8_vid_inference,
+    inputs = inputs_vid,
+    outputs = outputs_vid,
+    title = "Detect Thiên Việt productions",
+    cache_examples = False,
     
-# )
+)
 
 inputs = [
     gr.inputs.Image(type="filepath", label="Input Image"),
@@ -130,37 +130,37 @@ outputs =gr.outputs.Image(type="filepath", label="Output Image")
 
 title = "Detect Thiên Việt productions"
 
-# interface_image = gr.Interface(
-#     fn=yolov8_img_inference,
-#     inputs=[
-#         gr.Image(type='pil'),
-#         gr.Dropdown(["linhcuem/checker_TB_yolov8_ver1", "linhcuem/chamdiemgianhang_yolov8_ver21"], 
-#                         default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
-#         gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
-#         gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
-#         gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
-#     ],
-#     outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)],
-#     title=title,
-#     examples=image_path,
-#     cache_examples=True if image_path else False,
-    
-# )
-
-# gr.TabbedInterface(
-#     [interface_image, interface_vid],
-#     tab_names=['Image inference', 'Video inference']
-# ).queue().launch()
-
-demo_app = gr.Interface(
+interface_image = gr.Interface(
     fn=yolov8_img_inference,
-    inputs=inputs,
-    outputs=outputs,
+    inputs=[
+        gr.Image(type='pil'),
+        gr.Dropdown(["linhcuem/checker_TB_yolov8_ver1"], 
+                        default="linhcuem/checker_TB_yolov8_ver1", label="Model"),
+        gr.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+        gr.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+        gr.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+    ],
+    outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)],
     title=title,
     examples=image_path,
-    cache_examples=True,
-    theme='huggingface',
+    cache_examples=True if image_path else False,
+    
 )
-demo_app.launch(debug=True, enable_queue=True)
+
+gr.TabbedInterface(
+    [interface_image, interface_vid],
+    tab_names=['Image inference', 'Video inference']
+).queue().launch()
+
+# demo_app = gr.Interface(
+#     fn=yolov8_img_inference,
+#     inputs=inputs,
+#     outputs=outputs,
+#     title=title,
+#     examples=image_path,
+#     cache_examples=True,
+#     theme='huggingface',
+# )
+# demo_app.launch(debug=True, enable_queue=True)
 
 # interface_image.launch(debug=True, enable_queue=True)