Update app.py

app.py

@@ -5,6 +5,7 @@ except:
     os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
 
 import cv2
+import supervision as sv
 import torch
 from matplotlib.pyplot import axis
 import gradio as gr
@@ -41,6 +42,16 @@ my_metadata.thing_classes = ["None", "BAD_BILLBOARD","BROKEN_SIGNAGE","CLUTTER_S
 if not torch.cuda.is_available():
     cfg.MODEL.DEVICE = "cpu"
 
+    
+def predict_frame(frame,_):
+    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3
+    predictor = DefaultPredictor(cfg)
+    outputs = predictor(frame)
+    v = Visualizer(frame[:,:,::-1], my_metadata, scale=1.2, instance_mode=ColorMode.IMAGE )
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    return out.get_image()
+
+
 
 def inference(image_url, image, min_score):
     if image_url:
@@ -64,6 +75,11 @@ def inference(image_url, image, min_score):
     return out.get_image()
 
 
+def infer_video(video_path):
+    sv.process_video(source_path=video_path, target_path=f"result.mp4", callback=predict_frame)
+    return f"result.mp4"
+
+
 title = "Smartathon Phase2 Demo - Baseer"
 description = "This demo introduces an interactive playground for our trained Detectron2 model."
 article = '<p>Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</p>'
@@ -85,52 +101,39 @@ article = '<p>Detectron model is available from our repository <a href="https://
 #     #examples=['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg']
 
 
-with gr.Blocks(title=title,
-                    css=".gradio-container {background:white;}"
-               ) as demo:
 
-    gr.HTML("""<h4 style="font-weight:bold; text-align:center; color:navy;">"Smartathon Phase2 Demo - Baseer"</h4>""")
-    # #
-    #gr.HTML("""<h5 style="color:navy;">1- Select an example by clicking a thumbnail below.</h5>""")
-    gr.HTML("""<h5 style="color:navy;">1- Select an example by clicking a thumbnail below.<br>
-                                       2- Or upload an image by clicking on the canvas.<br>
-                                       3- Or insert direct url of an image.</h5>""")
 
+    gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=input_image)
+
+
+
+
+
+    
     with gr.Row():
         with gr.Column():
             #gr.HTML("""<h5 style="color:navy;">3- Or insert direct url of an image.</h5>""")
             input_url = gr.Textbox(label="Image URL", placeholder="")
             #gr.HTML("""<h5 style="color:navy;">2- Or upload an image by clicking on the canvas.<br></h5>""")
-            input_image = gr.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")
+            input_image = gr.Image(type="filepath", image_mode="RGB", sources="upload", label="Input Image")
+            input_video = gr.Video(format="mp4",sources="upload", label="Input video" )
             gr.HTML("""<h5 style="color:navy;">4- You can use this slider to control boxes min score: </h5>""")
             sliderr = gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score")
         output_image = gr.Image(type="pil", label="Output")
+        output_video = gr.Video(format="mp4", label="Output")
+    
+    
+    img_interface = gr.Interface(
+        fn=inference,
+       inputs=[input_url,input_image,sliderr], outputs=[output_image], api_name="find"
+    )
+    video_interface =  gr.Interface(
+        fn=infer_video,
+       inputs=[input_video], outputs=[output_video], api_name="vid"
+    )
+    demo = gr.TabbedInterface([img_interface, video_interface], ["Image Upload", "Video Upload"])
+    
+    demo.launch()
 
-    # gr.Interface(
-    #     inference,
-    #     [gr.inputs.Textbox(label="Image URL", placeholder=""),
-    #      gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image"),
-    #      gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score"),
-    #     ],
-
-
-    gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=input_image)
-
-    #gr.HTML("""<br/>""")
-
-
-
-
-
-    gr.HTML("""<h5 style="color:navy;">5- Then, click "Submit" button to predict object instances. It will take about 15-20 seconds (on cpu)</h5>""")
-    send_btn = gr.Button("Submit")
-    send_btn.click(fn=inference, inputs=[input_url,input_image,sliderr], outputs=[output_image], api_name="find")
-
-    #gr.HTML("""<h5 style="color:navy;">Reference</h5>""")
-    #gr.HTML("""<ul>""")
-    gr.HTML("""<h5 style="color:navy;">Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</h5>""")
-    #gr.HTML("""</ul>""")
 
 
-#demo.queue()
-demo.launch() # debug=True)