update application

app.py

@@ -3,7 +3,6 @@ import numpy as np
 import torch
 import cv2
 import os
-from random import randint
 from vision.ssd.mobilenetv1_ssd import create_mobilenetv1_ssd, create_mobilenetv1_ssd_predictor
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -44,6 +43,15 @@ def detection(image):
     s = f"Found {len(probs)} objects"
     return image, s
 
+def video_detection(frames):
+    out = cv2.VideoWriter('outpy.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30, (640, 320))
+    for frame in frames:
+        out.write(frame)
+
+    out.release()
+    return "outpy.avi"
+
+
 title = " AISeed AI Application Demo "
 description = "# A Demo of Deep Learning for Object Detection"
 example_list = [["examples/" + example] for example in os.listdir("examples")]
@@ -51,18 +59,35 @@ example_list = [["examples/" + example] for example in os.listdir("examples")]
 with gr.Blocks() as demo:
     demo.title = title
     gr.Markdown(description)
-    with gr.Row():
-        with gr.Column():
-            im = gr.Image(label="Input Image")
-            im_2 = gr.Image(label="Output Image")
-        with gr.Column():
-            text = gr.Textbox(label="Number of objects")
-            btn1 = gr.Button(value="Who wears mask?")
-            btn1.click(detection, inputs=[im], outputs=[im_2, text])
-            
-            gr.Examples(examples=example_list,
-                inputs=[im],
-                outputs=[im_2])
-            
+    with gr.Tabs():
+        with gr.TabItem("for Images"):
+            with gr.Row():
+                with gr.Column():
+                    im = gr.Image(label="Input Image")
+                    im_2 = gr.Image(label="Output Image")
+                with gr.Column():
+                    text = gr.Textbox(label="Number of objects")
+                    btn1 = gr.Button(value="Who wears mask?")
+                    btn1.click(detection, inputs=[im], outputs=[im_2, text])
+                    
+                    gr.Examples(examples=example_list,
+                        inputs=[im],
+                        outputs=[im_2])
+        # with gr.TabItem("for Videos"):
+        #     with gr.Row():
+        #         with gr.Column():
+        #             text1 = gr.Textbox(label="Number of objects")
+        #         with gr.Column():
+        #             text2 = gr.Textbox(label="Number of objects")
+        
+        with gr.Tab("for streaming"):
+            with gr.Row():
+                with gr.Column():
+                    input_video = gr.Video(source="webcam", label="Input")
+                    btn3 = gr.Button(value="Who wears mask?")
+                output_video = gr.Video(label="Output Video")
+                btn3.click(video_detection, inputs=[input_video], outputs=[output_video])
+
+                        
 if __name__ == "__main__":
     demo.launch()