Initial commit

app.py

@@ -11,6 +11,7 @@ from utils.plots import plot_one_box
 from utils.torch_utils import time_synchronized
 import time
 from ultralytics import YOLO
+import track.py
 
 def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
     # Resize and pad image while meeting stride-multiple constraints
@@ -174,6 +175,9 @@ def inference2(video,model_link,iou_threshold,confidence_threshold):
     return 'output.mp4',np.mean(fps_video)
 
 
+def MODT():
+  return track.MOT()
+
 
 examples_images = ['data/images/1.jpg',
             'data/images/2.jpg',
@@ -218,8 +222,7 @@ with gr.Blocks() as demo:
     video_button_detect.click(inference2, inputs=[video_input,video_drop,
                                            video_iou_threshold,video_conf_threshold],            
                                         outputs=[video_output,fps_video])
-    video_button_track.click(inference2, inputs=[video_input,video_drop,
-                                                 video_iou_threshold,video_conf_threshold],          
-                             outputs=[video_output,fps_video])
+    video_button_track.click(MODT,inputs=[],          
+                             outputs=[video_output])
 
 demo.launch(debug=True,enable_queue=True)

track.py

@@ -370,7 +370,12 @@ def MOT():
     save_dir = increment_path('runs/track/exp', exist_ok=False) 
     input = os.path.join(save_dir,'out.mp4')
     tracking_method='strongsort'
-    outpath = 'output/' + tracking_method + '_output.mp4' 
+    #outpath = 'output/' + tracking_method + '_output.mp4' 
+    outpath = 'output/'+ 'output.mp4' 
+    command = f"ffmpeg -i {input} -vf fps=30 -vcodec libx264 {outpath}"
+    print(command)
+    os.system(command)
     #!ffmpeg -i $input -vf fps=30 -vcodec libx264 $outpath tbd
+    return outpath