Added tracking in seperate tab

app.py

@@ -179,8 +179,9 @@ def inference_comp(image,iou_threshold,confidence_threshold):
     v7_out, v7_fps = inference(image, "yolov7",iou_threshold,confidence_threshold)
     return v7_out,v8_out,v7_fps,v8_fps
 
-def MODT(sourceVideo, model_link, trackingmethod):
-    model_path = 'weights/'+str(model_link)+'.pt'
+def MODT(sourceVideo, trackingmethod):    
+    #model_path = 'weights/'+str(model_link)+'.pt'
+    model_path = 'weights/yolov8m.pt'
     return MOT(model_path, trackingmethod, sourceVideo), 30
 
 examples_images = ['data/images/1.jpg',
@@ -212,7 +213,6 @@ with gr.Blocks() as demo:
             video_output = gr.Video(type="pil", label="Output Video",format="mp4")
         fps_video = gr.Number(0,label='FPS')
         video_drop = gr.Dropdown(label="Model", choices=models,value=models[0])
-        tracking_drop = gr.Dropdown(label="Tracker", choices=trackers,value=trackers[0])
         video_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
         video_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
         gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
@@ -235,6 +235,18 @@ with gr.Blocks() as demo:
             v7_fps_image = gr.Number(0,label='v7 FPS')        
             v8_fps_image = gr.Number(0,label='v8 FPS')
         gr.Examples(examples=examples_images,inputs=image_comp_input,outputs=[image_comp_output_v7,image_comp_output_v8])
+    
+    with gr.Tab("Video Tacking"):
+        gr.Markdown("## MOT using YoloV8 detection with tracking")
+        with gr.Row():
+            videotr_input = gr.Video(type='pil', label="Input Video", source="upload")
+            videotr_output = gr.Video(type="pil", label="Output Video",format="mp4")
+        fpstr_video = gr.Number(0,label='FPS')
+        tracking_drop = gr.Dropdown(choices=trackers,value=trackers[0], label="Select the tracking method")
+        videotr_iou_threshold = gr.Slider(label="IOU Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.45)
+        videotr_conf_threshold = gr.Slider(label="Confidence Threshold",interactive=True, minimum=0.0, maximum=1.0, value=0.25)
+        gr.Examples(examples=examples_videos,inputs=video_input,outputs=video_output)
+        video_button_track = gr.Button("Track")
         
 
     text_button.click(inference, inputs=[image_input,image_drop,
@@ -247,7 +259,7 @@ with gr.Blocks() as demo:
                                             image_comp_iou_threshold,
                                             image_comp_conf_threshold],
                                             outputs=[image_comp_output_v7,image_comp_output_v8,v7_fps_image,v8_fps_image])
-    video_button_track.click(MODT,inputs=[video_input,video_drop, tracking_drop],          
-                             outputs=[video_output, fps_video])
+    video_button_track.click(MODT,inputs=[videotr_input, tracking_drop],          
+                             outputs=[videotr_output, fpstr_video])
 
 demo.launch(debug=True,enable_queue=True)

track.py

@@ -29,22 +29,18 @@ if str(ROOT / 'trackers' / 'strongsort') not in sys.path:
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 import logging
-#from yolov8.ultralytics.nn.autobackend import AutoBackend
+
 from ultralytics.nn.autobackend import AutoBackend
-#from yolov8.ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadStreams
 from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadStreams
-#from yolov8.ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
-#from yolov8.ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
 from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, SETTINGS, callbacks, colorstr, ops
-
-#from yolov8.ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow, print_args, check_requirements
 from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_imshow, print_args, check_requirements
 from ultralytics.yolo.utils.files import increment_path
 from ultralytics.yolo.utils.torch_utils import select_device
 from ultralytics.yolo.utils.ops import Profile, non_max_suppression, scale_boxes, process_mask, process_mask_native
 from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
 
+
 from trackers.multi_tracker_zoo import create_tracker
 
 
@@ -85,6 +81,8 @@ def run(
         vid_stride=1,  # video frame-rate stride
         retina_masks=False,
 ):
+    #print the inputs
+    print(f"model used : {yolo_weights}, tracking method : {tracking_method}")
 
     source = str(source)
     save_img = not nosave and not source.endswith('.txt')  # save inference images
@@ -108,6 +106,8 @@ def run(
     # Load model
     device = select_device(device)
     is_seg = '-seg' in str(yolo_weights)
+
+  
     model = AutoBackend(yolo_weights, device=device, dnn=dnn, fp16=half)
     stride, names, pt = model.stride, model.names, model.pt
     imgsz = check_imgsz(imgsz, stride=stride)  # check image size
@@ -390,8 +390,10 @@ def MOT(yoloweights, trackingmethod, sourceVideo):
     save_dir = increment_path('exp', exist_ok=True) 
     input = os.path.join(save_dir,'out.mp4')
     outpath = 'output.mp4'  #'output/'+ 'output.mp4' 
+    if os.path.exists(outpath):
+        os.remove(outpath)
+    
     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