fffiloni commited on
Commit
e3a7f2f
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1 Parent(s): 83fd490

Update app.py

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Files changed (1) hide show
  1. app.py +6 -5
app.py CHANGED
@@ -246,11 +246,11 @@ def get_mask_sam_process(
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  # predictor.reset_state(inference_state) # if any previous tracking, reset
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  ### HANDLING WORKING FRAME
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- new_working_frame = None
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  # Add new point
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  if working_frame is None:
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  ann_frame_idx = 0 # the frame index we interact with, 0 if it is the first frame
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- new_working_frame = "frames_output_images/frame_0.jpg"
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  else:
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  # Use a regular expression to find the integer
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  match = re.search(r'frame_(\d+)', working_frame)
@@ -258,8 +258,8 @@ def get_mask_sam_process(
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  # Extract the integer from the match
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  frame_number = int(match.group(1))
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  ann_frame_idx = frame_number
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- new_working_frame = f"frames_output_images/frame_{ann_frame_idx}.jpg"
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- print(f"NEW_WORKING_FRAME PATH: {new_working_frame}")
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  ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers)
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@@ -288,7 +288,7 @@ def get_mask_sam_process(
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  plt.close()
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  torch.cuda.empty_cache()
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- return "output_first_frame.jpg", frame_names, inference_state
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  def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, progress=gr.Progress(track_tqdm=True)):
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  #### PROPAGATION ####
@@ -510,6 +510,7 @@ with gr.Blocks() as demo:
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  output_result,
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  stored_frame_names,
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  stored_inference_state,
 
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  ]
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  )
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  # predictor.reset_state(inference_state) # if any previous tracking, reset
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  ### HANDLING WORKING FRAME
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+ # new_working_frame = None
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  # Add new point
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  if working_frame is None:
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  ann_frame_idx = 0 # the frame index we interact with, 0 if it is the first frame
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+ working_frame = "frame_0.jpg"
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  else:
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  # Use a regular expression to find the integer
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  match = re.search(r'frame_(\d+)', working_frame)
 
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  # Extract the integer from the match
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  frame_number = int(match.group(1))
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  ann_frame_idx = frame_number
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+
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+ print(f"NEW_WORKING_FRAME PATH: {working_frame}")
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  ann_obj_id = 1 # give a unique id to each object we interact with (it can be any integers)
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  plt.close()
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  torch.cuda.empty_cache()
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+ return "output_first_frame.jpg", frame_names, inference_state, gr.update(value=working_frame, visible=True)
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  def propagate_to_all(video_in, checkpoint, stored_inference_state, stored_frame_names, video_frames_dir, vis_frame_type, progress=gr.Progress(track_tqdm=True)):
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  #### PROPAGATION ####
 
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  output_result,
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  stored_frame_names,
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  stored_inference_state,
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+ working_frame,
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  ]
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  )
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