dhkim2810 commited on
Commit
cabd05c
1 Parent(s): 6cf5a6c

Add debug print

Browse files
Files changed (1) hide show
  1. app.py +9 -3
app.py CHANGED
@@ -5,6 +5,7 @@ import numpy as np
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  import torch
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  from mobile_sam import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
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  from PIL import ImageDraw
 
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  from utils.tools import box_prompt, format_results, point_prompt
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  from utils.tools_gradio import fast_process
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@@ -111,6 +112,8 @@ def segment_with_points(
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  global global_points
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  global global_point_label
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  input_size = int(input_size)
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  w, h = image.size
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  scale = input_size / max(w, h)
@@ -118,18 +121,21 @@ def segment_with_points(
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  new_h = int(h * scale)
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  image = image.resize((new_w, new_h))
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  scaled_points = np.array(
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  [[int(x * scale) for x in point] for point in global_points]
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  )
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  scaled_point_label = np.array(global_point_label)
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  if scaled_points.size == 0 and scaled_point_label.size == 0:
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  print("No points selected")
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  return image, image
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- print(scaled_points, scaled_points is not None)
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- print(scaled_point_label, scaled_point_label is not None)
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-
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  nd_image = np.array(image)
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  predictor.set_image(nd_image)
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  masks, scores, logits = predictor.predict(
 
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  import torch
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  from mobile_sam import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
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  from PIL import ImageDraw
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+
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  from utils.tools import box_prompt, format_results, point_prompt
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  from utils.tools_gradio import fast_process
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  global global_points
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  global global_point_label
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+ print("Original Image : ", image.size)
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+
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  input_size = int(input_size)
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  w, h = image.size
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  scale = input_size / max(w, h)
 
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  new_h = int(h * scale)
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  image = image.resize((new_w, new_h))
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+ print("Scaled Image : ", image.size)
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+ print("Scale : ", scale)
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+
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  scaled_points = np.array(
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  [[int(x * scale) for x in point] for point in global_points]
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  )
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  scaled_point_label = np.array(global_point_label)
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+ print(scaled_points, scaled_points is not None)
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+ print(scaled_point_label, scaled_point_label is not None)
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+
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  if scaled_points.size == 0 and scaled_point_label.size == 0:
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  print("No points selected")
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  return image, image
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  nd_image = np.array(image)
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  predictor.set_image(nd_image)
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  masks, scores, logits = predictor.predict(