GoBoKyung commited on
Commit
5ffae2f
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1 Parent(s): 074b669
Files changed (1) hide show
  1. app.py +10 -33
app.py CHANGED
@@ -1,8 +1,6 @@
1
  import gradio as gr
2
- from matplotlib import gridspec
3
- import matplotlib.pyplot as plt
4
- import numpy as np
5
  from PIL import Image
 
6
  import tensorflow as tf
7
  from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
8
  import os
@@ -15,7 +13,7 @@ model = TFSegformerForSemanticSegmentation.from_pretrained(
15
  )
16
 
17
  def ade_palette():
18
- """ADE20K ํŒ”๋ ˆํŠธ - ๊ฐ ํด๋ž˜์Šค๋ฅผ RGB ๊ฐ’์— ๋งคํ•‘ํ•ฉ๋‹ˆ๋‹ค."""
19
  return [
20
  [204, 87, 92],
21
  [112, 185, 212],
@@ -48,39 +46,18 @@ colormap = np.asarray(ade_palette())
48
 
49
  def label_to_color_image(label):
50
  if label.ndim != 2:
51
- raise ValueError("2-D ์ž…๋ ฅ ๋ผ๋ฒจ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.")
52
 
53
  if np.max(label) >= len(colormap):
54
- raise ValueError("๋ผ๋ฒจ ๊ฐ’์ด ๋„ˆ๋ฌด ํฝ๋‹ˆ๋‹ค.")
55
  return colormap[label]
56
 
57
- def draw_plot(pred_img, seg):
58
- fig = plt.figure(figsize=(20, 15))
59
-
60
- grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])
61
-
62
- plt.subplot(grid_spec[0])
63
- plt.imshow(pred_img)
64
- plt.axis('off')
65
- LABEL_NAMES = np.asarray(labels_list)
66
- FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
67
- FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
68
-
69
- unique_labels = np.unique(seg.numpy().astype("uint8"))
70
- ax = plt.subplot(grid_spec[1])
71
- plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
72
- ax.yaxis.tick_right()
73
- plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
74
- plt.xticks([], [])
75
- ax.tick_params(width=0.0, labelsize=25)
76
- return fig
77
-
78
  def sepia(input_text):
79
- # ์ž…๋ ฅ ํ…์ŠคํŠธ๊ฐ€ ์œ ํšจํ•œ ํŒŒ์ผ ๊ฒฝ๋กœ์ธ์ง€ ํ™•์ธ
80
  if not os.path.isfile(input_text):
81
- return "์œ ํšจํ•˜์ง€ ์•Š์€ ํŒŒ์ผ ๊ฒฝ๋กœ์ž…๋‹ˆ๋‹ค. ์œ ํšจํ•œ ์ด๋ฏธ์ง€ ํŒŒ์ผ ๊ฒฝ๋กœ๋ฅผ ์ž…๋ ฅํ•ด์ฃผ์„ธ์š”."
82
 
83
- # ์ž…๋ ฅ ํ…์ŠคํŠธ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์ด๋ฏธ์ง€๋ฅผ ๋กœ๋“œ (์ด๋ฏธ์ง€ ํŒŒ์ผ ๊ฒฝ๋กœ๋กœ ๊ฐ€์ •)
84
  input_img = Image.open(input_text)
85
 
86
  inputs = feature_extractor(images=input_img, return_tensors="tf")
@@ -102,13 +79,13 @@ def sepia(input_text):
102
  pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
103
  pred_img = pred_img.astype(np.uint8)
104
 
105
- # Gradio์— ์ ํ•ฉํ•œ ํ˜•์‹์œผ๋กœ ์ด๋ฏธ์ง€ ๋ฐฐ์—ด ๋ณ€ํ™˜
106
  pred_img = Image.fromarray(pred_img)
107
 
108
  return pred_img
109
 
110
- # Gradio ์ธํ„ฐํŽ˜์ด์Šค ์ •์˜
111
  iface = gr.Interface(fn=sepia, inputs="image", outputs="image")
112
 
113
- # Gradio ์•ฑ ์‹œ์ž‘
114
  iface.launch()
 
1
  import gradio as gr
 
 
 
2
  from PIL import Image
3
+ import numpy as np
4
  import tensorflow as tf
5
  from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation
6
  import os
 
13
  )
14
 
15
  def ade_palette():
16
+ """ADE20K palette that maps each class to RGB values."""
17
  return [
18
  [204, 87, 92],
19
  [112, 185, 212],
 
46
 
47
  def label_to_color_image(label):
48
  if label.ndim != 2:
49
+ raise ValueError("Expect 2-D input label")
50
 
51
  if np.max(label) >= len(colormap):
52
+ raise ValueError("label value too large.")
53
  return colormap[label]
54
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
55
  def sepia(input_text):
56
+ # Check if the input text is a valid file path
57
  if not os.path.isfile(input_text):
58
+ return "Invalid file path. Please enter a valid image file path."
59
 
60
+ # Load the image using the input text (assumed to be a path to an image)
61
  input_img = Image.open(input_text)
62
 
63
  inputs = feature_extractor(images=input_img, return_tensors="tf")
 
79
  pred_img = np.array(input_img) * 0.5 + color_seg * 0.5
80
  pred_img = pred_img.astype(np.uint8)
81
 
82
+ # Convert the image array to a Pillow (PIL) image
83
  pred_img = Image.fromarray(pred_img)
84
 
85
  return pred_img
86
 
87
+ # Define the Gradio interface
88
  iface = gr.Interface(fn=sepia, inputs="image", outputs="image")
89
 
90
+ # Launch the Gradio app
91
  iface.launch()