yuragoithf commited on
Commit
ea62197
·
1 Parent(s): 4f37c6b

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +4 -10
app.py CHANGED
@@ -2,17 +2,13 @@ import os, io
2
  import cv2
3
  import gradio as gr
4
  import tensorflow as tf
5
- import urllib.request
6
  import numpy as np
7
  import keras.backend as K
8
- from transformers import pipeline
9
- from matplotlib import pyplot as plt
10
 
 
11
  from PIL import Image
12
- from matplotlib import cm
13
  from tensorflow import keras
14
 
15
- from matplotlib import cm
16
 
17
  resized_shape = (768, 768, 3)
18
  IMG_SCALING = (1, 1)
@@ -51,7 +47,7 @@ def dice_coef(y_true, y_pred, smooth=1):
51
  seg_model = keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
52
 
53
  # inputs = gr.inputs.Image(type="pil", label="Upload an image")
54
- image_output = gr.outputs.Image(type="pil", label="Output Image")
55
  # outputs = gr.outputs.HTML() #uncomment for single class output
56
 
57
  rows = 1
@@ -70,16 +66,14 @@ def gen_pred(img, model=seg_model):
70
  pred = np.squeeze(pred, axis=0)
71
  fig = plt.figure(figsize=(10, 7))
72
  fig.add_subplot(rows, columns, 1)
73
- plt.imshow(pred, interpolation='catrom')
 
74
  plt.axis('off')
75
- plt.title("Prediction")
76
  plt.show()
77
- # return "UI in developing process ..."
78
  return fig
79
 
80
  title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
81
  description = "Upload an image and get prediction mask"
82
- # css_code='body{background-image:url("file=wave.mp4");}'
83
 
84
  gr.Interface(fn=gen_pred,
85
  inputs=[gr.Image(type='pil')],
 
2
  import cv2
3
  import gradio as gr
4
  import tensorflow as tf
 
5
  import numpy as np
6
  import keras.backend as K
 
 
7
 
8
+ from matplotlib import pyplot as plt
9
  from PIL import Image
 
10
  from tensorflow import keras
11
 
 
12
 
13
  resized_shape = (768, 768, 3)
14
  IMG_SCALING = (1, 1)
 
47
  seg_model = keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
48
 
49
  # inputs = gr.inputs.Image(type="pil", label="Upload an image")
50
+ # image_output = gr.outputs.Image(type="pil", label="Output Image")
51
  # outputs = gr.outputs.HTML() #uncomment for single class output
52
 
53
  rows = 1
 
66
  pred = np.squeeze(pred, axis=0)
67
  fig = plt.figure(figsize=(10, 7))
68
  fig.add_subplot(rows, columns, 1)
69
+ # plt.imshow(pred, interpolation='catrom')
70
+ plt.imshow(pred)
71
  plt.axis('off')
 
72
  plt.show()
 
73
  return fig
74
 
75
  title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
76
  description = "Upload an image and get prediction mask"
 
77
 
78
  gr.Interface(fn=gen_pred,
79
  inputs=[gr.Image(type='pil')],