Commit
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ea62197
1
Parent(s):
4f37c6b
Update app.py
Browse files
app.py
CHANGED
@@ -2,17 +2,13 @@ import os, io
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import cv2
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import gradio as gr
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import tensorflow as tf
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import urllib.request
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import numpy as np
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import keras.backend as K
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from transformers import pipeline
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from matplotlib import pyplot as plt
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from PIL import Image
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from matplotlib import cm
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from tensorflow import keras
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from matplotlib import cm
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resized_shape = (768, 768, 3)
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IMG_SCALING = (1, 1)
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@@ -51,7 +47,7 @@ def dice_coef(y_true, y_pred, smooth=1):
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seg_model = keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
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# inputs = gr.inputs.Image(type="pil", label="Upload an image")
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image_output = gr.outputs.Image(type="pil", label="Output Image")
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# outputs = gr.outputs.HTML() #uncomment for single class output
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rows = 1
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@@ -70,16 +66,14 @@ def gen_pred(img, model=seg_model):
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pred = np.squeeze(pred, axis=0)
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fig = plt.figure(figsize=(10, 7))
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fig.add_subplot(rows, columns, 1)
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plt.imshow(pred, interpolation='catrom')
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plt.axis('off')
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plt.title("Prediction")
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plt.show()
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# return "UI in developing process ..."
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return fig
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title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
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description = "Upload an image and get prediction mask"
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# css_code='body{background-image:url("file=wave.mp4");}'
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gr.Interface(fn=gen_pred,
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inputs=[gr.Image(type='pil')],
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import cv2
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import gradio as gr
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import tensorflow as tf
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import numpy as np
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import keras.backend as K
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from matplotlib import pyplot as plt
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from PIL import Image
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from tensorflow import keras
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resized_shape = (768, 768, 3)
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IMG_SCALING = (1, 1)
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seg_model = keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
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# inputs = gr.inputs.Image(type="pil", label="Upload an image")
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# image_output = gr.outputs.Image(type="pil", label="Output Image")
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# outputs = gr.outputs.HTML() #uncomment for single class output
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rows = 1
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pred = np.squeeze(pred, axis=0)
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fig = plt.figure(figsize=(10, 7))
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fig.add_subplot(rows, columns, 1)
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# plt.imshow(pred, interpolation='catrom')
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plt.imshow(pred)
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plt.axis('off')
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plt.show()
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return fig
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title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
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description = "Upload an image and get prediction mask"
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gr.Interface(fn=gen_pred,
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inputs=[gr.Image(type='pil')],
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