Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from PIL import Image
+import io
+
+#from SegZoo
+def standardize(img):
+    #standardization using adjusted standard deviation
+
+    N = np.shape(img)[0] * np.shape(img)[1]
+    s = np.maximum(np.std(img), 1.0/np.sqrt(N))
+    m = np.mean(img)
+    img = (img - m) / s
+    del m, s, N
+    #
+    if np.ndim(img)==2:
+        img = np.dstack((img,img,img))
+    return img
+
+#load model
+filepath = './model/FRF_jan22_remap'
+model = tf.keras.models.load_model(filepath, compile = True)
+model.compile
+
+#segmentation
+def FRFsegment(input_img):
+    #img = tf.keras.preprocessing.image.load_img(input_img,target_size = (512, 512))
+    #img = tf.keras.preprocessing.image.img_to_array(img)
+    img = standardize(input_img)
+    img = np.expand_dims(img,axis=0)
+    
+    est_label = model.predict(img)
+    
+    
+#     est_label2 = np.flipud(model.predict((np.flipud(img)), batch_size=1))
+#     est_label3 = np.fliplr(model.predict((np.fliplr(img)), batch_size=1))
+#     est_label4 = np.flipud(np.fliplr(model.predict((np.flipud(np.fliplr(img))))))
+
+#     #soft voting - sum the softmax scores to return the new TTA estimated softmax scores
+#     pred = est_label + est_label2 + est_label3 + est_label4
+    
+    pred = est_label
+    
+#     print(pred.shape)
+    mask = np.argmax(np.squeeze(pred, axis=0),-1)
+#     print(np.amax(mask))
+#     print(np.amin(mask))
+    
+    #overlay plot
+    p = plt.imshow(input_img,cmap='gray')
+    p = plt.imshow(mask, alpha=0.4)
+    p = plt.axis("off")
+    return plt
+    
+#     #overlay plot to PIL
+#     p = plt.imshow(input_img,cmap='gray')
+#     p = plt.imshow(mask, alpha=0.6)
+#     p = plt.axis("off")
+#     buf = io.BytesIO()
+#     fig = plt.gcf()
+#     fig.savefig(buf)
+#     buf.seek(0)
+#     img = Image.open(buf)
+#     return img
+    
+#     #PIL
+#     #img = Image.fromarray(np.uint8(mask*(255/5)))
+
+#     return img
+
+#FRFSegapp = gr.Interface(FRFsegment, gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Image('plot'))
+FRFSegapp = gr.Interface(FRFsegment, gr.inputs.Image(shape=(512, 512)), "image")
+
+FRFSegapp.launch()