segmentation and sepia

.gitattributes

@@ -16,7 +16,7 @@
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 *.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import numpy as np
+import PIL
+
+import tensorflow as tf
+from tensorflow import keras
+
+def predict(img):
+  input = np.array(img, dtype='float32') / 255
+
+  sepia_filter = np.array([[.393, .769, .189], [.349, .686, .168], [.272, .534, .131]])
+  sepia_img = input.dot(sepia_filter.T)
+  sepia_img /= sepia_img.max()
+
+  mask = np.argmax(model.predict(np.expand_dims(input, axis=0)), axis=-1)[0]
+
+  result = np.copy(input)
+  for r in range(result.shape[0]):
+    for c in range(result.shape[1]):
+      if mask[r, c] != 1:
+        result[r, c] = sepia_img[r, c]
+
+  return PIL.Image.fromarray(np.uint8(result*255))
+
+
+model = keras.models.load_model('model')
+
+iface = gr.Interface(predict,\
+                    inputs = gr.Image(shape=(256, 256)),\
+                    outputs = gr.Image(shape=(256, 256), image_mode='rgb'),\
+                    examples = ["examples/english_setter_78.jpg",\
+                                "examples/Ragdoll_60.jpg",\
+                                "examples/pomeranian_74.jpg",\
+                                "examples/Persian_137.jpg",\
+                                "examples/saint_bernard_136.jpg"])
+
+iface.launch()

model/keras_metadata.pb

@@ -0,0 +1,3 @@
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model/saved_model.pb

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model/variables/variables.data-00000-of-00001

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model/variables/variables.index

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requirements.txt

@@ -0,0 +1,3 @@
+tensorflow
+Pillow
+numpy