Update app.py

app.py

@@ -5,20 +5,77 @@ from keras.models import load_model
 from tensorflow_addons.layers import InstanceNormalization
 import matplotlib.pyplot as plt
 import numpy as np
+import tensorflow as tf
+
 
 cust = {'InstanceNormalization': InstanceNormalization}
-model=load_model('g_model_AtoB_002160.h5',cust)
-# model=load_model('g_model_AtoB_000540.h5',cust)
+model=load_model('g-cycleGAN-photo2monet-500images-epoch10.h5',cust)
 
 path  = [['ex1.jpg'], ['ex2.jpg'], ['ex3.jpg'],['ex4.jpg'],['ex5.jpg']]
 
+# preprocess
+AUTOTUNE = tf.data.AUTOTUNE
+BUFFER_SIZE = 400
+BATCH_SIZE = 1
+IMG_WIDTH = 256
+IMG_HEIGHT = 256
+
+def resize(image,height,width):
+  '''
+    Resizing the image
+  '''
+  resized_image = tf.image.resize(image,[height,width],method = tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+  return resized_image
+
+def normalize(input_image):
+  # def normalize(real_image, input_image)
+  input_image = (input_image/127.5) - 1
+  return input_image
+#   real_image = (real_image/127.5) - 1
+#   return real_image,input_image
+
+def load(img_file):
+  '''
+    load the image. Since we need only the target image and a 
+    gray scale version of the same, we are going to load one 
+    and create the other from it
+  '''
+  img = tf.io.read_file(img_file)
+  img = tf.io.decode_jpeg(img)
+
+#   w = tf.shape(img)[1]
+#   w = w//2
+
+#   real_image = img[:,:w,:]
+  
+  real_image = tf.cast(img,tf.float32)
+
+  return real_image
+
+def load_image_test(image_file):
+  '''
+  We are not using random jitter here and thus creating
+  a gray scale image after resizing.
+  '''
+  re = load(image_file)
+  re = resize(re,IMG_HEIGHT,IMG_WIDTH)
+#   inp = tf.image.rgb_to_grayscale(re)
+#   re,inp = normalize(re,inp)
+#   inp = re
+#   re, inp = normalize(re,inp)
+  re = normalize(re)
+#   return inp,re
+  return re
+
+
 def show_preds_image(image_path):
-    A = plt.imread(image_path)
+    A = load_image_test(image_path)
     # A = (A - 127.5) / 127.5
     A = np.expand_dims(A,axis=0)
-    B = model.predict(A)
-    B = np.squeeze(B,axis=0)
-    B = (B + 1) / 2.0
+    B = model(A)
+    # B = np.squeeze(B,axis=0)
+    B = B[0]
+    B = B * 0.5 + 0.5
     return B
  
 inputs_image = [