path update

util.py

@@ -1,70 +1,70 @@
-import tensorflow as tf
-from tensorflow.keras.applications.vgg16 import preprocess_input
-from tensorflow.keras.preprocessing.image import img_to_array
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import numpy as np
-import pickle
-
-CapGenerator = tf.keras.models.load_model('models/CapGen.h5')
-VGGMod = tf.keras.models.load_model('models/VGGModel.h5')
-max_length = 35
-
-with open('models/tokenizer.pickle', 'rb') as handle:
-    tokenizer = pickle.load(handle)
-
-vocab_size = len(tokenizer.word_index) + 1
-
-def idx_to_word(integer, tokenizer):
-    for word, index in tokenizer.word_index.items():
-        if index == integer:
-            return word
-    return None
-
-def predict_caption(model, image, tokenizer, max_length=max_length):
-    # add start tag for generation process
-    in_text = 'startseq'
-    # iterate over the max length of sequence
-    for i in range(max_length):
-        # encode input sequence
-        sequence = tokenizer.texts_to_sequences([in_text])[0]
-        # pad the sequence
-        sequence = pad_sequences([sequence], max_length)
-        # predict next word
-        yhat = model.predict([image, sequence], verbose=0)
-        # get index with high probability
-        yhat = np.argmax(yhat)
-        # convert index to word
-        word = idx_to_word(yhat, tokenizer)
-        # stop if word not found
-        if word is None:
-            break
-        # append word as input for generating next word
-        in_text += " " + word
-        # stop if we reach end tag
-        if word == 'endseq':
-            break
-
-    return in_text
-  
-def feature_extractor(image):
-
-    # Img to np array
-    image = img_to_array(image)
-
-    # Reshaping
-    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
-
-    # Preprocessing for passing through VGG16
-    image = preprocess_input(image)
-
-    feature = VGGMod.predict(image, verbose=0)
-    
-    return feature
-
-def generate_caption(image_name):
-    
-    y_pred = predict_caption(CapGenerator, feature_extractor(image_name), tokenizer, max_length)
-    y_pred = y_pred[8:-7].upper()
-    
-    return y_pred
+import tensorflow as tf
+from tensorflow.keras.applications.vgg16 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import numpy as np
+import pickle
+
+CapGenerator = tf.keras.models.load_model('CapGen.h5')
+VGGMod = tf.keras.models.load_model('VGGModel.h5')
+max_length = 35
+
+with open('models/tokenizer.pickle', 'rb') as handle:
+    tokenizer = pickle.load(handle)
+
+vocab_size = len(tokenizer.word_index) + 1
+
+def idx_to_word(integer, tokenizer):
+    for word, index in tokenizer.word_index.items():
+        if index == integer:
+            return word
+    return None
+
+def predict_caption(model, image, tokenizer, max_length=max_length):
+    # add start tag for generation process
+    in_text = 'startseq'
+    # iterate over the max length of sequence
+    for i in range(max_length):
+        # encode input sequence
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        # pad the sequence
+        sequence = pad_sequences([sequence], max_length)
+        # predict next word
+        yhat = model.predict([image, sequence], verbose=0)
+        # get index with high probability
+        yhat = np.argmax(yhat)
+        # convert index to word
+        word = idx_to_word(yhat, tokenizer)
+        # stop if word not found
+        if word is None:
+            break
+        # append word as input for generating next word
+        in_text += " " + word
+        # stop if we reach end tag
+        if word == 'endseq':
+            break
+
+    return in_text
+  
+def feature_extractor(image):
+
+    # Img to np array
+    image = img_to_array(image)
+
+    # Reshaping
+    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
+
+    # Preprocessing for passing through VGG16
+    image = preprocess_input(image)
+
+    feature = VGGMod.predict(image, verbose=0)
+    
+    return feature
+
+def generate_caption(image_name):
+    
+    y_pred = predict_caption(CapGenerator, feature_extractor(image_name), tokenizer, max_length)
+    y_pred = y_pred[8:-7].upper()
+    
+    return y_pred