More cleanup

src/model/Siamese_Network.py

@@ -4,6 +4,9 @@
 import random
 import tensorflow as tf
 from pathlib import Path
+
+from utils import preprocess_image, preprocess_triplets
+
 from tensorflow.keras import layers
 from tensorflow.keras import optimizers
 from tensorflow.keras import metrics
@@ -16,32 +19,6 @@
 anchor_images_path = cache_dir / "left"
 positive_images_path = cache_dir / "right"
 
-def preprocess_image(filename):
-    """
-    Load the specified file as a JPEG image, preprocess it and
-    resize it to the target shape.
-    """
-
-    image_string = tf.io.read_file(filename)
-    image = tf.image.decode_jpeg(image_string, channels=3)
-    image = tf.image.convert_image_dtype(image, tf.float32)
-    image = tf.image.resize(image, target_shape)
-    return image
-
-
-def preprocess_triplets(anchor, positive, negative):
-    """
-    Given the filenames corresponding to the three images, load and
-    preprocess them.
-    """
-
-    return (
-        preprocess_image(anchor),
-        preprocess_image(positive),
-        preprocess_image(negative),
-    )
-
-
 class DistanceLayer(layers.Layer):
     """
     This layer is responsible for computing the distance between the anchor
@@ -190,4 +167,5 @@ def metrics(self):
 siamese_model = SiameseModel(siamese_network)
 siamese_model.compile(optimizer=optimizers.Adam(0.0001))  # 0..0001
 siamese_model.fit(train_dataset, epochs=7, validation_data=val_dataset)
-embedding.save("siamese_feature.h5")
+embedding.save_weights("weights/embedding_weights.h5")
+siamese_model.save_weights("weights/siamese_weights.h5")

src/model/Siamese_Predictor.py

@@ -1,28 +1,17 @@
-# from Siamese_Network import preprocess_image
+import threading
 import tensorflow as tf
+import time
+
+from utils import preprocess_image
+
 from tensorflow.keras.applications import resnet
-import argparse
 from tensorflow.keras import metrics
-import time
+
 from watchdog.observers import Observer
 from watchdog.events import PatternMatchingEventHandler
-import threading
-
-def preprocess_image(filename):
-    """
-    Load the specified file as a JPEG image, preprocess it and
-    resize it to the target shape.
-    """
-    target_shape = (200, 200)
-
-    image_string = tf.io.read_file(filename)
-    image = tf.image.decode_jpeg(image_string, channels=3)
-    image = tf.image.convert_image_dtype(image, tf.float32)
-    image = tf.image.resize(image, target_shape)
-    return image
 
 # load the model
-embedding = tf.keras.models.load_model("siamese_feature.h5", compile=False)
+embedding = tf.keras.models.load_model("siamese_weights.h5", compile=False)
 print("Model is done loading")
 
 # load david base

src/model/utils.py

@@ -0,0 +1,28 @@
+import tensorflow as tf
+
+target_shape = (200, 200)
+
+def preprocess_image(filename):
+    """
+    Load the specified file as a JPEG image, preprocess it and
+    resize it to the target shape.
+    """
+
+    image_string = tf.io.read_file(filename)
+    image = tf.image.decode_jpeg(image_string, channels=3)
+    image = tf.image.convert_image_dtype(image, tf.float32)
+    image = tf.image.resize(image, target_shape)
+    return image
+
+
+def preprocess_triplets(anchor, positive, negative):
+    """
+    Given the filenames corresponding to the three images, load and
+    preprocess them.
+    """
+
+    return (
+        preprocess_image(anchor),
+        preprocess_image(positive),
+        preprocess_image(negative),
+    )