From d8d2b5463eb1af6162069cfa2c4e99fe87022f90 Mon Sep 17 00:00:00 2001
From: raphael <raphael@maenle.net>
Date: Thu, 12 Aug 2021 15:25:37 +0200
Subject: [PATCH] adds preloading weights into existing siamese network

---
 evaluate.py          | 20 ++++++++++++--------
 train_lambda_coco.py | 11 +++++++++--
 2 files changed, 21 insertions(+), 10 deletions(-)

diff --git a/evaluate.py b/evaluate.py
index 3ad92fc..709886c 100644
--- a/evaluate.py
+++ b/evaluate.py
@@ -1,21 +1,25 @@
 import tensorflow.keras as keras
 from PIL import Image
 import numpy as np
-import pdb
+import ipdb
 
-def getI(path):
-    return np.asarray(Image.open(path).convert('RGB').resize((100, 100))) / 255
+class g:
+    def __init__(self, path):
+        self.image = np.asarray(Image.open(path).convert('RGB').resize((100, 100))) / 255
+
+    def show(self):
+        self.image.show()
 
 def predict(image1, image2):
-    return model.predict([np.array([image2]), np.array([image1])]) 
+    return model.predict([np.array([image2.image]), np.array([image1.image])]) 
 
-model = keras.models.load_model('./siamese_checkpoint')
-image1 = getI('data/fruits/fruits-360/Training/Avocado/r_254_100.jpg')
-image2 = getI('data/fruits/fruits-360/Training/Avocado/r_250_100.jpg')
+model = keras.models.load_model('../siamese_100x100_pretrainedb_vgg16')
+image1 = g('data/fruits/fruits-360/Training/Avocado/r_254_100.jpg')
+image2 = g('data/fruits/fruits-360/Training/Avocado/r_250_100.jpg')
 
 print(predict(image1, image2))
 
-pdb.set_trace()
+ipdb.set_trace()
 
 
 
diff --git a/train_lambda_coco.py b/train_lambda_coco.py
index 89d18af..8fa66f5 100644
--- a/train_lambda_coco.py
+++ b/train_lambda_coco.py
@@ -12,8 +12,15 @@ from siamese import SiameseNetwork
 import pdb
 
 import os, math, numpy as np
+
+
+# import Image handling and 
+# set do not import metadata
+import PIL
 from PIL import Image
 
+PIL.ImageFile.LOAD_TRUNCATED_IMAGES=True
+
 batch_size = 128
 num_classes = 131
 
@@ -21,9 +28,9 @@ num_classes = 131
 img_rows, img_cols = 100, 100
 
 def createTrainingData():
-    base_dir = './classified/'
+    base_dir = './data/combined/'
     train_test_split = 0.7
-    no_of_files_in_each_class = 200
+    no_of_files_in_each_class = 200 
 
     #Read all the folders in the directory
     folder_list = os.listdir(base_dir)