From 8c32100d4e07dd469f7da6102ba5bfe4dd9c9815 Mon Sep 17 00:00:00 2001
From: vallimaylv <116088249+vallimaylv@users.noreply.github.com>
Date: Sun, 13 Nov 2022 20:05:57 +0530
Subject: [PATCH] Update global.py

---
 global.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/global.py b/global.py
index 059b324..69b0e33 100644
--- a/global.py
+++ b/global.py
@@ -13,7 +13,7 @@
 # tunable-parameters
 #--------------------
 images_per_class = 80
-fixed_size       = tuple((300, 300))
+fixed_size       = tuple((500, 500))
 train_path       = "dataset/train"
 h5_data          = 'output/data.h5'
 h5_labels        = 'output/labels.h5'
@@ -49,7 +49,7 @@ def fd_histogram(image, mask=None):
 train_labels = os.listdir(train_path)
 
 # sort the training labels
-train_labels.sort()
+#train_labels.sort()
 print(train_labels)
 
 # empty lists to hold feature vectors and labels
@@ -65,7 +65,7 @@ def fd_histogram(image, mask=None):
     current_label = training_name
 
     # loop over the images in each sub-folder
-    for x in range(1,images_per_class+1):
+    for x in range(0,images_per_class):
         # get the image file name
         # file = dir + "/" + str(x) + ".jpg"
         file = dir + "/" + "{}_{}.jpg".format(training_name, int(training_name)*100 + x)
@@ -98,7 +98,7 @@ def fd_histogram(image, mask=None):
 print("[STATUS] feature vector size {}".format(np.array(global_features).shape))
 
 # get the overall training label size
-print("[STATUS] training Labels {}".format(np.array(labels).shape))
+print("[STATUS] training Labels size {}".format(np.array(labels).shape))
 
 # encode the target labels
 targetNames = np.unique(labels)