From 7d857cda957ee1230eeb6c7ffcd61447e486960f Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Sun, 5 May 2019 13:21:37 +0200
Subject: [PATCH] updates

---
 utils/datasets.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/utils/datasets.py b/utils/datasets.py
index 7561b629..14708550 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -130,7 +130,7 @@ class LoadWebcam:  # for inference
 
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=416, batch_size=16, augment=False):
+    def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True):
         with open(path, 'r') as f:
             img_files = f.read().splitlines()
             self.img_files = list(filter(lambda x: len(x) > 0, img_files))
@@ -144,8 +144,8 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             for x in self.img_files]
 
         # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
-        self.train_rectangular = False
-        if self.train_rectangular:
+        self.pad_rectangular = rect
+        if self.pad_rectangular:
             bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
             nb = bi[-1] + 1  # number of batches
             from PIL import Image
@@ -185,8 +185,8 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             try:
                 with open(file, 'r') as f:
                     self.labels[i] = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
-            except:  # missing label file
-                pass
+            except:
+                pass  # missing label file
 
     def __len__(self):
         return len(self.img_files)
@@ -195,7 +195,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         img_path = self.img_files[index]
         label_path = self.label_files[index]
 
-        # if hasattr(self, 'imgs'):
+        # if hasattr(self, 'imgs'):  # preloaded
         #    img = self.imgs[index]  # BGR
         img = cv2.imread(img_path)  # BGR
         assert img is not None, 'File Not Found ' + img_path
@@ -219,7 +219,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
         # Letterbox
         h, w, _ = img.shape
-        if self.train_rectangular:
+        if self.pad_rectangular:
             new_shape = self.batch_shapes[self.batch[index]]
             img, ratio, padw, padh = letterbox(img, new_shape=new_shape, mode='rect')
         else: