updates

utils/datasets.py

@@ -92,34 +92,39 @@ class LoadWebcam:  # for inference
 class LoadImagesAndLabels:  # for training
     def __init__(self, path, batch_size=1, img_size=608, augment=False):
         with open(path, 'r') as file:
-            self.img_files = file.readlines()
-            self.img_files = [x.replace('\n', '') for x in self.img_files]
+            self.img_files = file.read().splitlines()
             self.img_files = list(filter(lambda x: len(x) > 0, self.img_files))
 
+        self.nF = len(self.img_files)  # number of image files
+        self.nB = math.ceil(self.nF / batch_size)  # number of batches
+        assert self.nF > 0, 'No images found in %s' % path
+
         self.label_files = [x.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
                             for x in self.img_files]
 
-        self.nF = len(self.img_files)  # number of image files
-        self.nB = math.ceil(self.nF / batch_size)  # number of batches
         self.batch_size = batch_size
         self.img_size = img_size
         self.augment = augment
-
-        assert self.nF > 0, 'No images found in %s' % path
+        iter(self)
 
     def __iter__(self):
         self.count = -1
         self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
         return self
 
+    def __getitem__(self, index):
+        return self.load_images(index, index + 1)
+
     def __next__(self):
-        self.count += 1
-        if self.count == self.nB:
+        self.count += 1  # batches
+        if self.count >= self.nB:
             raise StopIteration
 
-        ia = self.count * self.batch_size
-        ib = min((self.count + 1) * self.batch_size, self.nF)
+        ia = self.count * self.batch_size  # start index
+        ib = min(ia + self.batch_size, self.nF)  # end index
+        return self.load_images(ia, ib)
 
+    def load_images(self, ia, ib):
         img_all, labels_all, img_paths, img_shapes = [], [], [], []
         for index, files_index in enumerate(range(ia, ib)):
             img_path = self.img_files[self.shuffled_vector[files_index]]