updates

utils/datasets.py

@@ -24,7 +24,7 @@ class load_images():  # for inference
         self.nF = len(self.files)  # number of image files
         self.height = img_size
 
-        assert self.nF > 0, 'No images found in path %s' % path
+        assert self.nF > 0, 'No images found in ' + path
 
     def __iter__(self):
         self.count = -1
@@ -41,7 +41,7 @@ class load_images():  # for inference
         assert img0 is not None, 'Failed to load ' + img_path
 
         # Padded resize
-        img, ratio, padw, padh = letterbox(img0, height=self.height, color=(127.5, 127.5, 127.5))
+        img, _, _, _ = letterbox(img0, height=self.height)
 
         # Normalize RGB
         img = img[:, :, ::-1].transpose(2, 0, 1)
@@ -58,13 +58,12 @@ class load_images():  # for inference
 class load_images_and_labels():  # for training
     def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
         self.path = path
-        # self.img_files = sorted(glob.glob('%s/*.*' % path))
         with open(path, 'r') as file:
             self.img_files = file.readlines()
 
         self.img_files = [path.replace('\n', '') for path in self.img_files]
-        self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
-                            self.img_files]
+        self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt')
+                            for path 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
@@ -73,7 +72,7 @@ class load_images_and_labels():  # for training
         self.multi_scale = multi_scale
         self.augment = augment
 
-        assert self.nB > 0, 'No images found in path %s' % path
+        assert self.nB > 0, 'No images found in %s' % path
 
     def __iter__(self):
         self.count = -1
@@ -128,7 +127,7 @@ class load_images_and_labels():  # for training
                 cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
 
             h, w, _ = img.shape
-            img, ratio, padw, padh = letterbox(img, height=height, color=(127.5, 127.5, 127.5))
+            img, ratio, padw, padh = letterbox(img, height=height)
 
             # Load labels
             if os.path.isfile(label_path):
@@ -189,7 +188,7 @@ class load_images_and_labels():  # for training
         return self.nB  # number of batches
 
 
-def letterbox(img, height=416, color=(0, 0, 0)):  # resize a rectangular image to a padded square
+def letterbox(img, height=416, color=(127.5, 127.5, 127.5)):  # resize a rectangular image to a padded square
     shape = img.shape[:2]  # shape = [height, width]
     ratio = float(height) / max(shape)  # ratio  = old / new
     new_shape = (round(shape[1] * ratio), round(shape[0] * ratio))