diff --git a/utils/datasets.py b/utils/datasets.py
index 4fa74ac3..3d831e71 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -240,7 +240,7 @@ class LoadStreams:  # multiple IP or RTSP cameras
             raise StopIteration
 
         # Letterbox
-        img = [letterbox(x, new_shape=self.img_size, auto=self.rect, interp=cv2.INTER_LINEAR)[0] for x in img0]
+        img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0]
 
         # Stack
         img = np.stack(img, 0)
@@ -508,8 +508,7 @@ def load_image(self, index):
         h0, w0 = img.shape[:2]  # orig hw
         r = self.img_size / max(h0, w0)  # resize image to img_size
         if r < 1 or (self.augment and (r != 1)):  # always resize down, only resize up if training with augmentation
-            interp = cv2.INTER_LINEAR if self.augment else cv2.INTER_AREA  # LINEAR for training, AREA for testing
-            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
+            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR)
         return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
     else:
         return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized
@@ -589,8 +588,7 @@ def load_mosaic(self, index):
     return img4, labels4
 
 
-def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
-              auto=True, scaleFill=False, scaleup=True, interp=cv2.INTER_AREA):
+def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
     # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
     shape = img.shape[:2]  # current shape [height, width]
     if isinstance(new_shape, int):
@@ -616,7 +614,7 @@ def letterbox(img, new_shape=(416, 416), color=(114, 114, 114),
     dh /= 2
 
     if shape[::-1] != new_unpad:  # resize
-        img = cv2.resize(img, new_unpad, interpolation=interp)  # INTER_AREA is better, INTER_LINEAR is faster
+        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
     top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
     left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
     img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
@@ -651,9 +649,8 @@ def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10,
 
     # Combined rotation matrix
     M = S @ T @ R  # ORDER IS IMPORTANT HERE!!
-    changed = (border != 0) or (M != np.eye(3)).any()
-    if changed:
-        img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_AREA, borderValue=(114, 114, 114))
+    if (border != 0) or (M != np.eye(3)).any():  # image changed
+        img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
 
     # Transform label coordinates
     n = len(targets)