Improved non-latin Annotator() plotting (#7488)

* Improved non-latin labels Annotator plotting

May resolve https://github.com/ultralytics/yolov5/issues/7460

* Update train.py

* Update train.py

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* add progress arg

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utils/plots.py

@@ -19,7 +19,7 @@ import torch
 from PIL import Image, ImageDraw, ImageFont
 
 from utils.general import (CONFIG_DIR, FONT, LOGGER, Timeout, check_font, check_requirements, clip_coords,
-                           increment_path, is_ascii, is_chinese, try_except, xywh2xyxy, xyxy2xywh)
+                           increment_path, is_ascii, try_except, xywh2xyxy, xyxy2xywh)
 from utils.metrics import fitness
 
 # Settings
@@ -72,11 +72,12 @@ class Annotator:
     # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
     def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
         assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
-        self.pil = pil or not is_ascii(example) or is_chinese(example)
+        non_ascii = not is_ascii(example)  # non-latin labels, i.e. asian, arabic, cyrillic
+        self.pil = pil or non_ascii
         if self.pil:  # use PIL
             self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
             self.draw = ImageDraw.Draw(self.im)
-            self.font = check_pil_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
+            self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
                                        size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
         else:  # use cv2
             self.im = im