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

train.py

@@ -48,13 +48,13 @@ from utils.datasets import create_dataloader
 from utils.downloads import attempt_download
 from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
                            check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
-                           intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,
-                           print_args, print_mutation, strip_optimizer)
+                           intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights, methods,
+                           one_cycle, print_args, print_mutation, strip_optimizer)
 from utils.loggers import Loggers
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
-from utils.plots import plot_evolve, plot_labels
+from utils.plots import check_font, plot_evolve, plot_labels
 from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -105,6 +105,8 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     init_seeds(1 + RANK)
     with torch_distributed_zero_first(LOCAL_RANK):
         data_dict = data_dict or check_dataset(data)  # check if None
+        if not is_ascii(data_dict['names']):  # non-latin labels, i.e. asian, arabic, cyrillic
+            check_font('Arial.Unicode.ttf', progress=True)
     train_path, val_path = data_dict['train'], data_dict['val']
     nc = 1 if single_cls else int(data_dict['nc'])  # number of classes
     names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names

utils/general.py

@@ -424,13 +424,13 @@ def check_file(file, suffix=''):
         return files[0]  # return file
 
 
-def check_font(font=FONT):
+def check_font(font=FONT, progress=False):
     # Download font to CONFIG_DIR if necessary
     font = Path(font)
     if not font.exists() and not (CONFIG_DIR / font.name).exists():
         url = "https://ultralytics.com/assets/" + font.name
         LOGGER.info(f'Downloading {url} to {CONFIG_DIR / font.name}...')
-        torch.hub.download_url_to_file(url, str(font), progress=False)
+        torch.hub.download_url_to_file(url, str(font), progress=progress)
 
 
 def check_dataset(data, autodownload=True):

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