Automatic Chinese fonts plotting (#4951)

* Automatic Chinese fonts plotting

* Default PIL=False

models/common.py

@@ -18,7 +18,7 @@ from PIL import Image
 from torch.cuda import amp
 
 from utils.datasets import exif_transpose, letterbox
-from utils.general import colorstr, increment_path, is_ascii, make_divisible, non_max_suppression, save_one_box, \
+from utils.general import colorstr, increment_path, make_divisible, non_max_suppression, save_one_box, \
     scale_coords, xyxy2xywh
 from utils.plots import Annotator, colors
 from utils.torch_utils import time_sync
@@ -356,7 +356,6 @@ class Detections:
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
-        self.ascii = is_ascii(names)  # names are ascii (use PIL for UTF-8)
         self.files = files  # image filenames
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
@@ -369,13 +368,13 @@ class Detections:
     def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
         crops = []
         for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
-            str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
+            s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
             if pred.shape[0]:
                 for c in pred[:, -1].unique():
                     n = (pred[:, -1] == c).sum()  # detections per class
-                    str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                 if show or save or render or crop:
-                    annotator = Annotator(im, pil=not self.ascii)
+                    annotator = Annotator(im, example=str(self.names))
                     for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                         label = f'{self.names[int(cls)]} {conf:.2f}'
                         if crop:
@@ -386,11 +385,11 @@ class Detections:
                             annotator.box_label(box, label, color=colors(cls))
                     im = annotator.im
             else:
-                str += '(no detections)'
+                s += '(no detections)'
 
             im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:
-                LOGGER.info(str.rstrip(', '))
+                LOGGER.info(s.rstrip(', '))
             if show:
                 im.show(self.files[i])  # show
             if save: