Automatic Chinese fonts plotting (#4951)

* Automatic Chinese fonts plotting

* Default PIL=False

detect.py

@@ -23,7 +23,7 @@ if str(ROOT) not in sys.path:
 from models.experimental import attempt_load
 from utils.datasets import LoadImages, LoadStreams
 from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \
-    increment_path, is_ascii, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
+    increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \
     strip_optimizer, xyxy2xywh
 from utils.plots import Annotator, colors
 from utils.torch_utils import load_classifier, select_device, time_sync
@@ -108,7 +108,6 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
             output_details = interpreter.get_output_details()  # outputs
             int8 = input_details[0]['dtype'] == np.uint8  # is TFLite quantized uint8 model
     imgsz = check_img_size(imgsz, s=stride)  # check image size
-    ascii = is_ascii(names)  # names are ascii (use PIL for UTF-8)
 
     # Dataloader
     if webcam:
@@ -190,7 +189,7 @@ def run(weights='yolov5s.pt',  # model.pt path(s)
             s += '%gx%g ' % img.shape[2:]  # print string
             gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
             imc = im0.copy() if save_crop else im0  # for save_crop
-            annotator = Annotator(im0, line_width=line_thickness, pil=not ascii)
+            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
             if len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()