Auto-UTF handling (#4594)

detect.py

@@ -21,9 +21,9 @@ sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
 
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
-from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
+from utils.general import check_img_size, check_requirements, check_imshow, colorstr, is_ascii, non_max_suppression, \
     apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
-from utils.plots import colors, Annotator
+from utils.plots import Annotator, colors
 from utils.torch_utils import select_device, load_classifier, time_sync
 
 
@@ -105,6 +105,7 @@ 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:
@@ -181,7 +182,7 @@ 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=False)
+            annotator = Annotator(im0, line_width=line_thickness, pil=not ascii)
             if len(det):
                 # Rescale boxes from img_size to im0 size
                 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()