Create Annotator() class (#4591)

* Add Annotator() class

* Download Arial

* 2x for loop

* Cleanup

* tuple 2 list

* max_size=1920

* bold logging results to

* tolist()

* im = annotator.im

* PIL save in detect.py

* Smart asarray in detect.py

* revert to cv2.imwrite

* Cleanup

* Return result asarray

* Add `Profile()` profiler

* CamelCase Timeout

* Resize after mosaic

* pillow>=8.0.0

* daemon imwrite

* Add cv2 support

* Remove plot_wh_methods and plot_one_box

* pil=False for hubconf.py annotations

* im.shape bug fix

* colorstr common.py

* join daemons

* Update t.daemon

* Removed daemon saving

models/common.py

@@ -18,8 +18,9 @@ from PIL import Image
 from torch.cuda import amp
 
 from utils.datasets import exif_transpose, letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
-from utils.plots import colors, plot_one_box
+from utils.general import colorstr, non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, \
+    save_one_box
+from utils.plots import colors, Annotator
 from utils.torch_utils import time_sync
 
 LOGGER = logging.getLogger(__name__)
@@ -370,12 +371,14 @@ class Detections:
                     n = (pred[:, -1] == c).sum()  # detections per class
                     str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                 if show or save or render or crop:
+                    annotator = Annotator(im, pil=False)
                     for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                         label = f'{self.names[int(cls)]} {conf:.2f}'
                         if crop:
                             save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
                         else:  # all others
-                            im = plot_one_box(box, im, label=label, color=colors(cls))
+                            annotator.box_label(box, label, color=colors(cls))
+                    im = annotator.im
             else:
                 str += '(no detections)'
 
@@ -388,7 +391,7 @@ class Detections:
                 f = self.files[i]
                 im.save(save_dir / f)  # save
                 if i == self.n - 1:
-                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
             if render:
                 self.imgs[i] = np.asarray(im)