Detection cropping+saving feature addition for detect.py and PyTorch Hub (#2827)

* Update detect.py

* Update detect.py

* Update greetings.yml

* Update cropping

* cleanup

* Update increment_path()

* Update common.py

* Update detect.py

* Update detect.py

* Update detect.py

* Update common.py

* cleanup

* Update detect.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

models/common.py

@@ -13,7 +13,7 @@ from PIL import Image
 from torch.cuda import amp
 
 from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
+from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
 from utils.plots import color_list, plot_one_box
 from utils.torch_utils import time_synchronized
 
@@ -311,29 +311,33 @@ class Detections:
         self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
         self.s = shape  # inference BCHW shape
 
-    def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
+    def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
         colors = color_list()
-        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
-            str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
+        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]} '
             if pred is not None:
                 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
-                if show or save or render:
+                if show or save or render or crop:
                     for *box, conf, cls in pred:  # xyxy, confidence, class
                         label = f'{self.names[int(cls)]} {conf:.2f}'
-                        plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
-            img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
+                        if crop:
+                            save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
+                        else:  # all others
+                            plot_one_box(box, im, label=label, color=colors[int(cls) % 10])
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:
                 print(str.rstrip(', '))
             if show:
-                img.show(self.files[i])  # show
+                im.show(self.files[i])  # show
             if save:
                 f = self.files[i]
-                img.save(Path(save_dir) / f)  # save
+                im.save(save_dir / f)  # save
                 print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
             if render:
-                self.imgs[i] = np.asarray(img)
+                self.imgs[i] = np.asarray(im)
 
     def print(self):
         self.display(pprint=True)  # print results
@@ -343,10 +347,14 @@ class Detections:
         self.display(show=True)  # show results
 
     def save(self, save_dir='runs/hub/exp'):
-        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp')  # increment save_dir
-        Path(save_dir).mkdir(parents=True, exist_ok=True)
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True)  # increment save_dir
         self.display(save=True, save_dir=save_dir)  # save results
 
+    def crop(self, save_dir='runs/hub/exp'):
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True)  # increment save_dir
+        self.display(crop=True, save_dir=save_dir)  # crop results
+        print(f'Saved results to {save_dir}\n')
+
     def render(self):
         self.display(render=True)  # render results
         return self.imgs