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>

detect.py

@@ -10,19 +10,19 @@ from numpy import random
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
-    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
+    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
 from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
-def detect(save_img=False):
+def detect():
     source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
     save_img = not opt.nosave and not source.endswith('.txt')  # save inference images
     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
         ('rtsp://', 'rtmp://', 'http://', 'https://'))
 
     # Directories
-    save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
+    save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)  # increment run
     (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
 
     # Initialize
@@ -84,7 +84,7 @@ def detect(save_img=False):
             if webcam:  # batch_size >= 1
                 p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
             else:
-                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
+                p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
 
             p = Path(p)  # to Path
             save_path = str(save_dir / p.name)  # img.jpg
@@ -108,9 +108,12 @@ def detect(save_img=False):
                         with open(txt_path + '.txt', 'a') as f:
                             f.write(('%g ' * len(line)).rstrip() % line + '\n')
 
-                    if save_img or view_img:  # Add bbox to image
-                        label = f'{names[int(cls)]} {conf:.2f}'
-                        plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
+                    if save_img or opt.save_crop or view_img:  # Add bbox to image
+                        c = int(cls)  # integer class
+                        label = f'{names[c]} {conf:.2f}'
+                        plot_one_box(xyxy, im0, label=label, color=colors[c], line_thickness=3)
+                        if opt.save_crop:
+                            save_one_box(xyxy, im0s, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
 
             # Print time (inference + NMS)
             print(f'{s}Done. ({t2 - t1:.3f}s)')
@@ -157,6 +160,7 @@ if __name__ == '__main__':
     parser.add_argument('--view-img', action='store_true', help='display results')
     parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
     parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
     parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
     parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')