Adding --save-dir and --save-conf options to test.py (#1182)

* Adding --output and --save-conf options to test.py

* Update help fields

* Update test.py

* Make arguments and comments uniform with test.py

* Remove previous and print save_dir on finish

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

detect.py

 
 def detect(save_img=False):
     out, source, weights, view_img, save_txt, imgsz = \
-        opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
+        opt.save_dir, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
     webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
 
     # Initialize
     set_logging()
     device = select_device(opt.device)
-    if os.path.exists(out):
-        shutil.rmtree(out)  # delete output folder
-    os.makedirs(out)  # make new output folder
+    if os.path.exists(out):  # output dir
+        shutil.rmtree(out)  # delete dir
+    os.makedirs(out)  # make new dir
     half = device.type != 'cpu'  # half precision only supported on CUDA
 
     # Load model
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
     parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam
-    parser.add_argument('--output', type=str, default='inference/output', help='output folder')  # output folder
     parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
     parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     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='output confidences in --save-txt labels')
+    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+    parser.add_argument('--save-dir', type=str, default='inference/output', help='directory to save results')
     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')
     parser.add_argument('--augment', action='store_true', help='augmented inference')

test.py

          dataloader=None,
          save_dir=Path(''),  # for saving images
          save_txt=False,  # for auto-labelling
+         save_conf=False,
          plots=True):
     # Initialize/load model and set device
     training = model is not None
         set_logging()
         device = select_device(opt.device, batch_size=batch_size)
         save_txt = opt.save_txt  # save *.txt labels
-        if save_txt:
-            out = Path('inference/output')
-            if os.path.exists(out):
-                shutil.rmtree(out)  # delete output folder
-            os.makedirs(out)  # make new output folder
 
         # Remove previous
-        for f in glob.glob(str(save_dir / 'test_batch*.jpg')):
-            os.remove(f)
+        if os.path.exists(save_dir):
+            shutil.rmtree(save_dir)  # delete dir
+        os.makedirs(save_dir)  # make new dir
+
+        if save_txt:
+            out = save_dir / 'autolabels'
+            if os.path.exists(out):
+                shutil.rmtree(out)  # delete dir
+            os.makedirs(out)  # make new dir
 
         # Load model
         model = attempt_load(weights, map_location=device)  # load FP32 model
                 x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1])  # to original
                 for *xyxy, conf, cls in x:
                     xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                    line = (cls, conf, *xywh) if save_conf else (cls, *xywh)  # label format
                     with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
-                        f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+                        f.write(('%g ' * len(line) + '\n') % line)
 
             # Clip boxes to image bounds
             clip_coords(pred, (height, width))
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     parser.add_argument('--verbose', action='store_true', help='report mAP by class')
     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-dir', type=str, default='runs/test', help='directory to save results')
     opt = parser.parse_args()
     opt.save_json |= opt.data.endswith('coco.yaml')
     opt.data = check_file(opt.data)  # check file
              opt.save_json,
              opt.single_cls,
              opt.augment,
-             opt.verbose)
+             opt.verbose,
+             save_dir=Path(opt.save_dir),
+             save_txt=opt.save_txt,
+             save_conf=opt.save_conf,
+             )
+
+        print('Results saved to %s' % opt.save_dir)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
         for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: