add ovlap_suppression branch

detect.py

 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, overlap_box_suppression
 from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
         # Apply NMS
         pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
+
+        pred = overlap_box_suppression(pred, opt.ovlap_thres)
+
         t2 = time_synchronized()
 
         # Apply Classifier
     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('--ovlap-thres', type=float, default=0.6, help='overlap threshold for OBS')
     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')

utils/general.py

 
     return output
 
+def overlap_box_suppression(prediction, ovlap_thres = 0.6):
+    """Runs overlap_box_suppression on inference results
+        delete the box that overlap of boxes bigger than ovlap_thres
+    Returns:
+         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+    """
+    def box_iob(box1, box2):
+        def box_area(box):
+            return (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
+
+        area1 = box_area(box1)  # (N,)
+        area2 = box_area(box2)  # (M,)
+
+        # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+        lt = torch.max(box1[:, None, :2], box2[:, :2])  # [N,M,2] # N中一个和M个比较；
+        rb = torch.min(box1[:, None, 2:], box2[:, 2:])  # [N,M,2]
+        wh = (rb - lt).clamp(min=0) #小于0的为0  clamp 钳；夹钳；
+        inter = wh[:, :, 0] * wh[:, :, 1]
+
+        return torch.squeeze(inter / area1), torch.squeeze(inter / area2)
+
+    output = [torch.zeros((0, 6), device=prediction[0].device)] * len(prediction)
+    for i, x in enumerate(prediction):
+        keep = []           # 最终保留的结果， 在boxes中对应的索引；
+        boxes = x[:, 0:4]
+        scores = x[:, 4]
+        cls = x[:, 5]
+        idxs = scores.argsort()
+        while idxs.numel() > 0:
+            keep_idx = idxs[-1]
+            keep_box = boxes[keep_idx][None, ]    # [1, 4]
+            keep.append(keep_idx)
+            if idxs.size(0) == 1:
+                break
+            idxs = idxs[:-1]        # 将得分最大框 从索引中删除； 剩余索引对应的框 和 得分最大框 计算iob；
+            other_boxes = boxes[idxs]
+            this_cls = cls[keep_idx]
+            other_cls = cls[idxs]
+            iobs1, iobs2 = box_iob(keep_box, other_boxes)   # 一个框和其余框比较 1XM
+            idxs = idxs[((iobs1 <= ovlap_thres) & (iobs2 <= ovlap_thres)) | (other_cls != this_cls)]
+        keep = idxs.new(keep)       # Tensor
+        output[i] = x[keep]
+    return output
 
 def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
     # Strip optimizer from 'f' to finalize training, optionally save as 's'