remove 'print' function for debug

deep_sort/deep_sort.py

@@ -12,7 +12,7 @@ __all__ = ['DeepSort']
 
 
 class DeepSort(object):
-    def __init__(self, model_path, model_config=None, max_dist=0.015, min_confidence=0.3, nms_max_overlap=0.9, max_iou_distance=0.45, max_age=20, n_init=10, nn_budget=1000, use_cuda=True):
+    def __init__(self, model_path, model_config=None, max_dist=0.005, min_confidence=0.3, nms_max_overlap=0.9, max_iou_distance=0.45, max_age=20, n_init=10, nn_budget=1000, use_cuda=True):
         self.min_confidence = min_confidence
         self.nms_max_overlap = nms_max_overlap
 

deep_sort/sort/iou_matching.py

@@ -24,18 +24,7 @@ def iou(bbox, candidates):
 
     """
     length = len(candidates)
-    # print("----iou test---------")
-    # print(bbox)
-    # bbox[0] -= bbox[2]
-    # bbox[1] -= bbox[3]
-    # bbox[2] *= 3
-    # bbox[3] *= 3
-    # candidates[:, 0] -= candidates[:, 2]
-    # candidates[:, 1] -= candidates[:, 3]
-    # candidates[:, 2] *= 3
-    # candidates[:, 3] *= 3
-    # print(bbox)
-    # print("----iou test---------")
+
     bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
     candidates_tl = candidates[:, :2]
     candidates_br = candidates[:, :2] + candidates[:, 2:]
@@ -49,12 +38,13 @@ def iou(bbox, candidates):
     area_intersection = wh.prod(axis=1)
     area_bbox = bbox[2:].prod()
     area_candidates = candidates[:, 2:].prod(axis=1)
+    
+    # should be consious
     gious = []
     for i in range(length):
         gious.append(float((area_bbox + area_candidates[i] - area_intersection[i])/(max(bbox[0]+bbox[2],candidates[i][0]+candidates[i][2])-min(bbox[0],candidates[i][0]))/(max(bbox[1]+bbox[3],candidates[i][1]+candidates[i][3])-min(bbox[1],candidates[i][1]))) )
+        
     # return area_intersection / (area_bbox + area_candidates - area_intersection)
-    print(f"gious:{gious}")
-    print(area_intersection / (area_bbox + area_candidates - area_intersection))
     return np.array(gious)
 
 

deep_sort/sort/linear_assignment.py

@@ -55,7 +55,6 @@ def min_cost_matching(
 
     cost_matrix = distance_metric(
         tracks, detections, track_indices, detection_indices)
-    print(f"iou_distance:{cost_matrix}")
     cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
 
     row_indices, col_indices = linear_assignment(cost_matrix)

deep_sort/sort/tracker.py

@@ -99,8 +99,6 @@ class Tracker:
             cost_matrix = linear_assignment.gate_cost_matrix(
                 self.kf, cost_matrix, tracks, dets, track_indices,
                 detection_indices)
-            print("cost:")
-            print(cost_matrix)
             return cost_matrix
 
         # Split track set into confirmed and unconfirmed tracks.

param.py

@@ -11,7 +11,7 @@ class Parameters(object):
         self.classes      = [0]
         self.agnostic_nms = None
         self.augment      = None
-        self.query_index  = 2
-        self.gallary_index= 3
+        self.query_index  = 1
+        self.gallary_index= 2
         pass