--- a
+++ b/CrowdVoting/crowd_vote.py
@@ -0,0 +1,102 @@
+
+import numpy as np
+"""
+This is the code for crowd voting. 
+
+@all_bbox_file: the txt file contains all the bbox for all the patients.
+                Format: jpg name,Ymin,Xmin,Ymax,Xmax,score
+                jpg name format:patientId_frameId.jpg
+                eg:48_20.jpg,532.9326,388.33987,576.0951,432.8795,0.10115564
+
+@patient_ids: an array of patient id
+              eg:["18","48","60"]
+
+Output: The information of voted bounding box; one patient, one row, one bounding box.
+        The output is written to crowd_vote_result.txt. Format: jpg name,Ymin,Xmin,Ymax,Xmax,score
+                                                        jpg name format:patientId_frameId.jpg
+        eg:18_10.jpg,491.96744,113.14628,533.2568,153.23444,0.9348341,0.7855689961758875
+           48_24.jpg,357.22125,299.84146,395.42416,330.64755,0.9572147,0.6061841445094583
+           60_33.jpg,692.35065,340.7826,740.7708,380.7977,0.9680265,0.9008500634163339
+
+"""
+
+all_bbox_file = "all_bbox.txt" 
+patient_ids = [18,48,60]
+
+def main(all_bbox_file, patient_ids):
+    vote_result = list()
+    for patient_id in patient_ids:
+        vote_result.append(crowd_vote(all_bbox_file, str(patient_id)))
+    return vote_result
+
+#this is the function for calculating IoU
+def bbox_iou(a, b):
+    epsilon = 1e-5
+    # COORDINATES OF THE INTERSECTION BOX
+    y1 = max(a[0], b[0])
+    x1 = max(a[1], b[1])
+    y2 = min(a[2], b[2])
+    x2 = min(a[3], b[3])
+
+    # AREA OF OVERLAP - Area where the boxes intersect
+    width = (x2 - x1)
+    height = (y2 - y1)
+    # handle case where there is NO overlap
+    if (width < 0) or (height < 0):
+        return 0.0
+    area_overlap = width * height
+
+    # COMBINED AREA
+    area_a = (a[2] - a[0]) * (a[3] - a[1])
+    area_b = (b[2] - b[0]) * (b[3] - b[1])
+    area_combined = area_a + area_b - area_overlap
+
+    # RATIO OF AREA OF OVERLAP OVER COMBINED AREA
+    iou = area_overlap / (area_combined+epsilon)
+    return iou
+
+#this is the function for crowd voting
+def crowd_vote(all_bbox_file, patient_id):
+    #read corresponding patient bbox info 
+    with open(all_bbox_file,'r') as f:
+        lines = f.readlines()
+        info=list()
+        box_sum=list()
+        for line in lines:
+            get_id=line.split("_")
+            if(get_id[0]!=patient_id):
+                continue
+            box= line.strip().split(".jpg,")[1].split(",")
+            cur_box=[-1,-1,-1,-1]
+            for i in range(4):
+                cur_box[i]=float(box[i])
+            box_sum.append(cur_box)
+            info.append(line)        
+
+    #make the matrix
+    n_frame=len(box_sum)
+    fusion=np.zeros(shape=(n_frame,n_frame))
+    for i in range(n_frame):
+        for j in range(n_frame):
+            if (i==j):
+                fusion[i,j]=0
+            else:
+                fusion[i,j]=bbox_iou(box_sum[i],box_sum[j])
+
+    #calculate avg and vote
+    max_mean=-1
+    vote=-1
+    for i in range(n_frame):
+        cur_mean = np.mean(fusion[i])
+        if(cur_mean>max_mean):
+            vote=i
+            max_mean=cur_mean
+
+    return(info[vote])
+
+
+if __name__ == '__main__':
+    vote_result=main(all_bbox_file, patient_ids)
+    with open('crowd_vote_result.txt', 'w') as f:
+        for item in vote_result:
+            f.write("%s" % item)
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