--- a
+++ b/CrowdVoting/evaluation.py
@@ -0,0 +1,130 @@
+"""
+This is the code for analyzing detection results, calculating patient score. Generate .txt for all the bbox labels respectively and name them by patientID_frameId.txt
+                                                                             Format: Ymin Xmin Ymax Xmax
+                                                                             eg., 361.0 293.0 397.0 325.0 
+                                                                             file name example: 48_12.txt
+                                                                             
+@detect_res_file: the txt file contains the detection results. 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
+@test_id: the tested patient or you wanna test all patients, input[]
+          eg:[18,48,60]
+
+The output is written into eva_result.txt: format: patient id(missing if all patients are tested), patient score
+                                            eg.18,1.0
+"""   
+
+import numpy as np
+
+detect_res_file = "crowd_vote_result.txt"
+patient_ids=[18]
+
+def main(detect_res_file, patient_ids):
+    if len(patient_ids)==0:
+        return(str(patient_score_eva(detect_res_file,-1)))
+    else:
+        score=list()
+        for patient_id in patient_ids:
+            cur_score = str(patient_score_eva(detect_res_file,patient_id))
+            score.append(str(patient_id)+","+cur_score)
+        return score
+
+
+#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 calculating patient score
+def patient_score_eva(file_name,patient_id):
+    with open(file_name,'r') as f:
+        lines = f.readlines()
+        info=list()
+        img_name=list()
+        label_boxes = dict()
+        for line in lines:
+            cur_patient = int(line.split("_")[0])
+            if cur_patient==patient_id or patient_id==-1:
+                sp=line.split("jpg")
+                cur_img_name = sp[0]            
+                
+                cur_frame = int(cur_img_name.split("_")[0].replace(".",""))
+                cur_box_info = sp[1].split(",")
+
+                #find the label for cur img
+                if cur_img_name in label_boxes:
+                    label_box=label_boxes[cur_img_name]
+                else:
+                    cur_img_label = cur_img_name.replace(".",".txt")
+                    label_box=[-1,-1,-1,-1]
+                    with open(cur_img_label) as f:
+                            cur_content = f.read().strip('\n')
+                            ls_label_box = cur_content.split(" ")                
+                            for i in range(4):  
+                                label_box[i]=float(ls_label_box[i])
+                    label_boxes[cur_img_name]=label_box            
+                
+                #read loc for cur img and calculate iou
+                cur_box=[-1,-1,-1,-1]
+                for i in range(4):
+                    cur_box[i]=float(cur_box_info[i+1])        
+                cur_iou = bbox_iou(cur_box,label_box)
+                #read score for cur img
+                cur_score = float(cur_box_info[5].replace("\n",""))
+
+                cur_info=[cur_patient,cur_frame,cur_iou,cur_score]
+                info.append(cur_info)
+                img_name.append(cur_img_name)
+
+    m=np.array(info)
+    select_res=list()
+
+    #if one frame has several bounding boxes, then selecet the one with max score
+    for cur_img_name in set(img_name):
+        cur_patient = int(cur_img_name.split("_")[0])
+        cur_frame = int(cur_img_name.split("_")[0].replace(".",""))
+        max_score=-1
+        select_img = -1
+        for i in range(len(m)):
+            if m[i,0]==cur_patient and m[i,1]==cur_frame and m[i,3]>max_score:
+                max_score=m[i,1]
+                select_img=i
+        select_res.append(m[select_img,])
+
+    #find the number of res with iou>0
+    r=np.array(select_res)
+    count=0
+    for i in range(len(r)):
+        if r[i,2]>0:
+            count+=1
+
+    patient_score = count/len(r)
+    return patient_score
+
+
+if __name__ == '__main__':
+    eva_result=main(detect_res_file, patient_ids)
+    with open('eva_result.txt', 'w') as f:
+        for item in eva_result:
+            f.write("%s" % item)
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