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)