"""

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)