save tracking results into csv file for oxuva long-term tracking dataset (from txt to csv)

save tracking results into csv file for oxuva long-term tracking dataset (from txt to csv)

2019-10-25 09:42:03

 

Official Tools: OxUvA long-term tracking benchmark [ECCV'18] [Github] 

Project page: https://oxuva.github.io/long-term-tracking-benchmark/ 

 

import os 
import numpy as np 
import cv2
import time
import oxuva
import pdb 

# export PYTHONPATH="/home/wangxiao/THOR/long-term-tracking-benchmark-master/python:$PYTHONPATH"


# txtPath = '/home/wangxiao/THOR/benchmark/results/OXUVA/Tracker/' 
# txtFiles = os.listdir(txtPath) 

# csv_path = './oxuva_csv_results/'

# for index in range(len(txtFiles)):

#     txtName = txtFiles[index] 
#     pointPosition = txtName.find('.')
#     videoName = txtName[:pointPosition] 

#     preds = np.loadtxt(txtPath + txtName, delimiter=',') 
#     preds = preds.tolist()

#     spacePosition = txtName.find('_')
#     if spacePosition: 
#         obj = 1 
#     else: 
#         obj = txtName[spacePosition:spacePosition+1] 

#     preds_file = os.path.join(csv_path, '{}_{}.csv'.format(videoName, obj))

#     tmp_preds_file = os.path.join(csv_path, '{}_{}.csv.tmp'.format(videoName, obj))
#     with open(tmp_preds_file, 'w', encoding='utf-8-sig') as fp:
#         pdb.set_trace() 

#         oxuva.dump_predictions_csv(videoName, obj, preds, fp)
#     os.rename(tmp_preds_file, preds_file)


#     pdb.set_trace() 




import json
import pdb
import cv2
import os
import pandas as pd
resultpath= '/home/wangxiao/tracking_results_oxuva/' 
videopath="/home/wangxiao/dataset/OxUvA/images/test/"
videos=os.listdir(videopath)
txtFiles = os.listdir(resultpath) 




for i in range(len(videos)):
    txtName = videos[i] + "_oxuva-baseline.txt" 
    preds = np.loadtxt(resultpath + txtName, delimiter=',') 

    print("==>> txtName: ", txtName) 
    xmin=[]
    xmax=[]
    ymin=[]
    ymax=[]
    video_ids=[]
    obj_ids=[]
    frame_nums=[]
    presents=[]
    scores=[]
    video_id=videos[i][0:7]
    if(len(videos[i])==7):
        obj_id='obj0000'
    elif(videos[i][-1]=='2'):
        obj_id='obj0001'
    else:
        obj_id='obj0002'
    present='True'
    score=0.5
    # l=result['res']

    imgs=os.listdir(videopath+videos[i]+'/')
    imgs = np.sort(imgs) 
    # pdb.set_trace() 

    image=cv2.imread(videopath+videos[i]+'/'+imgs[0])
    imgh=image.shape[0]
    imgw=image.shape[1]


    for j in range(len(imgs)):

        # pdb.set_trace() 

        x=preds[j][0]
        y=preds[j][1]
        w=preds[j][2]
        h=preds[j][3]

        ## results relative to original image size. 
        x1=x/imgw
        x2=(x+w)/imgw
        y1=y/imgh
        y2=(y+h)/imgh

        x1=round(x1,4)
        x2=round(x2,4)
        y1=round(y1,4)
        y2=round(y2,4)

        frame=imgs[j][0:6]

        if(frame=='000000'):
            frame_num=0
        else:
            frame_num=frame.lstrip('0')

        xmin.append(x1)
        xmax.append(x2)
        ymin.append(y1)
        ymax.append(y2)
        video_ids.append(video_id)
        obj_ids.append(obj_id)
        frame_nums.append(frame_num)
        presents.append(present)
        scores.append(score)

    # pdb.set_trace() 

    dataframe=pd.DataFrame({'video_id':video_ids,'object_id':obj_ids,'frame_num':frame_nums,'present':presents,\
                            'score':scores,'xmin':xmin,'xmax':xmax,'ymin':ymin,'ymax':ymax})
    savepath='./oxuva_csv_results/' +videos[i][0:7]+'_'+obj_id+'.csv'
    columns=['video_id','object_id','frame_num','present','score','xmin','xmax','ymin','ymax']

    dataframe.to_csv(savepath,index=False,columns=columns,header=None)


    # pdb.set_trace() 

 

 

========= Results 

 

 

 

 

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