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 =

#     else:

#         obj = txtName[spacePosition:spacePosition+]

#     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][:]

    if(len(videos[i])==):

        obj_id='obj0000'

    elif(videos[i][-]==''):

        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[])

    imgh=image.shape[]

    imgw=image.shape[]

    for j in range(len(imgs)):

        # pdb.set_trace()

        x=preds[j][]

        y=preds[j][]

        w=preds[j][]

        h=preds[j][]

        ## results relative to original image size.

        x1=x/imgw

        x2=(x+w)/imgw

        y1=y/imgh

        y2=(y+h)/imgh

        x1=round(x1,)

        x2=round(x2,)

        y1=round(y1,)

        y2=round(y2,)

        frame=imgs[j][:]

        if(frame==''):

            frame_num=

        else:

            frame_num=frame.lstrip('')

        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][:]+'_'+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

==