import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')

import importlib
import argparse
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str)
parser.add_argument('--config', type=str)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--iteration', type=str, default='')
parser.add_argument('-g', '--gpu', type=str)
parser.add_argument('-n', '--normal', action='store_true')
parser.add_argument('-e', '--edge', action='store_true')
parser.add_argument('-cr', '--collision_ratio', action='store_true')
parser.add_argument('-a', '--all', action='store_true')
args = vars(parser.parse_args())

if args['debug']:
    from configs.iccvw.pyr_64_128_256_512_cape_f_e2e import config as config_1
else:
    config_module = importlib.import_module(args['config'])
    config_1 = config_module.config


import time
import argparse
import tqdm
import random
import numpy as np
import plotly.graph_objects as go
import plotly.express as ex
from data.utils import Frame




def calculate(type=''):
    errors = dict()
    errors_mean = dict()

    with open(f'{folder}/{hood_csv}', 'r') as f:
        lines_hood = f.readlines()
    with open(f'{folder}/{ours_csv}', 'r') as f:
        lines_ours = f.readlines()

    for line in lines_hood:
        line = line.split(',')
        category = line[2]

        if category not in errors:
            errors[category] = []
        errors[category].append(float(line[3]))

    for category in errors:
        errors_mean[category] = round(np.mean(errors[category]), 5)
    print(errors_mean) 

    mean = round(np.mean(list(errors_mean.values())), 5)
    print(f'Final HOOD error{type}: {mean}')

    for line in lines_ours:
        line = line.split(',')
        category = line[2]

        if category not in errors:
            errors[category] = []
        errors[category].append(float(line[3]))

    for category in errors:
        errors_mean[category] = round(np.mean(errors[category]), 5)
    print(errors_mean) 

    mean = round(np.mean(list(errors_mean.values())), 5)
    print(f'Final OURS error{type}: {mean}')


if __name__ == '__main__':

    folder = '/data-local/127_2/results/hood'
    hood_csv = 'hood_2_hood.csv'
    ours_csv = 'hood_2_ours.csv'

    calculate()