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""" only part codes """

# read groundtruth and predictions
dataDir= Path('datasets/coco')
valImageRoot = dataDir / 'val2017'
groudtruth_path = dataDir / 'annotations/instances_val2017.json'
prediction_path = 'output/inference/coco_instances_results.json'

cocoGt=COCO(groudtruth_path)
cocoDt = cocoGt.loadRes(prediction_path)

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import json

from pathlib import Path
import glob
import os
import shutil
from tqdm import tqdm
import numpy as np

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import os
import mxnet as mx
from mxnet import ndarray as nd
from easydict import EasyDict as edict
import json
import copy

SRC_SYM = './models/vgg-softmax-emore/model'
TGT_SYM = './models/vgg-softmax-emore-bnMerged/model'

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# drawMatches numpy version
def draw_matches(img1, kp1, img2, kp2, matches, color=None): 
    """Draws lines between matching keypoints of two images.  
    Keypoints not in a matching pair are not drawn.
    Places the images side by side in a new image and draws circles 
    around each keypoint, with line segments connecting matching pairs.
    You can tweak the r, thickness, and figsize values as needed.
    Args:
        img1: An openCV image ndarray in a grayscale or color format.
        kp1: ndarray [n1, 2]

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"""
Usage:
    python gpu_analysis.py <gpu_load.txt> <gpu_num>
"""
import sys

gpu_num = int(sys.argv[2])
total = 0
utilized = 0
for line in open(sys.argv[1]).readlines():

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"""
This file read a .abf file, find the peaks and troughs, and calculate delta values

Requirements:
pyabf
numpy
matplotlib
"""
import numpy as np
import sys

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"""
Show three windows to visualize predictions, ground truth labels and real colors
press key t to synchronize the camera setting in win_follow
"""
import open3d as o3d
import numpy as np
import copy
import threading
import time
from easydict import EasyDict as edict

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# Non_maximum_suppression

import numpy as np

def nms(dets, scores, thresh):
    '''
    dets is a numpy array : num_dets, 4
    scores ia  nump array : num_dets,
    '''
    x1 = dets[:, 0]

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#include <iostream>
#include <chrono>
#include <ctime>
#include <cmath>

class Timer
{
public:
    void start()
    {

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while true
do
    m=`nvidia-smi --format=csv --query-gpu=memory.used 2>&1 | awk '{if(NR>1 && NR<=2)print $1}'`
    if [ $m -lt 2000 ]
    then
        echo executing
        exec python tools/train.py --gpus 0,1 configs/res18.yaml --name res18_base
    else
        echo memory $m
        sleep 1