#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Script to convert

@author: alex

"""

#!/bin/bash

# make this a callable script.... and add the gpu number to the port!

# python3 runstuff.py 1 (2 or 3)

import subprocess, sys

import numpy as np

import itertools

import os

import pandas as pd

from pathlib import Path

os.environ['DLClight']="True"

import deeplabcut

def convertpaths2unixstyle(projectname,basepath):

config=os.path.join(basepath,projectname,'config.yaml')

#add appending to config path...

config=os.path.join(basepath,config)

cfg=deeplabcut.utils.read_config(config)

cfg['project_path']=os.path.join(basepath,projectname) #config.split('config.yaml')[0]

#videos = cfg['video_sets'].keys()

#video_names = [Path(i).stem for i in videos]

#folders = [Path(config).parent / 'labeled-data' /Path(i) for i in video_names]

folders=[os.path.join(cfg['project_path'],'labeled-data',fn) for fn in os.listdir(os.path.join(cfg['project_path'],'labeled-data')) if "_labeled" not in fn]

print("Creating images with labels by %s." %cfg['scorer'])

print(folders)

for folder in folders:

#try:

print(os.path.join(str(folder),'CollectedData_' + cfg['scorer'] + '.h5'))

DataCombined = pd.read_hdf(os.path.join(str(folder),'CollectedData_' + cfg['scorer'] + '.h5'), 'df_with_missing')

DataCombined.to_csv(os.path.join(str(folder),"CollectedData_" + "original" + ".csv"))

DataCombined.to_hdf(os.path.join(os.path.join(str(folder),'CollectedData_' + "original" + '.h5')),'df_with_missing',format='table', mode='w')

#frame = pd.DataFrame(a, columns = index, index = self.index)

imindex=[]

for fn in DataCombined.index:

imindex.append(os.path.join('labeled-data',folder.split('/')[-1],'img'+fn.split('img')[1]))

for j,bpt in enumerate(cfg['bodyparts']):

index = pd.MultiIndex.from_product([[cfg['scorer']], [bpt], ['x', 'y']],names=['scorer', 'bodyparts', 'coords'])

frame = pd.DataFrame(DataCombined[cfg['scorer']][bpt].values, columns = index, index = imindex)

if j==0:

dataFrame=frame

else:

dataFrame = pd.concat([dataFrame, frame],axis=1)

dataFrame.to_csv(os.path.join(str(folder),"CollectedData_" + cfg['scorer'] + ".csv"))

dataFrame.to_hdf(os.path.join(os.path.join(str(folder),'CollectedData_' + cfg['scorer'] + '.h5')),'df_with_missing',format='table', mode='w')

deeplabcut.utils.write_config(config,cfg)

return cfg,DataCombined

#path where your project sits (in colaboratory), e.g. see

# for our example: https://github.com/AlexEMG/DeepLabCut/blob/master/examples/Colab_TrainNetwork_VideoAnalysis.ipynb

basepath='/content/drive/My Drive/DeepLabCut/examples/'

projectname='Reaching-Mackenzie-2018-08-30'

cfg,DataCombined=convertpaths2unixstyle(projectname,basepath)