isVoid

#include <iostream>
#include <stack>
#include <queue>
using namespace std;

struct ListNode {
    int val;
    ListNode *next;
    ListNode(int x) : val(x), next(NULL) {}
};

isVoid

import os
import subprocess
import random

def isVideo(f):
    ext = f.split(".")[-1]
    if ext == "mp4":
        return True
    return False

isVoid

@echo off
for %%x in (*.ARW) do call :for_body %%x >> a7s.txt
exit /b

:for_body
	echo %1
	dcraw.exe -i -v %1 | findstr /i /B /c:"Camera Multip"
exit/b

isVoid

import os

root = "F:\\Camera_Calibration\\A7S2\\16000"

for dirPath, _, files in os.walk(root):
    for f in files:
        # print (os.path.join(dirPath, f)[-22:])
        print (f[9:-4])
        try:
            os.mkdir(os.path.join(root, f[9: -4]))

isVoid

import os
import subprocess
import random

def isVideo(f):
    ext = f.split(".")[-1]
    if ext == "mp4":
        return True
    return False

isVoid

def augment(images, labels,
            resize=None, # (width, height) tuple or None
            horizontal_flip=False,
            vertical_flip=False,
            rotate=0, # Maximum rotation angle in degrees
            crop_probability=0, # How often we do crops
            crop_min_percent=0.6, # Minimum linear dimension of a crop
            crop_max_percent=1.,  # Maximum linear dimension of a crop
            mixup=0):  # Mixup coeffecient, see https://arxiv.org/abs/1710.09412.pdf
  if resize is not None:

isVoid

app.project.bitsPerChannel = 16
myFootage = app.project.importFileWithDialog()[0]

footageName = myFootage.file.fsName
$.writeln(footageName)

var location = "B.M. Playa"
//var m = footageName.match(/\d{4}_\d{1,2}_\d{1,2}_.[^\\]*/gi)[0]
var n = footageName.match(/\d{1}_\d{2}_\d{4}/gi)[0]
$.writeln(location + n)

isVoid

import tensorflow as tf
import numpy as np
from tqdm import tqdm

class RNN(object):

    def __init__(self, params):
        self.params = params

    def rnn_cell(self, x, ht0, hidden_size = 100, reuse = True):