Addam Dominec addam

## remap_values_migration.py
from django.db.migrations.operations.base import Operation
from django.db import transaction


class Delete:
    pass


"""
RemapValues changes the values of a field

## lock.js
function createLock() {
  const clients = new Map()

  function nop() {
  }

  const lock = (id) => {
    const waiting = clients.get(id)
    if (!waiting) {
      const queue = []

## feather_dilate.py
def feather_dilate(stack: np.ndarray, radius=20):
    """Feather dilate a stack of images

    stack has dimensions: [image_id, x, y]
    The result is similar to dilation but with feather edges"""
    ms = max_steepness
    msq = ms * 2**0.5
    for x in range(1, stack.shape[2]):
        stack[:,:, x] = (np.maximum(stack[:,:, x], stack[:,:, x-1] - ms))
    for x in range(stack.shape[2]-2, -1, -1):

## stream_docker_stats.py
import subprocess
import time
from sys import argv

run_args = "docker stats --format {{.MemUsage}}".split()
if len(argv) > 1:
    run_args.append(argv[1])
background = subprocess.Popen(run_args, stdout=subprocess.PIPE)
units = {'Ki': 1e3, 'Mi': 1e6, 'Gi': 1e9, 'Ti': 1e12}
print("timestamp_seconds", "memory_bytes", flush=True)

## qrclipboard.py
#!/usr/bin/python3
from PyQt5.QtWidgets import QApplication, QLabel, QMessageBox
from pyzbar import pyzbar
import numpy as np

def qimage2mat(qimage):
    qimage.convertToFormat(4)
    ptr = qimage.bits()
    ptr.setsize(qimage.byteCount())
    arr = np.array(ptr).reshape([qimage.height(), qimage.width(), 4])

## normalize_background.py
from sys import argv
import cv2
import numpy as np
import scipy

iterations = 2

def grid(shape):
  y = np.linspace(0, 1, shape[0])
  x = np.linspace(0, 1, shape[1])

## classifier.py
import cv2
import numpy as np
from tkinter import Tk
from tkinter.filedialog import askopenfilename

descriptors = []

# evaluate the similarity between two point clouds
# enormous robustness against outliers is necessary
def score(a, b):

## classifier.html
<!doctype html>
<html>
<head>
<meta charset=utf-8>
<title>Classifier</title>
<script>
let descriptors = [];

qs = (sel) => document.querySelector(sel);

## timer.html
<html>
<meta charset=utf-8>
<title>Timer</title>
<style>
@keyframes blinker {
  50% {
    opacity: 0;
  }
}
body {

## sorter.html
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<script src="list.js"></script>
<script>
// generate list.js from images in current directory:
// (echo 'imageList = ['; ls *.{jpg,jpeg,JPG,JPEG} | sed -E 's/(.*)/"\1",/p'; echo ']') > list.js
// generate list.js from images in subdirectories of the current one:
// (echo 'imageList = ['; ( for d in *; do ls "$d"/*.{jpg,jpeg,JPG,JPEG}; done) | sed -E 's/(.*)/"\1",/p'; echo ']') > list.js
	from django.db.migrations.operations.base import Operation
	from django.db import transaction


	class Delete:
	pass


	"""
	RemapValues changes the values of a field
	function createLock() {
	const clients = new Map()

	function nop() {
	}

	const lock = (id) => {
	const waiting = clients.get(id)
	if (!waiting) {
	const queue = []
	def feather_dilate(stack: np.ndarray, radius=20):
	"""Feather dilate a stack of images

	stack has dimensions: [image_id, x, y]
	The result is similar to dilation but with feather edges"""
	ms = max_steepness
	msq = ms * 2**0.5
	for x in range(1, stack.shape[2]):
	stack[:,:, x] = (np.maximum(stack[:,:, x], stack[:,:, x-1] - ms))
	for x in range(stack.shape[2]-2, -1, -1):
	import subprocess
	import time
	from sys import argv

	run_args = "docker stats --format {{.MemUsage}}".split()
	if len(argv) > 1:
	run_args.append(argv[1])
	background = subprocess.Popen(run_args, stdout=subprocess.PIPE)
	units = {'Ki': 1e3, 'Mi': 1e6, 'Gi': 1e9, 'Ti': 1e12}
	print("timestamp_seconds", "memory_bytes", flush=True)
	#!/usr/bin/python3
	from PyQt5.QtWidgets import QApplication, QLabel, QMessageBox
	from pyzbar import pyzbar
	import numpy as np

	def qimage2mat(qimage):
	qimage.convertToFormat(4)
	ptr = qimage.bits()
	ptr.setsize(qimage.byteCount())
	arr = np.array(ptr).reshape([qimage.height(), qimage.width(), 4])
	from sys import argv
	import cv2
	import numpy as np
	import scipy

	iterations = 2

	def grid(shape):
	y = np.linspace(0, 1, shape[0])
	x = np.linspace(0, 1, shape[1])
	import cv2
	import numpy as np
	from tkinter import Tk
	from tkinter.filedialog import askopenfilename

	descriptors = []

	# evaluate the similarity between two point clouds
	# enormous robustness against outliers is necessary
	def score(a, b):
	<!doctype html>
	<html>
	<head>
	<meta charset=utf-8>
	<title>Classifier</title>
	<script>
	let descriptors = [];

	qs = (sel) => document.querySelector(sel);
	<html>
	<meta charset=utf-8>
	<title>Timer</title>
	<style>
	@keyframes blinker {
	50% {
	opacity: 0;
	}
	}
	body {