Alexey Stern AlexeyGy

## restore_date.py
import os
import argparse
import re
from datetime import datetime
import subprocess

parser = argparse.ArgumentParser(description='Restore creation dates')
parser.add_argument('dir', help='Directory to parse for PXL files.')
args = parser.parse_args()

## change.patch
diff --git a/woo-min-max-quantity.php b/woo-min-max-quantity.php
index f9614ef..c35e2b9 100644
--- a/woo-min-max-quantity.php
+++ b/woo-min-max-quantity.php
@@ -86,7 +86,7 @@ function _wcmmax_options_page()

   <table>
   <tr valign="top">
-  <th scope="row"><label for="_wcmmax_options_option_name">Custom alert message for maximum Quantity limit </label></th>
+  <th scope="row"><label for="_wcmmax_options_option_name">Custom alert message for maximum Quantity limit. Use $maximum to have the maximum in the text.</label></th>

## javascript-added.js
        function take_snapshot() {
          Webcam.snap(function (data_uri) {
            // snap complete, image data is in 'data_uri' see https://github.com/jhuckaby/webcamjs/blob/master/DOCS.md
            Webcam.upload(
              data_uri,
              "https://raspberrypi:5000/upload",
              function (response_code, response_data) {
                if (response_code === 200) {
                  var parsed_response_data = JSON.parse(response_data);
                  clearCanvas();

## app.py
import datetime
import os
from logging import info
from typing import List

import cv2 as cv
import numpy as np
from flask import (
    Flask,
    jsonify,

## test_detecting.py
# this script performs a testwise detection of the content of test-images
import unittest

import cv2 as cv
from model import recognize, set_up_inference

NET = None


class TestDetection(unittest.TestCase):

## model.py
import os
from logging import info
from typing import List

import numpy as np
import cv2 as cv

INPUT_FOLDER = "model/"  # where we read the neural network from

# the image size that the neural network is trained to work on.

## set_up.py
import os
from logging import info
from typing import List

import numpy as np
import cv2 as cv

INPUT_FOLDER = "model/"  # where we read the neural network from

# a pretrained model from OpenVino, see https://docs.openvinotoolkit.org/2018_R5/_docs_Retail_object_detection_pedestrian_rmnet_ssd_0013_caffe_desc_person_detection_retail_0013.html

## model.py
import os
from logging import info
from typing import List

import numpy as np
import cv2 as cv

INPUT_FOLDER = "model/"  # where we read the neural network from

# the image size that the neural network is trained to work on.

## test_detecting.py
import unittest

import cv2 as cv
from model import recognize, set_up_inference


class TestDetection(unittest.TestCase):
    def setUp(self):
        self.net = set_up_inference()

## index-reduced.html
<!DOCTYPE html>
<html>
  <head>
    <meta charset="utf-8" />
    <title>webcam</title>
  </head>

  <body>
    <script src="webcam.js"></script>
    <div>
	import os
	import argparse
	import re
	from datetime import datetime
	import subprocess

	parser = argparse.ArgumentParser(description='Restore creation dates')
	parser.add_argument('dir', help='Directory to parse for PXL files.')
	args = parser.parse_args()
	diff --git a/woo-min-max-quantity.php b/woo-min-max-quantity.php
	index f9614ef..c35e2b9 100644
	--- a/woo-min-max-quantity.php
	+++ b/woo-min-max-quantity.php
	@@ -86,7 +86,7 @@ function _wcmmax_options_page()

	<table>
	<tr valign="top">
	- <th scope="row"><label for="_wcmmax_options_option_name">Custom alert message for maximum Quantity limit </label></th>
	+ <th scope="row"><label for="_wcmmax_options_option_name">Custom alert message for maximum Quantity limit. Use $maximum to have the maximum in the text.</label></th>
	import datetime
	import os
	from logging import info
	from typing import List

	import cv2 as cv
	import numpy as np
	from flask import (
	Flask,
	jsonify,
	# this script performs a testwise detection of the content of test-images
	import unittest

	import cv2 as cv
	from model import recognize, set_up_inference

	NET = None


	class TestDetection(unittest.TestCase):
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8" />
	<title>webcam</title>
	</head>

	<body>
	<script src="webcam.js"></script>
	<div>