Free one time use cameras software for linux

Multiple Time Sheets is a simple tool to help you keep track of how many hours you work and for whom.
It differs from most time-tracking software because its designed to work like paper that magically totals up hours.
Main features:
- Uses text files, requiring no database.
- Supports only one user per app, for simpler code.
- Sends and tracks invoices, and payments thereof.
- Features a rudimentary to-do list that displays your list as an outline.
- Sends you a backup of your data automatically.
- Assume the user prefers free-form data entry in text files rather than typing into forms.
- Uses the htmlMimeMail.php class by Richard Heye (phpguru.org).
- CSV and OPML exports of some data.
- Automatic hyperlinking from MTS to your favorite web-based software.
Enhancements:
- This release added a feature that replaces text patterns with links so that strings like "Bug 10" can link to a bug tracking application.
- CSV export was added for the timesheet along with OPML export for the to-do list.

Python Traffic Analyzer is a Python base class and sample driver script written to retrieve and manipulate images from the TrafficLand cameras and calculate a numeric value representing the current traffic flow.

PyTrAn, an example driver script, an image collector and an image mask creator are available for download from the link shown at the bottom. To use the PyTrAn package begin by choosing a camera that you wish to analyze, for this example well use the camera captioned above.

We want to construct a mask over the area of the image that we are interested in, namely the road. In this particular example the road takes up the majority of the image but that is not always the case.

We will apply the mask over captured images to fine tune the area over which we are looking for movement. To create the mask we will first need to collect a sequential series of snapshots from the target camera. The image_collector.py script was written for this task:

$ mkdir mask_200003
$ cd mask_200003
$ ../image_collector.py 200003 30
Collecting 30 images...
30

Done.

The script is hard coded to capture images on a 2-second delay. The delay is necessary to ensure the image has changed. I believe 2-seconds to be the absolute minimum. Once complete, 30 images numbered 1 through 30 will be created in the current directory.

We construct a mask from these captured images by creating a diff-image for each sequential image pair and then adding each diff-image together. Naturally, a script was written to automate this task as well:

$ ../mask_maker.py 1 30
Creating a diff for each sequential image pair.
Diffing 29

Creating the initial mask from the first image pair.

Adding the rest of the diffs to the mask.
Masking 29

Done.

A number of .diff files are generated in this process. These files repesent the movement between individual sequence pairs.

The .diff files are simply intermediary files, the important bit is the mask file, which is generated as the sum of all differences.

The mask file may be dirty (as in this case) and require manual cleanup. The basic shape of the road however is clearly visible, evidence that we can with minimal effort automate the mask generation process. Also, this run was conducted at night, day-time images yield better results.

There are a few final steps we need to take before we can use the example PyTrAn driver script. First we need to convert the mask to ASCII (noraw) format:

$ pnmnoraw mask > mask_200003.ascii

Then we need to open an ImageMagick display window and get its X-window-ID using xwininfo. Finally, update camera_id and window_id in pytran_sampling.py and launch the driver:

$ ../pytran_sampling.py
DEBUG> grabbing frame from camera 200003
DEBUG> rotating image: pytran.this > pytran.last
DEBUG> refreshing image in 3 secs
taking a 5 minute sample at various thresholds.
DEBUG> grabbing frame from camera 200003
DEBUG> generating frame diff on pytran.last, pytran.this
DEBUG> displaying image: pytran.diff
DEBUG> converting pytran.diff to ascii
DEBUG> calculating traffic ratio...
ratio[5]: 55%
DEBUG> calculating traffic ratio...
ratio[10]: 52%
...
...
5 minute sample[5]: 67.88
5 minute sample[10]: 42.66
5 minute sample[15]: 30.57
5 minute sample[20]: 23.03
5 minute sample[25]: 18.39
5 minute sample[30]: 14.79
5 minute sample[35]: 12.42
5 minute sample[40]: 10.53
5 minute sample[45]: 9.06
5 minute sample[50]: 7.85

The sampling script will take 5 minute samples at varying color thresholds. The optimal threshold must be manually chosen. Furthermore, you will need to sample the traffic ratios during both heavy and light traffic times to get a good feel for your acceptable range. Also, keep in mind that the traffic ratio value is simply the percent change detected, or in other words the movement detected within the masked region. This means that a completely empty road will register similar values to a road so congested it looks like a parking lot. The time of day can be combined with the traffic ration to determine the logical truth.

With this task implemented and abstracted more complex systems can be built. When I find the time Id like to create a system that will take multiple potential travel routes and times, and during the travel time e-mail the traveler with the best route to take. Another idea I had would be to record the traffic flow values for each camera, for each day and for each half hour interval. Travelers and other interested parties can then analyze traffic patterns to determine the fastest route dependant on date/time.

Network Time Tools (NTT) is a set of network tools designed to provide monitoring of a network and the services on that network, and provide various reports on the hosts/services and optional alerts via email, pager, and cellphones. It also has the capability to send messages on user-specified services to email, pagers and cell phones.
Main features:
- Bandwidth measurement based on time intervals
- Checks Service availibilty on specified Hosts for the following protocols:
- ftp
- ssh
- telnet
- dns
- http ( specific pages )
- www ( simple head )
- pop
- nntp
- imap
- irc
- smtp
- Ability to send alerts based on user specified services to:
- pagers
- cell phones
- email
- A frontend that displays a daily report that can be viewed by a web browser or cell phone.
- views specific servers
- views specific services
- views bandwidth measurements
- view alerts
- Data stored in a Mysql Database
- Easily configurable via an XML config file
- Runs in Daemon or one-shot mode

Time Manager is a cgi-bin script that keeps track on how much time you have spent at work. This works with the user signing on and off whenever he or she arrives or leaves.
Time Manager relies heavily on the Date::Manip module which has to be installed before running the script.
Enhancements:
- made a new monthly stats
- reversed change entry list
- actions like signon/off are now linked up with the main menu giving an action-status line at the bottom of the page.
- added some buttons where lists could get long (weekly stats)

Time::Skew is a Perl module that computes local clock skew with respect to a remote clock.

SYNOPISI

use Time::Skew

# Init Convex Hull and timing data
my $hull=[];
my $result={};

# Iterate data point introduction
Time::Skew::convexhull($result,$datapoint,$hull);

This module supports the computation of the skew between two clocks: the (relative) skew is the speed with which two clocks diverge. For instance, if yesterday two clocks, at the same time, showed respectively 10:00 and 10:05, while today when the former shows 10:00 the latter shows 10:04, we say that their relative skew is 1 minute/24 hours, roughly 7E-4.

The module contains one single subroutine, which accepts as input a pair of timestamps, associated to a message from host A to host B: the timestamps correspond to the time when the message was sent, and to the time when message is received. Each timestamp reflects the value of the local clock where the operation takes place: the clock of host A for the send, the clock of B for the receive.

Please note that the module does _not_ contain any message exchange facility, but only the mathematics needed to perform the skew approximation, once timestamps are known.

The subroutine takes as argument:

a reference to a hash where values related to the timing of the network path from A to B;
a 2-elems array (a data point in the sequel) containing the timestamp of the receive event, and the differece between the send timestamp and the receive timestamp for one message;
a stack containing some data points, those that form the convex hull.

The usage is very simple, and is illustrated by the following example:

#!/usr/bin/perl -w
use strict;
use Time::Skew;

# Initialize data
my $hull=[];
my $result={};
while ( 1 ) {
# Exchange message and acquire a new data point
my $datapoint = acquire();
# Call the convexhull subroutine
Time::Skew::convexhull($result,$datapoint,$hull);
# After first message some results are still undefined
( defined $result->{skewjitter} ) || next;
# here you can use the results

};
}

The data returned in the "result" hash is the following:

result->{skew} the clock skew;
result->{skewjitter} the variance of the skew estimate, used to estimate convergence;
result->{jitter} difference between the current delay and the previous delay;
result->{delay} the communication delay, decremented by a constant (yet unknown) value, used to compute communication jitter;
result->{elems} the number of data points in the convex hull;
result->{select} the index of the data point in the convex hull used to compute the skew;
result->{itimestamp} the timestamp, first element in the data point just passed to the subroutine;
result->{delta} the timestamp difference, second element in the data point just passed to the subroutine;

The data returned in the "hull" stack is a series of data points, selected from those passed to successive calls of the subroutine. The number of data points in the "hull" stack usually does not exceed 20 units.

The algorithm is very fast: each call consists in scanning at most all data points in the "hull" stack, performing simple arithmetic operations for each element.

The algorithm must be fed with a sequence of data points before returning significant results. The accuracy of the estimate keeps growing while new data points are passed to the subroutine. A rough rule of thumb to evaluate estimate accuracy is to observe the skew jitter, and assume it corresponds to the skew estimate accuracy. Paths with quite regular communication delay (small jitter) converge faster.

Time and Attendance is software that is designed to track time and attendance. Attendance tracking is key to any club or organization, and Time and Attendance can easily bring this task online.
Its Web-based interface allows you to enter events as either public or private, and only public events are shown on the public attendance page.
Installation:
-copy all files to your web host
-use phpmyadmin or your mysql interface to run site.sql against your database.
-open site.xml and edit the database section with your database details.
-go to index.php and login with username of admin with a password of test.
-be sure to change the passwords for the admin and regular user.
Setup the site.xml file with your database settings as follows.
< database type="mysql" >
< server >database server address< /server >
< login >database login< /login >
< password >database password< /password >
< default >mysql database name< /default >
< /database >
Add this to your htaccess file to prevent viewing of the xml config file.
< Files ~ ".xml" >
Order allow,deny
Deny from all
Satisfy All
< /Files >
Enhancements:
- Some bugs found in the main form library for the time attendance system were fixed.

Time::Convert is a Perl interface to converting unix seconds to years, days, hours and minutes.

SYNOPSIS

use Time::Convert;
my $convert = new Time::Convert;

EXAMPLE

use Time::Convert;
my $convert = new Time::Convert;
$REPLY = $convert->ConvertSecs(time);
print($REPLY);