Free digital camera software for linux

DigicaMerge project is a commandline tool to merge directories of pictures taken with digital cameras. If youve got a digital camera, your hard disk probably contains many directories full of pictures all named with the same names.
This utility allows you to merge such directories contents into a new directory, and renames all the pictures on the fly, ensuring no filename clash will occur.
You can define your own naming scheme, using either a set of predefined variables or any recognized Exif tag which may be present in your pictures, and also specify a pattern to select only certain files.
Installation:
- Extract it :
$ gzip -d digicamerge-x.xx.tar.gz | tar -xf -
where x.xx is digicamerges latest version number.
- Install it :
Go to digicamerges directory:
$ cd digicamerge-x.xx
Just type:
$ python setup.py install
You may need to be logged in with sufficient privileges (e.g. root)
This will generally install digicamerge in /usr/local/bin or an equivalent path depending on your system.
- Launch it :
Just type :
$ digicamerge
And read the long help message which contains examples.
Enhancements:
- A command line option was added to automatically remove duplicate pictures when merging directories.
- The manual page is now included in the package.

DigiCam is a command-line interface to Kodak DC21x Digital Cameras. I own a Kodak DC215Zoom and its pretty shitty.

Ive compared images my camera makes with others, and I guess there is just something subtly screwed with my camera.

Focus mechanics or something. Ill probably dump this crap and get a real camera one of these days.

What you can do with it:

* Display camera or specific picture information;
* Display number of pictures in the camera;
* Delete any picture from the camera;
* Delete all pictures from the camera in one step;
* Take picture, save to file, delete picture from the camera in one step;
* Retrieve any picture and save to file;
* Retrieve all pictures, and save to current directory as imageNNN.jpg;
* Set resolution to High or Low before taking a picture;
* Set picture quality before taking a picture;
* Set Flash mode before taking a picture;
* Set Zoom value before taking a picture;

Run "cam" without any parameters for a complete list of possible options. Some options can be combined to make cam usable inside scripts. Please use common sense, and dont combine options like -d and -R, or -t and -d

Digital Product Management Library SDK (DPML SDK) is a management platform for Java-based component development, resource management, and deployment.
The project is composed of the Transit resource management layer, the Depot multi-project build system, the Metro component management runtime engine, and the Station application management console.
Enhancements:
- Support was enhanced for the creation of composite components using URL resolvable parts (where a part is a building block defining a classloader chain, component, and deployment scenario).

Happy Camel is intended to combine your digital camera with your GPS device. It you feed it a list of digital photos and a tracklog, it figures out where these images were taken.

Happy Camel can embed this position in the EXIF-data of your photos and create a .kmz file for Google Earth displaying your photos at the right positions along the tracklog.

Usage:
Happy Camel is a command-line tool, in which you should basically provide a directory with images and the position on your disk of a tracklog file. Running happycamel --help should provide you with all the necessary information.

Now what?
So, your pictures have their GPS-coordinates embedded. Now what? Good question.
Of course you can have Happy Camel make a nice Google Earth file which shows a smalle version of your images in ther proper place. There are also several other resources which could display your images on a map, like Yuan.cc and Flickrmap, which require to have your photos on Flickr.

Theres currently a lot going on around GPS and photos. More options will probably present themselves.

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.

Fedora Digital Object Repository is an open source software which gives organizations a flexible service-oriented architecture for managing and delivering their digital content. At its core is a powerful digital object model that supports multiple views of each digital object and the relationships among digital objects.
Digital objects can encapsulate locally-managed content or make reference to remote content. Dynamic views are possible by associating web services with objects. Digital objects exist within a repository architecture that supports a variety of management functions. All functions of Fedora, both at the object and repository level, are exposed as web services. These functions can be protected with fine-grained access control policies.
This unique combination of features makes Fedora an attractive solution in a variety of domains. Some examples of applications that are built upon Fedora include library collections management, multimedia authoring systems, archival repositories, institutional repositories, and digital libraries for education.
Enhancements:
- This is a significant release of Fedora that includes a complete repackaging as a proper Web application.
- A new installer application makes it easy to setup and run.
- It now uses Servlet Filters for authentication.
- The Fedora repository can also be configured to calculate and store checksums for content.
- The RDF-based Resource Index has been tuned for better performance.
- A new high-performing RDBMS-backed triplestore has been developed that can now be plugged into the RI.

The Digital Invisible Ink Toolkit is a Java steganography tool that can hide any sort of file inside a digital image (regarding that the message will fit, and the image is 24 bit colour). Digital Invisible Ink Toolkit will work on Linux and Max OS, even on Windows because it is written in Java and thus platform independent.

There are four highly customisable algorithms in the tool, as well as an open-source implementation of RS Analysis (an extremely good steganalysis method). The tool has the additional advantage of being able to simulate hiding - so you can get an accurate map of where the information is hidden.

The compiled version can be run by simply double clicking the .jar file, or by running at a command line with the following options (you will need to run at the command line if you are using big pictures, such as those greater than 500x500 pixels or it will run out of memory):

java -jar -Xmx512m diit_1-1.jar

Where -Xmx512m tells the virtual machine to use 512MB of physical RAM (at most) - please change to suit your own machine specifications.