#snake software on bear hardware

[hzdtrees]

Fog

[morefandomscenarios]

Imagine...

... building some kind of machinery/project with Kid.

He builds hardware and you build software. He assembles parts into a functioning machine and you instruct said machine. There's a special bond that develops between the two of you because of the activity, connects you in a more intimate way than how his superglue attaches two slabs of metals together, which you don't think other people would ever understand. It's by no means an easy job, and it takes you weeks - months - until your efforts bear fruit, and the journey there is painful.

Most of the time Kid fails. And fails. And fails some more. Parts aren't locking together as expected, tools are getting misplaced, measurements are done wrongly, components are missing, the things he ordered don't arrive on time. He scowls, yells in anger, throws a wrench or two, and breaks some windows. In these times, it's up to you to make sure he doesn't accidentally murder someone, and you find that it's so much easier to deal with him than cold, soulless, unforgiving lines of codes - with a promise of food, impromptu movie night, sex and a lazy morning, he's all set and ready to continue from wherever it is he left off, this time with a cooler head and renewed vigor.

Most of the time you too fail. And fail. And fail some more. Encounter logical errors in codes, forget a semicolon, miss a capitalization on variable names, forget to charge your laptop and lose a file you've been working on for almost two hours, nearly throw your water bottle across the room because you should have used snake case instead of camel case. You pull on your hairs, down your fifth coffee for the day, and consider laying on the ground to contemplate life. In these times, Kid either kicks a wall with you or holds you close as you cry, surprisingly gentle as he mumbles about stupid machines making your lives harder than it's supposed to be and insists on getting into a bath together because you both need it, badly. You're back up and running again the next day, determined and freshly rested.

And when you succeed, when you can visibly see what you've built doing what it's supposed to be doing, see what you've managed to accomplish? When Kid laughs out loud and throws you into the air, hair greasy, tank top drenched in sweat and oil and grime?

All worth it.

A/N: Wow this got super self-indulgent real fast I'm sorry slkdfjlsdf where my fellow software devs at?

[technocinema]

Failures in Photogrammetry

I had the chance to try out my home made photogrammetry calibration system in Joshua Tree last week. With temperatures in triple digits throughout the day, the only reasonable time for this type of activity is early in the morning when the Mojave is also at its most beautiful and wildlife are at their most active. I was surrounded by quail and lizards (no snakes) while setting up and plumbing/leveling the posts a little before dawn and a coyote loped by a few yards away, seemingly indifferent to my presence.

I have long suspected that photogrammetry would supply the key to my practical investigation of the conjunction of data and images following on the theoretical and historical research I did on this subject for Technologies of Vision. Although I have done small-scale experiments in the past with a hardware-based system (Occipital Structure Sensor) that captured infrared depth information to an iPad along with photographic textures, the Joshua Tree environment was orders of magnitude larger and more complex. In addition, my intended processing platform is the entirely software-based Metashape by Agisoft (previously PhotoScan), which generates all depth information from the photographs themselves. 

Note: if you have somehow stumbled on this post and are hoping to learn the correct way to use Metashape, please look elsewhere. The internet is full of people -- many of whom don’t really know what they’re doing -- offering authoritative advice on how to use this particular piece of software and I don’t want to add myself to the list. While it’s true that my eventual goal is to gain proficiency with various photogrammetry workflows, I’m currently deriving at least equal pleasure from my near total inability to execute any part of this process as intended. Even the measuring sticks in the above image are of my own conception and may not serve any useful purpose whatsoever.

I captured this segment of landscape three separate times, all using a DJI Mavic 2 Pro, but the drone was only in flight for one of the three capture sessions, partly because of unpredictable winds, but also because of not wanting to be reported on by the neighbors. The Joshua Tree Highlands area has declared itself a no-fly zone out of respect for the wildlife, so when I did put the drone up, I flew a grid pattern that was low and slow, capturing video rather than still images to maximize efficiency. As a side note, Metashape is able to import video directly, from which it extracts individual frames like the one above, but something went wrong at the photo alignment stage, and these images -- which should have generated my most comprehensive spatial mapping -- instead produced a faux-landscape that was more exquisitely distorted than any of the other processes I tried. 

The thumbnail array below comes from one of my terrestrial capture sessions, the first of which, in my exuberance, consisted of well over 500 images. When this image set took 30+ hours to generate a dense point cloud, I re-photographed the entire area at what I hoped would be a more reasonable total of around 70 images. Even when not in flight, the Mavic generates metadata for each image, including GPS coordinates and information about elevation, camera angle, focal length, etc. Metashape uses this data to help spatialize the images in a 3D environment, creating a visual effect that bears a distinct resemblance to the Field-Works series created -- I have no doubt through a much more laborious process -- by the Japanese media artist Masaki Fujihata beginning in the early 2000s. 

When I wrote about Masaki’s work for TechnoVision, I offered Field-Works (above) as a good object that celebrated the gaps and imperfections in spatialized image systems, in contrast with the totalizing impulse and literal aspiration to world-domination represented by Google Earth. With this practice-based research, then -- my own “Field-Works” -- I remain equally driven by the desire to succeed at capturing a photorealistic landscape, with dimensional data accurate enough to inform an architectural plan, and a secret hope -- even an expectation -- that the result will instead turn out to be an interesting failure -- more like Clement Valla’s dazzling collection Postcards from Google Earth (below), where algorithmically generated photographic textures ooze and stretch in a failed attempt to conceal irregularities in the landscape. 

In fact, my first experiment with transforming drone images into dimensional data resulted in just such an outcome. This scene from the back yard (where proximity to LAX also dictates a no-fly zone) grew increasingly interesting with each stage of distortion from image to point cloud to mesh to textured model. The drone never went higher than about 12 feet on two circular passes of about 10 images each and the model was deliberately selected to confound the software. The mesh platform of the wagon, covered with leaves that changed position when blown by the rotors of the Mavic confused the software enough to yield a kind of molten surface reminiscent of Valla, an effect that I have not been able to reproduce since. 

This first attempt was created using a free, open source Mac-compatible program called Regard 3D. Although I now have access to PCs with decent graphics processing capability through the VR lab at UCLA, I preferred to stay in the Mac environment to avoid multiple trips across town. In fact, the majority of this post was written while one photogrammetry program or another was rendering models, meshes or depth maps in the background. Although the results from Regard 3D were more than gratifying, I went ahead and purchased an educational license for Metashape and then immediately upgraded to the Pro version when I realized all the features that were withheld from the standard version. Metashape has the advantage of robust documentation -- going back to its days as PhotoScan -- and in-application help features as well as a very active community forum that seems relatively welcoming to people who don’t know what they’re doing. 

For my second backyard test, I chose slightly more conventional (solid) surfaces and included some reference markers -- 8′ measuring sticks and Agisoft Ground Control Points (GCPs -- seen in lower right and left corners of the image above) to see if these would help with calibration for the mapping in Joshua Tree. Metashape handled the process effortlessly, resulting in a near-photorealistic 3D model. The measuring sticks allowed me to confirm the scale and size of objects in the final model, but the GCPs could not have functioned as intended because I didn’t manually enter their GPS coordinates. Instead, the software seems to have relied on GPS data from the Mavic and I’m not sure my handheld GPS unit would have been any more accurate at locating the GCPs anyway. In fact, when I got to Joshua Tree, although I dutifully printed out and took a bunch of GCP markers like the one below with me, I forgot to use them and didn’t miss having them when reconstructing the landscape. 

Although images like this, which are designed to be read by non-human eyes, have been in public circulation for decades -- QR codes, bar codes, etc. -- they continue to fascinate me as fulcrum-objects located at the intersection of data and images. When Metashape “sees” these patterns in an imported image, it ties them to a system of spatial coordinates used to verify the lat/long data captured by the drone. When used correctly, the visual/human register takes precedence over the data systems of machines and satellites. Although unintentional, my failure to use the GCPs may have been a gesture of unconscious resistance to these systems’ fetishization of Cartesian precision. 

After 36 hours of processing my initial set of 570 images (mapped by location above on the left), Metashape produced a “dense point cloud” (right) representing all vertices where the software identified the same visual feature in more than one image. Although the area I intended to map was only about 5000 square feet, the software found vertices extending in all directions -- literally for miles -- across the Joshua basin. A bounding box (below) is used to exclude outlying vertices from the point cloud and to define a limited volume for the next stages of modeling. 

The point cloud can also be viewed using the color information associated with each vertex point (below). This begins to resemble a low-resolution photographic representation of the landscape, which also reveals the areas (resembling patches of snow on the ground) where image-data is missing. Likewise, although the bounding box has removed most outlying information from the distant hillsides, many vertices have still been incorrectly mapped, appearing as sparse clouds against the white background. Metashape has the ability to scan and remove these artifacts automatically by calculating the confidence with which each vertex has been rendered, but they can also be deleted manually. Still not fully trusting the software, I removed the majority of these orphan points by hand, which also allowed me to exclude areas of the model that I knew would not be needed even though they might have a high confidence value. Under other circumstances, of course, I am totally opposed to “cleaning” data to minimize artifacts and imperfections, but the 30+ hours of processing required to render the initial cloud had left me scarred and impatient. 

The next step is to create a wireframe mesh that defines the surfaces of the model, transforming the point cloud into a dimensional web of triangles. With tens of millions of potential vertices to incorporate, the software may be set to limit the mesh to a maximum number of triangular faces, which in turn, determines the final precision of the model. 

At this point, the model can be rotated in space and a strong sense of the overall contours of the landscape is readily apparent. The aesthetics of the wireframe are also exquisite in their own right -- admittedly perhaps as a result of my fascination with the economical graphic environment of the early 80s Atari game Battlezone (below) -- and there is always a part of me that wants to export and use the wireframe as-is. In fact, I assume this is possible -- though I haven’t yet tried it -- and I look forward to figuring out how to create 3D flythroughs and/or navigable environments with these landscapes rendered as an untextured mesh. 

The final stage in creating the model is for Metashape to generate textures based on the original photographs that are mapped onto the mesh surfaces. At this stage, gaps in the point cloud are filled in algorithmically, eliminating the snow effect on the ground. By limiting the size of the model and deleting as many artifacts as possible, the final texturing was relatively quick, even at high resolution, and the resulting model is exportable in a variety of 3D formats. 

Metashape also offers the ability to generate digital elevation maps (DEMs), which register relative heights of the landscape signified by color or shape. I’m reasonably certain that the DEM image below is the result of some egregious error, but it remains among my favorite outputs of this exercise. 

The final image included here is the single frame used by the photogrammetry software for texture mapping. Although it gives the appearance at first glance of being an aerial photograph of a strangely featureless landscape, this file is actually an indexed palette of all the color and texture combinations present on all surfaces of the 3D model. If photogrammetry constitutes the key technology operating in the interstices between data and images, these files are arguably the most liminal of all its components. Neither mimetic nor computational, they provide visual information (“pixel-data”) that is necessary to the perceptual phenomenon of photorealism, while denying the pleasures of recognition. In a literal sense, this single image contains every color and texture that may be seen in the 3D model, rendered here as an abstraction fully legible only to the eyes of the machine. 

[gaminggadgets]

Der Vorgänger des Commodore 64 bekommt jetzt auch einen Nachbau mit zig legendären Spielen der 1980er Jahre. Das bietet der TheVIC20.

Die Macher des TheC64 Mini und des TheC64 bringen mit dem TheVIC20 einen weiteren Nachbau eines Commodore-Heimrechners in den Handel. Bereits im Oktober soll die Replik des C64-Vorläufers erscheinen.

Das bietet der TheVIC20

Hersteller Retro Games bringt den ursprünglich 1981 veröffentlichten Vic-20 noch einmal in den Handel. Bis 1984 wurde der Heimcomputer hergestellt, stolze 2,5 Millionen Mal wurde dieser bis 1985 verkauft. Der C64 knüpfte mit verbesserter Hardware an die Erfolge des Vic-20 an.

Das “Remake” besitzt die originalen Ausmaße des Computer-Urgesteins, im Inneren steckt allerdings zeitgemäße Technik. Das heißt: Ihr könnt das Gerät problemlos über HDMI an euren Fernseher anschließen. Nostalgiker schalten via Software einen CRT-Filter ein. Die Tastatur ist voll funktionsfähig, ihr könnt also auch regulär im Basic des TheVIC20 programmieren. Das Vic-20-Basic ist vorinstalliert.

Der Joystick. (Foto: Retro Games)

Mit schöner Verpackung. (Foto: Retro Games)

Die Tastatur ist voll funktionsfähig. (Foto: Retro Games)

Das Auswahl-Menü. (Foto: Retro Games)

Hier wählt ihr passende Spielmodi aus. (Foto: Retro Games)

Aber: TheVIC20 emuliert den Vic-20, genauso übrigens den Commodore 64. Denn ihr könnt auch auf das C64-Basic sowie vorinstallierte Spiele vom Vic-20 (28) und C64 (36) zugreifen. Zusätzlich lassen sich eigene Spiele via USB-Stick nutzen. An drei weiteren USB-Ports schließt ihr weitere Joysticks und Gamepads an. Für Multiplayer-Action ist also gesorgt.

Zocken wie damals

Interessant: Ein an einen Competition Pro erinnernder Joystick liegt dem Lieferumfang bei. Der kommt bei den zahlreichen Games zum Einsatz, genau genommen diese:

Vic-20-Spiele des TheVIC20

Abductor

Andes Attack

Arcadia

Bewitched

Blitzkrieg

Brainstorm

Catcha Snatcha

Connect 4

Encounter

Frantic

Frog Chase

Gridrunner

Harvester

Headbanger’s Heaven

Hell Gate

Laser Zone

Martians

Matrix

Mega Vault

Metagalactic Llamas Battle at the Edge of Time

Psychedelia

Snake

Starquest

Subspace Striker

Tank Battle

Traxx

Wacky Waiters

Zor

Commodore 64 auf dem TheVIC20

Alleykat

Bear Bovver

Boulder Dash

California Games

Chips Challenge

Cyberdyne Warrior

Cybernoid II

Destroyer

Firelord

Galencia Mini

Gribbly’s Day Out

Heartland

Impossible Mission

IO

Iridis Alpha

Jumpman

Mega Apocalypse

Nebulus

Netherworld

Paradroid

Pitstop II

Planet of Death

Robin of the Wood

Speedball 2

Spindizzy

Street Sports Baseball

Street Sports Basketball

Summer Games II

Super Cycle

Sword of Fargoal

Temple of Apshai Trilogy

The Arc of Yesod

Uridium

Winter Games

World Games

Zynaps

Wer braucht den TheVIC20?

Zugegeben: Wenn ihr bereits einen TheC64 besitzt, stellt sich schon die Frage, wieso ihr euch noch einen TheVIC20 zulegen solltet. Vermutlich richtet sich der Computer an Retro-Liebhaber, die damals mit dem VIC-20 aufgewachsen sind. Qualitativ besser dürften dennoch die C64-Spiele sein, mich persönlich reizen die VIC-20-Games eher in Maßen.

Seht ihr das anders: Für rund 120 Euro soll das gute Stück verkauft werden. Eine Vorbestellung ist derzeit nur für Briten auf Amazon UK möglich. Ob es der TheVIC20 nach Deutschland schafft? Das wird sich zeigen. Die Frage, die ich mir derzeit viel dringender stelle ist: Wann kommt endlich ein TheAmiga500 oder ein TheAmiga1200?

Der Vorgänger des legendären Commodore 64 bekommt eine Neuauflage. Das bietet der TheVIC20. #thevic20 Der Vorgänger des Commodore 64 bekommt jetzt auch einen Nachbau mit zig legendären Spielen der 1980er Jahre.

[candelariafitzpa-blog]

This Story Behind team Will Haunt You For life!

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