About five years ago I wrote a little program to help me find numeric patterns in arrays. Basically I needed to find byte patterns like 0xFFFE 0xE000 in the data from DICOM files. Something like MATLAB’s strfind function, except more general than looking for substrings in strings.

So I wrote a rather naïve algorithm to do just that. It wasn’t very fast, but it worked for exploratory use. But then today I needed to do something like that in production code. So I asked one of my coworkers for tips with a faster implementation.

Turns out the strfind works just as well on numeric arrays as it does on strings. And it’s really fast, too.

% "data" is a 1,200,000 element UINT8 array.
% It contains 29 instances of the pattern.
tic; offsetTable = strfind(data, [254 255 0 224]); toc

Elapsed time is 0.027390 seconds.

If you’re familiar with the C++ Standard Library, you can start to use your wrapped mxArray like an STL container. I use the term “like” advisedly, since it’s not actually a container. To reduce memory overhead, I don’t make a copy of the data that gets put into ArrayWrapper. Consequently, it doesn’t model the container concept that element lifetimes are the same as the “container” lifetime. You’re shouldn’t count on being able to use ArrayWrapper with any ole STL function. But then again, you might be able to use it with some; I haven’t tried.

#include "mex.h"
#include <cstddef>

// Treat an mxArray like a container.
// (See Josuttis. "The C++ Standard Library." 1999. p. 219-220)
template<class T>
class ArrayWrapper {
  private:
    T      *v;
    mwSize thesize; 

  public:
    // Type definitions
    typedef T         value_type;
    typedef T*        iterator;
    typedef const T*  const_iterator;
    typedef T&        reference;
    typedef const T&  const_reference;
    typedef mwSize    size_type;
    typedef ptrdiff_t difference_type;

    // Constructor
    ArrayWrapper(const mxArray *theArray)
    {
        v = static_cast<T*>(mxGetData(theArray));
        thesize = mxGetNumberOfElements(theArray);
    }

    // Iterator support
    iterator begin() { return v; }
    const_iterator begin() const { return v; }
    iterator end() { return v + thesize; }
    const_iterator end() const { return v + thesize; }

    // Direct element access
    reference operator[](size_type i) { return v[i]; }
    const_reference operator[](size_type i) const { return v[i]; }

    // Size is constant
    size_type size() const { return thesize; }
    size_type max_size() const { return thesize; }
};

void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
    const mxArray *inputArray = prhs[0];

    ArrayWrapper<uint8_T> inData(inputArray);

    // 1- Count the number of samples in the image.
    mwSize count = 0;
    ArrayWrapper<uint8_T>::const_iterator i = inData.begin();
    while (i++ != inData.end())
    {
        count++;
    }

    mexPrintf("The image has %ld samples.n", count);

    // 2 - Halve the image values.
    mxArray *outputArray = mxDuplicateArray(prhs[0]);
    ArrayWrapper<uint8_T> outData(outputArray);

    i = inData.begin();
    ArrayWrapper<uint8_T>::iterator j = outData.begin();
    while (i != inData.end())
    {
        *(j++) = *(i++) * 0.5;
    }

    plhs[0] = outputArray;
}

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Driving back to East Glacier one day, we decided to count the different kinds of (larger) animals we saw on our trip: mule deer, prairie dogs, black bears, mountain goats, brook trout, magpies, ravens, goldfinch, pronghorn, elk, bison, pelicans, marmots, chipmunks (two kinds), squirrels (tree and ground), bunnies, hummingbirds, coyote, cows, camels, llamas, horses, osprey, golden eagles, robins, blue jays, geese, ducks, mosquitos, doggies, hawk, dragonflies, grizzly bears, bighorn sheep, bees, and hippies.

Here are photographs of some of these animals.

	Hummingbird (Rocky Mountain NP, Colorado)
	Yellow-bellied marmot (Rocky Mountain NP, Colorado)
	Mallard (Rocky Mountain NP, Colorado)
	Yellow-bellied marmot (Rocky Mountain NP, Colorado)
	Elk (Rocky Mountain NP, Colorado)
	Elk (Rocky Mountain NP, Colorado)
	Chipmunk (Rocky Mountain NP, Colorado)
	Pronghorn antelope (Natrona County, Wyoming)
	Ravens (Yellowstone NP, Wyoming)
	Elk (Yellowstone NP, Wyoming)
	Bison (Yellowstone NP, Wyoming)
	Bison (Yellowstone NP, Wyoming)
	Bison (Yellowstone NP, Wyoming)
	Elk (Yellowstone NP, Wyoming)
	Coyote (Yellowstone NP, Wyoming)
	Deer (Yellowstone NP, Wyoming)
	Black bear (Yellowstone NP, Wyoming)
	Osprey (Glacier NP, Montana)
	Cows (Glacier NP, Montana)
	Deer (Waterton Lakes NP, Alberta)
	Black bear (Waterton Lakes NP, Alberta)
	Deer (Glacier NP, Montana)
	Grizzly bears (Blackfeet Nation, Montana)
	Bighorn sheep (Glacier NP, Montana)
	Mountain goat (Glacier NP, Montana)
	Mountain goats (Glacier NP, Montana)

Alright. Alright. But you’re only going to get a rough timeline.

	Downtown Denver
	Coors Field, Denver
	At low elevation, ca. 7,000 feet (Rocky Mountain NP, Colorado)
	On our first hike, ca. 8,500 feet (Rocky Mountain NP, Colorado)
	Lunch spot (Rocky Mountain NP, Colorado)
	On our second hike, ca. 9,500 feet (Rocky Mountain NP, Colorado)
	On our second hike, ca. 10,000 feet (Rocky Mountain NP, Colorado)
	On our second hike, Lake Haiyaha (Rocky Mountain NP, Colorado)
	Where’s the parking lot? (Rocky Mountain NP, Colorado)
	On Trail Ridge Road, ca. 13,000 feet (Rocky Mountain NP, Colorado)
	Elk on the tundra (Rocky Mountain NP, Colorado)
	Mother bear and cubs (Rocky Mountain NP, Colorado)
	Arty elks (Rocky Mountain NP, Colorado)
	Carbon County, Wyoming
	Downtown Casper, Wyoming
	Learning how to use the big camera (Casper, Wyoming)
	Freeland Cemetery (Natrona County, Wyoming)
	Freeland Cemetery (Natrona County, Wyoming)
	Freeland Cemetery (Natrona County, Wyoming)
	In the Boone and Crockett lodge at the Buffalo Bill Historical Center (Cody, Wyoming)
	In the Whitney museum at the BBHC (Cody, Wyoming)
	A typical prairie scene (Cody, Wyoming)
	Another typical prairie scene (Cody, Wyoming)
	Yet another typical prairie scene (Cody, Wyoming)
	Yellowstone NP, Wyoming
	Early summer flowers (Yellowstone NP, Wyoming)
	Bison amid the flowers (Yellowstone NP, Wyoming)
	Old Faithful (Yellowstone NP, Wyoming)
	Grand Prismatic Pool (Yellowstone NP, Wyoming)
	Home, Sweet Home (Yellowstone NP, Wyoming)
	On our third hike (Yellowstone NP, Wyoming)
	On our third hike, The Grand Canyon of the Yellowstone (Yellowstone NP, Wyoming)
	On our third hike, the upper falls (Yellowstone NP, Wyoming)
	Waiting for Old Faithful (Yellowstone NP, Wyoming)
	Old Faithful (Yellowstone NP, Wyoming)
	Old Faithful Inn (Yellowstone NP, Wyoming)
	27ºF (Yellowstone NP, Wyoming)
	Early summer flowers (Yellowstone NP, Wyoming)
	Mammoth Hot Springs (Yellowstone NP, Wyoming)
	Dried up hot springs (Yellowstone NP, Wyoming)
	European tourists (Yellowstone NP, Wyoming)
	The Golden Gate (Yellowstone NP, Wyoming)
	Looking for the elusive sage moose Alces sagifora (Yellowstone NP, Wyoming)
	On our fourth hike, the summer trip to Mordor (Yellowstone NP, Wyoming)
	Lower falls of the Yellowstone (Yellowstone NP, Wyoming)
	The Beartooth Highway, Wyoming
	Baby cousin (Billings, Montana)
	Happy baby, Happy mom (Billings, Montana)
	In “The Montana Room” (Billings, Montana)
	The Montana Capitol (Helena, Montana)
	North of Great Falls, Montana
	On the way to Glacier NP, Montana
	Blackfeet Nation, Montana
	Snapshooting (Blackfeet Nation, Montana)
	On the way to Saint Mary (Blackfeet Nation, Montana)
	On our way to Saint Mary (Blackfeet Nation, Montana)
	The most popular view in the park (Glacier NP, Montana)
	Logan Pass (Glacier NP, Montana)
	Logan Pass (Glacier NP, Montana)
	Clearing storm, Gunsight Pass (Glacier NP, Montana)
	Gunsight Pass (Glacier NP, Montana)
	Beautiful mountain weather (Waterton Lakes NP, Alberta)
	The Prince of Wales Hotel (Waterton Lakes NP, Alberta)
	Bear grass (Waterton Lakes NP, Alberta)
	On our fifth hike, clearing snow (Glacier NP, Montana)
	On our fifth hike, Mount Oberlin (Glacier NP, Montana)
	On our fifth hike, the boardwalk (Glacier NP, Montana)
	On our fifth hike, the snowfield (Glacier NP, Montana)
	On our fifth hike, the baby mountain goat (Glacier NP, Montana)
	Merriwether Luis, the wayward conquistador (Marias Pass, Montana)
	Petting a sea star (Seattle, Washington)
	Funky coral (Seattle, Washington)
	Happy to have coffee at the Space Needle (Seattle, Washington)
	Happy in-laws (Seattle, Washington)
	Calder stabile (Seattle, Washington)
	Look who’s smart enough to wear a hat to Safeco (Seattle, Washington)
	Another great evening for a great baseball game (Seattle, Washington)
	Doing the large format thing (Mount Saint Helens National Volcanic Monument, Washington)
	Mid-summer wildflowers (Mount Saint Helens NVM, Washington)
	Mid-summer wildflowers (Mount Saint Helens NVM, Washington)
	A Zen moment at the Japanese Garden (Portland, Oregon)
	Mind like water (Portland, Oregon)
	Watching the sea lions (Portland, Oregon)
	Sea lions (Portland, Oregon)
	Crocs (Portland, Oregon)

It’s taking me longer than I had hoped, but you can expect to see photographs and details about our Western swing soon. . . .

I picked up twenty sheets of developed 4×5″ film from the lab today. Although I made hundreds of photographs with my digital camera, these were certainly the most enjoyable to produce and also the ones that filled me with the most trepidation. I’m pleased to report that the results were rather good. Not 100% what I would like . . . but then again I’m a perfectionist who is getting spoiled by the quick (and virtuous) feedback cycle afforded by digital capture and editing.

I really only used my large format camera about a dozen times on the trip, since I bracket most of my exposures, making an extra photograph with a different amount of light reaching the film. The goal is to have a better chance at getting the “right” exposure. On those dozen occasions, the responses from the people around me ran the gamut from indifference to excited interest. I talked to a few people while composing the scene with my head under the focusing cloth; disembodied voices asking me about how my camera works. There were also several people who thought that because I was incapable of seeing them, I also couldn’t hear their conversations about me.

I think my favorite conversation was with a British fellow about my age in Yellowstone.

“That’s some serious gear.” Most people’s first realization that something is up occurs when I unfold the camera as it sits on the tripod. “Are you a professional?”

“No. I’m just a guy with a very expensive hobby; but I’m having a lot of fun.”

Over the next minutes, I attached my wide-angle lens to the camera, set up a photograph of the Grand Canyon of the Yellowstone, focused the camera, took a few meter readings, set the exposure time and aperture, and switched closed the shutter. At that point almost everything is done. I just had to insert the film holder and trip the shutter.

“WOW! That’s some serious gear!” Something about the Quickload film holder touched a geeky, gadget-loving part in my onlooker. I put a sheet of film in the holder, waited for the wind to subside a bit, and tripped the cable release.

“That’s it?” While I find something immensely charming in the mechanical sound of the shutter winding down the fraction of a second that it’s open, most people think it’s anticlimactic, as though fireworks should shoot out from the camera. But then again, I suppose we’re accustomed to thinking that if someone spends fifteen minutes getting a camera ready, the result should be a poster-sized print that magically appears.

The funny thing is, my mom had the same reaction. She wanted to see how my view camera works, so we collaboratively made the image you see above. And I have to admit, it was a bit disappointing that I had to make her wait three weeks to see the result.

But I talked to several very nice people, and a few even took me up on the offer to pop under the focusing hood and see the image on the ground glass. That reaction is the one that makes me the happiest. It usually goes something like this: “It’s dark under here. . . . WHOA! That’s amazing.”

Anyway, enough accentuating the positive. Let’s talk about mistakes.

Fourteen: My ability to get the “correct” exposure sucks (to put it bluntly). As I mentioned, I have been taking a second exposure, usually 1/2 stop brighter, in an effort to get it right. The darker images — which use the exposure values suggested by my meter — are usually 1/2 to one-and-a-half stops underexposed. So I’m going to change my exposure compensation and start bracketing in whole stops. (And eventually I’m going to get an instant film holder to check the images in the field and finally be able to show onlookers something tangible.)

Fifteen: I forgot the filter compensation factor for my polarizing filter. I guessed two stops at maximum effect and seem to have gotten it about right.

Sixteen: Camera shake is quite visible in a 20 square-inch image. Evidently, I need to wait for the camera to settle after the wind stops blowing and after I pull the dark slide on the film. A couple of the image were a bit blurry and not because of focus.

(And for the curious, I’m working on my “ghetto film scanner,” since I still don’t have a scanner that accepts 4×5. The images lack quite a bit of resolution, dynamic range, and color fidelity. But by adding an opaque mask around my film on the light table, I’ve at least managed to get rid of some of the annoying fringing at the edges of the images. I still must use Photoshop to crop the image, correct the perspective, and “fix” the color; and I don’t feel right using it for anything other than showing off here.

I made a couple photographs of my pathetic setup. Yes, it’s made with two hanging file folders taped together which are held flat by whatever I happen to be reading. (Right now that’s the excellent Devil in the White City.)

Like a copy stand but not as functional

The film holder and light shield

While we were in Billings, we stopped into Action Toys, the best little farm toy store in the world. Here are the fruits of my most recent trip. (Yes, I have a spending limit when I go in.)

Farm equipment, 1/64 scale

So what do we have here?

• AGCO DT200 tractor
• John Deere/Bauer Built DB90 - 36 Row 30″ Planter — In real-life this plants 90 feet of corn (or other row crops) at once. I haven’t dared fold it open yet; this is how you would see it as you passed it on the county highway.
• John Deere 4920 Dry Box Spreader ‘cuz even my little farm demands high yields.
• John Deere 7720 Titan II Combine ca. 1985 but still seen all over Iowa

The tractor pulling the planter is a John Deere 9420T with 425HP engine. I didn’t buy it this trip, but there’s no way the Agco’s 200ish horsepower engine is going to get the field planted, especially when it’s been raining.

We will return you to regularly scheduled programming soon. . . .

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