conversion | Laravel Unit Conversion

calloc is allocating the amount of memory you asked for on the heap. The pointer is allocated by your compiler either in registers or on the stack. In this case, calloc is actually allocating enough memory for 4 ints on the heap (which on most systems is going to be 16 bytes, but for the arduino uno it would be 8 because the sizeof(int) is 2), then storing the pointer to that allocated memory in your register/stack location.

For the bonus question: Arduino uses C++ instead of C, and that means that it uses C++'s stronger type system. void * and int * are different types, so it's complaining. You should cast the return value of malloc when using C++.

First of all: a Rat with a denominator of 0 is a perfectly legal Rational value. So creating a Rat with a 0 denominator will not throw an exception on creation.

I see two issues really:

• how do you represent a Rat with a denominator of 0 as a string?
• how do you want your program to react to such a Rat?

When you represent a Rats as a string, there is a good chance you will lose precision:

After some time, I did find a solution for this problem. An important thing to know is that, by default, train_crop_size and vis_crop_size are 513x513.

The issue was due to vis_crop_size being smaller than the input images, so vis_crop_size is needed to be greater than the max dimension of the biggest image.

In case you want to use export_model.py, you must use the same logic than vis.py, so your masks are not cropped to 513 by default.

Vision uses images (hence the name ;-) ). If you don't want to use images, you need to use the Core ML API directly.

If the output from the first model really is an image, it's easiest to change that model's output type to an image so that you get a CVPixelBuffer instead of an MLMultiArray. Then you can directly pass this CVPixelBuffer into the next model using Vision.

Seems to be a prime case for fancy indexing (advanced indexing).

Something like this should do the trick, though I didn't verify it on an actual image. I've added a section to reconstruct the image in the beginning, because it is easier to work with the array as a whole than broken into parts. Likely, you can refactor this and avoid splitting it to begin with.

Don't do it; go the route suggested of using a date range instead

Always seek to avoid creating queries that manipulate table data before a comparison is done. Suppose you have a table with ten million datetimes in, and they're all indexed

The database will probably use the index for this:

I'm going by what I remember from the last time I investigated this. Grain of salt and all that.

the fixed point format splits the integer and fractional parts of your (x,y)-coordinates into different maps.

it's "compact" in that CV_32FC2 or 2x CV_32FC1 uses 8 bytes per pixel, while CV_16SC2 + CV_16UC1 uses 6 bytes per pixel. also it's integer-only, so using it can free up floating point compute resources for other work.

the integer parts go into the first map, which is 2-channel. no surprises there.

the fractional parts are converted to 5-bit integers, i.e. they're multiplied by 32. then they're packed together, lowest 5 bits from one coordinate, higher next 5 bits from the other one.

the resulting funny number has a range of 0 .. 1023, or 0b00000_00000 .. 0b11111_11111, which encodes fractional parts (0.0, 0.0) and (0.96875, 0.96875) respectively (that's 31/32).

during remap...

the integer map is used to look up, for every resulting pixel, several pixels in the source image required for interpolation.

the fractional map is taken as an index into an "interpolation table", which is internal to OpenCV. it contains whatever factors and shifts required to correctly blend the several sampled pixels into one resulting pixel, all using integer math. I guess there are multiple tables, one for each interpolation method (linear, cubic, ...).

You can create a conversion proxy object for your return type. This moves the return type deduction into a template conversion operation instead.

I'm the author of the article you referenced.

As discussed in Discover concurrency in SwiftUI, views can make use of the new .task { } and .refreshable { } modifiers to fetch data asynchronously.

So you now have the following options to call you async code: