Improve speed of msgify(point_cloud_2_np) by 115x

[apockill]

I noticed that on large point clouds, converting from numpy to PointCloud2 was extremely time consuming. It took 2.4 seconds to convert a point cloud representing (x. y, z, rgb) for a structured array of shape (1944, 1200). This cloud is about 40 megabytes when serialized.

Background

As I looked into it, I found that the speed problem was not actually in cloud_arr.tostring(), but rather inside of the PointCloud2.data property, when it calls self._data = array.array('B', value).

Reference code:

class PointCloud2:
	...

	@data.setter
    def data(self, value):
        if isinstance(value, array.array):
            assert value.typecode == 'B', \
                "The 'data' array.array() must have the type code of 'B'"
            self._data = value
            return
        self._data = array.array('B', value)

From looking into it, it appears that the entire byte array is iterated upon painstakingly and copied by array.array('B', value),

To resolve this, I used a memoryview and array.array.frombytes() to reduce the copying and iteration. In fact, the only copy is here, which uses memcpy and is quite efficient- as opposed to currently where it iterates over each byte as the array.array object is built.

Benchmarks

Current Implementation:
msgify(large_point_cloud): 2.46 seconds

This PR:
msgify(large_point_cloud): 0.0211 seconds

[velovix]

@tpanzarella Would you mind taking a look at this PR? It would be super helpful to have this in 😁

[tpanzarella]

Yes. For some reason I was not getting alerts about the PRs to the repo. I should be able to take a look today. Sorry for the delays. The premise of the PR sounds great.