PyPI 官网下载 | mediagrains-2.7.0.tar.gz

# mediagrains A python library for handling grain-based media in a python-native style. ## Introduction Provides constructor functions for various types of grains and classes that nicely wrap those grains, as well as a full serialisation and deserialisation library for GSF format. Please read the pydoc documentation for more details. Some useful tools for handling the Grain Sequence Format (GSF) file format are also included - see [Tools](#tools). ## Installation ### Requirements * A working Python 3.6+ installation * BBC R&D's internal deb repository set up as a source for apt (if installing via apt-get) * The tool [tox](https://tox.readthedocs.io/en/latest/) is needed to run the unittests, but not required to use the library. ### Steps ```bash # Install from pip $ pip install mediagrains # Install via apt-get $ apt-get install python3-mediagrains # Install directly from source repo $ git clone git@github.com:bbc/rd-apmm-python-lib-mediagrains.git $ cd rd-apmm-python-lib-mediagrains $ pip install -e . ``` ## Usage As an example of using this in your own code the following is a simple program to load the contents of a GSF file and print the timestamp of each grain. ```Python console >>> from mediagrains.gsf import load >>> f = open('examples/video.gsf', "rb") >>> (head, segments) = load(f) >>> print('\n'.join(str(grain.origin_timestamp) for grain in segments[1])) 1420102800:0 1420102800:20000000 1420102800:40000000 1420102800:60000000 1420102800:80000000 1420102800:100000000 1420102800:120000000 1420102800:140000000 1420102800:160000000 1420102800:180000000 ``` Alternatively to create a new video grain in 10-bit planar YUV 4:2:0 and fill it with colour-bars: ```Python console >>> from mediagrains import VideoGrain >>> from uuid import uuid1 >>> from mediagrains.cogenum import CogFrameFormat, CogFrameLayout >>> src_id = uuid1() >>> flow_id = uuid1() >>> grain = VideoGrain(src_id, flow_id, cog_frame_format=CogFrameFormat.S16_422_10BIT, width=1920, height=1080) >>> colours = [ ... (0x3FF, 0x000, 0x3FF), ... (0x3FF, 0x3FF, 0x000), ... (0x3FF, 0x000, 0x000), ... (0x3FF, 0x3FF, 0x3FF), ... (0x3FF, 0x200, 0x3FF), ... (0x3FF, 0x3FF, 0x200) ] >>> x_offset = [0, 0, 0] >>> for colour in colours: ... i = 0 ... for c in grain.components: ... for x in range(0,c.width//len(colours)): ... for y in range(0,c.height): ... grain.data[c.offset + y*c.stride + (x_offset[i] + x)*2 + 0] = colour[i] & 0xFF ... grain.data[c.offset + y*c.stride + (x_offset[i] + x)*2 + 1] = colour[i] >> 8 ... x_offset[i] += c.width//len(colours) ... i += 1 ``` (a more natural interface for accessing data exists in the form of numpy arrays. See later.) The object grain can then be freely used for whatever video processing is desired, or it can be serialised into a GSF file as follows: ```Python console >>> from mediagrains.gsf import dump >>> f = open('dummyfile.gsf', 'wb') >>> dump(f, [grain]) >>> f.close() ``` The encoding module also supports a "progressive" mode where an encoder object is created and a dump started, then grains can be added and will be written to the output file as they are added. ```Python console >>> from uuid import uuid1 >>> from mediagrains import Grain >>> from mediagrains.gsf import GSFEncoder >>> src_id = uuid1() >>> flow_id = uuid1() >>> f = open('dummyfile.gsf', 'wb') >>> enc = GSFEncoder(f) >>> seg = enc.add_segment() # This must be done before the call to start_dump >>> enc.start_dump() # This writes the file header and starts the export >>> seg.add_grain(Grain(src_id, flow_id)) # Adds a grain and writes it to the file >>> seg.add_grain(Grain(src_id, flow_id)) # Adds a grain and writes it to the file >>> seg.add_grain(Grain(src_id, flow_id)) # Adds a grain and writes it to the file >>> enc.end_dump() # This ends the export and finishes off the file >>> f.close() ``` If the underlying file is seekable then the end_dump call will upade all segment metadata to list the correct grain count, otherwise the counts will be left at -1. In addition the library contains a relatively rich grain comparison mechanism in the submodule `comparison`. An example of useage is as follows: ```python >>> from mediagrains.comparison import compare_grain >>> from mediagrains.testsignalgenerator import LumaSteps >>> from uuid import uuid1 >>> from mediatimestamp import Timestamp >>> src_id = uuid1() >>> flow_id = uuid1() >>> ots = Timestamp() >>> gen = LumaSteps(src_id, flow_id, 1920, 1080, origin_timestamp=ots) >>> a = next(gen) >>> b = next(gen) >>> print(compare_grain(a, b)) ❌ Grains do not match ✅ <a/b>.height == 1080 ✅ Binary data <a/b>.data are equal ✅ <a/b>.format == <CogFrameFormat.U8_444: 8192> ✅ <a/b>.length == 6220800 ❌ a.origin_timestamp - b.origin_timestamp == -0:40000000, not the expected 0:0 ❌ a.sync_timestamp - b.sync_timestamp == -0:40000000, not the expected 0:0 ✅ <a/b>.flow_id == UUID('7ff37130-d904-11e8-9fa5-5065f34ed007') ✅ <a/b>.grain_type == 'video' ✅ Lists match ✅ len(<a/b>.timelabels) == 0 ✅ <a/b>.layout == <CogFrameLayout.FULL_FRAME: 0> ✅ <a/b>.width == 1920 ✅ <a/b>.source_id == UUID('7bf845f6-d904-11e8-9fa5-5065f34ed007') ✅ <a/b>.rate == Fraction(25, 1) ✅ a.creation_timestamp - b.creation_timestamp == 0:0 as expected ✅ <a/b>.duration == Fraction(1, 25) ``` This output gives a relatively detailed breakdown of the differences between two grains, both as a printed string (as seen above) and also in a data-centric fashion as a tree structure which can be interrogated in code. ### Numpy arrays An additional feature is provided in the form of numpy array access to the data in a grain. As such the above example of creating colourbars can be done more easily: ```Python console >>> from mediagrains.numpy import VideoGrain >>> from uuid import uuid1 >>> from mediagrains.cogenums import CogFrameFormat, CogFrameLayout >>> src_id = uuid1() >>> flow_id = uuid1() >>> grain = VideoGrain(src_id, flow_id, cog_frame_format=CogFrameFormat.S16_422_10BIT, width=1920, height=1080) >>> colours = [ ... (0x3FF, 0x000, 0x3FF), ... (0x3FF, 0x3FF, 0x000), ... (0x3FF, 0x000, 0x000), ... (0x3FF, 0x3FF, 0x3FF), ... (0x3FF, 0x200, 0x3FF), ... (0x3FF, 0x3FF, 0x200) ] >>> for c in range(0, 3): ... for x in range(0, grain.components[c].width): ... for y in range(0, grain.components[c].height): ... grain.component_data[c][x, y] = colours[x*len(colours)//grain.components[c].width][c] ``` ## Documentation The API is well documented in the docstrings of the module mediagrains, to view: ```bash pydoc mediagrains ``` ## Tools Some tools are installed with the library to make working with the Grain Sequence Format (GSF) file format easier. * `wrap_video_in_gsf` - Provides a means to read raw video essence and generate a GSF file. * `wrap_audio_in_gsf` - As above, but for audio. * `extract_from_gsf` - Read a GSF file and dump out the raw essence within. * `gsf_probe` - Read metadata about the segments in a GSF file. For example, to generate a GSF file containing a test pattern from `ffmpeg`, dump the metadata and then play it out again: ```bash ffmpeg -f lavfi -i testsrc=duration=20:size=1920x1080:rate=25 -pix_fmt yuv422p10le -c:v rawvideo -f rawvideo - | \ wrap_video_in_gsf - output.gsf --size 1920x1080 --format S16_422_10BIT --rate 25 gsf_probe output.gsf extract_gsf_essence output.gsf - | ffplay -f rawvideo -pixel_format yuv422p10 -video_size 1920x1080 -framerate 25 pipe:0 ``` To do the same with a sine wave: ```bash ffmpeg -f lavfi -i "sine=frequency=1000:duration=5" -f s16le -ac 2 - | wrap_audio_in_gsf - output_audio.gsf --sample-rate 44100 gsf_probe output_audio.gsf extract_gsf_essence output_audio.gsf - | ffplay -f s16le -ac 2 -ar 44100 pipe:0 ``` ## Devel