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https://github.com/kkroening/ffmpeg-python.git
synced 2025-08-07 18:39:47 +08:00
Detect: Add NVidia decoders data
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Ross Patterson
parent
8982e9fff2
commit
9f00ffd995
@ -38,7 +38,11 @@ API_TO_HWACCEL = {
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NVIDIA_GPU_MATRIX_URL = (
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'https://developer.nvidia.com/video-encode-decode-gpu-support-matrix')
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NVIDIA_LINE_SUFFIXES = {'geforce': ['gtx titan', 'gtx', 'gt', 'rtx']}
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NVIDIA_CODEC_COLUMN_PREFIXES = {'h.264': 'h264', 'h.265': 'hevc'}
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NVIDIA_CODEC_COLUMN_PREFIXES = {
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'mpeg-1': 'mpeg1video', 'mpeg-2': 'mpeg2video',
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'vc-1': 'vc1',
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'vp8': 'vp8', 'vp9': 'vp9',
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'h.264': 'h264', 'h.265': 'hevc'}
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def get_hwaccel_data():
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@ -75,14 +79,18 @@ def get_nvidia_data():
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(
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nvenc_recent, nvenc_consumer, nvenc_workstation, nvenc_virt,
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nvdec_recent, nvdec_consumer, nvdec_workstation, nvdec_virt) = tables
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nvidia = collections.OrderedDict(
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lines=[], model_lines=collections.OrderedDict(),
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boards=collections.OrderedDict())
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nv_coders = dict(
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encoders=(
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nvenc_recent, nvenc_consumer, nvenc_workstation, nvenc_virt),
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decoders=(
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nvdec_recent, nvdec_consumer, nvdec_workstation, nvdec_virt))
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nvidia = collections.OrderedDict(lines=[])
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# Compile aggregate data needed to parse individual rows
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for nvenc_table in (
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nvenc_recent, nvenc_consumer, nvenc_workstation, nvenc_virt):
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for board in nvenc_table['BOARD']:
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for nv_coder_table in tables:
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for board in nv_coder_table['BOARD']:
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if board == 'BOARD':
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continue
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line = board.replace('\xa0', ' ').split(None, 1)[0].lower()
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if line not in nvidia['lines']:
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nvidia['lines'].append(line)
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@ -90,62 +98,79 @@ def get_nvidia_data():
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for line_suffix in reversed(line_suffixes):
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nvidia['lines'].insert(0, ' '.join((line, line_suffix)))
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for nvenc_table in (
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nvenc_recent, nvenc_consumer, nvenc_workstation, nvenc_virt):
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for nvenc_row_idx, nvenc_row in nvenc_table.iterrows():
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nvenc_row_values = {
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idx: cell for idx, cell in enumerate(nvenc_row[1:]) if (
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cell and
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not (isinstance(cell, float) and math.isnan(cell)))}
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if not nvenc_row_values:
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# Divider row
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continue
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for coder_type, nv_coder_tables in nv_coders.items():
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coder_data = nvidia[coder_type] = collections.OrderedDict(
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model_lines=collections.OrderedDict(),
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boards=collections.OrderedDict())
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for nv_coder_table in nv_coder_tables:
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for nv_coder_row_idx, nv_coder_row in nv_coder_table.iterrows():
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nv_coder_row_values = {
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idx: cell for idx, cell in enumerate(nv_coder_row[1:]) if (
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cell and
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not (isinstance(cell, float) and math.isnan(cell)))}
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if not nv_coder_row_values:
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# Divider row
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continue
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# Assemble the data for this row to use for each model or range
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model_data = collections.OrderedDict()
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for key, value in nvenc_row.items():
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if value in {'YES', 'NO'}:
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model_data[key] = value == 'YES'
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else:
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model_data[key] = value
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model_data['BOARD'] = model_data['BOARD'].replace('\xa0', ' ')
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# Add keys for the data for the ffmpeg codec names for fast lookup
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for codec_prefix, codec in NVIDIA_CODEC_COLUMN_PREFIXES.items():
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for column_idx, column in enumerate(nvenc_row.keys()):
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if column.lower().startswith(codec_prefix):
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model_data[codec] = nvenc_row[column_idx] == 'YES'
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break
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nvidia['boards'][model_data['BOARD']] = model_data
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# Assemble the data for this row to use for each model or range
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model_data = collections.OrderedDict()
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for key, value in nv_coder_row.items():
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if isinstance(key, tuple):
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if key[0] == key[1]:
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key = key[0]
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else:
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key = ' '.join(key)
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if value in {'YES', 'NO'}:
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model_data[key] = value == 'YES'
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else:
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model_data[key] = value
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model_data['BOARD'] = model_data['BOARD'].replace('\xa0', ' ')
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# Add keys for the ffmpeg codec names for fast lookup
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for codec_prefix, codec in (
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NVIDIA_CODEC_COLUMN_PREFIXES.items()):
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for column_idx, column in enumerate(nv_coder_row.keys()):
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if isinstance(column, tuple):
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if column[0] == column[1]:
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column = column[0]
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else:
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column = ' '.join(column)
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if column.lower().startswith(codec_prefix):
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model_data[codec] = nv_coder_row[
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column_idx] == 'YES'
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break
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coder_data['boards'][model_data['BOARD']] = model_data
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_detect._parse_models(
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model_lines=nvidia['lines'],
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boards=model_data['BOARD'].lower(),
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model_data=model_data['BOARD'],
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model_lines_data=nvidia['model_lines'])
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_detect._parse_models(
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model_lines=nvidia['lines'],
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boards=model_data['BOARD'].lower(),
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model_data=model_data['BOARD'],
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model_lines_data=coder_data['model_lines'])
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# Clean up some annoying clashes between the titan model line and GeForce
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# GTX model numbers
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for model_line, model_line_suffixes in NVIDIA_LINE_SUFFIXES.items():
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models_data = nvidia['model_lines'][model_line]['models']
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for model_num in list(models_data):
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for model_line_suffix in model_line_suffixes:
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if model_num.startswith(model_line_suffix + ' '):
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models_data[model_num[
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len(model_line_suffix + ' '):]] = models_data.pop(
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model_num)
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for titan_model_num in {'black', 'xp'}:
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nvidia['model_lines']['geforce gtx']['models'][
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'titan ' + titan_model_num] = nvidia['model_lines'][
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'titan']['models'].pop(titan_model_num)
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for titan_model_num in list(nvidia['model_lines'][
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'geforce gtx titan']['models'].keys()):
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nvidia['model_lines']['geforce gtx']['models'][
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'titan ' + titan_model_num] = nvidia['model_lines'][
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'geforce gtx titan']['models'].pop(titan_model_num)
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nvidia['model_lines']['geforce gtx']['models']['titan'] = nvidia[
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'model_lines']['geforce gtx']['models']['titan black']
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del nvidia['model_lines']['geforce gtx']['models']['titan ']
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del nvidia['model_lines']['geforce gtx titan']
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# Cleanup any deviations from the convention where models from
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# multiple lines are in the same BOARD cell
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for model_line, model_line_data in coder_data['model_lines'].items():
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for line, line_suffixes in NVIDIA_LINE_SUFFIXES.items():
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if not model_line.startswith(line):
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continue
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for model_num, boards in list(
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model_line_data['models'].items()):
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for line_suffix in line_suffixes:
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if not model_num.startswith(line_suffix + ' '):
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continue
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coder_data['model_lines'][
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' '.join((line, line_suffix))]['models'][
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model_num[len(line_suffix + ' '):]
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] = model_line_data['models'].pop(model_num)
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# Clean up some annoying clashes between the titan model line and
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# GeForce GTX model numbers
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del coder_data['model_lines']['geforce gtx titan']['models']['']
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coder_data['model_lines']['geforce gtx titan']['models'][
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'xp'] = coder_data['model_lines']['titan']['models'].pop('xp')
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coder_data['model_lines']['geforce gtx titan']['models'][
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'black'] = titan_black = coder_data['model_lines'][
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'titan']['models'].pop('black')
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coder_data['model_lines']['geforce gtx']['models'][
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'titan'] = titan_black
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return nvidia
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7331
ffmpeg/detect.json
7331
ffmpeg/detect.json
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