view vamp/collect.py @ 99:7764eb74a3c6

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'''A high-level interface to the vampyhost extension module, for quickly and easily running Vamp audio analysis plugins on audio files and buffers.'''

import vampyhost
import load
import process
import frames

import numpy as np

def get_feature_step_time(sample_rate, step_size, output_desc):
    if output_desc["sample_type"] == vampyhost.ONE_SAMPLE_PER_STEP:
        return vampyhost.frame_to_realtime(step_size, sample_rate)
    elif output_desc["sample_type"] == vampyhost.FIXED_SAMPLE_RATE:
        return vampyhost.RealTime('seconds', 1.0 / output_desc["sample_rate"])
    else:
        return 1

def timestamp_features(sample_rate, step_size, output_desc, features):
    n = -1
    if output_desc["sample_type"] == vampyhost.ONE_SAMPLE_PER_STEP:
        for f in features:
            n = n + 1
            t = vampyhost.frame_to_realtime(n * step_size, sample_rate)
            f["timestamp"] = t
            yield f
    elif output_desc["sample_type"] == vampyhost.FIXED_SAMPLE_RATE:
        output_rate = output_desc["sample_rate"]
        for f in features:
            if "has_timestamp" in f:
                n = int(f["timestamp"].to_float() * output_rate + 0.5)
            else:
                n = n + 1
            f["timestamp"] = vampyhost.RealTime('seconds', float(n) / output_rate)
            yield f
    else:
        for f in features:
            yield f

def fill_timestamps(results, sample_rate, step_size, output_desc):

    output = output_desc["identifier"]
    
    selected = [ r[output] for r in results ]

    stamped = timestamp_features(sample_rate, step_size, output_desc, selected)

    for s in stamped:
        yield s

def deduce_shape(output_desc):
    if output_desc["has_duration"]:
        return "individual"
    if output_desc["sample_type"] == vampyhost.VARIABLE_SAMPLE_RATE:
        return "individual"
    if not output_desc["has_fixed_bin_count"]:
        return "individual"
    if output_desc["bin_count"] == 0:
        return "individual"
    if output_desc["bin_count"] == 1:
        return "vector"
    return "matrix"


def reshape(results, sample_rate, step_size, output_desc):

    output = output_desc["identifier"]
    shape = deduce_shape(output_desc)
    out_step = get_feature_step_time(sample_rate, step_size, output_desc)

    if shape == "vector":
        rv = ( out_step,
               np.array([r[output]["values"][0] for r in results], np.float32) )
    elif shape == "matrix":
        #!!! todo: check that each feature has the right number of bins?
        outseq = [r[output]["values"] for r in results]
        rv = ( out_step, np.array(outseq, np.float32) )
    else:
        rv = list(fill_timestamps(results, sample_rate, step_size, output_desc))

    return rv

        
def collect(data, sample_rate, key, output = "", parameters = {}):
    """Process audio data with a Vamp plugin, and make the results from a
    single plugin output available as a single structure.

    The provided data should be a 1- or 2-dimensional list or NumPy
    array of floats. If it is 2-dimensional, the first dimension is
    taken to be the channel count.

    The returned results will be those calculated by the plugin with
    the given key and returned through its output with the given
    output identifier. If the requested output is the empty string,
    the first output provided by the plugin will be used.

    If the parameters dict is non-empty, the plugin will be configured
    by setting its parameters according to the (string) key and
    (float) value data found in the dict.

    The structure in which the results are returned depends upon the
    output descriptor for the requested plugin output, as follows:

    * If the plugin output emits single-valued features at a fixed
      sample-rate, then this function will return a tuple of step time
      (the time in seconds between consecutive feature values) and a
      one-dimensional NumPy array of feature values. An example of
      such a feature might be a loudness curve against time.

    * If the plugin output emits multiple-valued features, with an
      equal number of bins per feature, at a fixed sample-rate, then
      this function will return a tuple of step time (the time in
      seconds between consecutive feature values) and a
      two-dimensional NumPy array of feature values. An example of
      such a feature might be a spectrogram.

    * Otherwise this function will return a list of features, where
      each feature is represented as a dictionary containing a
      timestamp (always) and a duration (optionally), a label
      (string), and a 1-dimensional array of float values.

    If you would prefer to obtain features as they are calculated
    (where the plugin supports this) and with the format in which the
    plugin returns them, via an asynchronous generator function, use
    vamp.process() instead.
    """
    
    plugin, step_size, block_size = load.load_and_configure(data, sample_rate, key, parameters)

    if output == "":
        output_desc = plugin.get_output(0)
        output = output_desc["identifier"]
    else:
        output_desc = plugin.get_output(output)

    ff = frames.frames_from_array(data, step_size, block_size)

    results = process.process_frames_with_plugin(ff, sample_rate, step_size, plugin, [output])

    rv = reshape(results, sample_rate, step_size, output_desc)

    plugin.unload()
    return rv


#!!! restore & complete this once process is refactored:
        
# def collect_frames(ff, channels, sample_rate, step_size, key, output = "", parameters = {}):

#     plug = vampyhost.load_plugin(key, sample_rate,
#                                  vampyhost.ADAPT_INPUT_DOMAIN +
#                                  vampyhost.ADAPT_BUFFER_SIZE +
#                                  vampyhost.ADAPT_CHANNEL_COUNT)

#     plug.set_parameter_values(parameters)

#     if not plug.initialise(channels, step_size, block_size):
#         raise "Failed to initialise plugin"

#     if output == "":
#         output_desc = plugin.get_output(0)
#         output = output_desc["identifier"]
#     else:
#         output_desc = plugin.get_output(output)

#     results = process.process_frames_with_plugin(ff, sample_rate, step_size, plugin, [output])

#     rv = reshape(results, sample_rate, step_size, output_desc)

#     plugin.unload()
#     return rv