mirror of
https://github.com/correl/dejavu.git
synced 2024-11-23 19:19:53 +00:00
A fairly big batch of changes, blame me to forget committing
Changes in no particular order: - Replaced all use cases of wavfile/wave and extract_channels with the new decoder.read function - Added a 'recognize' method to the Dejavu class. This is a shortcut for recognizing songs. - Renamed 'do_fingerprint' into 'fingerprint_directory' - Removed parameters not required anymore from fingerprint_directory - Cleaned up fingerprint.py - Made fingerprint.generate_hashes a generator - WaveFileRecognizer is now FileRecognizer and can take any formats supported by pydub - Fixed MicrophoneRecognizer to actually run, previous version had many small mistakes - Renamed 'fingerprint_worker' to '_fingerprint_worker' to signify it is not to be used publicly - Moved 'chunkify' outside the Dejavu class - Cleaned up pep8 styling mistakes in all edited files.
This commit is contained in:
Wessie
parent
7895bae23e
commit
29029238fc
4 changed files with 120 additions and 205 deletions
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@ -1,22 +1,20 @@
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from dejavu.database import SQLDatabase
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import dejavu.decode as decoder
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import dejavu.decoder as decoder
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import fingerprint
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from scipy.io import wavfile
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from multiprocessing import Process
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import wave, os
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from multiprocessing import Process, cpu_count
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import os
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import random
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DEBUG = False
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class Dejavu(object):
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def __init__(self, config):
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super(Dejavu, self).__init__()
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self.config = config
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# initialize db
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self.db = SQLDatabase(**config.get("database", {}))
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#self.fingerprinter = Fingerprinter(self.config)
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self.db.setup()
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# get songs previously indexed
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@ -29,27 +27,27 @@ class Dejavu(object):
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self.songnames_set.add(song_name)
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print "Added: %s to the set of fingerprinted songs..." % song_name
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def chunkify(self, lst, n):
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"""
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Splits a list into roughly n equal parts.
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http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
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"""
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return [lst[i::n] for i in xrange(n)]
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def do_fingerprint(self, path, output, extensions, nprocesses):
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def fingerprint_directory(self, path, extensions, nprocesses=None):
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# Try to use the maximum amount of processes if not given.
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if nprocesses is None:
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try:
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nprocesses = cpu_count()
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except NotImplementedError:
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nprocesses = 1
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# convert files, shuffle order
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files = decoder.find_files(path, extensions)
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files = list(decoder.find_files(path, extensions))
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random.shuffle(files)
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files_split = self.chunkify(files, nprocesses)
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files_split = chunkify(files, nprocesses)
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# split into processes here
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processes = []
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for i in range(nprocesses):
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# create process and start it
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p = Process(target=self.fingerprint_worker,
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args=(files_split[i], self.db, output))
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p = Process(target=self._fingerprint_worker,
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args=(files_split[i], self.db))
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p.start()
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processes.append(p)
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@ -57,55 +55,37 @@ class Dejavu(object):
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for p in processes:
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p.join()
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# delete orphans
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# print "Done fingerprinting. Deleting orphaned fingerprints..."
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# TODO: need a more performant query in database.py for the
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#self.fingerprinter.db.delete_orphans()
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def fingerprint_worker(self, files, sql_connection, output):
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def _fingerprint_worker(self, files, db):
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for filename, extension in files:
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# if there are already fingerprints in database, don't re-fingerprint or convert
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# if there are already fingerprints in database,
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# don't re-fingerprint
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song_name = os.path.basename(filename).split(".")[0]
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if DEBUG and song_name in self.songnames_set:
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if song_name in self.songnames_set:
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print("-> Already fingerprinted, continuing...")
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continue
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channels, Fs = decoder.read(filename)
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# insert song name into database
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song_id = sql_connection.insert_song(song_name)
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song_id = db.insert_song(song_name)
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for c in range(len(channels)):
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channel = channels[c]
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print "-> Fingerprinting channel %d of song %s..." % (c+1, song_name)
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hashes = fingerprint.fingerprint(channel, Fs=Fs)
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sql_connection.insert_hashes(song_id, hashes)
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db.insert_hashes(song_id, hashes)
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# only after done fingerprinting do confirm
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sql_connection.set_song_fingerprinted(song_id)
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def extract_channels(self, path):
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"""
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Reads channels from disk.
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Returns a tuple with (channels, sample_rate)
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"""
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channels = []
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Fs, frames = wavfile.read(path)
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wave_object = wave.open(path)
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nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
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#assert Fs == self.fingerprinter.Fs
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for channel in range(nchannels):
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channels.append(frames[:, channel])
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return (channels, Fs)
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db.set_song_fingerprinted(song_id)
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def fingerprint_file(self, filepath, song_name=None):
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# TODO: replace with something that handles all audio formats
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channels, Fs = self.extract_channels(path)
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channels, Fs = decoder.read(filepath)
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if not song_name:
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song_name = os.path.basename(filename).split(".")[0]
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song_name = os.path.basename(filepath).split(".")[0]
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song_id = self.db.insert_song(song_name)
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for data in channels:
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@ -141,7 +121,6 @@ class Dejavu(object):
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largest_count = diff_counter[diff][sid]
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song_id = sid
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if DEBUG:
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print("Diff is %d with %d offset-aligned matches" % (largest, largest_count))
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# extract idenfication
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@ -151,9 +130,6 @@ class Dejavu(object):
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else:
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return None
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if DEBUG:
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print("Song is %s (song ID = %d) identification took %f seconds" % (songname, song_id, elapsed))
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# return match info
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song = {
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"song_id": song_id,
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@ -162,3 +138,15 @@ class Dejavu(object):
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}
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return song
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def recognize(self, recognizer, *options, **kwoptions):
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r = recognizer(self)
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return r.recognize(*options, **kwoptions)
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def chunkify(lst, n):
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"""
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Splits a list into roughly n equal parts.
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http://stackoverflow.com/questions/2130016/splitting-a-list-of-arbitrary-size-into-only-roughly-n-equal-parts
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"""
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return [lst[i::n] for i in xrange(n)]
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@ -1,17 +1,11 @@
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import numpy as np
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import matplotlib.mlab as mlab
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import matplotlib.pyplot as plt
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import matplotlib.image as mpimg
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from scipy.io import wavfile
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from scipy.ndimage.filters import maximum_filter
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from scipy.ndimage.morphology import generate_binary_structure, iterate_structure, binary_erosion
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from dejavu.database import SQLDatabase
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import os
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import wave
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import sys
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import time
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from scipy.ndimage.morphology import (generate_binary_structure,
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iterate_structure, binary_erosion)
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import hashlib
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import pickle
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IDX_FREQ_I = 0
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IDX_TIME_J = 1
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@ -25,8 +19,8 @@ DEFAULT_AMP_MIN = 10
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PEAK_NEIGHBORHOOD_SIZE = 20
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MIN_HASH_TIME_DELTA = 0
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def fingerprint(channel_samples,
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Fs=DEFAULT_FS,
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def fingerprint(channel_samples, Fs=DEFAULT_FS,
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wsize=DEFAULT_WINDOW_SIZE,
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wratio=DEFAULT_OVERLAP_RATIO,
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fan_value=DEFAULT_FAN_VALUE,
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@ -53,8 +47,8 @@ def fingerprint(channel_samples,
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# return hashes
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return generate_hashes(local_maxima, fan_value=fan_value)
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def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
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def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
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# http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.morphology.iterate_structure.html#scipy.ndimage.morphology.iterate_structure
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struct = generate_binary_structure(2, 1)
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neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
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@ -62,8 +56,11 @@ def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
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# find local maxima using our fliter shape
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local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
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background = (arr2D == 0)
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eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
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detected_peaks = local_max - eroded_background # this is a boolean mask of arr2D with True at peaks
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eroded_background = binary_erosion(background, structure=neighborhood,
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border_value=1)
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# Boolean mask of arr2D with True at peaks
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detected_peaks = local_max - eroded_background
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# extract peaks
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amps = arr2D[detected_peaks]
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@ -85,97 +82,37 @@ def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
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ax.scatter(time_idx, frequency_idx)
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ax.set_xlabel('Time')
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ax.set_ylabel('Frequency')
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ax.set_title("Spectrogram of \"Blurred Lines\" by Robin Thicke");
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ax.set_title("Spectrogram of \"Blurred Lines\" by Robin Thicke")
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plt.gca().invert_yaxis()
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plt.show()
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return zip(frequency_idx, time_idx)
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def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
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"""
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Hash list structure:
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sha1-hash[0:20] time_offset
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sha1_hash[0:20] time_offset
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[(e05b341a9b77a51fd26, 32), ... ]
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"""
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fingerprinted = set() # to avoid rehashing same pairs
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hashes = []
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for i in range(len(peaks)):
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for j in range(fan_value):
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if i+j < len(peaks) and not (i, i+j) in fingerprinted:
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if (i + j) < len(peaks) and not (i, i + j) in fingerprinted:
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freq1 = peaks[i][IDX_FREQ_I]
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freq2 = peaks[i + j][IDX_FREQ_I]
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t1 = peaks[i][IDX_TIME_J]
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t2 = peaks[i + j][IDX_TIME_J]
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t_delta = t2 - t1
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if t_delta >= MIN_HASH_TIME_DELTA:
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h = hashlib.sha1("%s|%s|%s" % (str(freq1), str(freq2), str(t_delta)))
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hashes.append((h.hexdigest()[0:20], t1))
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h = hashlib.sha1(
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"%s|%s|%s" % (str(freq1), str(freq2), str(t_delta))
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)
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yield (h.hexdigest()[0:20], t1)
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# ensure we don't repeat hashing
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fingerprinted.add((i, i + j))
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return hashes
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# TODO: move all of the below to a class with DB access
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class Fingerprinter():
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def __init__(self, config,
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Fs=DEFAULT_FS,
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wsize=DEFAULT_WINDOW_SIZE,
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wratio=DEFAULT_OVERLAP_RATIO,
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fan_value=DEFAULT_FAN_VALUE,
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amp_min=DEFAULT_AMP_MIN):
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self.config = config
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self.Fs = Fs
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self.dt = 1.0 / self.Fs
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self.window_size = wsize
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self.window_overlap_ratio = wratio
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self.fan_value = fan_value
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self.noverlap = int(self.window_size * self.window_overlap_ratio)
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self.amp_min = amp_min
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def fingerprint(self, samples, path, sid, cid):
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"""Used for learning known songs"""
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hashes = self.process_channel(samples, song_id=sid)
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print "Generated %d hashes" % len(hashes)
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self.db.insert_hashes(hashes)
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# TODO: put this in another module
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def match(self, samples):
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"""Used for matching unknown songs"""
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hashes = self.process_channel(samples)
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matches = self.db.return_matches(hashes)
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return matches
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# TODO: this function has nothing to do with fingerprinting. is it needed?
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def print_stats(self):
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iterable = self.db.get_iterable_kv_pairs()
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counter = {}
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for t in iterable:
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sid, toff = t
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if not sid in counter:
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counter[sid] = 1
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else:
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counter[sid] += 1
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for song_id, count in counter.iteritems():
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song_name = self.song_names[song_id]
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print "%s has %d spectrogram peaks" % (song_name, count)
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# this does... what? this seems to only be used for the above function
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def set_song_names(self, wpaths):
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self.song_names = wpaths
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# TODO: put this in another module
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@ -1,14 +1,8 @@
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from multiprocessing import Queue, Process
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from dejavu.database import SQLDatabase
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import dejavu.fingerprint as fingerprint
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from dejavu import Dejavu
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from scipy.io import wavfile
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import wave
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import dejavu.decoder as decoder
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import numpy as np
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import pyaudio
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import sys
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import time
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import array
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class BaseRecognizer(object):
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@ -26,25 +20,17 @@ class BaseRecognizer(object):
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def recognize(self):
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pass # base class does nothing
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class WaveFileRecognizer(BaseRecognizer):
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def __init__(self, dejavu, filename=None):
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super(WaveFileRecognizer, self).__init__(dejavu)
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self.filename = filename
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class FileRecognizer(BaseRecognizer):
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def __init__(self, dejavu):
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super(FileRecognizer, self).__init__(dejavu)
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def recognize_file(self, filename):
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Fs, frames = wavfile.read(filename)
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Fs, frames = decoder.read(filename)
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self.Fs = Fs
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wave_object = wave.open(filename)
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nchannels, sampwidth, framerate, num_frames, comptype, compname = wave_object.getparams()
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channels = []
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for channel in range(nchannels):
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channels.append(frames[:, channel])
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t = time.time()
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match = self._recognize(*channels)
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match = self._recognize(*frames)
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t = time.time() - t
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if match:
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@ -52,50 +38,53 @@ class WaveFileRecognizer(BaseRecognizer):
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return match
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def recognize(self):
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return self.recognize_file(self.filename)
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def recognize(self, filename):
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return self.recognize_file(filename)
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class MicrophoneRecognizer(BaseRecognizer):
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default_chunksize = 8192
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default_format = pyaudio.paInt16
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default_channels = 2
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default_samplerate = 44100
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CHUNK = 8192 # 44100 is a multiple of 1225
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FORMAT = pyaudio.paInt16
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CHANNELS = 2
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RATE = 44100
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def __init__(self, dejavu, seconds=None):
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def __init__(self, dejavu):
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super(MicrophoneRecognizer, self).__init__(dejavu)
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self.audio = pyaudio.PyAudio()
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self.stream = None
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self.data = []
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self.channels = CHANNELS
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self.chunk_size = CHUNK
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self.rate = RATE
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self.channels = self.default_channels
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self.chunksize = self.default_chunk
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self.samplerate = self.default_samplerate
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self.recorded = False
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def start_recording(self, channels=CHANNELS, rate=RATE, chunk=CHUNK):
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self.chunk_size = chunk
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def start_recording(self, channels=default_channels,
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samplerate=default_samplerate,
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chunksize=default_chunksize):
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self.chunksize = chunksize
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self.channels = channels
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self.recorded = False
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self.rate = rate
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self.samplerate = samplerate
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if self.stream:
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self.stream.stop_stream()
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self.stream.close()
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self.stream = self.audio.open(format=FORMAT,
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self.stream = self.audio.open(
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format=self.default_format,
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channels=channels,
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rate=rate,
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rate=samplerate,
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input=True,
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frames_per_buffer=chunk)
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frames_per_buffer=chunksize,
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)
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self.data = [[] for i in range(channels)]
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def process_recording(self):
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data = self.stream.read(self.chunk_size)
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data = self.stream.read(self.chunksize)
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nums = np.fromstring(data, np.int16)
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for c in range(self.channels):
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self.data[c].extend(nums[c::c+1])
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self.data[c].extend(nums[c::len(self.channels)])
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def stop_recording(self):
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self.stream.stop_stream()
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@ -111,13 +100,14 @@ class MicrophoneRecognizer(BaseRecognizer):
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def get_recorded_time(self):
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return len(self.data[0]) / self.rate
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|
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def recognize(self):
|
||||
def recognize(self, seconds=None):
|
||||
self.start_recording()
|
||||
for i in range(0, int(self.rate / self.chunk * self.seconds)):
|
||||
for i in range(0, int(self.samplerate / self.chunksize
|
||||
* seconds)):
|
||||
self.process_recording()
|
||||
self.stop_recording()
|
||||
return self.recognize_recording()
|
||||
|
||||
|
||||
class NoRecordingError(Exception):
|
||||
pass
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue