Update dejavu/fingerprint.py

dejavu/fingerprint.py

@@ -1,167 +1,167 @@
-from __future__ import absolute_import
-from __future__ import unicode_literals
-import os
-import numpy as np
-import matplotlib.mlab as mlab
-import matplotlib.pyplot as plt
-from scipy.ndimage.filters import maximum_filter
-from scipy.ndimage.morphology import (
-    generate_binary_structure, iterate_structure, binary_erosion
-)
-import hashlib
-from operator import itemgetter
-from six.moves import range
-from six.moves import zip
-
-IDX_FREQ_I = 0
-IDX_TIME_J = 1
-
-######################################################################
-# Sampling rate, related to the Nyquist conditions, which affects
-# the range frequencies we can detect.
-DEFAULT_FS = os.getenv('DEFAULT_FS', 44100)
-
-######################################################################
-# Size of the FFT window, affects frequency granularity
-DEFAULT_WINDOW_SIZE = os.getenv('DEFAULT_WINDOW_SIZE', 4096)
-
-######################################################################
-# Ratio by which each sequential window overlaps the last and the
-# next window. Higher overlap will allow a higher granularity of offset
-# matching, but potentially more fingerprints.
-DEFAULT_OVERLAP_RATIO = os.getenv('DEFAULT_OVERLAP_RATIO', 0.5)
-
-######################################################################
-# Degree to which a fingerprint can be paired with its neighbors --
-# higher will cause more fingerprints, but potentially better accuracy.
-DEFAULT_FAN_VALUE = os.getenv('DEFAULT_FAN_VALUE', 15)
-
-######################################################################
-# Minimum amplitude in spectrogram in order to be considered a peak.
-# This can be raised to reduce number of fingerprints, but can negatively
-# affect accuracy.
-DEFAULT_AMP_MIN = os.getenv('DEFAULT_AMP_MIN', 10)
-
-######################################################################
-# Number of cells around an amplitude peak in the spectrogram in order
-# for Dejavu to consider it a spectral peak. Higher values mean less
-# fingerprints and faster matching, but can potentially affect accuracy.
-PEAK_NEIGHBORHOOD_SIZE = os.getenv('PEAK_NEIGHBORHOOD_SIZE', 20)
-
-######################################################################
-# Thresholds on how close or far fingerprints can be in time in order
-# to be paired as a fingerprint. If your max is too low, higher values of
-# DEFAULT_FAN_VALUE may not perform as expected.
-MIN_HASH_TIME_DELTA = os.getenv('MIN_HASH_TIME_DELTA', 0)
-MAX_HASH_TIME_DELTA = os.getenv('MAX_HASH_TIME_DELTA', 200)
-
-######################################################################
-# If True, will sort peaks temporally for fingerprinting;
-# not sorting will cut down number of fingerprints, but potentially
-# affect performance.
-PEAK_SORT = True
-
-######################################################################
-# Number of bits to throw away from the front of the SHA1 hash in the
-# fingerprint calculation. The more you throw away, the less storage, but
-# potentially higher collisions and misclassifications when identifying songs.
-FINGERPRINT_REDUCTION = os.getenv('FINGERPRINT_REDUCTION', 20)
-
-
-def fingerprint(
-    channel_samples,
-    Fs=DEFAULT_FS,
-    wsize=DEFAULT_WINDOW_SIZE,
-    wratio=DEFAULT_OVERLAP_RATIO,
-    fan_value=DEFAULT_FAN_VALUE,
-    amp_min=DEFAULT_AMP_MIN
-):
-    """
-    FFT the channel, log transform output, find local maxima, then return
-    locally sensitive hashes.
-    """
-    # FFT the signal and extract frequency components
-    arr2D = mlab.specgram(
-        channel_samples,
-        NFFT=wsize,
-        Fs=Fs,
-        window=mlab.window_hanning,
-        noverlap=int(wsize * wratio)
-    )[0]
-
-    # apply log transform since specgram() returns linear array
-    arr2D = 10 * np.log10(arr2D)
-    arr2D[arr2D == -np.inf] = 0  # replace infs with zeros
-
-    # find local maxima
-    local_maxima = get_2D_peaks(arr2D, plot=False, amp_min=amp_min)
-
-    # return hashes
-    return generate_hashes(local_maxima, fan_value=fan_value)
-
-
-def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
-    # http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.morphology.iterate_structure.html#scipy.ndimage.morphology.iterate_structure
-    struct = generate_binary_structure(2, 1)
-    neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
-
-    # find local maxima using our fliter shape
-    local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
-    background = (arr2D == 0)
-    eroded_background = binary_erosion(
-        background, structure=neighborhood, border_value=1
-    )
-
-    # Boolean mask of arr2D with True at peaks
-    detected_peaks = local_max ^ eroded_background
-
-    # extract peaks
-    amps = arr2D[detected_peaks]
-    j, i = np.where(detected_peaks)
-
-    # filter peaks
-    amps = amps.flatten()
-    peaks = list(zip(i, j, amps))
-    peaks_filtered = [x for x in peaks if x[2] > amp_min]  # freq, time, amp
-
-    # get indices for frequency and time
-    frequency_idx = [x[1] for x in peaks_filtered]
-    time_idx = [x[0] for x in peaks_filtered]
-
-    if plot:
-        # scatter of the peaks
-        fig, ax = plt.subplots()
-        ax.imshow(arr2D)
-        ax.scatter(time_idx, frequency_idx)
-        ax.set_xlabel('Time')
-        ax.set_ylabel('Frequency')
-        ax.set_title("Spectrogram")
-        plt.gca().invert_yaxis()
-        plt.show()
-
-    return list(zip(frequency_idx, time_idx))
-
-
-def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
-    """
-    Hash list structure:
-       sha1_hash[0:20]    time_offset
-    [(e05b341a9b77a51fd26, 32), ... ]
-    """
-    if PEAK_SORT:
-        peaks.sort(key=itemgetter(1))
-
-    for i in range(len(peaks)):
-        for j in range(1, fan_value):
-            if (i + j) < len(peaks):
-
-                freq1 = peaks[i][IDX_FREQ_I]
-                freq2 = peaks[i + j][IDX_FREQ_I]
-                t1 = peaks[i][IDX_TIME_J]
-                t2 = peaks[i + j][IDX_TIME_J]
-                t_delta = t2 - t1
-
-                if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
-                    key = u"{}|{}|{}".format(freq1, freq2, t_delta)
-                    h = hashlib.sha1(key.encode('utf-8'))
-                    yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)
+from __future__ import absolute_import
+from __future__ import unicode_literals
+import os
+import numpy as np
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+from scipy.ndimage.filters import maximum_filter
+from scipy.ndimage.morphology import (
+    generate_binary_structure, iterate_structure, binary_erosion
+)
+import hashlib
+from operator import itemgetter
+from six.moves import range
+from six.moves import zip
+
+IDX_FREQ_I = 0
+IDX_TIME_J = 1
+
+######################################################################
+# Sampling rate, related to the Nyquist conditions, which affects
+# the range frequencies we can detect.
+DEFAULT_FS = os.getenv('DEFAULT_FS', 44100)
+
+######################################################################
+# Size of the FFT window, affects frequency granularity
+DEFAULT_WINDOW_SIZE = os.getenv('DEFAULT_WINDOW_SIZE', 4096)
+
+######################################################################
+# Ratio by which each sequential window overlaps the last and the
+# next window. Higher overlap will allow a higher granularity of offset
+# matching, but potentially more fingerprints.
+DEFAULT_OVERLAP_RATIO = os.getenv('DEFAULT_OVERLAP_RATIO', 0.5)
+
+######################################################################
+# Degree to which a fingerprint can be paired with its neighbors --
+# higher will cause more fingerprints, but potentially better accuracy.
+DEFAULT_FAN_VALUE = os.getenv('DEFAULT_FAN_VALUE', 20)  # Increased from 15 for better accuracy
+
+######################################################################
+# Minimum amplitude in spectrogram in order to be considered a peak.
+# This can be raised to reduce number of fingerprints, but can negatively
+# affect accuracy.
+DEFAULT_AMP_MIN = os.getenv('DEFAULT_AMP_MIN', 8)  # Lowered from 10 to detect more peaks
+
+######################################################################
+# Number of cells around an amplitude peak in the spectrogram in order
+# for Dejavu to consider it a spectral peak. Higher values mean less
+# fingerprints and faster matching, but can potentially affect accuracy.
+PEAK_NEIGHBORHOOD_SIZE = os.getenv('PEAK_NEIGHBORHOOD_SIZE', 15)  # Reduced from 20 for more peaks
+
+######################################################################
+# Thresholds on how close or far fingerprints can be in time in order
+# to be paired as a fingerprint. If your max is too low, higher values of
+# DEFAULT_FAN_VALUE may not perform as expected.
+MIN_HASH_TIME_DELTA = os.getenv('MIN_HASH_TIME_DELTA', 0)
+MAX_HASH_TIME_DELTA = os.getenv('MAX_HASH_TIME_DELTA', 200)
+
+######################################################################
+# If True, will sort peaks temporally for fingerprinting;
+# not sorting will cut down number of fingerprints, but potentially
+# affect performance.
+PEAK_SORT = True
+
+######################################################################
+# Number of bits to throw away from the front of the SHA1 hash in the
+# fingerprint calculation. The more you throw away, the less storage, but
+# potentially higher collisions and misclassifications when identifying songs.
+FINGERPRINT_REDUCTION = os.getenv('FINGERPRINT_REDUCTION', 20)
+
+
+def fingerprint(
+    channel_samples,
+    Fs=DEFAULT_FS,
+    wsize=DEFAULT_WINDOW_SIZE,
+    wratio=DEFAULT_OVERLAP_RATIO,
+    fan_value=DEFAULT_FAN_VALUE,
+    amp_min=DEFAULT_AMP_MIN
+):
+    """
+    FFT the channel, log transform output, find local maxima, then return
+    locally sensitive hashes.
+    """
+    # FFT the signal and extract frequency components
+    arr2D = mlab.specgram(
+        channel_samples,
+        NFFT=wsize,
+        Fs=Fs,
+        window=mlab.window_hanning,
+        noverlap=int(wsize * wratio)
+    )[0]
+
+    # apply log transform since specgram() returns linear array
+    arr2D = 10 * np.log10(arr2D)
+    arr2D[arr2D == -np.inf] = 0  # replace infs with zeros
+
+    # find local maxima
+    local_maxima = get_2D_peaks(arr2D, plot=False, amp_min=amp_min)
+
+    # return hashes
+    return generate_hashes(local_maxima, fan_value=fan_value)
+
+
+def get_2D_peaks(arr2D, plot=False, amp_min=DEFAULT_AMP_MIN):
+    # http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.morphology.iterate_structure.html#scipy.ndimage.morphology.iterate_structure
+    struct = generate_binary_structure(2, 1)
+    neighborhood = iterate_structure(struct, PEAK_NEIGHBORHOOD_SIZE)
+
+    # find local maxima using our fliter shape
+    local_max = maximum_filter(arr2D, footprint=neighborhood) == arr2D
+    background = (arr2D == 0)
+    eroded_background = binary_erosion(
+        background, structure=neighborhood, border_value=1
+    )
+
+    # Boolean mask of arr2D with True at peaks
+    detected_peaks = local_max ^ eroded_background
+
+    # extract peaks
+    amps = arr2D[detected_peaks]
+    j, i = np.where(detected_peaks)
+
+    # filter peaks
+    amps = amps.flatten()
+    peaks = list(zip(i, j, amps))
+    peaks_filtered = [x for x in peaks if x[2] > amp_min]  # freq, time, amp
+
+    # get indices for frequency and time
+    frequency_idx = [x[1] for x in peaks_filtered]
+    time_idx = [x[0] for x in peaks_filtered]
+
+    if plot:
+        # scatter of the peaks
+        fig, ax = plt.subplots()
+        ax.imshow(arr2D)
+        ax.scatter(time_idx, frequency_idx)
+        ax.set_xlabel('Time')
+        ax.set_ylabel('Frequency')
+        ax.set_title("Spectrogram")
+        plt.gca().invert_yaxis()
+        plt.show()
+
+    return list(zip(frequency_idx, time_idx))
+
+
+def generate_hashes(peaks, fan_value=DEFAULT_FAN_VALUE):
+    """
+    Hash list structure:
+       sha1_hash[0:20]    time_offset
+    [(e05b341a9b77a51fd26, 32), ... ]
+    """
+    if PEAK_SORT:
+        peaks.sort(key=itemgetter(1))
+
+    for i in range(len(peaks)):
+        for j in range(1, fan_value):
+            if (i + j) < len(peaks):
+
+                freq1 = peaks[i][IDX_FREQ_I]
+                freq2 = peaks[i + j][IDX_FREQ_I]
+                t1 = peaks[i][IDX_TIME_J]
+                t2 = peaks[i + j][IDX_TIME_J]
+                t_delta = t2 - t1
+
+                if t_delta >= MIN_HASH_TIME_DELTA and t_delta <= MAX_HASH_TIME_DELTA:
+                    key = u"{}|{}|{}".format(freq1, freq2, t_delta)
+                    h = hashlib.sha1(key.encode('utf-8'))
+                    yield (h.hexdigest()[0:FINGERPRINT_REDUCTION], t1)