File indexing completed on 2024-05-12 04:52:38

 #!/usr/bin/env python3
 
 # correlation.py
 import subprocess
 import numpy
 import sys
 
 # seconds to sample audio file for
 sample_time = 500  # number of points to scan cross correlation over
 span = 150  # step size (in points) of cross correlation
 step = 1  # minimum number of points that must overlap in cross correlation
 # exception is raised if this cannot be met
 min_overlap = 20  # report match when cross correlation has a peak exceeding threshold
 threshold = 0.8
 
 
 # calculate fingerprint
 def calculate_fingerprints(filename):
     # Convert to wav
     tmpFile = "/tmp/out1.wav"
     # subprocess.getoutput('ffmpeg -y -i %s -filter:a "atempo=0.5" -vn %s' % (filename, tmpFile))
     subprocess.getoutput("ffmpeg -y -i %s -vn %s" % (filename, tmpFile))
     fpcalc_out = subprocess.getoutput(
         "fpcalc -raw -length %i %s" % (sample_time, tmpFile)
     )
     fingerprint_index = fpcalc_out.find("FINGERPRINT=") + 12
     # convert fingerprint to list of integers
     intlist = fpcalc_out[fingerprint_index:]
     fingerprints = list(map(int, intlist.split(",")))
     return fingerprints
     # returns correlation between lists
 
 
 def correlation(listx, listy):
     if len(listx) == 0 or len(listy) == 0:
         # Error checking in main program should prevent us from ever being
         # able to get here.
         raise Exception("Empty lists cannot be correlated.")
     if len(listx) > len(listy):
         listx = listx[: len(listy)]
     elif len(listx) < len(listy):
         listy = listy[: len(listx)]
 
     covariance = 0
     for i in range(len(listx)):
         covariance += 32 - bin(listx[i] ^ listy[i]).count("1")
     covariance = covariance / float(len(listx))
     return covariance / 32
     # return cross correlation, with listy offset from listx
 
 
 def cross_correlation(listx, listy, offset):
     if offset > 0:
         listx = listx[offset:]
         listy = listy[: len(listx)]
     elif offset < 0:
         offset = -offset
         listy = listy[offset:]
         listx = listx[: len(listy)]
     if min(len(listx), len(listy)) < min_overlap:
         # Error checking in main program should prevent us from ever being
         # able to get here.
         return
     # raise Exception('Overlap too small: %i' % min(len(listx), len(listy)))
     return correlation(listx, listy)
     # cross correlate listx and listy with offsets from -span to span
 
 
 def compare(listx, listy, span, step):
     if span > min(len(listx), len(listy)):
         # Error checking in main program should prevent us from ever being
         # able to get here.
         raise Exception(
             "span >= sample size: %i >= %i\n" % (span, min(len(listx), len(listy)))
             + "Reduce span, reduce crop or increase sample_time."
         )
 
     corr_xy = []
     for offset in numpy.arange(-span, span + 1, step):
         corr_xy.append(cross_correlation(listx, listy, offset))
     return corr_xy
     # return index of maximum value in list
 
 
 def max_index(listx):
     max_index = 0
     max_value = listx[0]
     for i, value in enumerate(listx):
         if value > max_value:
             max_value = value
             max_index = i
     return max_index
 
 
 def get_max_corr(corr, source, target):
     max_corr_index = max_index(corr)
     max_corr_offset = -span + max_corr_index * step
     # print("max_corr_index = ", max_corr_index, "max_corr_offset = ", max_corr_offset)
     # report matches
     if corr[max_corr_index] > threshold:
         print(
             (
                 "MATCH with correlation of %.4f at offset %i"
                 % (corr[max_corr_index], max_corr_offset)
             )
         )
     else:
         print(
             (
                 "NO MATCH (Correlation: %.4f at offset %i)"
                 % (corr[max_corr_index], max_corr_offset)
             )
         )
 
 
 def correlate(source, target):
     fingerprint_source = calculate_fingerprints(source)
     fingerprint_target = calculate_fingerprints(target)
     corr = compare(fingerprint_source, fingerprint_target, span, step)
     max_corr_offset = get_max_corr(corr, source, target)
 
 
 if __name__ == "__main__":
     correlate(sys.argv[1], sys.argv[2])