Reads a binary .mood file to get RGB values and some basic stats about track and samples

PyMood.py

#!/usr/bin/env python3
import argparse
import statistics
global moodbarSamples
moodbarSamples = 1000 # This value is according to Moodbar definition (https://en.wikipedia.org/wiki/Moodbar)


##  --------------------  Script execution context  --------------------  ##


# Script main function
# Usage : `$ python ./PyMood.py -v /path/to/file.mood`
# Verbose argument is optional, file path isn't (obviously)
# Will build a MoodbarFile object (see class definition) from a .mood binary file
# The main purpose of this is to extract Bass, medium and high interresting values
# Futur features in ManaZeak project will analyse those data (see https://github.com/ManaZeak/ManaZeak)
# DM me (https://github.com/ArthurBeaulieu) if you have any questions about moodbar
def main():
    # Init argparse arguments
    ap = argparse.ArgumentParser()
    ap.add_argument('filePath', help='The .mood file path on system') # Mandatory .mood file path
    ap.add_argument('-v', '--verbose', help='Log detailled progress when running', action='store_true') # Optional verbose option
    args = vars(ap.parse_args()) # Parse sent arguments according to definition
    # Open .mood given as an argument file
    with open(args['filePath'], 'rb') as file:
        intArray = convertStreamToArray(file) # Bytes to int
        samples = convertArrayToSamples(intArray) # Int array to MoodbarSample array
        output = MoodbarFile(samples) # MoodbarSample array to MoodbarFile object
        if args['verbose'] == True: # Display MoodbarFile if verbose arg
            output.display()
        file.close() # Safely close .mood file


# Take a file an iterate through its bytes to converts byte value into int array
def convertStreamToArray(file):
    output = [] # Output int array
    byte = file.read(1) # Get first byte of given file
    while byte: # While bytes are found
        byte = file.read(1) # Get next byte
        output.append(int.from_bytes(byte, byteorder='big')) # Append its int value in output
    return output # Send back int array


# Convert 3k length int array into 1k length MoodbarSample array
def convertArrayToSamples(array):
    output = [] # Output MoodbarSample array
    for i in  range(0, moodbarSamples, 3): # Iterate 3 by 3 over int array (RGB values)
        # Append MoodbarSample with given R, G and B values
        output.append(MoodbarSample(array[i], array[i + 1], array[i + 2]))
    return output # Send back MoodbarSample array


##  --------------------  Util classes  --------------------  ##


# The script output object
class MoodbarFile:
    def __init__(self, samples):
        self.samples = samples # Store MoodbarSamples in MoodbarFile
        # Basic file stats
        self.mean = 0
        self.median = 0
        self.mode = 0
        # Bass frequencies stats
        self.rMean = 0
        self.rMedian = 0
        self.rMode = 0
        # Middle frequencies stats
        self.gMean = 0
        self.gMedian = 0
        self.gMode = 0
        # High frequencies stats
        self.bMean = 0
        self.bMedian = 0
        self.bMode = 0
        # Compute all class attributes
        self._computeInternals()

    # The internal method will fill all MoodbarFile internals according to given samples
    def _computeInternals(self):
        rs, gs, bs, means, medians, modes = [], [], [], [], [], [] # Init working arrays
        for sample in self.samples:
            rs.append(sample.r)
            gs.append(sample.g)
            bs.append(sample.b)
            means.append(sample.mean)
            medians.append(sample.median)
            modes.append(sample.mode)
        # Basic stats
        self.mean = statistics.mean(means)
        self.median = statistics.median(medians)
        self.mode = statistics.mode(modes)
        # Bass frequencies stats
        self.rMean = statistics.mean(rs)
        self.rMedian = statistics.median(rs)
        self.rMode = statistics.mode(rs)
        # Middle frequencies stats
        self.gMean = statistics.mean(gs)
        self.gMedian = statistics.median(gs)
        self.gMode = statistics.mode(gs)
        # High frequencies stats
        self.bMean = statistics.mean(bs)
        self.bMedian = statistics.median(bs)
        self.bMode = statistics.mode(bs)

    # Log MoodbarFile
    def display(self):
        print('  Track global colors')
        print('> Track mean color is rgb({}, {}, {})'.format(self.mean, self.mean, self.mean))
        print('> Track median color is rgb({}, {}, {})'.format(self.mean, self.mean, self.mean))
        print('> Track mode color is rgb({}, {}, {})\n'.format(self.mean, self.mean, self.mean))
        print('  Bass colors')
        print('> Bass mean color is rgb({}, {}, {})'.format(self.rMean, self.rMean, self.rMean))
        print('> Bass median color is rgb({}, {}, {})'.format(self.rMedian, self.rMedian, self.rMedian))
        print('> Bass mode color is rgb({}, {}, {})\n'.format(self.rMode, self.rMode, self.rMode))
        print('  Medium colors')
        print('> Medium mean color is rgb({}, {}, {})'.format(self.gMean, self.gMean, self.gMean))
        print('> Medium median color is rgb({}, {}, {})'.format(self.gMedian, self.gMedian, self.gMedian))
        print('> Medium mode color is rgb({}, {}, {})\n'.format(self.gMode, self.gMode, self.gMode))
        print('  High colors')
        print('> High mean color is rgb({}, {}, {})'.format(self.bMean, self.bMean, self.bMean))
        print('> High median color is rgb({}, {}, {})'.format(self.bMedian, self.bMedian, self.bMedian))
        print('> High mode color is rgb({}, {}, {})'.format(self.bMode, self.bMode, self.bMode))


# One single mood sample (track length by 1k section)
class MoodbarSample:
    def __init__(self, r, g, b):
        self.r = r # Sample bass value
        self.g = g # Sample medium value
        self.b = b # Sample high value
        # Basic sample stats
        self.mean = statistics.mean([r, g, b]) # Sample mean color
        self.median = statistics.median([r, g, b]) # Sample median color
        self.mode = statistics.mode([r, g, b]) # Sample mode color


# Script start point
if __name__ == '__main__':
    main()