def process_folder(folder_path, quantization, temporal_granularity):
# Get instrus and prs from a folder name name
pr0, instru0, _, name0, pr1, instru1, _, name1 = build_data_aux.get_instru_and_pr_from_folder_path(folder_path, quantization)
# Temporal granularity
if temporal_granularity == 'event_level':
pr0 = warp_pr_aux(pr0, get_event_ind_dict(pr0))
pr1 = warp_pr_aux(pr1, get_event_ind_dict(pr1))
# Get trace from needleman_wunsch algorithm
# Traces are binary lists, 0 meaning a gap is inserted
trace_0, trace_1, this_sum_score, this_nbId, this_nbDiffs = needleman_chord_wrapper(sum_along_instru_dim(pr0), sum_along_instru_dim(pr1))
# Wrap dictionnaries according to the traces
assert(len(trace_0) == len(trace_1)), "size mismatch"
pr0_warp = warp_dictionnary_trace(pr0, trace_0)
pr1_warp = warp_dictionnary_trace(pr1, trace_1)
# Get pr warped and duration# In fact we just discard 0 in traces for both pr
trace_prod = [e1 * e2 for (e1,e2) in zip(trace_0, trace_1)]
duration = sum(trace_prod)
if duration == 0:
return [None]*7
pr0_aligned = remove_zero_in_trace(pr0_warp, trace_prod)
pr1_aligned = remove_zero_in_trace(pr1_warp, trace_prod)
return pr0_aligned, instru0, name0, pr1_aligned, instru1, name1, duration
def process_folder(folder_path, quantization, unit_type, temporal_granularity, gapopen=3, gapextend=1):
# Get instrus and prs from a folder name name
pr0, instru0, _, name0, pr1, instru1, _, name1 = get_instru_and_pr_from_folder_path(folder_path, quantization)
# Unit type
pr0 = Unit_type.from_rawpr_to_type(pr0, unit_type)
pr1 = Unit_type.from_rawpr_to_type(pr1, unit_type)
# Temporal granularity
if temporal_granularity == 'event_level':
event_0 = get_event_ind_dict(pr0)
event_1 = get_event_ind_dict(pr1)
pr0 = warp_pr_aux(pr0, event_0)
pr1 = warp_pr_aux(pr1, event_1)
else:
event_0 = None
event_1 = None
# Align tracks
pr0_aligned, trace_0, pr1_aligned, trace_1, trace_prod, duration = align_tracks(pr0, pr1, unit_type, gapopen, gapextend)
# Clean events
event0_aligned = clean_event(event_0, trace_0, trace_prod)
event1_aligned = clean_event(event_1, trace_1, trace_prod)
# Find which pr is orchestra, which one is piano
pr_piano, event_piano, instru_piano, name_piano,\
pr_orch, event_orch, instru_orch, name_orch,\
duration =\
discriminate_between_piano_and_orchestra(pr0_aligned, event0_aligned, instru0, name0,
pr1_aligned, event1_aligned, instru1, name1,
duration)
return pr_piano, event_piano, instru_piano, name_piano, pr_orch, event_orch, instru_orch, name_orch, duration
def get_dim_matrix(index_files_dict, meta_info_path='temp.p', quantization=12, temporal_granularity='frame_level', logging=None):
# Determine the temporal size of the matrices
# If the two files have different sizes, we use the shortest (to limit the use of memory,
# we better contract files instead of expanding them).
# Get instrument names
instrument_list_from_dico = build_dico().keys()
instru_mapping = {}
# instru_mapping = {'piano': {'pitch_min': 24, 'pitch_max':117, 'ind_min': 0, 'ind_max': 92},
# 'harp' ... }
T_dict = {} # indexed by set_identifier
for set_identifier, index_files in index_files_dict.iteritems():
logging.info("##########")
logging.info(set_identifier)
# Get the full size of the tracks and instrument present
T = 0
for index_file in index_files:
# Read the csv file indexing the database
with open(index_file, 'rb') as f:
for folder_path in f:
folder_path = folder_path.rstrip()
logging.info(folder_path)
if not os.path.isdir(folder_path):
continue
# Read pr
try:
pr0, instru0, T0, name0, pr1, instru1, T1, name1 = build_data_aux.get_instru_and_pr_from_folder_path(folder_path, quantization)
except:
with open('log', 'wb') as f:
f.write('Bad file' + folder_path + '\n')
continue
# Temporal granularity
if temporal_granularity == 'event_level':
new_event_0 = get_event_ind_dict(pr0)
pr0 = warp_pr_aux(pr0, new_event_0)
new_event_1 = get_event_ind_dict(pr1)
pr1 = warp_pr_aux(pr1, new_event_1)
# Get T
trace_0, trace_1, this_sum_score, this_nbId, this_nbDiffs = needleman_chord_wrapper(sum_along_instru_dim(pr0), sum_along_instru_dim(pr1))
trace_prod = [e1 * e2 for (e1,e2) in zip(trace_0, trace_1)]
T += sum(trace_prod)
# Modify the mapping from instrument to indices in pianorolls and pitch bounds
instru_mapping = build_data_aux.instru_pitch_range(instrumentation=instru0,
pr=pr0,
instru_mapping=instru_mapping,
instrument_list_from_dico=instrument_list_from_dico,
)
# remark : instru_mapping would be modified if it is only passed to the function,
# f(a) where a is modified inside the function
# but i prefer to make the reallocation explicit
# a = f(a) with f returning the modified value of a.
# Does it change anything for computation speed ? (Python pass by reference,
# but a slightly different version of it, not clear to me)
instru_mapping = build_data_aux.instru_pitch_range(instrumentation=instru1,
pr=pr1,
instru_mapping=instru_mapping,
instrument_list_from_dico=instrument_list_from_dico
)
# Delete prs
del pr0, pr1, instru0, instru1
T_dict[set_identifier] = T
# Build the index_min and index_max in the instru_mapping dictionary
counter = 0
for k, v in instru_mapping.iteritems():
if k == 'piano':
index_min = 0
index_max = v['pitch_max'] - v['pitch_min']
v['index_min'] = index_min
v['index_max'] = index_max
continue
index_min = counter
counter = counter + v['pitch_max'] - v['pitch_min']
index_max = counter
v['index_min'] = index_min
v['index_max'] = index_max
# Instanciate the matrices
########################################
########################################
########################################
temp = {}
temp['instru_mapping'] = instru_mapping
temp['quantization'] = quantization
temp['T'] = T_dict
temp['N_orchestra'] = counter
pickle.dump(temp, open(meta_info_path, 'wb'))
return
########################################################
# orch = sum_along_instru_dim(pr)[start_ind:end_ind]
# orch[np.nonzero(orch)] = 1
# orch[0, 30] = 1
# orch[0, 92] = 1
#######################################################
#######################################################
pianofile = Read_midi(piano_path, quantization)
pr_piano = pianofile.read_file()
#######################################################
#######################################################
# Event level representation
event_piano = get_event_ind_dict(pr_piano)
event_orch = get_event_ind_dict(pr_orch)
pr_piano = warp_pr_aux(pr_piano, event_piano)
pr_orch = warp_pr_aux(pr_orch, event_orch)
########################################################
def align_tracks(pr0, pr1, unit_type, gapopen, gapextend):
# Get trace from needleman_wunsch algorithm
# First extract binary representation, whatever unit_type is
pr0_binary = Unit_type.from_type_to_binary(pr0, unit_type)
pr1_binary = Unit_type.from_type_to_binary(pr1, unit_type)
pr0_trace = sum_along_instru_dim(pr0_binary)
pr1_trace = sum_along_instru_dim(pr1_binary)
# Traces are computed from binaries matrices
