def initialiseWAV(my_json): # -> 1716 secs, no probs
print("Creating the WAV files.")
navigate.set_path_to_dataset_MP3_sorted(my_json)
all_datasets = utils.get_list_subs(
) # List all the sub folders of the dataset folder.
threads = []
for index_d, dataset in enumerate(all_datasets):
if (len(os.listdir(my_json['dataset_mp3_sorted_path'] + '\\' +
dataset)) >= 30):
navigate.set_path(
my_json["dataset_mp3_sorted_path"] + "\\" + dataset
) # Set the path to the first sub dataset directory, which contains tracks.
actual_dataset_path = navigate.get_actual_path()
items_dataset = navigate.get_list_subs(
) # Return all the track of the actual folder.
my_thread = WavThread("Wav Thread " + str(index_d), my_json,
actual_dataset_path, dataset, items_dataset)
threads.append(my_thread)
navigate.set_path_to_dataset_MP3_sorted(my_json)
else:
print("There is less than 30 songs in the ", dataset,
" dataset. I will not export it.")
navigate.set_path_to_project_path(my_json)
for thread in threads:
thread.start()
for thread in threads:
thread.join()
navigate.set_path_to_project_path(my_json)
def shapeDataConv2DGTZAN(my_json, percent_train):
"""
overview : Shape the datas to return the train and test sets.
input :
- my_json : .json file with all the datas we need.
- percent_train : float -> [0.0 : 1.0] which represent the percentage
of images which will be include in the train set.
"""
print("Shaping the data to get the Train and Test sets.")
navigate.set_path_to_dataset_image_GTZAN(my_json)
X, Y = [], []
path = navigate.get_actual_path()
maxi = len(utils.get_list_subs())
convert_str = "L" # L=B&W, RGB
for index_dir, dir_x in enumerate(utils.get_list_subs()):
navigate.set_path(dir_x)
for index_fig, fig in enumerate(utils.get_list_subs()):
im = Image.open(navigate.get_actual_path() + "\\" + fig).convert(
convert_str) # 640 * 480 pixels
width, height = im.size
pix = np.array(im.getdata())
im.close()
X.append(pix)
Y.append(dir_x)
navigate.set_path_to_dataset_image_GTZAN(my_json)
if (convert_str == "L"):
X = reshapeEntriesConv2D(X, width, height, 1)
if (convert_str == "RGB"):
X = reshapeEntriesConv2D(X, width, height, 3)
Y = reshapeOutputsDense(Y)
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=(1 - percent_train))
return X_train, Y_train, X_test, Y_test, width, height
def set_security_autorizations_images(my_json):
print("Setting the autorization of the image folders.")
navigate.set_path_to_dataset_image(my_json)
for dir_x in get_list_subs():
navigate.set_path(dir_x)
# print("Path : ",navigate.get_actual_path(),".\n")
os.chmod(navigate.get_actual_path(), 0o777)
navigate.set_path_to_dataset_image(my_json)
def initialiseFigs(my_json, dpi, height, width, legend,
colormesh): # 1444 secs
# We can't use threat, because matplotlib isn't thread protected.
print("Creating the Images.")
navigate.set_path_to_dataset_WAV(my_json)
all_datasets = utils.get_list_subs(
) # List all the sub folders of the dataset folder.
navigate.set_path_to_dataset_image(my_json)
for dataset in all_datasets:
if not (os.path.exists(dataset)):
os.makedirs(dataset)
navigate.set_path_to_dataset_WAV(my_json)
processes = []
outputs_shaped = model.shapeOutputs(my_json)
right_array = 0
flag = False
for index_d, dataset in enumerate(all_datasets):
for item in outputs_shaped:
if (item['category_name'] == dataset):
right_array = item["category_array"]
flag = True
navigate.set_path(
dataset
) # Set the path to the first sub dataset directory, which contains tracks.
actual_dataset_path = navigate.get_actual_path()
items_dataset = utils.get_list_subs(
) # Return all the track of the actual folder.
if (len(items_dataset) >= 50 and flag == True): #
my_process = ImageProcess("Image process " + str(index_d),
my_json,
actual_dataset_path,
dataset,
items_dataset,
right_array,
my_json["dataset_image_path"] + "\\" +
dataset,
dpi,
height,
width,
legend=legend,
colormesh=colormesh)
processes.append(my_process)
navigate.set_path_to_dataset_WAV(my_json)
processes = [mp.Process(target=p.run, args=()) for p in processes]
for p in processes:
p.start()
for p in processes:
p.join()
navigate.set_path_to_project_path(my_json)
def shapeDataDense(my_json, percent_train, encoder=False):
"""
overview : Shape the datas to return the train and test sets.
input :
- my_json : .json file with all the datas we need.
- percent_train : float -> [0.0 : 1.0] which represent the percentage
of images which will be include in the train set.
"""
print("Shaping the data to get the Train and Test sets.")
navigate.set_path_to_dataset_WAV_GTZAN(my_json)
X, Y = [], []
path = navigate.get_actual_path()
maxi = len(utils.get_list_subs())
encoder = LabelEncoder()
for index_dir, dir_x in enumerate(utils.get_list_subs()):
navigate.set_path(dir_x)
for index_fig, audio_path in enumerate(utils.get_list_subs()):
print("Index folder : ", index_dir + 1, "/10 -- File ",
index_fig + 1, "/100.")
audio, freq = lr.load(audio_path, mono=True)
chroma_stft = lr.feature.chroma_stft(y=audio, sr=freq)
rmse = lr.feature.rmse(y=audio)
spec_cent = lr.feature.spectral_centroid(y=audio, sr=freq)
spec_bw = lr.feature.spectral_bandwidth(y=audio, sr=freq)
rolloff = lr.feature.spectral_rolloff(y=audio, sr=freq)
zcr = lr.feature.zero_crossing_rate(audio)
mfcc = lr.feature.mfcc(y=audio, sr=freq)
data = [
np.mean(chroma_stft),
np.mean(rmse),
np.mean(spec_cent),
np.mean(spec_bw),
np.mean(rolloff),
np.mean(zcr)
]
for mfcc_x in mfcc:
data.append(np.mean(mfcc_x))
X.append(data)
Y.append(dir_x)
break
navigate.set_path_to_dataset_WAV_GTZAN(my_json)
X = reshapeEntriesDense(X)
Y = reshapeOutputsDense(Y)
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=(1 - percent_train))
return X_train, Y_train, X_test, Y_test
def initialiseMP3Sorted(my_json): # -> 1748 secs, no probs
print("Sorting the MP3 files.")
datas = pd.read_csv(tracks,
encoding="utf-8",
usecols=["album", 'track.19', "track.7", "track.16"],
dtype={
"album": object,
'track.19': object,
"track.7": object,
"track.16": object
})
datas = datas.rename(
columns={
"album": "track_id",
'track.19': "title",
"track.7": "genre_top",
"track.16": "number"
})
threads = []
navigate.set_path_to_datasets(my_json)
if not (os.path.exists("Mp3Sorted")):
os.makedirs("Mp3Sorted")
navigate.set_path_to_dataset_MP3(my_json)
for index_dir, x_dir in enumerate(utils.get_list_subs()):
if (x_dir != 'checksums' and x_dir != "fma_metadata"
and x_dir != "README.txt"):
navigate.set_path(x_dir)
actual_dataset_path = navigate.get_actual_path()
items_dataset = navigate.get_list_subs(
) # Return all the track of the actual folder.
my_thread = SortMP3Thread("MP3 Sorted Thread " + str(index_dir),
my_json, actual_dataset_path, x_dir,
items_dataset, datas)
threads.append(my_thread)
else:
print("This is not a directory of mp3 tracks.")
navigate.set_path_to_dataset_MP3(my_json)
navigate.set_path_to_project_path(my_json)
for thread in threads:
thread.start()
for thread in threads:
thread.join()
navigate.set_path_to_project_path(my_json)
def initialiseFigsGTZAN(my_json, dpi, height, width, legend): # 1444 secs
# We can't use threat, because matplotlib isn't thread protected.
print("Creating the Images.")
navigate.set_path_to_dataset_WAV_GTZAN(my_json)
all_datasets = utils.get_list_subs(
) # List all the sub folders of the dataset folder.
navigate.set_path_to_dataset_image_GTZAN(my_json)
for dataset in all_datasets:
if not (os.path.exists(dataset)):
os.makedirs(dataset)
navigate.set_path_to_dataset_WAV_GTZAN(my_json)
processes = []
for index_d, dataset in enumerate(all_datasets):
navigate.set_path(
dataset
) # Set the path to the first sub dataset directory, which contains tracks.
actual_dataset_path = navigate.get_actual_path()
items_dataset = utils.get_list_subs(
) # Return all the track of the actual folder.
if (len(items_dataset) >= 50): #
my_process = ImageProcessGTZAN(
"Image GTZAN process " + str(index_d),
my_json,
actual_dataset_path,
dataset,
items_dataset,
my_json["dataset_images_gtzan_path"] + "\\" + dataset,
dpi,
height,
width,
legend=legend)
processes.append(my_process)
navigate.set_path_to_dataset_WAV_GTZAN(my_json)
processes = [mp.Process(target=p.run, args=()) for p in processes]
for p in processes:
p.start()
for p in processes:
p.join()
navigate.set_path_to_project_path(my_json)
def shapeDataConv2D(my_json, percent_train, encoder=False):
"""
overview : Shape the datas to return the train and test sets.
input :
- my_json : .json file with all the datas we need.
- percent_train : float -> [0.0 : 1.0] which represent the percentage
of images which will be include in the train set.
"""
print("Shaping the data to get the Train and Test sets.")
navigate.set_path_to_dataset_image(my_json)
X, Y = [], []
path = navigate.get_actual_path()
maxi = len(utils.get_list_subs())
for index_dir, dir_x in enumerate(utils.get_list_subs()):
navigate.set_path(dir_x)
for index_fig, fig in enumerate(utils.get_list_subs()):
im = Image.open(navigate.get_actual_path() + "\\" + fig).convert(
'L') # 640 * 480 pixels
width, height = im.size
pix = np.array(im.getdata()).reshape(width, height)
if (encoder):
category = str(im.info["category"])
else:
category = ast.literal_eval(im.info["category_shaped"])
im.close()
X.append(pix)
Y.append(category)
navigate.set_path_to_dataset_image(my_json)
if (encoder):
Y = encoder.fit_transform(Y)
n_classes = encoder.classes_.size
Y = Y.reshape(len(Y), 1)
else:
Y = reshapeOutputs(Y)
X = reshapeEntriesConv2D(X, width, height)
X_train, X_test, Y_train, Y_test = train_test_split(
X, Y, test_size=(1 - percent_train))
return X_train, Y_train, X_test, Y_test, width, height
