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makeRhythmsBatch.py
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makeRhythmsBatch.py
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import numpy, wave,matplotlib
import matplotlib.pyplot as plt
import numpy as np
import os
import librosa
import librosa.display
from PIL import Image
import re
import shutil
#COOKED_DIR = 'F:\项目\花城音乐项目\样式数据\音乐样本2019-01-29\节奏二\\'
#plotpath = 'F:\\specgram\\'
#savepath = 'F:\\mfcc_pic\\2\\'
score = 0
def load_and_trim(path):
audio, sr = librosa.load(path)
energy = librosa.feature.rmse(audio)
frames = np.nonzero(energy >= np.max(energy) / 5)
indices = librosa.core.frames_to_samples(frames)[1]
audio = audio[indices[0]:indices[-1]] if indices.size else audio[0:0]
return audio, sr
#path_index = np.array(['1.31MP3','2.2MP3','2019-01-29'])
#save_path = 'F:/项目/花城音乐项目/参考代码/tensorflow_models_nets-master/rhythm/train/'
path_index = np.array(['1.31MP3','2.2MP3','2.18MP3','2019-01-29'])
save_path = 'F:/项目/花城音乐项目/参考代码/tensorflow_models_nets-master/rhythm/train/'
tmp = ['A','B','C','D','E']
dis_dir = 'F:/项目/花城音乐项目/参考代码/tensorflow_models_nets-master/rhythm/train'
scr_dir = 'F:/项目/花城音乐项目/参考代码/tensorflow_models_nets-master/rhythm/val'
def clear_dir(dis_dir,scr_dir):
for i in tmp:
d_dir = dis_dir + '/' + i
shutil.rmtree(d_dir)
os.mkdir(d_dir)
s_dir = scr_dir + '/' + i
shutil.rmtree(s_dir)
os.mkdir(s_dir)
#清空文件夹
clear_dir(save_path,save_path)
for i in range(1,5):
COOKED_DIR = 'F:/项目/花城音乐项目/样式数据/ALL/旋律/'+ path_index[i-1] + '/'
#savepath = 'F:\\mfcc_pic\\'+ str(i) +'\\'
for root, dirs, files in os.walk(COOKED_DIR):
print("Root = ", root, "dirs = ", dirs, "files = ", files)
index = 0
for filename in files:
print(filename)
if filename.find('wav') <= 0:
continue
else:
index = index + 1
path_one = COOKED_DIR + filename
y, sr = load_and_trim(path_one)
CQT = librosa.amplitude_to_db(librosa.cqt(y, sr=16000), ref=np.max)
librosa.display.specshow(CQT)
#plt.ylabel('Frequency')
#plt.xlabel('Time(s)')
#plt.show()
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(4, 4)
if "." in filename:
Filename = filename.split(".")[0]
plt.axis('off')
plt.axes().get_xaxis().set_visible(False)
plt.axes().get_yaxis().set_visible(False)
#plt.rcParams['savefig.dpi'] = 300 # 图片像素
#plt.figure(figsize=(10, 10))
#plt.rcParams['figure.dpi'] = 300 # 分辨率
if filename.find('标准') > 0:
saveFileName = '100-A'
savepath = 'F:/项目/花城音乐项目/参考代码/tensorflow_models_nets-master/rhythm/train/A/'
# elif filename.find('分') > 0:
# score = filename.split("分")[0].split(".")[1] # 提取分值
# elif filename.find('(') > 0:
# score = filename.split("(")[2].split(")")[0] # 提取分值
else:
#score = filename.split("（")[2].split("）")[0] # 提取分值
score = re.sub("\D", "", filename) # 筛选数字
if str(score).find("100") > 0:
score = 100
else:
score = int(score) % 100
if int(score) >=90:
grade = 'A'
savepath = save_path + 'A/'
elif int(score) >= 75:
grade = 'B'
savepath = save_path + 'B/'
elif int(score) >=60:
grade = 'C'
savepath = save_path + 'C/'
elif int(score) >=1:
grade = 'D'
savepath = save_path + 'D/'
else:
grade = 'E'
savepath = save_path + 'E/'
saveFileName = str(score) + '-' + grade
file_sum = os.listdir(savepath)
#saveFileName = str(len(file_sum)+1) + '-' + filename.split(".wav")[0] + '-' + saveFileName
saveFileName = str(len(file_sum) + 1) + '-' + saveFileName
plt.savefig(savepath + saveFileName + '.jpg', bbox_inches='tight', pad_inches=0)
plt.clf()
saveFileName = ''
'''
infile = plotpath + Filename + '.jpg'
outfile = savepath + saveFileName + '.jpg'
im = Image.open(infile).convert("L")
(x, y) = im.size # read image size
x_s = 224 # define standard width
y_s = 224 # calc height based on standard width
out = im.resize((x_s, y_s), Image.ANTIALIAS) # resize image with high-quality
out.save(outfile)
print
'original size: ', x, y
print
'adjust size: ', x_s, y_s
'''