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check_onset_by_single.py
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check_onset_by_single.py
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import re
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
from create_base import *
from create_labels_files import *
from myDtw import *
import tensorflow as tf
import numpy as np
import pdb
import cv2
import os
import glob
import slim.nets.alexnet as alaxnet
import os
from create_tf_record import *
import tensorflow.contrib.slim as slim
def clear_dir(dis_dir):
shutil.rmtree(dis_dir)
os.mkdir(dis_dir)
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
def get_single_onsets(filename,curr_num):
y, sr = librosa.load(filename)
#librosa.display.waveplot(y, sr=sr)
#onset_frames = librosa.onset.onset_detect(y=y, sr=sr)
onset_frames,onsets_frames_strength = get_onsets_by_all(y,sr)
onset_times = librosa.frames_to_time(onset_frames, sr=sr)
#plt.vlines(onset_times, 0, y.max(), color='r', linestyle='--')
onset_samples = librosa.time_to_samples(onset_times)
#print(onset_samples)
#plt.subplot(len(onset_times),1,1)
#plt.show()
for i in range(0, len(onset_times)):
start = onset_samples[i] - sr/2
if start < 0:
start =0
end = onset_samples[i] + sr/2
#y2 = [x if i> start and i<end else 0 for i,x in enumerate(y)]
y2 = [x for i,x in enumerate(y) if i> start and i<end]
#y2 = [0.03 if i> start and i<end else 0.02 for i,x in enumerate(y)]
y2[int(len(y2) / 2)] = np.max(y) # 让图片展示归一化
t = librosa.samples_to_time([onset_samples[i]-start], sr=sr)
plt.vlines(t, -1*np.max(y), np.max(y), color='r', linestyle='--') # 标出节拍位置
y2 = np.array(y2)
#print("len(y2) is {}".format(len(y2)))
#print("(end - start)*sr is {}".format((end - start)*sr))
#plt.subplot(len(onset_times),1,i+1)
#y, sr = librosa.load(filename, offset=2.0, duration=3.0)
librosa.display.waveplot(y2, sr=sr)
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.savefig(savepath + str(curr_num) + '.png', bbox_inches='tight', pad_inches=0)
plt.clf()
curr_num += 1
#plt.show()
return onset_frames,onsets_frames_strength,curr_num
def predict(image_dir,onset_frames,onsets_frames_strength,models_path):
class_nums = 2
onsets = []
onsets_strength = {}
batch_size = 1 #
resize_height = 224 # 指定存储图片高度
resize_width = 224 # 指定存储图片宽度
depths = 3
data_format = [batch_size, resize_height, resize_width, depths]
[batch_size, resize_height, resize_width, depths] = data_format
#labels = np.loadtxt(labels_filename, str, delimiter='\t')
input_images = tf.placeholder(dtype=tf.float32, shape=[None, resize_height, resize_width, depths], name='input')
with slim.arg_scope(alaxnet.alexnet_v2_arg_scope()):
out, end_points = alaxnet.alexnet_v2(inputs=input_images, num_classes=class_nums, dropout_keep_prob=1.0, is_training=False)
# 将输出结果进行softmax分布,再求最大概率所属类别
score = tf.nn.softmax(out,name='pre')
class_id = tf.argmax(score, 1)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, models_path)
images_list=sorted(glob.glob(os.path.join(image_dir,'*.jpg')), key=os.path.getmtime)
#sorted(glob.glob('*.png'), key=os.path.getmtime)
score_total = 0
index = 0
for image_path in images_list:
im=read_image(image_path,resize_height,resize_width,normalization=True)
im=im[np.newaxis,:]
#pred = sess.run(f_cls, feed_dict={x:im, keep_prob:1.0})
pre_score,pre_label = sess.run([score,class_id], feed_dict={input_images:im})
max_score=pre_score[0,pre_label]
#print("{} is: pre labels:{},name:{} score: {}".format(image_path, pre_label, labels[pre_label], max_score))
print("{} is predicted as label::{} ".format(image_path,pre_label[0]))
if 1 == pre_label[0]:
#score_total += 1
onsets.append(onset_frames[index])
onsets_strength[onset_frames[index]] = onsets_frames_strength.get(onset_frames[index])
else:
pass
index +=1
print("valuation accuracy is {}".format(score_total/len(images_list)))
sess.close()
return onsets,onsets_strength
if __name__ == '__main__':
# savepath = 'e:/test_image/'
savepath = './single_onsets/data/test/'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏1.3(95).wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏1（二）(100).wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏1_40227（100）.wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏1林(70).wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏2_40314（100）.wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏2_40409（98）.wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏2林(25).wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏2语(85).wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏10_40411（85）.wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏10-04（80）.wav'
# filename = 'F:/项目/花城音乐项目/样式数据/2.27MP3/节奏/节奏九（2）（95）.wav'
labels_filename = './single_onsest/data/label.txt'
models_path = './single_onsets/models/alex/model.ckpt-10000'
# 清空文件夹
image_dir = './single_onsets/data/test'
if not os.path.exists(image_dir):
os.mkdir(image_dir)
# clear_dir(image_dir)
'''
一次性自动生成所有图片
'''
# 要切割的文件路径列表
dir_list = ['./mp3/2.18WAV/','./mp3/2.27WAV/']
# 每个文件夹下要处理的图片数量（超过最大会按最大值处理）
num = 200
# 文件名计数
curr_num = 1
for dir in dir_list:
file_list = os.listdir(dir)
if num > len(file_list):
num = len(file_list)
for i in range(0,num):
onset_frames, onsets_frames_strength,curr_num = get_single_onsets(dir+file_list[i],curr_num)
print("onset_frames,onsets_frames_strength is {},{}".format(onset_frames,onsets_frames_strength))
# if onset_frames:
# onsets, onsets_strength = predict(image_dir,onset_frames,onsets_frames_strength,models_path)
# print("onsets, onsets_strength is {},{}".format(onsets, onsets_strength))
# y, sr = librosa.load(filename)
# librosa.display.waveplot(y, sr=sr)
# onsets_time = librosa.frames_to_time(onsets, sr=sr)
# onset_frames_time = librosa.frames_to_time(onset_frames,sr = sr)
# plt.vlines(onsets_time, -1 * np.max(y), np.max(y), color='r', linestyle='solid')
# plt.vlines(onset_frames_time, -1 * np.max(y), np.max(y), color='b', linestyle='dashed')
# plt.show()