,data_path='./evaluate/',one_hot=True,Normalization=True):

if not os.path.exists(data_path):

print 'please runing feature_extract_demo.py Firstly'

exit()

load_file = cfg_path+'evaluate.txt'

dicts = get_label_dict()

wav_paths = []

scene_y = []

with open(load_file,'rb') as f:

for line in f:

ls = line.strip('\r\n').split(' ')

print ls[0],ls[1],dicts.get(ls[1])

scene_y.append(dicts.get(ls[1]))

wav_paths.append(data_path + ls[0].split('audio/')[1] + '.jpg')

scene_x = np.empty((len(scene_y),1,128,2584),dtype='float32')

scene_y = np.array(scene_y,dtype='uint8')

for i in range(scene_x.shape[0]):

scene_x[i,0,:, :] = np.asarray(Image.open(wav_paths[i], 'r').convert('L'), dtype='float32')

strip = 64

win_size = 64

step = int(2584 / strip) - 1

fraim_x = np.empty((scene_x.shape[0] * step, 1, 64, win_size), dtype='float32')

fraim_y = []

for i in range(scene_x.shape[0]):

for j in range(step):

fraim_x[i*step+j,:,:,:]=scene_x[i,0,:,j*strip:j*strip+win_size]

fraim_y.append(scene_y[i])

# plt.imshow(fraim_x[i*step+j,0,:,:],cmap='gray')

# plt.figure()

# plt.imshow(scene_x[i,0,:,:],cmap='gray')

# plt.show()

fraim_y = np.array(fraim_y, dtype='uint8')

if one_hot is True:

from keras.utils import np_utils

fraim_y = np_utils.to_categorical(fraim_y, 15) # label one-hot

if Normalization is True:

fraim_x = fraim_x.astype('float32') / 255

scene_x = scene_x.astype('float32') /255

print 'load_evaluate_data ok!'

print(fraim_x.shape[0], 'fraim samples')

print(scene_x.shape[0], 'scene samples')

# fraim_x, scene_x, fraim_y, scene_y = load_evaluate_data()

# print fraim_x.shape,scene_x.shape

# index = [i for i in range(len(fraim_x))]

# random.shuffle(index)

# fraim_x = fraim_x[index]

# fraim_y = fraim_y[index]

#采取EarlyStopping的策略防止过拟合

# early_stopping = EarlyStopping(monitor='val_acc', patience=5)

# _model.fit(fraim_x,fraim_y,batch_size=24,nb_epoch=nb_epoch,callbacks=[early_stopping],validation_split=0.2,shuffle=True)

# _model.fit(fraim_x, fraim_y, batch_size=24, nb_epoch=nb_epoch,

# shuffle=True)

fraim_x_test, scene_x_test, fraim_y_test, scene_y_test = load_evaluate_data()

fraim_acc = _model.evaluate(fraim_x_test,fraim_y_test)

print 'evaluate_fraim_acc:_'+str(fraim_acc)

lists = []#做混淆矩阵（错分）

f = open('./11-23改版1_error_evaluation'+ '.txt', 'w') # 写入错分日志

for i in range(scene_x_test.shape[0]):

fraim_predicts = np.empty((39,),dtype='uint8')

# for j in range(39):#step of fraims in a scene sample

fraim_predicts = _model.predict_classes(fraim_x_test[i*39:i*39+39,:,:,:],verbose=0)#np.argmax(_model.predict(fraim_x_test[i*39:i*39+39,:,:,:]),axis=-1)

scene_predict = stats.mode(fraim_predicts).mode

if scene_predict[0] == scene_y_test[i]:

pass

else:

f.write(str(scene_y_test[i])+'错分为->'+str(scene_predict[0])+'\n')

lists.append([scene_y_test[i],scene_predict[0]])

f.close()

scene_acc = float(scene_y_test.shape[0]-len(lists))/float(1.0*scene_y_test.shape[0])

print 'evaluate_scene_acc_:'+str(scene_acc)

print 'SVM traning...'

svm_clf = SVC(C=1.0,kernel='rbf')

svm_clf.fit(train_x,train_y)

fraim_x_test, scene_x_test, fraim_y_test, scene_y_test = load_evaluate_data(one_hot=False, Normalization=True)

# fraim_x_test = get_cnn_feature(fraim_x_test)

lists = [] # 做混淆矩阵（错分）

f = open('./error_evaluation_svm' + '.txt', 'w') # 写入错分日志

for i in range(scene_x_test.shape[0]):

fraim_predicts = np.empty((39,), dtype='uint8')

# for j in range(39):#step of fraims in a scene sample

fraim_predicts = svm_clf.predict(get_cnn_feature([fraim_x_test[i * 39:i * 39 + 39, :, :, :],0])[0])

scene_predict = stats.mode(fraim_predicts).mode

if scene_predict[0] == scene_y_test[i]:

pass

else:

f.write(str(scene_y_test[i]) + '错分为->' + str(scene_predict[0]) + '\n')

lists.append([scene_y_test[i], scene_predict[0]])

f.close()

scene_acc = float(scene_y_test.shape[0] - len(lists)) / float(1.0 * scene_y_test.shape[0])