def compare(elbow_input, knee_input, job_result, file_path):
#Compare elbow angles and plot
elbow_ans = np.load('elbow_2.npy')
elbow_input_trim, elbow_ans_trim, elbow_score, elbow_trim_frame = dtw.DTW(
elbow_input, elbow_ans)
ax1.plot(elbow_input_trim, label='Uploaded')
ax1.plot(elbow_ans_trim, label='Suggested')
ax1.legend(loc='best')
ax1.get_xaxis().set_visible(False)
ax1.set_title('Elbow Angle Comparison')
#Compare knee angles and plot
knee_ans = np.load('knee_2.npy')
knee_input_trim, knee_ans_trim, knee_score, knee_trim_frame = dtw.DTW(
knee_input, knee_ans)
ax2.plot(knee_input_trim, label='Uploaded')
ax2.plot(knee_ans_trim, label='Suggested')
ax2.legend(loc='best')
ax2.get_xaxis().set_visible(False)
ax2.set_title('Knee Angle Comparison')
bar.get_tk_widget().pack(side="right")
# print(elbow_score)
# print(knee_score)
# print(get_average(elbow_input_trim))
# print(get_average(elbow_ans_trim))
# print(get_average(knee_input_trim))
# print(get_average(knee_ans_trim))
if elbow_score < 1000 and knee_score < 1000:
print_text = "Overall, Good Job!"
else:
print_text = ""
if elbow_score > 1000:
if get_average(elbow_input_trim) > get_average(elbow_ans_trim):
print_text += "Overall, bend your elbows a little more. "
else:
print_text += "Overall, bend your elbows a little less. "
if knee_score > 1000:
if get_average(knee_input_trim) > get_average(knee_ans_trim):
print_text += "Overall, bend your knees a little more. "
else:
print_text += "Overall, bend your knees a little less. "
info_label.config(text=print_text)
timer_display_elbow(0, elbow_trim_frame, elbow_input_trim, elbow_ans_trim,
knee_trim_frame, knee_input_trim, knee_ans_trim,
job_result, file_path)
def __init__(self, samplingFrequency=8000, framePeriod=25e-3, hopPeriod=10e-3, trainDir="./train_audio/",
thresh=1.2):
self.samplingFrequency = samplingFrequency
self.framePeriod = framePeriod
self.hopPeriod = hopPeriod
self.trainDir = trainDir
self.hopLength = int(samplingFrequency * hopPeriod)
self.frameLength = int(samplingFrequency * framePeriod)
self.referenceMFCC = []
for file_name in os.listdir(trainDir):
if file_name.endswith(".wav"):
(fs, data) = wv.read(trainDir + file_name)
num_frames = int(data.shape[0] / self.hopLength) - int(np.ceil(self.frameLength / self.hopLength))
MFCC_calculator = mfcc.MFCC()
MFCC_MATRIX = np.empty([39, abs(num_frames)])
for k in range(num_frames):
MFCC_MATRIX[:, k] = MFCC_calculator.compute_mfcc(
data[k * self.hopLength: k * self.hopLength + self.frameLength])
self.referenceMFCC.append(MFCC_MATRIX)
DTW_calculator = dtw.DTW()
distance_list = [DTW_calculator.compute_distance(np.transpose(matrix), np.transpose(matrix2)) for matrix in
self.referenceMFCC for matrix2 in self.referenceMFCC]
self.thresh = np.mean(np.array((distance_list))) * thresh
def distance(self, fileName):
"""
This function is used for calculating the DTW distance between the test utterance and each of the 10 training utterances.
:param fileName: Name of test utterance .wav file
:type fileName: str
:returns: List of DTW distances of test utterance with each training utterance
:rtype: list
"""
if isinstance(fileName, (bytes, bytearray)):
data = np.fromstring(fileName)
else:
(fs, data) = wv.read(fileName)
DTW_calculator = dtw.DTW()
num_frames = int(data.shape[0] / self.hopLength) - int(np.ceil(self.frameLength / self.hopLength))
if num_frames <= 0:
return 10000
MFCC_calculator = mfcc.MFCC()
MFCC_MATRIX = abs(np.empty([39, num_frames]))
for k in range(num_frames):
MFCC_MATRIX[:, k] = MFCC_calculator.compute_mfcc(
data[k * self.hopLength: k * self.hopLength + self.frameLength])
distance_list = [DTW_calculator.compute_distance(np.transpose(matrix), np.transpose(MFCC_MATRIX)) for matrix in
self.referenceMFCC]
return distance_list
def run_dtw(train, valid):
dtw_alg = dtw.DTW()
dtw_alg.train(train[0], train[1])
dtw_alg.test(valid[0], valid[1])