while (cap.isOpened() and cap.get(cv2.CAP_PROP_POS_MSEC) <
(int(args.e) * 1000)):
ret, frame = cap.read()
if frame == None:
break
if args.f == features[0]:
h = ft.colorhist(frame)
elif args.f == features[1]:
h = temporal_diff(prev_frame, frame, 10)
elif args.f == features[2] or args.f == features[3]:
audio_frame = frame_to_audio(frame_nbr, frame_rate, fs, wav_data)
if args.f == features[2]:
h = np.mean(audio_frame**2)
elif args.f == features[3]:
h, mspec, spec = ft.extract_mfcc(audio_frame, fs)
elif args.f == features[4]:
colorhist = ft.colorhist(frame)
h = colorhist_diff(prev_colorhist, colorhist)
prev_colorhist = colorhist
if h != None:
query_features.append(h)
prev_frame = frame
frame_nbr += 1
# Compare with database
video_types = ('*.mp4', '*.MP4', '*.avi')
audio_types = ('*.wav', '*.WAV')
def process_videos(video_list, indx):
total = len(video_list)
progress_count = 0
for video in video_list:
progress_count += 1
print 'processing: ', video, ' (', progress_count, ' of ', total, ')'
cap = cv2.VideoCapture(video)
frame_rate = get_frame_rate(video)
total_frames = get_frame_count(video)
total_audio_frames = get_frame_count_audio(video)
# get corresponding audio file
filename, fileExtension = os.path.splitext(video)
audio = filename + '.wav'
fs, wav_data = read(audio)
colorhists = []
sum_of_differences = []
audio_powers = []
mfccs = []
colorhist_diffs = []
prev_colorhist = None
prev_frame = None
frame_nbr = 0
while (cap.isOpened()):
ret, frame = cap.read()
if frame == None:
break
audio_frame = frame_to_audio(frame_nbr, frame_rate, fs, wav_data)
# check if audio frame is long enough for mfcc transformation
if len(audio_frame) >= int(0.01 * fs):
power = np.mean(audio_frame**2)
audio_powers.append(power)
ceps, mspec, spec = ft.extract_mfcc(audio_frame, fs)
mfccs.append(ceps)
# calculate sum of differences
if not prev_frame == None:
tdiv = temporal_diff(prev_frame, frame, 10)
#diff = np.absolute(prev_frame - frame)
#sum = np.sum(diff.flatten()) / (diff.shape[0]*diff.shape[1]*diff.shape[2])
sum_of_differences.append(tdiv)
colorhist = ft.colorhist(frame)
colorhists.append(colorhist)
if not prev_colorhist == None:
ch_diff = colorhist_diff(prev_colorhist, colorhist)
colorhist_diffs.append(ch_diff)
prev_colorhist = colorhist
prev_frame = frame
frame_nbr += 1
print 'end:', frame_nbr
# prepare descriptor for database
# mfccs = descr['mfcc'] # Nx13 np array (or however many mfcc coefficients there are)
# audio = descr['audio'] # Nx1 np array
# colhist = descr['colhist'] # Nx3x256 np array
# tempdif = descr['tempdiff'] # Nx1 np array
descr = {}
descr['mfcc'] = np.array(mfccs)
descr['audio'] = np.array(audio_powers)
descr['colhist'] = np.array(colorhists)
descr['tempdiff'] = np.array(sum_of_differences)
descr['chdiff'] = np.array(colorhist_diffs)
indx.add_to_index(video, descr)
print 'added ' + video + ' to database'
indx.db_commit()
)
parser.add_argument("wav_path", help="Path to wavfile")
parser.add_argument("-n",
help="Number of mfcc components that should be visualized",
default=13)
args = parser.parse_args()
# Read the wavfile
fs, data = wavfile.read(args.wav_path)
print 'Processing wavfile: ' + args.wav_path + ' ... '
print 'Calculating MFCCs ... '
# extract mfcc coefficents
ceps, mspec, spec = ft.extract_mfcc(data, fs)
print 'Generating spectogram ... '
# create pyplot figure with custom title
fig = plt.figure()
title = 'Analysis of ' + args.wav_path
fig.canvas.set_window_title(title)
# in upper subplot draw the spectogram
plt.subplot(2, 1, 1)
plt.specgram(data, Fs=fs)
plt.title('Spectogram of raw audio data')
plt.xlabel('time [s]')
plt.ylabel('Frequency [Hz]')
def process_videos(video_list, indx):
total = len(video_list)
progress_count = 0
for video in video_list:
progress_count += 1
print 'processing: ',video, ' (' ,progress_count, ' of ' ,total,')'
cap = cv2.VideoCapture(video)
frame_rate = get_frame_rate(video)
total_frames = get_frame_count(video)
total_audio_frames = get_frame_count_audio(video)
# get corresponding audio file
filename, fileExtension = os.path.splitext(video)
audio = filename + '.wav'
fs, wav_data = read(audio)
colorhists = []
sum_of_differences = []
audio_powers = []
mfccs = []
colorhist_diffs = []
prev_colorhist = None
prev_frame = None
frame_nbr = 0
while(cap.isOpened()):
ret, frame = cap.read()
if frame == None:
break
audio_frame = frame_to_audio(frame_nbr, frame_rate, fs, wav_data)
# check if audio frame is long enough for mfcc transformation
if len(audio_frame) >= 256:
power = np.mean(audio_frame**2)
audio_powers.append(power)
ceps, mspec, spec = ft.extract_mfcc(audio_frame, fs)
mfccs.append(ceps)
# calculate sum of differences
if not prev_frame == None:
tdiv = temporal_diff(prev_frame, frame, 10)
#diff = np.absolute(prev_frame - frame)
#sum = np.sum(diff.flatten()) / (diff.shape[0]*diff.shape[1]*diff.shape[2])
sum_of_differences.append(tdiv)
colorhist = ft.colorhist(frame)
colorhists.append(colorhist)
if not prev_colorhist == None:
ch_diff = colorhist_diff(prev_colorhist, colorhist)
colorhist_diffs.append(ch_diff)
prev_colorhist = colorhist
prev_frame = frame
frame_nbr += 1
print 'end:', frame_nbr
# prepare descriptor for database
# mfccs = descr['mfcc'] # Nx13 np array (or however many mfcc coefficients there are)
# audio = descr['audio'] # Nx1 np array
# colhist = descr['colhist'] # Nx3x256 np array
# tempdif = descr['tempdiff'] # Nx1 np array
descr = {}
descr['mfcc'] = np.array(mfccs)
descr['audio'] = np.array(audio_powers)
descr['colhist'] = np.array(colorhists)
descr['tempdiff'] = np.array(sum_of_differences)
descr['chdiff'] = np.array(colorhist_diffs)
indx.add_to_index(video,descr)
print 'added ' + video + ' to database'
indx.db_commit()
cap.set(cv2.CAP_PROP_POS_MSEC, int(args.s)*1000)
while(cap.isOpened() and cap.get(cv2.CAP_PROP_POS_MSEC) < (int(args.e)*1000)):
ret, frame = cap.read()
if frame == None:
break
if args.f == features[0]:
h = ft.colorhist(frame)
elif args.f == features[1]:
h = temporal_diff(prev_frame, frame, 10)
elif args.f == features[2] or args.f == features[3]:
audio_frame = frame_to_audio(frame_nbr, frame_rate, fs, wav_data)
if args.f == features[2]:
h = np.mean(audio_frame**2)
elif args.f == features[3]:
h, mspec, spec = ft.extract_mfcc(audio_frame, fs)
elif args.f == features[4]:
colorhist = ft.colorhist(frame)
h = colorhist_diff(prev_colorhist, colorhist)
prev_colorhist = colorhist
if h != None:
query_features.append(h)
prev_frame = frame
frame_nbr += 1
# Compare with database
video_types = ('*.mp4', '*.MP4', '*.avi')
