def get_audio_energy(path, filename, fps):
full_path = path + filename
time, data = audio_to_array(full_path)
sample_rate = sndfile(full_path).samplerate()
total_frames = 0
avg_e = 0.0
for i in xrange(0, len(time), int(sample_rate / fps)):
data[i] = abs(data[i])
total_frames = total_frames + 1
avg_e = avg_e + data[i]
avg_e = avg_e / total_frames
t = []
for i in xrange(0, len(time), int(sample_rate / fps)):
t.append(data[i])
r = kaiser.convolve_kaiser(70, 5, t)
energy = []
max_r = max(r)
for i in range(len(r)):
v = r[i] / max_r * 100.0 * (1.0 - avg_e)
energy.append(v)
return energy
def get_audio_energy_ffmpeg(path, filename, fps):
full_path = path + filename
reader = pf.FFMpegReader()
reader.open(full_path, pf.TS_AUDIO)
audio_track = reader.get_tracks()[0]
smp_rate = audio_track.get_samplerate()
smp_in_frame = smp_rate / fps
count = 0
fno = 1
s = 0
result = []
max_value = 1
try:
while True:
frame = audio_track.get_next_frame()
for f in frame:
d = abs((f[0] + f[1]) / 2)
s = s + d
count = count + 1
if count >= smp_in_frame:
if max_value < s:
max_value = s
fno = fno + 1
result.append(s)
count = 0
s = 0
except IOError:
print "Energy complete"
avg_e = 0.0
t = []
for i in result:
v = float(i) / max_value
avg_e = avg_e + v
t.append(v)
avg_e = avg_e / fno
print avg_e,
r = kaiser.convolve_kaiser(70, 5, t)
energy = []
max_r = max(r)
for i in range(len(r)):
v = r[i] / max_r * 100.0 * (1.0 - avg_e)
energy.append(v)
return energy
def get_arousal(video_path, video_file, audio_path, audio_file):
print "Calculating arousal for file", video_path, video_file
fps = shot_motion.get_fps(video_path, video_file)
print "FPS:", fps
print "Shots and motions started"
rhythm, motion = shot_motion.get_shot_motion(video_path, video_file)
print "Shots and motions completed"
print "Audio energy started"
e = energy.get_audio_energy_ffmpeg(audio_path, audio_file, fps)
print "Audio energy completed"
a = []
for i in range(len(rhythm)):
v = (rhythm[i] + motion[i] + e[i]) / 3
a.append(v)
arousal = kaiser.convolve_kaiser(150, 5, a)
return a, arousal
def get_shot_motion(path, filename, verbose=False):
fullpath = path + filename
# psyco.full()
total_histo_size = 0
# create ffmpeg reader instance
reader = pf.FFMpegReader()
# open media file with PIL support
reader.open(fullpath, pf.TS_VIDEO_PIL)
# get video track
video_track = reader.get_tracks()[0]
fps = video_track.get_fps()
idx = 0 # frame index
histo = []
motion_vecs = []
print "Extracting shots and motions from", path, filename
try:
last_frame = None
histo_size = 0
while True:
frame = video_track.get_next_frame()
idx = idx + 1
# Compare current frame and the last frame for histogram change
h2 = frame.histogram()
mv = video_track.get_current_frame_motion_vectors()
m = calc_motion_val(mv, frame.size)
# Compute motion vectors
if math.isnan(m):
m = calc_motion_val(mv, frame.size, True)
motion_vecs.append(m)
if last_frame == None:
last_frame = frame
for i in h2:
histo_size += i
continue
h1 = last_frame.histogram()
r = float(histo_change(h1, h2)) / histo_size / 2
last_frame = frame
histo.append(r)
except IOError:
print "Frame read completed"
reader.close()
print "Detecting shots..."
shots = detect_shots(histo, fps)
print "Calculating shot change rate..."
rhythm = calc_shot_change_rate(shots, idx)
ret_rhythm = kaiser.convolve_kaiser(70, 5, rhythm)
ret_motion = kaiser.convolve_kaiser(70, 5, motion_vecs)
return ret_rhythm, ret_motion
