meta, data = loadwav(args.soundfile)
print("Samplerate: %d" % meta.rate)
print("Channels: %d" % meta.channels)
print("Length: %d samples, %d seconds" % (meta.samples, meta.seconds))
blocksize = meta.rate // args.fps
blocks = meta.samples // blocksize
print("%d Frames at %d samples" % (blocks, blocksize))
term_width = 100
N = blocksize
T = 1.0 / blocksize * 1.25
for n, b in enumerate(audio_chunks(data, blocksize)):
img = np.zeros((args.height, args.width, 3), np.uint8)
if args.multichannel and meta.channels > 1:
reflect = [(1, 1), (-1, 1), (1, -1), (-1, -1)]
for i in range(meta.channels - 1):
img = render_frame(img,
spectrum(b.T[i], N),
threshold=args.threshold,
thickness=args.thickness,
spread=args.spread or rms(b.T[i]) * 4,
width=args.width,
height=args.height)
else:
if meta.channels > 1:
b = b.T[0]
img = render_frame(img,
parser.add_argument('-r',
'--rate',
dest='rate',
type=float,
action='store',
help='rate')
args = parser.parse_args()
image = np.zeros((args.height, args.width, 3), dtype=np.uint8)
meta, audio = ap.loadwav(args.audiofile)
blocksize = meta.rate // args.fps
blocks = meta.samples // blocksize
scroll = blocksize // args.height
last_img = None
for i, block in enumerate(ap.audio_chunks(audio, blocksize)):
img = last_img if last_img is not None else image
img = render_frame(
img,
block.T[0] if meta.channels > 1 else block,
blocksize,
args.width,
args.height,
raw=args.raw,
)
cv2.imwrite(os.path.join(args.outdir, '{0:05d}.png'.format(i + 1)),
img)
last_img = np.zeros(img.shape, img.dtype)
# scroll left
last_img[:, 0:args.width - scroll] = img[:, scroll:]
# progress
meta, data = loadwav(args.soundfile)
blocksize: int = meta.rate // args.fps
blocks: int = meta.samples // blocksize
files = glob(args.infile)
if args.shape:
try:
shape = tuple(map(int, args.shape.split(',')))
assert len(shape) == 2
except (AssertionError, ValueError):
print('Can not parse shape parameter, must be: x,y')
else:
shape = None
for n, data_pair in enumerate(zip(audio_chunks(data, blocksize), cycle(files))):
block, imgfile = data_pair
padded = "{0:05d}".format(n)
bitmap = cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)
if args.mode == 'fft':
block = spectrum(block, meta.rate)
if shape:
block = np.reshape(block, shape)
image = fftconvolve(bitmap, block * args.amplify, mode='same')
cv2.imwrite(path.join(args.outdir, f'{padded}.png'), image)
percent_finished = int(n / blocks * 100)
print(f'{percent_finished:>3}%', end='\r', flush=True)
bitmap = cv2.imread(imagefiles[0])
if bitmap is None:
print("Problem loading the first Imagefile")
sys.exit(-1)
print("image loaded")
junk = np.fromfile(args.junkfile, dtype='u1')
np.roll(junk, args.offset)
print("junk loaded")
height, width, colors = bitmap.shape
blocksize = meta.rate // args.fps
blocks = meta.samples // blocksize
juncnt = 0
for n, b in enumerate(audio_chunks(data.T[0], blocksize), 1):
#if os.path.exists(os.path.join(args.outdir, "frame_%06d.png"%n)):
# continue
if args.length is not None and n-args.start_frame >= args.length:
continue
if n < args.start_frame or n > blocks:
continue
elif len(imagefiles) > 1:
bitmap = cv2.imread(imagefiles[n-1 % len(imagefiles)])
frame = bitmap.copy()
shift = np.interp(np.arange(height), np.arange(blocksize), b)
for line in range(height):
v_line = shift[line] * shift[line]
if v_line > args.threshold:
frame[line] = frame[line]