def drawSamples(drawing, data, width=600, height=400, show_progress=False):
size = len(data)
grain = width / size
path = Path(
stroke_width=1,
stroke='black',
fill='black',
fill_opacity=0.0,
)
path.M(0, height / 2)
for i, d in enumerate(data):
x = i * grain
y = d * height * 0.5 + height * 0.5
path.L(x, y)
if show_progress:
progress(i, size)
drawing.append(path)
return drawing
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,
spectrum(b, N),
threshold=args.threshold,
thickness=args.thickness,
spread=args.spread or rms(b) * 4,
width=args.width,
height=args.height)
cv2.imwrite(os.path.join(args.outdir, '{0:05d}.png'.format(n + 1)),
img)
progress(n, blocks)
sys.stdout.write("\n")
console("info", "connecting to MQTT broker...")
sleep(2)
client.loop_start()
# Open camera
resolution = (1024, 768)
vs = VideoStream( \
usePiCamera = True, \
resolution = resolution, \
framerate = 2 \
).start()
console("info", "Opening Camera...")
startProgress("Opening Camera")
for _ in range(10):
sleep(1)
progress(10 * _)
endProgress()
sleep(1)
# Capture frames
console("info", "Capturing frames")
cnt = 0
while True:
sys.stdout.flush()
sys.stdout.write("\r[+] frame %d" % cnt)
cnt += 1
frame = imutils.resize(vs.read(), width=newwidth, height=newheight)
# print(frame)
retval, buffer = cv2.imencode('.jpg', frame)
jpg_as_text = base64.b64encode(buffer)
# print(jpg_as_text)
def progress(msg, br=False):
lib.progress("[AUGMENT] %s"%msg,br)
### mk_analysis_expStratifiedRecall
from __future__ import division
import sys
_,data_dirpath,nDiv,method_name,topK = sys.argv
nDiv,topK = int(nDiv),int(topK)
from lib import progress, gen_path, gen_data_filename, gen_mk_out_filename
### items
fin_filename = gen_path(data_dirpath, "items.dat")
items = []
with open(fin_filename) as fin:
progress("reading %s..."%fin_filename)
for line in fin:
i = int(line)
items.append(i)
recall_at = {}
for d in range(1, nDiv):
### read train data
fin_filename = gen_path(method_name, gen_data_filename(d, nDiv, "train"))
train_ratings = []
with open(fin_filename) as fin:
progress("reading %s..."%fin_filename)
for line in fin:
u,i,r,t = line.split()
u,i,r,t = int(u),int(i),float(r),int(t)
train_ratings.append((u,i,r,t))
### read test data
fin_filename = gen_path(method_name, gen_data_filename(d, nDiv, "test"))
def progress(msg, br=False):
lib.progress("[TRAIN] %s"%msg,br)
'--size',
dest='size',
type=int,
action='store',
default=50,
help='flocksize')
ap.add_argument(
'-S',
'--speed',
dest='speed',
type=int,
action='store',
help='glider speed. if not specified, speed is randomized 1..5')
args = ap.parse_args()
flock = GliderFlock(
width=args.width,
height=args.height,
length=args.length,
size=args.size,
speed=args.speed,
)
for i in range(args.iterations):
flock.step()
cv2.imwrite(os.path.join(args.outdir, '{:05d}.png'.format(i)),
flock.array * 255)
progress(i, args.iterations)
stdout.write('\n')
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
lib.progress(i, blocks)