print imfile
# random block voting
bvw = BoVW()
bvw.load(model)
rbv = BlockVote(
Vote(bvw), RandBlockIter(nblock, pdiv, sdiv, md, Md))
votes = rbv.run(img)
if display:
rbv.show()
# corase segmentation
coarse = Votes2Img(img.shape[:2]).run(votes)
# final segmentation
grid = GridClassifier(MyKNN(labels, nn=nneigh), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
res = grid.show()
if display:
imshow(res)
if save:
base = pbase(imfile)
outfile = pjoin(save, AddSuffix('out', 'jpg').run(base))
imwrite(outfile, res)
outfile = pjoin(save, AddSuffix('out', 'pck').run(base))
grid.save(outfile)
from mscr.grid import Grid, GridClassifier
imgf, model, w, h, nn, display, save = parse_args()
img = load_gray(imgf)
print '#-----------------------'
print imgf
bvw = BoVW()
bvw.load(model)
ubv = BlockVote(Vote(bvw), TrivialBlockIter(w, h))
votes = ubv.run(img)
coarse = Votes2Img(img.shape[:2]).run(votes)
grid = GridClassifier(MyKNN(labels, nn=nn), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
res = grid.show()
if display:
imshow(res)
if save:
base = pbase(imgf)
outfile = pjoin(save, AddSuffix('out', 'jpg').run(base))
imwrite(outfile, res)
outfile = pjoin(save, AddSuffix('out', 'pck').run(base))
grid.save(outfile)
"utf-8")
],
format="%(asctime)s %(name)-1s -- [%(levelname)s]: %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
vtlog = logging.getLogger("vthell_autoschedule")
console = logging.StreamHandler(sys.stdout)
console.setLevel(logging.INFO)
formatter1 = logging.Formatter("[%(asctime)s] %(message)s")
console.setFormatter(formatter1)
vtlog.addHandler(console)
vtlog.info("Collecting existing jobs...")
vthell_jobs = glob.glob(pjoin(BASE_VTHELL_PATH, "jobs", "*.json"))
vthell_jobs = [psplit(pbase(job))[0] for job in vthell_jobs]
class ValidationError(Exception):
def __init__(self, msg):
super().__init__(msg)
class StreamData:
"""Stream Information parser that used info like Jetri.co API
Required schema (You can other stuff yourself.)
{
"id": str,
"title": str
"channel": str,
"startTime": int/str
print '#------------------'
print imfile
# random block voting
bvw = BoVW()
bvw.load(model)
rbv = BlockVote(Vote(bvw), RandBlockIter(nblock, pdiv, sdiv, md, Md))
votes = rbv.run(img)
if display:
rbv.show()
# corase segmentation
coarse = Votes2Img(img.shape[:2]).run(votes)
# final segmentation
grid = GridClassifier(MyKNN(labels, nn=nneigh), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
res = grid.show()
if display:
imshow(res)
if save:
base = pbase(imfile)
outfile = pjoin(save, AddSuffix('out', 'jpg').run(base))
imwrite(outfile, res)
outfile = pjoin(save, AddSuffix('out', 'pck').run(base))
grid.save(outfile)
from mscr.grid import Grid, GridClassifier
imgf, model, w, h, nn, display, save = parse_args()
img = load_gray(imgf)
print '#-----------------------'
print imgf
bvw = BoVW()
bvw.load(model)
ubv = BlockVote(Vote(bvw), TrivialBlockIter(w, h))
votes = ubv.run(img)
coarse = Votes2Img(img.shape[:2]).run(votes)
grid = GridClassifier(MyKNN(labels, nn=nn), Grid(microsize))
grid.run(img, coarse)
grid.finalize()
res = grid.show()
if display:
imshow(res)
if save:
base = pbase(imgf)
outfile = pjoin(save, AddSuffix('out', 'jpg').run(base))
imwrite(outfile, res)
outfile = pjoin(save, AddSuffix('out', 'pck').run(base))
grid.save(outfile)
