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)