# Path: blockplayer/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# Path: blockplayer/preprocess.py
# def threshold_and_mask(depth,bg):
#     def m_():
#     def m2_():
#     def m3_():
# 
# Path: blockplayer/dataset.py
# def advance(skip=1):
# def setup_opencl():
# def load_dataset(pathname):
# def load_gt():
# def load_random_dataset():
# def download():
# 
# Path: blockplayer/colormap.py
# def color_map(depth):


import numpy as np
import pylab
import opennpy
import cv
from blockplayer import config
from blockplayer import preprocess
from blockplayer import dataset
from blockplayer import colormap
from blockplayer import classify
    except IndexError:
        return

    cv.ShowImage('mask',mask.astype('u1')*255)

    global label_image
    label_image = classify.predict(depth)
    cv.ShowImage('label_image', ((label_image[0]+1)*100*mask).astype('u1'))
    pylab.waitforbuttonpress(0.03)


def resume():
    try:
        while 1:
            once()
    except IOError:
        return


def start(dset=None, frame_num=0):
    global modelmat
    modelmat = None
    if not FOR_REAL:
        if dset is None:
            dataset.load_random_dataset()
        else:
            dataset.load_dataset(dset)
        while dataset.frame_num < frame_num:
            dataset.advance()
    else:

# Path: blockplayer/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# Path: blockplayer/preprocess.py
# def threshold_and_mask(depth,bg):
#     def m_():
#     def m2_():
#     def m3_():
# 
# Path: blockplayer/dataset.py
# def advance(skip=1):
# def setup_opencl():
# def load_dataset(pathname):
# def load_gt():
# def load_random_dataset():
# def download():
# 
# Path: blockplayer/colormap.py
# def color_map(depth):


# Path: demos/demo_classify.py
import numpy as np
import pylab
import opennpy
import cv
from blockplayer import config
from blockplayer import preprocess
from blockplayer import dataset
from blockplayer import colormap
from blockplayer import classify

    except IndexError:
        return

    cv.ShowImage('mask',mask.astype('u1')*255)

    global label_image
    label_image = classify.predict(depth)
    cv.ShowImage('label_image', ((label_image[0]+1)*100*mask).astype('u1'))
    pylab.waitforbuttonpress(0.03)


def resume():
    try:
        while 1:
            once()
    except IOError:
        return


def start(dset=None, frame_num=0):
    global modelmat
    modelmat = None
    if not FOR_REAL:
        if dset is None:
            dataset.load_random_dataset()
        else:
            dataset.load_dataset(dset)
        while dataset.frame_num < frame_num:
            dataset.advance()
    else:

        config.load('data/newest_calibration')