# 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/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# Path: blockplayer/hashalign.py
# def features_weave(Ar):
# def find_features(A):
# def diff_coord(a, b, ashape):
# def apply_correction(grid, bx, by, bz,rot):
# def match_features(A, B, sq):
# def correction2modelmat(R_aligned, x, y, z, r):
# def find_best_alignment(occA, vacA, occB, vacB,
#                         R_aligned=None, prev_R_Correct=None):
#     def error(occA, vacA, occB, vacB):
#     def error_weave(occA, vacA, occB, vacB, bx, bz, term):
#     WX, WY, WZ = Ar.shape
#     R = expmap.axis2rot(np.array([0,-r*np.pi/2,0]))
#     A,B = occA, occB
#             R = correction2modelmat(R_aligned, 0, 0, 0, r)
# 
# Path: blockplayer/grid.py
# def initialize():
# def gt2grid(gtstr, chars='*rR'):
# def grid2gt(occ):
# def initialize_with_groundtruth(GT):
# def window_correction(occA, vacA, occB, vacB):
#     def error(t):
# def xcorr_correction(A, B):
#     def convolve(p1, p2):
#     def best(cc):
# def show_votegrid(vg, color=(1,0,0), opacity=1):
# def has_previous_estimate():
# def apply_correction(grid, bx,bz,rot):
# def center(R_aligned, occ_new, vac_new):
# def nearest(R_previous, R_aligned):
# def align_with_previous(R_aligned, occ_new, vac_new):
# def stencil_carve(depth, rect, R_correct, occ, vac, rgb=None):
# def merge_with_previous(occ_, vac_, occ_stencil, vac_stencil, color_=None):
# def update_previous_estimate(R_correct):
#     B = apply_correction(B, x, z, r)
#     B = apply_correction(B, bx, bz, br)
#     X,Y,Z = np.array(gridmax)-gridmin
#     M = [expmap.axis2rot(np.array([0,-i*np.pi/2,0])) for i in range(4)]


import glob
import os
import numpy as np
import cPickle as pickle
import pylab
import re
            import re
        import re
from blockplayer import dataset
from blockplayer import config
from blockplayer import hashalign
from blockplayer import grid
        custom = os.path.join('data/sets/', name, 'gt.txt')
        if os.path.exists(custom):
            # Try dataset directory first
            fname = custom
        else:
            # Fall back on generic ground truth file
            match = re.match('.*_z(\d)m_(.*)', name)
            if match is None:
                continue
            
            number = int(match.groups()[0])
            fname = 'data/experiments/gt/gt%d.txt' % number
        
        # Load the ground truth file
        removed = None
        added = None
        with open(fname) as fp:
            s = fp.read()
            config.GT = grid.gt2grid(s, chars='*')
            removed = grid.gt2grid(s, chars='rR')
            added = grid.gt2grid(s, chars='aA')

        number, kind = re.match('.*_z(\d)m_(.*)$', name).groups()
        number = int(number)

        with open(os.path.join('data/experiments/output/',name,
                               'final_output.txt'),'r') as f:
            final_output = f.read()

        out = grid.gt2grid(final_output)

# 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/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# Path: blockplayer/hashalign.py
# def features_weave(Ar):
# def find_features(A):
# def diff_coord(a, b, ashape):
# def apply_correction(grid, bx, by, bz,rot):
# def match_features(A, B, sq):
# def correction2modelmat(R_aligned, x, y, z, r):
# def find_best_alignment(occA, vacA, occB, vacB,
#                         R_aligned=None, prev_R_Correct=None):
#     def error(occA, vacA, occB, vacB):
#     def error_weave(occA, vacA, occB, vacB, bx, bz, term):
#     WX, WY, WZ = Ar.shape
#     R = expmap.axis2rot(np.array([0,-r*np.pi/2,0]))
#     A,B = occA, occB
#             R = correction2modelmat(R_aligned, 0, 0, 0, r)
# 
# Path: blockplayer/grid.py
# def initialize():
# def gt2grid(gtstr, chars='*rR'):
# def grid2gt(occ):
# def initialize_with_groundtruth(GT):
# def window_correction(occA, vacA, occB, vacB):
#     def error(t):
# def xcorr_correction(A, B):
#     def convolve(p1, p2):
#     def best(cc):
# def show_votegrid(vg, color=(1,0,0), opacity=1):
# def has_previous_estimate():
# def apply_correction(grid, bx,bz,rot):
# def center(R_aligned, occ_new, vac_new):
# def nearest(R_previous, R_aligned):
# def align_with_previous(R_aligned, occ_new, vac_new):
# def stencil_carve(depth, rect, R_correct, occ, vac, rgb=None):
# def merge_with_previous(occ_, vac_, occ_stencil, vac_stencil, color_=None):
# def update_previous_estimate(R_correct):
#     B = apply_correction(B, x, z, r)
#     B = apply_correction(B, bx, bz, br)
#     X,Y,Z = np.array(gridmax)-gridmin
#     M = [expmap.axis2rot(np.array([0,-i*np.pi/2,0])) for i in range(4)]


# Path: experiments/exp_avg.py
import glob
import os
import numpy as np
import cPickle as pickle
import pylab
import re
            import re
        import re
from blockplayer import dataset
from blockplayer import config
from blockplayer import hashalign
from blockplayer import grid

        custom = os.path.join('data/sets/', name, 'gt.txt')
        if os.path.exists(custom):
            # Try dataset directory first
            fname = custom
        else:
            # Fall back on generic ground truth file
            match = re.match('.*_z(\d)m_(.*)', name)
            if match is None:
                continue
            
            number = int(match.groups()[0])
            fname = 'data/experiments/gt/gt%d.txt' % number
        
        # Load the ground truth file
        removed = None
        added = None
        with open(fname) as fp:
            s = fp.read()
            config.GT = grid.gt2grid(s, chars='*')
            removed = grid.gt2grid(s, chars='rR')
            added = grid.gt2grid(s, chars='aA')

        number, kind = re.match('.*_z(\d)m_(.*)$', name).groups()
        number = int(number)

        with open(os.path.join('data/experiments/output/',name,
                               'final_output.txt'),'r') as f:
            final_output = f.read()

        out = grid.gt2grid(final_output)

        gt = config.GT