# Path: blockplayer/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# 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/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: 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)


import os
import shutil
import numpy as np
import django.conf
import time
import re
import simplejson as json
import glob
import cPickle as pickle
from django import template
from blockplayer import config
from blockplayer import dataset
from blockplayer import grid
from blockplayer import hashalign
            added = grid.gt2grid(s, chars='aA')

        try:
            with open(os.path.join('data/experiments/output/', name,
                    'output.pkl'),'r') as f:
                output = pickle.load(f)
        except IOError:
            continue
        
        with open(os.path.join('data/experiments/output/',name,
                'final_output.txt'),'r') as f:
            final_output = f.read()

        folder = os.path.join(out_path, name)
        os.mkdir(folder)

        print "(%d/%d)" % (x+1,total), name
        out = grid.gt2grid(final_output)
        gt = config.GT

        try:
            c,_ = hashalign.find_best_alignment(out, 0*out, gt, 0*gt)
        except ValueError:
            red = gt | added
            yellow = 0*out
            green = 0*out
            purple = 0*out
            blue = 0*out
        else:
            gt = hashalign.apply_correction(gt, *c)

# Path: blockplayer/config.py
# def load(dir_path):
# def save(dir_path):
# LH = 0.0192
# LW = 0.016
# GRIDRAD = 18
# 
# 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/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: 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: makewww/make_grid.py
import os
import shutil
import numpy as np
import django.conf
import time
import re
import simplejson as json
import glob
import cPickle as pickle
from django import template
from blockplayer import config
from blockplayer import dataset
from blockplayer import grid
from blockplayer import hashalign

            added = grid.gt2grid(s, chars='aA')

        try:
            with open(os.path.join('data/experiments/output/', name,
                    'output.pkl'),'r') as f:
                output = pickle.load(f)
        except IOError:
            continue
        
        with open(os.path.join('data/experiments/output/',name,
                'final_output.txt'),'r') as f:
            final_output = f.read()

        folder = os.path.join(out_path, name)
        os.mkdir(folder)

        print "(%d/%d)" % (x+1,total), name
        out = grid.gt2grid(final_output)
        gt = config.GT

        try:
            c,_ = hashalign.find_best_alignment(out, 0*out, gt, 0*gt)
        except ValueError:
            red = gt | added
            yellow = 0*out
            green = 0*out
            purple = 0*out
            blue = 0*out
        else:
            gt = hashalign.apply_correction(gt, *c)

            added = hashalign.apply_correction(added, *c)