page_elements2.py

#! /usr/bin/python
# encoding: utf-8
'''Page Elements'''


#from multiprocessing import Process

import cv2 as cv
import numpy as np
from sklearn.mixture import GMM
#import font_detector
from scipy.stats import mode as statsmode

# from classify import cls as fast_cls

from classify import label_chars, load_cls
from scipy.ndimage.interpolation import rotate
# from recognize import main as rec_main, construct_page
# from utils_extra import add_padding, trim, invert_bw
from utils import invert_bw
from feature_extraction import normalize_and_extract_features
from fast_utils import to255
# from yik import word_parts_set
from scipy.ndimage.filters import gaussian_filter1d
from scipy.signal import argrelmin
from scipy.interpolate import UnivariateSpline, splrep, splev
from collections import OrderedDict

class PageElements(object):
    '''Page Elements object - a representation of the tiff image as a set
    of elements (contours, bounding boxes) and measurements used for recognition
    
    Parameters:
    -----------
    img_arr: 2d numpy array containing pixel data of the image
    
    small_coef: int, default=2
        A scalar value used in filtering out small ("noise") objects in the
        image.
        
        This may be deprecated soon. It is useful in situations where you
        know the typeset being used and want to ensure filtering is not too
        lax or aggressive.
        
    Attributes:
    ------
    contours: list, a list of contours return by cv.findContours
    
    hierarchy: list, contour hierarchy exported by cv.findContours
    
    boxes: list, list of bounding boxes for the page
    
    indices: list, list of integers representing the indices for contours and
        boxes that have not been filtered
    
    char_mean, char_std, tsek_mean, tsek_std: float, parameters of the Gaussian
        distributions for letters and punctuation on the page (first pass)
    
    page_array: 2d array of containing newly drawn image with filtered blobs
        removed
    
    Methods:
    --------
    char_gaussians: class method for using 2 class GMM
    
    get_tops: helper function for getting the top y coordinates of all
        bounding boxes on the page (-filter boxes)
    '''
    

#     @timeout(25)
#     @profile
    def __init__(self, img_arr, fast_cls, small_coef=1, low_ink=False, \
                 page_type=None, flpath=None, detect_o=True,\
                 clear_hr = False): #lower coef means more filtering USE 3 for nying gyud
        self.img_arr = img_arr
        self.page_type = page_type
        self.flpath = flpath
        self.low_ink = low_ink
        self.detect_o = detect_o
#         self.clear_hr = clear_hr
#         self.cached_features = {}
#         self.cached_pred_prob = {}
        self.cached_features = OrderedDict()
        self.cached_pred_prob = OrderedDict()
#         self.low_ink = True 
#        if page_type == 'pecha':
#            self._contour_mode = cv.RETR_CCOMP
#        else:
        self._contour_mode = cv.RETR_TREE
        ### repeatedly called functions
        ones = np.ones
        uint8 = np.uint8
        predict = fast_cls.predict
        predict_proba = fast_cls.predict_proba
        self.contours, self.hierarchy = self._contours()
        self.boxes = []
        self.indices = []
        self.small_coef = small_coef
        
        FILTERED_PUNC = (u'།', u'་', u']', u'[')
        
        self._set_shape_measurements()
        if page_type == 'pecha':
            if clear_hr:
                print 'Warning: clear_hr called on pecha format. For clearing text'
                self.force_clear_hr()
            self.set_pecha_layout()
            if self.indices:
                content_parent = int(statsmode([self.hierarchy[0][i][3] for i in self.indices])[0])
            else:
                print 'no content found'
        else:

            content_parent = int(statsmode([hier[3] for hier in self.hierarchy[0]])[0])
            self.indices = self.get_indices()
#        if self.page_type != 'pecha':
            
            ### Find the parent with the most children. Call it 'content_parent'
#        content_parent = int(statsmode([self.hierarchy[0][i][3] for i in self.indices])[0])

#        width_measures = self.char_gaussians([b[2] for b in self.get_boxes() if (b[2] < .1*self.img_arr.shape[1]] and self.hierarchy[0][] ))

        outer_contours = []
        outer_widths = []

#        pg = np.ones_like(img_arr)
        
        ## Iterate through all contours
        for i in self.indices:
            cbox = self.get_boxes()[i]
            x,y,w,h = cbox
             ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS. Recently 
             # added the len(indices) < 40 as a way to prevent exaggerated
             # filtering of small lines where gaussian width measures
             # are meaningless due to small sample size (too few contours)
#             if self.hierarchy[0][i][3] == content_parent and (cbox[2] < .1*self.img_arr.shape[1] or len(self.indices) < 40 ): 
            if self.hierarchy[0][i][3] == content_parent and (cbox[2] < .1*self.img_arr.shape[1] or len(self.indices) < 40 ): 
#            if self.hierarchy[0][i][3] == content_parent and cbox[2] < 3*self.char_mean:  ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS
#            if self.hierarchy[0][i][3] == content_parent and cbox[2] < .075*self.img_arr.shape[1]:  ### THIS SECOND CONDITION IS CAUSING A LOT OF PROBLEMS
                outer_contours.append(i)
                outer_widths.append(cbox[2])
#                if cbox[2] > 50: print cbox[2],
#                x,y,w,h = cbox
#                 cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
            else:
#                 if cbox[2] > 100:
#                     print cbox
#                     raw_input('continue?')
                if cbox[2] > .66*self.img_arr.shape[1]:
                    print cbox[2] / float(self.img_arr.shape[1])
                if clear_hr and .995*self.img_arr.shape[1] > cbox[2] > \
                .66*self.img_arr.shape[1] and cbox[1] < .25*self.img_arr.shape[0]:
                    self.img_arr[0:cbox[1]+cbox[3], :] = 1
#                 print 'rejected box. too wide?', cbox[2] >= .1*self.img_arr.shape[1] 
#        print
#        print max(outer_widths)   
        width_measures = self.char_gaussians(outer_widths)
        
#         import Image
#         Image.fromarray(self.img_arr*255).show()
        
        
#         newarr = np.ones_like(img_arr)
#         for o in self.indices:
#             x,y,w,h = self.get_boxes()[o]
#             cv.rectangle(newarr, (x,y), (x+w, y+h), 0)
#             if self.hierarchy[0][o][3] == content_parent:
#                 self.draw_contour_and_children(o, newarr, (0,0))
#          
#         import Image
#         Image.fromarray(newarr*255).show()
#         import sys; sys.exit()
        for i,j in zip(['char_mean', 'char_std', 'tsek_mean', 'tsek_std'], width_measures):
            setattr(self, i, j)

#        print self.gmm.converged_
#        print self.char_mean, self.char_std
#        print self.tsek_mean, self.tsek_std

        self.small_contour_indices = []
#        self.contours = []
        self.indices = [] # Need to reset!19
        self.emph_symbols = []
        self.naros = []
#         print self.char_mean, self.char_std, self.tsek_mean
        for i in outer_contours:
            cbox = self.get_boxes()[i]
            # if small and has no children, put in small list (this could backfire with false interiors e.g. from salt and pepper noise)
            ## NOTE: previously small was defined as less than tsek_mean + 3xtsek std
            ## however, this wasn't always working. changing to less than charmean
            ## minus 2xchar std however should watch to see if is ok for many different inputs...
            
            x,y,w,h = cbox
            tmparr = ones((h,w), dtype=uint8)
            tmparr = self.draw_contour_and_children(i, tmparr, (-x,-y))

            features = normalize_and_extract_features(tmparr)
            self.cached_features[i] = features
            
            prprob = predict_proba(features)
            
#         all_feats = self.cached_features.values()
#         all_probs = predict_proba(all_feats)
#         all_probs = predict_proba(self.cached_features.values())
#         for ix,i in enumerate(outer_contours):
#             prprob = all_probs[ix]
#             if recognizer ==  'probout':
            mxinx = prprob.argmax()
            quick_prd = label_chars[mxinx]
            self.cached_pred_prob[i] = (mxinx, prprob[0])
#             self.cached_pred_prob[i] = (mxinx, prprob)
#             else:
#             quick_prd = label_chars[predict_proba(features).argmax()]
#                 quick_prd = label_chars[predict(features)[0]]
            
#             is_emph_symbol = quick_prd in set([u'༷', u'༵', u'༼', u'༽', u'—'])
            is_emph_symbol = quick_prd in set([u'༷', u'༵', u'༼', u'༽'])
#             is_emph_symbol = quick_prd in set([u'༼', u'༽'])
#             is_emph_symbol = quick_prd in set([u'༷', u'༵'])
#             is_emph_symbol = quick_prd in set([u'༼', u'༽', u'—'])
#             is_emph_symbol = quick_prd in set([u'༼', u'༽'])
#             is_emph_symbol = quick_prd == '~~' # use this line if don't want this to actually get anything
#             if is_emph_symbol: print 'found naro? ', is_emph_symbol
#                 import Image; Image.fromarray(tmparr*255).show()
            if is_emph_symbol:
                self.emph_symbols.append(i)
                
                print 'EMPHSYMBOLFOUND', quick_prd
#                 cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
            elif quick_prd == u'ོ' and self.detect_o:
                self.naros.append(i)
                
            elif cbox[2] < 7:
                
#             elif cbox[2] < 9:
                continue
#             elif (cbox[2] <= self.char_mean - 2*self.char_std and 
#             elif (cbox[2] <= self.char_mean - 3*self.char_std and 
#             elif (cbox[2] <= self.tsek_mean*1.5 and 
#             elif (cbox[2] <= self.tsek_mean*.0 and 
            elif (cbox[2] <= self.tsek_mean*3 and 
#             elif (cbox[2] <= self.char_mean - 4*self.char_std and 
#                   self.hierarchy[0][i][2] < 0 and 
                quick_prd in FILTERED_PUNC) and not self.low_ink: # default!!!
#                 quick_prd in (u'་')) and not self.low_ink:
#                 quick_prd not in word_parts_set) and not self.low_ink :
                self.small_contour_indices.append(i)
#                self.indices.append(i) #DEFAULT
#             elif (cbox[2] <= self.tsek_mean*.8 and 
#             elif (cbox[2] <= self.tsek_mean*.3 and 
#            elif (cbox[2] <= self.char_mean - 4*self.char_std and 
#                   self.hierarchy[0][i][2] < 0 and not self.low_ink):
#                 cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
#                 continue
            else:
#                 cv.rectangle(self.img_arr, (x,y), (x+w, y+h), 0)
                self.indices.append(i)

#                if  (cbox[2] <= self.tsek_mean*1.5 and 
##            elif (cbox[2] <= self.char_mean - 4*self.char_std and 
#                  self.hierarchy[0][i][2] < 0 and 
#                  quick_prd in (u'།', u'་')):
#                    self.small_contour_indices.append(i)
            
#            import Image
#            Image.fromarray(tmparr*255).convert('L').save('/tmp/examples/%04d.tif' % i)
            
#        print len(self.small_contour_indices), 'len small contour ind'
#         import Image
#         Image.fromarray(self.img_arr*255).show()
#        print scount
#         raw_input()
        if self.detect_o:
            print 'pre-filtered na-ro vowel', len(self.naros), 'found'    
        
#        for i in self.indices:
            #                if cbox[2] > 50: print cbox[2],
#            bx = self.boxes[i]
#            x,y,w,h = bx
#            cv.rectangle(img_arr, (x,y), (x+w, y+h), 0)

#         import Image
#         Image.fromarray(img_arr*255).show()
#        raw_input()
#        for i in self.indices:
#            if self.hierarchy[0][i][2] >= 0:
#                char = self.draw_contour_and_children(i)
#                
#                Image.fromarray(char*255).show()
#                raw_input()
#        from matplotlib import pyplot as plt
#        from matplotlib.mlab import normpdf
#        plt.subplot(111)
#        plt.title('tsek-char distributions, pre-segmentation')
#
##        widths = [self.boxes[i][2] for i in self.get_indices()]
#        n,bins,p = plt.hist(outer_widths, 200, range=(0,75), normed=True, color='#3B60FA')
#        plt.vlines([self.char_mean, self.tsek_mean], 0, np.array([max(n), max(n)]), linestyles='--')
#        plt.plot(bins, normpdf(bins, self.tsek_mean, self.tsek_std),  label='fit', linewidth=1)
#        plt.fill_between(bins, normpdf(bins, self.tsek_mean, self.tsek_std), color=(.58,.63,.8), alpha=0.09)
#        plt.plot(bins, normpdf(bins, self.char_mean, self.char_std), label='fit', linewidth=1)
#        plt.fill_between(bins, normpdf(bins, self.char_mean, self.char_std), color=(.58,.63,.8), alpha=0.01)
#        plt.show()

#        print self.tsek_mean, self.tsek_std
#        print len(self.boxes)
#        font_detector.save_info(self.char_mean, self.char_std, self.tsek_mean, self.tsek_std)
#         self.low_ink = False
        if self.low_ink:
            self._low_ink_setting()
    
#         allfeats = self.cached_features.values()
#         pp = predict_proba(allfeats)
    
    def force_clear_hr(self):
        boxes = self.get_boxes()
        for cbox in boxes:
            if .995*self.img_arr.shape[1] > cbox[2] > \
                    .66*self.img_arr.shape[1] and cbox[1] < .25*self.img_arr.shape[0]:
                        self.img_arr[0:cbox[1]+cbox[3], :] = 1

    def _low_ink_setting(self):
#         self.low_ink = True
        print 'IMPORTANT: Low ink setting=True'
        a = self.img_arr.copy()*255
        
        ############## Effects
        #**Default**#
#         erode_iter = 3
#         vertblur = 15
#         horizblur = 1
#         threshold = 170
          
        #**mild vertical blurring**#
#         erode_iter = 1
#         vertblur = 5
#         horizblur = 1
#         threshold = 127
        
        #**mild vertical blurring**#
        #**mild vertical blurring**#
        
        #**other**#
        erode_iter = 2
        vertblur = 35
        horizblur = 1
        threshold = 160
        
        
        #############
        
        
        a = cv.erode(a, None, iterations=erode_iter)
#        a = cv.blur(a, (1,int(self.char_mean*.8)))
        ##### parameters below are highly text-dependent unfortunately...
#         a = cv.blur(a, (9,61))
#         a = cv.blur(a, (9,61))
#         a = cv.blur(a, (int(.5*self.tsek_mean),int(3*self.tsek_mean)))
#         a = cv.blur(a, (1,15))
        a = cv.blur(a, (horizblur,vertblur))
#         a = cv.blur(a, (15,1))
#         a = cv.blur(a, (9,70))
#         a = cv.blur(a, (1,50))
#         ret, a = cv.threshold(a, 175, 255, cv.THRESH_BINARY)
        ret, a = cv.threshold(a, threshold, 255, cv.THRESH_BINARY)
#         ret, a = cv.threshold(a, 200, 255, cv.THRESH_BINARY)
#         ret, a = cv.threshold(a, 160, 255, cv.THRESH_BINARY)
        ctrs, hier = cv.findContours(a, mode=self._contour_mode , 
                                     method=cv.CHAIN_APPROX_SIMPLE)
        
        self.low_ink_boxes = [cv.boundingRect(c) for c in ctrs]
        self.low_ink_boxes = [i for i in self.low_ink_boxes if 
                              i[2] < 1.33*self.char_mean]
#        self.low_ink_boxes.sort(key=lambda x: x[1])
#         import Image
#         Image.fromarray(a*255).show()
#         import sys; sys.exit()
        del a, ctrs, hier

#        
#        self.low_ink_groups = dict((i,[]) for i in range(len(self.low_ink_boxes)))
#        self.low_ink_index = {}
##        print self.low_ink_boxes
#        imgdrawn = self.img_arr.copy()
#        for j, b in enumerate(self.low_ink_boxes):
#            bx, by, bw,bh = b
#            if bw < 1.33*self.char_mean:
##                print b
#                cv.rectangle(imgdrawn, (bx,by), (bx+bw,by+bh), 0)
#        import Image
##        Image.fromarray(imgdrawn*255).show()
#        Image.fromarray(a*255).show()
#        import sys; sys.exit()
#        
#        for i in self.indices: 
#        # By now, indices contains only non-tsek outer contours, so this is OK
#            x,y,w,h = self.get_boxes()[i]
#            
#            for j, b in enumerate(self.low_ink_boxes):
#                bx, by, bw,bh = b
#                if x >= bx and y >= by and x+w <= bx+bw and y+h <= by + bh:
#                    self.low_ink_groups[j].append(i)
#                    self.low_ink_index[i] = j
#                    break
        
        
    def _contours(self):

#        return cv.findContours(self.img_arr.copy(), mode=cv.RETR_CCOMP, method=cv.CHAIN_APPROX_SIMPLE)
#         return cv.findContours(self.img_arr.copy(), mode=self._contour_mode , method=cv.CHAIN_APPROX_SIMPLE)
        return cv.findContours(self.img_arr.copy(), mode=self._contour_mode, 
                               method=cv.CHAIN_APPROX_SIMPLE)

    
    def get_boxes(self):
        '''Retrieve bounding boxes. Create them if not yet cached'''
        if not self.boxes:
            self.boxes = self._boxes()
       
        return self.boxes
    
    def _boxes(self):
        return [cv.boundingRect(c) for c in self.contours]
    
    def get_indices(self):
        if not self.indices:
#            print self.tsek_mean, np.floor(self.tsek_std), np.ceil(self.tsek_std), self.tsek_std
            self.indices = [i for i, b in enumerate(self.get_boxes())] #if (
#               max(b[2], b[3]) <= 6 * self.char_mean   )] # and  # filter out too big
#            (b[2] > 10 or b[3] > 10 ))]
#               b[2] >= (np.floor(self.tsek_mean) - 
#               self.small_coef * np.floor(self.tsek_std))) ] # ... and too small
        return self.indices
        
    def _set_shape_measurements(self):
        width_measures = self.char_gaussians([b[2] for b in self.get_boxes() if
                                               b[2] < .1*self.img_arr.shape[1]])
        for i,j in zip(['char_mean', 'char_std', 'tsek_mean', 'tsek_std'], width_measures):
            setattr(self, i, j)
    
#        self._gaussians([b[2] for b in self.get_boxes() if b[2] < .1*self.img_arr.shape[1]])
#        self._draw_new_page()

    def update_shapes(self):
        self.contours, self.hierarchy = self._contours()
        self.boxes = self._boxes()
        self._set_shape_measurements()
        self.indices = [i for i, b in enumerate(self.get_boxes()) if (
               max(b[2], b[3]) <= 6 * self.char_mean )] 

#        self.indices = [i for i, b in enumerate(self.get_boxes()) if (
#               max(b[2], b[3]) <= 5 * self.char_mean and #)] # and  # filter out too big
#               b[2] >= (np.floor(self.tsek_mean) - 
#               self.small_coef * np.floor(self.tsek_std)))]
    
    def _draw_new_page(self):
        self.page_array = np.ones_like(self.img_arr)
        
        self.tall = set([i for i in self.get_indices() if 
                         self.get_boxes()[i][3] > 3*self.char_mean])
        
#        cv.drawContours(self.page_array, [self.contours[i] for i in 
#                        self.get_indices() if self.get_boxes()[i][2] <= self.tsek_mean + 3*self.tsek_std], 
#                        -1,0, thickness = -1)
#        
#        
#        self.page_array = cv.medianBlur(self.page_array, 19)
#        
#        cv.drawContours(self.page_array, [self.contours[i] for i in 
#                        self.get_indices() if self.get_boxes()[i][2] <= self.tsek_mean + 3*self.tsek_std], 
#                        -1,0, thickness = -1)
        cv.drawContours(self.page_array, [self.contours[i] for i in 
                        range(len(self.contours)) if 
                        self.get_boxes()[i][2] > self.smlmean + 3*self.smstd], 
                        -1,0, thickness = -1)
#        cv.drawContours(self.page_array, [self.contours[i] for i in 
#                        self.get_indices() if self.get_boxes()[i][3] <= 2*self.char_mean], 
#                        -1,0, thickness = -1)
#        cv.erode(self.page_array, None, self.page_array, iterations=2)
#        self.page_array = cv.morphologyEx(self.page_array, cv.MORPH_CLOSE, None,iterations=2)
        import Image
        Image.fromarray(self.page_array*255).show()
#        raw_input()
#        cv.dilate(self.page_array, None, self.page_array, iterations=1)
        
    @classmethod
    def char_gaussians(cls, widths):
        
        widths = np.array(widths)
        widths.shape = (len(widths),1)
        cls.median_width = np.median(widths)
        
        gmm = GMM(n_components = 2, n_iter=100)
        try:
            gmm.fit(widths)
        except ValueError:
            return (0,0,0,0)
        means = gmm.means_
        stds = np.sqrt(gmm.covars_)
        cls.gmm = gmm
        char_mean_ind = np.argmax(means)
        char_mean = float(means[char_mean_ind]) # Page character width mean
        char_std = float(stds[char_mean_ind][0]) # Page character std dev
        
        cls.tsek_mean_ind = np.argmin(means)
        
        tsek_mean = float(means[cls.tsek_mean_ind])
        tsek_std = float(stds[cls.tsek_mean_ind][0])
#        print gmm.converged_, 'converged'
        return (char_mean, char_std, tsek_mean, tsek_std)

#    def _gaussians(self, widths):
##        print widths
#        widths = np.array(widths)
#        widths.shape = (len(widths),1)
#        
#        gmm = GMM(n_components = 3, n_iter=100)
#        try:
#            gmm.fit(widths)
#        except ValueError:
#            return (0,0,0,0)
#        means = gmm.means_
#        stds = np.sqrt(gmm.covars_)
#        
#        argm = np.argmin(means)
#        self.smlmean = means[argm]
#        self.smstd = stds[argm]
        
#        cls.gmm = gmm
#        print gmm.converged_, 'converged'
#        from matplotlib import pyplot as plt
#        from matplotlib.mlab import normpdf
##        plt.subplot(211)
#        plt.title('tsek-char distributions, pre-segmentation')
#        
#        n,bins,p = plt.hist(widths, 200, range=(0,75), normed=True, color='#3B60FA')
##        plt.vlines(means, 0, np.array([max(n), max(n)]), linestyles='--')
#        for i, m in enumerate(means):
#            
#            plt.plot(bins, normpdf(bins, means[i], stds[i]),  label='fit', linewidth=1)
#            plt.fill_between(bins, normpdf(bins, means[i], stds[i]), color=(.58,.63,.8), alpha=0.09)
#
#        plt.show()

    def get_tops(self):       
        return [self.get_boxes()[i][1] for i in self.get_indices()]
    
#     @profile
    def draw_contour_and_children(self, root_ind, char_arr=None, offset=()):
        char_contours = [root_ind]
        root = self.hierarchy[0][root_ind]
        if root[2] >= 0:
            char_contours.append(root[2]) # add root's first child
            child_hier = self.hierarchy[0][root[2]] # get hier for 1st child
            if child_hier[0] >= 0: # if child has sib, continue to loop
                has_sibling = True
            else: has_sibling = False # ... else skip loop and draw
            
            while has_sibling:
                ind = child_hier[0] # get sibling's index
                char_contours.append(ind) # add sibling's index
                child_hier = self.hierarchy[0][ind] # get sibling's hierarchy
                if child_hier[0] < 0: # if sibling has sibling, continue loop
                    has_sibling = False
        
        if not hasattr(char_arr, 'dtype'):
            char_box = self.get_boxes()[root_ind]
            x,y,w,h = char_box
            char_arr = np.ones((h,w), dtype=np.uint8)
            offset = (-x, -y)
        cv.drawContours(char_arr, [self.contours[j] for j in char_contours], -1,0, thickness = -1, offset=offset)
        return char_arr
    
#     @profile
    def detect_num_lines(self, content_box_dict):
        '''content_box_dict has values {'chars':[], 'b':b, 'boxes':[], 
                                'num_boxes':0, 'num_chars':0}
        
        where chars are the indices of chars in the content box, b is the 
        the xywh dimensions of the box, boxes are the sub-boxes of the 
        document tree contained in this box (not box chars but large page-
        structuring boxes. 
        
        Note: page_type must be set to "pecha"
        '''
        
        cbx, cby, cbw, cbh = content_box_dict['b']
        
        
#        print self.img_arr.shape
#        print content_box_dict['b']
        
        cbox_arr = np.ones((cbh, cbw), dtype=self.img_arr.dtype)
        
        tsekmeanfloor = np.floor(self.tsek_mean)
        tsekstdfloor = np.floor(self.tsek_std)
        cv.drawContours(cbox_arr, [self.contours[i] for i in content_box_dict['chars']
                        if ((self.get_boxes()[i][2] > 
                        (tsekmeanfloor - 
               self.small_coef * tsekstdfloor)  or 
               self.get_boxes()[i][2] < .1*self.img_arr.shape[1]) and 
                            self.get_boxes()[i][3] > 10) 
                                   ], -1, 0, thickness=-1, offset=(-cbx, -cby))
        cbox_arr = cbox_arr[5:-5, :] # shorten from the top and bottom to help out trim in the event of small noise
#         cbox_arr = cbox_arr[0:-1, :] # shorten from the top and bottom to help out trim in the event of small noise
#         cbox_arr = trim(cbox_arr)
#         cbox_arr = cv.dilate(cbox_arr, None, iterations=3)
        cbox_arr = cv.erode(cbox_arr, None, iterations=5)
#         cbox_arr = cv.erode(cbox_arr, None, iterations=1)
#         cbox_arr = cv.blur(cbox_arr, (150, 3))
#         cbox_arr = cv.blur(cbox_arr*255, (75, 19))
#         cbox_arr = cv.blur(cbox_arr*255, (75, 19))
        cbox_arr = to255(cbox_arr)

        cv.blur(cbox_arr, (75, 19), dst=cbox_arr)
#         k = cv.blur(to255(cbox_arr), (75, 19))


        ####################
#         print 'warning: using non default (127) line count threshold'
#         ret, cbox_arr = cv.threshold(cbox_arr, 127, 1, cv.THRESH_BINARY)
        ####################
        ret, cbox_arr = cv.threshold(cbox_arr, 200, 1, cv.THRESH_BINARY) #DEFAULT!
        ###################



#         cbox_arr = cv.blur(cbox_arr, (90, 80))
#         cbox_arr = cv.blur(cbox_arr, (130, 100))
#        cbox_arr = cv.morphologyEx(cbox_arr, cv.MORPH_OPEN, None,iterations=6)
#         print cbox_arr[np.where(1.0>cbox_arr)]
#         import Image
#         Image.fromarray(cbox_arr*255).show()
#         sys.exit()
#         sc = 1/255.0
#         cbox_arr *= sc
        vsum = cbox_arr.sum(axis=1)

#        from scipy.ndimage.measurements import extrema
#         vsum_smoothed = gaussian_filter1d(vsum, 10)
        vsum_smoothed = gaussian_filter1d(vsum, 25) ###DEFAULT
#         vsum_smoothed = gaussian_filter1d(vsum, 13)
        len_vsum = len(vsum)
#        print vsum
#        print extrema(vsum)
#        print argrelmin(vsum)
#        print argrelmax(vsum)
#        from scipy.interpolate import interp1d
        
#        fx = interp1d(range(len(vsum)), vsum, kind='cubic')
        fx = UnivariateSpline(range(len_vsum), vsum_smoothed)
        tck = splrep(range(len_vsum), fx(range(len_vsum)))
        y = splev(range(len_vsum), tck, der=1)
        tck = splrep(range(len_vsum), y)
#        roots = sproot(tck)
#        print len(roots)
        mins = argrelmin(fx(range(len_vsum)))
#        mins = argrelmin(vsum_smoothed, order=2)
#        mins_min = min([vsum[m] for m in mins[0]])

        ### Filter false peaks that show up from speckles on page
#        mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= .05]
#        mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= .1]
#        mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= 1.5*self.char_mean/float(cbw)]
#         mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= .025]
        mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= .01]
#         mins = [m for m in mins[0] if (cbw - vsum[m])/float(cbw) >= .0075]
#        mins = [m for m in mins[0] ]
#        print mins, len_vsum
#        print len(mins[0])
#        print mins

#        for m in mins:
#            cbox_arr[m, :] = 1
            
#        
        self.num_lines = len(mins)
#         print self.num_lines
#         self.num_lines = 19
#        print self.num_lines
#        print self.num_lines
#         self.num_lines = 5
#        print self.num_lines
#        print dir(fx)
#        print fx
#        print dir(fx)
#        from scipy.optimize import minimize_scalar
#        print minimize_scalar(fx)
        
        
        #############################
#         plot b spline of image profile. number of minima is line number
#         (or should be...
#         from matplotlib import pyplot as plt
#         plt.plot(range(len(vsum)), fx(range(len(vsum))))
# #        plt.plot(range(len(vsum)), y) # alternatively, plt fist derivative of the b spline
# #        plt.bar(range(vsum.shape[0]), vsum) ## plot horiz profile as bar chart
#         plt.vlines(mins, 0, max(vsum))
#         plt.show()
        ################################
        
#        import sys
#        sys.exit()
        
    
    def draw_hough_outline(self, arr):
        
        arr = invert_bw(arr)
#         import Image
#         Image.fromarray(arr*255).show()
#        h = cv.HoughLinesP(arr, 2, np.pi/4, 5, minLineLength=arr.shape[0]*.10)
        h = cv.HoughLinesP(arr, 2, np.pi/4, 1, minLineLength=arr.shape[0]*.15, maxLineGap=5) #This
#         h = cv.HoughLinesP(arr, 2, np.pi/4, 1, minLineLength=arr.shape[0]*.15, maxLineGap=1)
#        h = cv.HoughLinesP(arr, 2, np.pi/4, 1, minLineLength=arr.shape[0]*.15)
        PI_O4 = np.pi/4
#        if h and h.any():
#        if self._page_type == 'pecha':
#            color = 1
#            thickness = 10
#        else: # Attempt to erase horizontal lines if page_type == book. 
#            # Why? Horizontal lines can break LineCluster if they are broken
#            # e.g. couldn't be filtered out prior to line_breaker.py
#            color = 0
#            thickness = 10
        if h is not None:
            for line in h[0]:
                new = (line[2]-line[0], line[3] - line[1])
                val = (new[0]/np.sqrt(np.dot(new, new)))
                theta = np.arccos(val)
                if theta >= PI_O4: # Vertical line
#                    print line[1] - line[3]
#                     cv.line(arr, (line[0], 0), (line[0], arr.shape[0]), 1, thickness=10)
                    if line[0] < .5*arr.shape[1]:
                        arr[:,:line[0]+12] = 0
                    else:
                        arr[:,line[0]-12:] = 0
                else: # horizontal line
                    if line[2] - line[0] >= .15 * arr.shape[1]:
#                         cv.line(arr, (0, line[1]), (arr.shape[1], line[1]), 1, thickness=50)
                        if line[1] < .5 *arr.shape[0]:
                            arr[:line[1]+17, :] = 0
                        else:
                            arr[line[1]-5:,:] = 0
        

        return ((arr*-1)+1).astype(np.uint8)

    def save_margin_content(self, tree, content_box):
        '''Look at margin content and try to OCR it. Save results in a pickle
        file of a dictionary object:
        d = {'left':['margin info 1', ...], 'right':['right margin info 1', etc]}
        
        Margin content is tricky since letters are often not defined as well
        as the main page content. The current OCR implementation also stumbles
        on text with very few characters. Page numbers don't do well for some
        reason...
        '''
        
        import cPickle as pickle
        import os
        content_box_right_edge = tree[content_box]['b'][0] + tree[content_box]['b'][2]
        inset = 20

        right_content = []
        left_content = []
        for brnch in tree:
            if brnch != content_box:
                outer_box = brnch

                if tree[outer_box]['num_chars'] != 0:
                    bx = tree[outer_box]['b']
                    arr = self.img_arr[bx[1]+inset:bx[1]+bx[3]-inset, bx[0]+inset:bx[0]+bx[2]-inset]

                    text = ''
                    if bx[0] > content_box_right_edge:
                        arr = rotate(arr, -90, cval=1)
                        text = construct_page(rec_main(arr, line_break_method='line_cut', page_type='book', page_info={'flname': 'margin content'}))
                        if text:
                            right_content.append(text)
                    else:
                        arr = rotate(arr, 90, cval=1)
                        text = construct_page(rec_main(arr, line_break_method='line_cut', page_type='book', page_info={'flname': 'margin content'}))
                        if text:
                            left_content.append(text)
        pklname = os.path.join(os.path.dirname(self.flpath), os.path.basename(self.flpath)[:-4]+'_margin_content.pkl')
        pickle.dump({'right':right_content, 'left':left_content}, open(pklname, 'wb'))
#        import sys; sys.exit()
    
#     @profile
    def set_pecha_layout(self):
#         a = cv.erode(self.img_arr.copy(), None,iterations=2)
        #         import Image
#         Image.fromarray(cbox_arr*255).show()
        a = self.img_arr.copy()
        
        if self.img_arr.shape[1] > 2*self.img_arr.shape[0]:
            self._page_type = 'pecha'
        else:
            self._page_type = 'book'
        
        if self._page_type == 'pecha': # Page is pecha format
            a = self.draw_hough_outline(a)
            
        self.img_arr = a.copy()
        self.update_shapes()
        
#        a= cv.morphologyEx(a, cv.MORPH_OPE#         if self._page_type == 'pecha': # Page is pecha format
#             a = self.draw_hough_outline(a)N, None,iterations=5)
#        a = cv.medianBlur(a, 9)
#         import Image
#         Image.fromarray(a*255).show()
        a = cv.GaussianBlur(a, (5, 5), 0)
#        print a.dtype
#        a = cv.GaussianBlur(a, (5, 5), 0)
#        a = self.img_arr.copy()
#         n = np.ones_like(a)
        contours, hierarchy = cv.findContours(a.copy(), mode=cv.RETR_TREE, method=cv.CHAIN_APPROX_SIMPLE)
        
        
        ## Most of this logic for identifying rectangles comes from the 
        ## squares.py sample in opencv source code.
        def angle_cos(p0, p1, p2):
            d1, d2 = (p0-p1).astype('float'), (p2-p1).astype('float')
            return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
        
        border_boxes = []
        
        for j,cnt in enumerate(contours):
            cnt_len = cv.arcLength(cnt, True)
            orig_cnt = cnt.copy()
            cnt = cv.approxPolyDP(cnt, 0.02*cnt_len, True)
            if len(cnt) == 4 and cv.contourArea(cnt) > 1000 and cv.isContourConvex(cnt):
                cnt = cnt.reshape(-1, 2)
                max_cos = np.max([angle_cos(cnt[i], 
                                            cnt[(i+1) % 4], cnt[(i+2) % 4] ) 
                                  for i in range(4)])
                if max_cos < 0.1:
#                    print 'got one at %d' % j
#                    n = np.ones_like(a)
                    b = cv.boundingRect(orig_cnt)
#                     if self.clear_hr:
#                         print 'Warning: you are clearing text on a pecha page'
#                         self.img_arr[0:b[1]+b[3], :] = 1
                    x,y,w,h = b
#                    b = [x+10, y+10, w-10, h-10]
                    border_boxes.append(b)
#                     cv.rectangle(n, (x,y), (x+w, y+h), 0)
#                     cv.drawContours(n, [cnt], -1,0, thickness = 5)
#                    import Image
#                    Image.fromarray(n*255).save('/tmp/rectangles_%d.png' % j )
        
#         import Image
#         Image.fromarray(n*255).show()
        border_boxes.sort(key=lambda b: (b[0],b[1]))
        #border_boxes = border_boxes
        
        def get_edges(b):
            l = b[0]
            r = b[0] + b[2]
            t = b[1]
            b = b[1] + b[3]
            return (l,r,t,b)

        def bid(b):
            return '%d-%d-%d-%d' % (b[0],b[1],b[2],b[3])
       
        tree = {}
        for b in border_boxes:
            tree[bid(b)] = {'chars':[], 'b':b, 'boxes':[], 'num_boxes':0, 'num_chars':0}    
        
        def b_contains_nb(b,nb):
            l1,r1,t1,b1 = get_edges(b)
            l2,r2,t2,b2 = get_edges(nb)
            return l1 <= l2 and r2 <= r1 and t1 <= t2 and b1 >= b2
            
        for i, b in enumerate(border_boxes):
            bx,by,bw,bh = b
            self.img_arr[by:by+1,bx+3:bx+bw-3] = 1
            self.img_arr[by+bh,by+bh-1:bx+3:bx+bw-3] = 1
            for nb in border_boxes[i+1:]:
                if b_contains_nb(b, nb):
                    tree[bid(b)]['boxes'].append(bid(nb))
                    tree[bid(b)]['num_boxes'] = len(tree[bid(b)]['boxes'])
        
        self.update_shapes()
#         import Image
#         Image.fromarray(self.img_arr*255).show()
        
        tree_keys = tree.keys()
        tree_keys.sort(key=lambda x: tree[x]['num_boxes'])
                
        ## Assign contours to boxes
        for i in self.get_indices():
            for k in tree_keys:
                box = tree[k]
                b = box['b']
                
#                print box['num_boxes']
                char_box = self.get_boxes()[i]
                if b_contains_nb(b, char_box):
                    tree[k]['chars'].append(i)
                    tree[k]['num_chars'] = len(tree[k]['chars'])
                    break
#        import pprint
#        pprint.pprint(tree)
        
        def qualified_box(bx):
            '''Helper function that ignores boxes that contain other boxes.
            This is useful for finding the main content box which should
            be among the innermost boxes that have no box children '''
            
            if tree[bx]['num_boxes'] == 0:
                return tree[bx]['num_chars']
            else:
                return -1
        
#        content_box = max(tree, key=lambda bx: tree[bx]['num_chars'])
        content_box = max(tree, key=qualified_box)
#        print tree[content_box]['num_chars']
#        self.indices = [i for i in tree[content_box]['chars'] if self.boxes[i][2] >= (np.floor(self.tsek_mean) - 
#               self.small_coef * np.floor(self.tsek_std))] 
#         self.indices = [i for i in tree[content_box]['chars'] if self.boxes[i][2] >= (np.floor(self.tsek_mean) - 
#                1.5 * np.floor(self.tsek_std))] 
        self.indices = [i for i in tree[content_box]['chars'] if self.boxes[i][2] >= 7] 
        
        
        self.detect_num_lines(tree[content_box])
#        self.save_margin_content(tree, content_box)


#        import Image
#        Image.fromarray(cbox_arr*255).show()
#        raw_input()


#                            codecs.open(os.path.join(os.path.dirname(self.flpath), os.path.basename(self.flpath)[:-4] + '_left_' + str(left_count)+'.txt'), 'w', 'utf-8').write(text)
#                            left_count += 1
#                        print construct_page(rec_main(arr, line_break_method='line_cluster', page_type='pecha', k_groups=1, page_info={'flname': 'margin content'}))
        
        

        
#        self.margins = {'left':[], 'right':[]}
#        import re
#        reg = re.compile(ur'([0-9]{1,4})')
#        for brnch in tree:
#            if brnch != content_box:
#                outer_box = brnch
#                chars = tree[outer_box]['chars']
##                
##                left = [] # container for chars left to the content box
##                right = []
##                
##                for c in chars:
##                    if self.boxes[c][0] < content_box_right_edge:
##                        left.append(c)
##                    else:
##                        right.append(c)
##                
##                sections = {}
##                if left:
##                    sections['left'] = combine_many_boxes([self.boxes[c] for c in left])
##                if right:
##                    sections['right'] = combine_many_boxes([self.boxes[c] for c in right])
##                import Image
##                for section in sections:
##                    x,y,w,h = sections[section]
###                    print w, self.tsek_mean
##                    if not w > .05*self.img_arr.shape[1]:
##                        arr = np.ones((h, w), dtype=self.img_arr.dtype)
##                        cv.drawContours(arr, [self.contours[i] for i in locals()[section]], -1, 0, thickness=-1, offset=(-x,-y))
##                        Image.fromarray(arr*255).show()
##                        if section == 'left':
##                            arr = rotate(arr, 90)
##                        else:
##                            arr = rotate(arr, -90)
##                        arr = add_padding(arr[3:-3, 3:-3], padding=5)
##                        area = w*h
##                        # The resulting blob shouldn't be mostly black or white
##                        # as either would suggest there are no actual 
##                        # characters in the arr
##                        if .25 < arr.sum() / float(area) < .95:
##                            text = construct_page(rec_main(arr, line_break_method='line_cut', page_type='book', page_info={'flname': section + ' margin content'}))
##                            self.margins[section].append((sections[section], text.strip()))
#                            
#                            
#                            
#                            
#                            
#                            
##        print self.margins
##                raw_input()
#                chars.sort(key=lambda x: self.boxes[x][1] + self.boxes[x][3])
#                chars = chars[::-1]
#                numbers = []
##                
##        #        print content_box
##                content_box_right_edge = tree[content_box]['b'][0] + tree[content_box]['b'][2]
#                parents = [self.hierarchy[0][c][-1] for c in chars]
#                common_parent = int(statsmode(parents)[0])
#                chars = [c for c in chars if self.hierarchy[0][c][-1] == common_parent]
#                for c in chars:
#        #            print self.boxes[c]
#        #            print hierarchy[0][c]
#        #            if self.hierarchy[0][c][-2] > -1:
#                    if self.hierarchy[0][c][-1] == 0 and self.boxes[c][0] > content_box_right_edge:
##                    if self.boxes[c][0] > content_box_right_edge:
#        #                x,y,w,h = self.get_boxes()[c]
#        #                arr = np.ones((h,w), dtype=self.img_arr.dtype)
#        #                cv.drawContours(arr, contours[c], -1, 0, offset=(-x,-y))
#        #                Image.fromarray(arr*255).show()
#                        outchar = self.draw_contour_and_children(c)
#                        outchar = rotate(outchar, -90)
#        #                Image.fromarray(outchar*255).show()
#                        feat = normalize_and_extract_features(outchar)
#                        char = label_chars[fast_cls.predict(feat)[0]]
#                        numbers.append(char)
#                num = ''.join(numbers)
#                res = reg.search(num)
#                if res:
#                    self.num = res.group(0)
#        for char in tree[content_box]['chars']:
#            b = self.get_boxes()[char]
#            x,y,w,h = b
##            cv.drawContours(n, [self.contours[char]], 
##                        -1,0, thickness = -1)
#            cv.rectangle(n, (x,y), (x+w, y+h), 0)
##            
#        import ImageDraw
#        import Image
#        im = Image.fromarray(n*255)
#        draw = ImageDraw.Draw(im)
#        for char in tree[content_box]['chars']:
##            label = str(self.hierarchy[0][char])
##            i = char
#            b = self.get_boxes()[char]
#            x,y,w,h = b
##            if self.hierarchy[0][i][0] < 0 and self.hierarchy[0][i][1] < 0 and self.hierarchy[0][i][2] < 0:
##            draw.text(self.get_boxes()[char][0:2], str(self.get_boxes()[char][3]))
#            pos = self.get_boxes()[char][0:2]
##            draw.text((pos[0]+pos[0]%5, pos[1]), str(self.hierarchy[0][char]))
#            draw.text((pos[0], pos[1]+self.get_boxes()[char][3]), str(w))
#        im.show()
#        im.save('/tmp/sample-hierarchy.png')
#        Image.fromarray(n*255).show()
        
        # Get chars in the margins.. The following won't work if 
        # There's one more than 1 box containing chars at the side of 
        # the main content box.. (this does happen....)
        
#        self.right_margin = None
#        self.left_margin = None
#        for k in tree:
#            if k != content_box:
#                box = tree[k]
#                if box['num_chars']:
#                    if box['b'][0] < tree[content_box]['b'][0]:
#                        self.left_margin = box
#                    elif box['b'][0] > tree[content_box]['b'][0]:
#                        self.right_margin = box