NEwPaint_Text

# -*- coding: utf-8 -*-
import os
import itertools
import codecs
import re
import datetime
import cairocffi as cairo
import editdistance
import numpy as np
from scipy import ndimage
import random
import pylab
from keras import backend as K
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers import Input, Dense, Activation
from keras.layers import Reshape, Lambda
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.layers.recurrent import GRU
from keras.optimizers import SGD
from keras.utils.data_utils import get_file
from keras.preprocessing import image
import keras.callbacks
import cv2

import matplotlib.pyplot as plt



# paints the string in a random location the bounding box
# also uses a random font, a slight random rotation,
# and a random amount of speckle noise
def noisy(noise_typ,image):
   if noise_typ == "gauss":
      row,col,ch= image.shape
      mean = 0
      var = 0.1
      sigma = var**0.5
      gauss = np.random.normal(mean,sigma,(row,col,ch))
      gauss = gauss.reshape(row,col,ch)
      noisy = image + gauss
      return noisy
   elif noise_typ == "s&p":
       row,col,ch = image.shape
       s_vs_p = 0.5
       amount = 0.004
       out = np.copy(image)
       # Salt mode
       num_salt = np.ceil(amount * image.size * s_vs_p)
       coords = [np.random.randint(0, i - 1, int(num_salt))
              for i in image.shape]
       out[coords] = 1

       # Pepper mode
       num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
       coords = [np.random.randint(0, i - 1, int(num_pepper))
              for i in image.shape]
       out[coords] = 0
       return out
   elif noise_typ == "poisson":
      vals = len(np.unique(image))
      vals = 2 ** np.ceil(np.log2(vals))
      noisy = np.random.poisson(image * vals) / float(vals)
      return noisy
   elif noise_typ =="speckle":
      row,col,ch = image.shape
      gauss = np.random.randn(row,col,ch)
      gauss = gauss.reshape(row,col,ch)
      noisy = image + image * gauss
      return noisy
def imsave(fname, arr, vmin=None, vmax=None, cmap='gray', format=None, origin=None):
    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
    from matplotlib.figure import Figure

    fig = Figure(figsize=arr.shape[::-1], dpi=1, frameon=False)
    canvas = FigureCanvas(fig)
    fig.figimage(arr, cmap=cmap, vmin=vmin, vmax=vmax, origin=origin)
    fig.savefig(fname, dpi=1, format=format)
def speckle(img):
    severity = np.random.uniform(0, 0.4)
    print(severity)
    blur = ndimage.gaussian_filter(np.random.randn(*img.shape) * severity, 1)
    img_speck = (img + blur)
    img_speck[img_speck > 1] = 1
    img_speck[img_speck <= 0] = 0
    return img_speck
def sp_noise(image,prob):

    output = np.zeros(image.shape,np.uint8)
    thres = 1 - prob
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            rdn = random.random()
            if rdn < prob:
                output[i][j] = 0
            elif rdn > thres:
                output[i][j] = 255
            else:
                output[i][j] = image[i][j]
    return output

def paint_text(text):
    newtext = ""
    import random
    banglachars = "অআইঈউঊঋএঐওঔকখগঘঙচছজঝঞটঠডঢণতথদধনপফবভমযরলশষসহড়ঢ়য়ঃৎং"
    # text = "নড়চড়"
    chars = []
    for i in range(0, len(banglachars), 3):
        chars.append(banglachars[i:i + 3])
    for i in range(0, len(text), 3):
        ch = text[i:i + 3]
        itsoke = 1
        for j in chars:
            if j == ch:
                itsoke = 0

    # text="অ"
    w = 280
    h = 68
    fontsize = 65
    x, y = 8, 54
    surface = cairo.ImageSurface(cairo.FORMAT_RGB24, w, h)
    import random

    FlagBlack = random.randint(0, 4)
    # FlagBlack = 1

    with cairo.Context(surface) as context:
        context.set_source_rgb(1,1,1)

        context.paint()
        # this font list works in CentOS 7
        multi_fonts = True

        # if multi_fonts:
        # fonts = ['Bangla', 'AponaLohit', 'Nikosh', 'Siyamrupali', 'kalpurush', 'AdorshoLipi', 'Likhan', 'Lohit Bengali',
        #          'SutonnyBanglaOMJ', 'Sagar', 'Rupali', 'Mukti','Solaimanlipi']
        fonts = ['SutonnyBanglaOMJ', 'Lohit Bengali', 'AponaLohit', 'Nikosh', 'Siyamrupali', 'SutonnyBanglaOMJ',
                 'kalpurush', 'AdorshoLipi', 'Bensen', 'SutonnyBanglaOMJ']
        # fonts = ['Bangla', 'AponaLohit', 'Nikosh', 'Siyamrupali', 'kalpurush', 'AdorshoLipi', 'Likhan', 'Lohit Bengali',
        #          'SutonnyBanglaOMJ', 'Sagar', 'Rupali', 'Mukti']
        f = np.random.choice(fonts)
        boldornotbold = 1

        if (f == "Solaimanlipi"):
            fontsize = random.randint(62, 65)
            x, y = 8, 54
        elif (f == "Lohit Bengali"):
            fontsize = random.randint(54, 58)
            x, y = 6, 54
        elif (f == "AponaLohit"):
            fontsize = random.randint(52, 56)
            x, y = 4, 54
        elif (f == "Nikosh"):
            fontsize = random.randint(72, 76)
            x, y = 8, 58
        elif (f == "Siyamrupali"):
            fontsize = random.randint(48, 52)
            x, y = 12, 54
        elif (f == "kalpurush"):
            fontsize = random.randint(62, 66)
            x, y = 12, 56
        elif (f == "AdorshoLipi"):
            fontsize = random.randint(52, 56)
            x, y = 9, 56
        elif (f == "Bensen"):
            fontsize = random.randint(70, 74)
            x, y = 10, 56
        elif (f == "SutonnyBanglaOMJ"):
            fontsize = random.randint(68, 72)
            x, y = 10, 56
        # context.select_font_face(np.random.choice(fonts), cairo.FONT_SLANT_NORMAL,
        #                              np.random.choice([cairo.FONT_WEIGHT_BOLD, cairo.FONT_WEIGHT_NORMAL]))
        context.select_font_face(f, cairo.FONT_SLANT_NORMAL,
                                 boldornotbold)
        # else:
        #    context.select_font_face('Mukti' , cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_BOLD)
        import random

        context.set_font_size(fontsize)
        box = context.text_extents(text)
        border_w_h = (4, 4)

        max_shift_x = w - box[2]
        max_shift_y = h - box[3] - border_w_h[1]
        top_left_x = np.random.randint(0, int(max_shift_x))

        top_left_y = np.random.randint(0, int(max_shift_y))

        context.move_to(x, y)
        # context.move_to(top_left_x - int(box[0]), top_left_y - int(box[1]))

        context.set_source_rgb(0,0,0)

        # print(text)
        context.show_text(text)

    buf = surface.get_data()
    a = np.frombuffer(buf, np.uint8)

    a.shape = (h, w, 4)
    # a = sp_noise(a,0.01)
    # plt.imshow(a,cmap='gray')
    # plt.show()
    a = a[:, :, 0]  # grab single channel
    # import cv2
    vis2=a
    # vis2 = cv2.cvtColor(a, cv2.COLOR_GRAY2BGR)
    ran = random.randint(1, 4)
    # print(ran)
    vis2 = cv2.resize(vis2, (280 / ran, 68 / ran))
    vis2 = cv2.resize(vis2, (280, 68))
    # a = imsave('dataset/file_'+str(random.randint(0,1999))+'.png',a)
    a=vis2

    a = cv2.adaptiveThreshold(a, 255, cv2.ADAPTIVE_THRESH_MEAN_C, \
                              cv2.THRESH_BINARY, 11, 2)
    a = np.expand_dims(a, 0)
    a = image.random_rotation(a, 3 * (w - top_left_x) / w + 1)
    a = speckle(a)
    a = np.reshape(a, (68, 280))


    import matplotlib.pyplot as plt

    plt.imshow(a,cmap='gray')
    plt.show()

    # a = a.astype(np.float32) / 255
    a = np.expand_dims(a, 0)



    # b=a
    # print(b.shape)
    # b=np.reshape(b,(68,280))
    # print(b.shape)
    # return a
for i in range(5):
    paint_text("০১-২৩৪")