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| 1 | +# MIT License |
| 2 | +# |
| 3 | +# Copyright (c) 2017 OsciiArt |
| 4 | +# |
| 5 | +# Permission is hereby granted, free of charge, to any person obtaining a copy |
| 6 | +# of this software and associated documentation files (the "Software"), to deal |
| 7 | +# in the Software without restriction, including without limitation the rights |
| 8 | +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| 9 | +# copies of the Software, and to permit persons to whom the Software is |
| 10 | +# furnished to do so, subject to the following conditions: |
| 11 | +# |
| 12 | +# The above copyright notice and this permission notice shall be included in all |
| 13 | +# copies or substantial portions of the Software. |
| 14 | +# |
| 15 | +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 16 | +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 17 | +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 18 | +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 19 | +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 20 | +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 21 | +# SOFTWARE. |
| 22 | + |
| 23 | +#!/usr/bin/env python |
| 24 | +# -*- coding: utf-8 -*- |
| 25 | + |
| 26 | +from keras.models import model_from_json |
| 27 | +import numpy as np |
| 28 | +import pandas as pd |
| 29 | +from PIL import Image |
| 30 | +import pickle |
| 31 | +import os |
| 32 | + |
| 33 | + |
| 34 | +# parameters |
| 35 | +model_path = "model/model.json" |
| 36 | +weight_path = "model/weight.hdf5" |
| 37 | +image_path = 'sample images/original images/21 original.png' # put the path of the image that you convert. |
| 38 | +new_width = 0 # adjust the width of the image. the original width is used if new_width = 0. |
| 39 | +input_shape = [64, 64, 1] |
| 40 | + |
| 41 | + |
| 42 | +def add_mergin(img, mergin): |
| 43 | + if mergin!=0: |
| 44 | + img_new = np.ones([img.shape[0] + 2 * mergin, img.shape[1] + 2 * mergin], dtype=np.uint8) * 255 |
| 45 | + img_new[mergin:-mergin, mergin:-mergin] = img |
| 46 | + else: |
| 47 | + img_new = img |
| 48 | + return img_new |
| 49 | + |
| 50 | + |
| 51 | +def pickleload(path): |
| 52 | + with open(path, mode='rb') as f: |
| 53 | + data = pickle.load(f) |
| 54 | + return data |
| 55 | + |
| 56 | + |
| 57 | +# load model |
| 58 | +json_string = open(model_path).read() |
| 59 | +model = model_from_json(json_string) |
| 60 | +model.load_weights(weight_path) |
| 61 | +print("model load done") |
| 62 | + |
| 63 | +char_list_path = "data/char_list.csv" |
| 64 | +char_list = pd.read_csv(char_list_path, encoding="cp932") |
| 65 | +print("len(char_list)", len(char_list)) |
| 66 | +# print(char_list.head()) |
| 67 | +char_list = char_list[char_list['frequency']>=10] |
| 68 | +char_list = char_list['char'].as_matrix() |
| 69 | + |
| 70 | +for k, v in enumerate(char_list): |
| 71 | + if v==" ": |
| 72 | + space = k |
| 73 | + break |
| 74 | +print("class index of 1B space:", space) |
| 75 | + |
| 76 | + |
| 77 | +mergin = (input_shape[0] - 18) // 2 |
| 78 | +img = Image.open(image_path) |
| 79 | +orig_width, orig_height = img.size |
| 80 | +if new_width==0: new_width = orig_width |
| 81 | +new_height = int(img.size[1] * new_width / img.size[0]) |
| 82 | +img = img.resize((new_width, new_height), Image.LANCZOS) |
| 83 | +img = np.array(img) |
| 84 | +if len(img.shape) == 3: |
| 85 | + img = img[:, :, 0] |
| 86 | + |
| 87 | +img_new = np.ones([img.shape[0]+2*mergin+18, img.shape[1]+2*mergin+18], |
| 88 | + dtype=np.uint8) * 255 |
| 89 | +img_new[mergin:mergin+new_height, mergin:mergin+new_width] = img |
| 90 | +img = (img_new.astype(np.float32)) / 255 |
| 91 | + |
| 92 | +char_dict_path = "data/char_dict.pkl" |
| 93 | +char_dict = pickleload(char_dict_path) |
| 94 | + |
| 95 | +print("len(char_dict)", len(char_dict)) |
| 96 | + |
| 97 | +output_dir = "output/" |
| 98 | +if not os.path.isdir(output_dir): |
| 99 | + os.makedirs(output_dir) |
| 100 | + |
| 101 | +for slide in range(18): |
| 102 | + print("converting:", slide) |
| 103 | + num_line = (img.shape[0] - input_shape[0]) // 18 |
| 104 | + img_width = img.shape[1] |
| 105 | + new_line = np.ones([1, img_width]) |
| 106 | + img = np.concatenate([new_line, img], axis=0) |
| 107 | + predicts = [] |
| 108 | + text = [] |
| 109 | + for h in range(num_line): |
| 110 | + w = 0 |
| 111 | + penalty = 1 |
| 112 | + predict_line = [] |
| 113 | + text_line = "" |
| 114 | + while w <= img_width - input_shape[1]: |
| 115 | + input_img = img[h*18:h*18+ input_shape[0], w:w+input_shape[1]] |
| 116 | + input_img = input_img.reshape([1,input_shape[0], input_shape[1], 1]) |
| 117 | + predict = model.predict(input_img) |
| 118 | + if penalty: predict[0, space] = 0 |
| 119 | + predict = np.argmax(predict[0]) |
| 120 | + penalty = (predict==space) |
| 121 | + char = char_list[predict] |
| 122 | + predict_line.append(char) |
| 123 | + char_width = char_dict[char].shape[1] |
| 124 | + w += char_width |
| 125 | + text_line += char |
| 126 | + predicts.append(predict_line) |
| 127 | + text.append(text_line+'\r\n') |
| 128 | + # print(text) |
| 129 | + |
| 130 | + img_aa = np.ones_like(img, dtype=np.uint8) * 255 |
| 131 | + |
| 132 | + for h in range(num_line): |
| 133 | + w = 0 |
| 134 | + for char in predicts[h]: |
| 135 | + # print("w", w) |
| 136 | + char_width = char_dict[char].shape[1] |
| 137 | + char_img = 255 - char_dict[char].astype(np.uint8) * 255 |
| 138 | + img_aa[h*18:h*18+16, w:w+char_width] = char_img |
| 139 | + w += char_width |
| 140 | + |
| 141 | + img_aa = Image.fromarray(img_aa) |
| 142 | + img_aa = img_aa.crop([0,slide,new_width, new_height+slide]) |
| 143 | + save_path = output_dir + os.path.basename(image_path)[:-4] + '_'\ |
| 144 | + + 'w' + str(new_width) \ |
| 145 | + + '_slide' + str(slide) + '.png' |
| 146 | + img_aa.save(save_path) |
| 147 | + |
| 148 | + f=open(save_path[:-4] + '.txt', 'w') |
| 149 | + f.writelines(text) |
| 150 | + f.close() |
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