File size: 13,289 Bytes
f6f8d06
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
import cv2, os, re, random
import numpy as np
# import tesserocr
# from tesserocr import PyTessBaseAPI, PSM, OEM



class TextSpan(object):
    def __init__(self, top_bnd=None, bottom_bnd=None, left_bnd=None, right_bnd=None):
        self.top = top_bnd
        self.bottom = bottom_bnd
        self.height = self.bottom - self.top if bottom_bnd is not None else None

        self.left = left_bnd
        self.right = right_bnd
        self.width = self.right - self.left if right_bnd is not None else None

    def set_top(self, top_bnd):
        self.top = top_bnd
        return True

    def set_bottom(self, bottom_bnd):
        if self.top is None or bottom_bnd <= self.top:
            return False
        self.bottom = bottom_bnd
        self.height = self.bottom - self.top
        return True

    def set_left(self, left_bnd):
        self.left = left_bnd
        return True
    
    def set_right(self, right_bnd):
        if self.left is None or right_bnd <= self.left:
            return False
        self.right = right_bnd
        self.width = right_bnd - self.left
        return True

    def __getitem__(self, index):
        if isinstance(index, int) and index >=0 and index < 4:
            return [self.left, self.top, self.right, self.bottom][index]
        else:
            raise AttributeError(f'Invalid key: {index}')

def split_step0(span, thresh, sumby_yaxis, thresh2=None) -> list[TextSpan]:
    candidate_pnts = (np.where(sumby_yaxis[span.top: span.bottom] > thresh)[0] + span.top).tolist()
    span_list = []
    if len(candidate_pnts) == 0:
        return None
    stride_tol = 1
    span0, span1 = TextSpan(candidate_pnts[0]), TextSpan()
    for pnt_ind in range(len(candidate_pnts)-1):
        if candidate_pnts[pnt_ind+1] - candidate_pnts[pnt_ind] > stride_tol:
            if not span0.set_bottom(candidate_pnts[pnt_ind]):
                continue
            span_list = split_step1(span0, span_list, thresh=thresh2, sumby_yaxis=sumby_yaxis)
            span1.set_top(candidate_pnts[pnt_ind+1])
            span0 = span1
            span1 = TextSpan()

    if len(candidate_pnts)-1 == 0:
        if candidate_pnts[0] == candidate_pnts[-1]:
            span_list = None
        else:
            span0 = TextSpan(candidate_pnts[0], candidate_pnts[-1])
            span_list = split_step1(span0, span_list, thresh=thresh2, sumby_yaxis=sumby_yaxis)
    elif span0.top != candidate_pnts[-1]:
        span0.set_bottom(candidate_pnts[-1])
        span_list = split_step1(span0, span_list, thresh=thresh2, sumby_yaxis=sumby_yaxis)

    return span_list



def split_step1(span, span_list, thresh=None, sumby_yaxis=None):
    if thresh is None:
        span_list.append(span)
        return span_list
    else:
        subspan_list = split_step0(span, thresh, sumby_yaxis)
        # print(np.var(sumby_yaxis[span.top:span.bottom]))
        if subspan_list is not None:

            _, maxspan = find_span(subspan_list, max)
            _, minspan = find_span(subspan_list, min)
            
            sum_height = sum(c.height for c in subspan_list)
            
            if maxspan.height / minspan.height > 2.5 or sum_height / span.height < 0.3 or len(subspan_list) == 1:
                subspan_list = None
        if subspan_list is not None and len(subspan_list) > 1:
            span_list += subspan_list
        else:
            span_list.append(span)
        return span_list



def shrink_span_list(src_img, span_list, shrink_vert_space=True, shrink_hor_space=True):
    height, width = src_img.shape[0], src_img.shape[1]

    sum_spacing = 0
    if shrink_vert_space:
        for ii in range(len(span_list)-1):
            line_spacing = span_list[ii+1].top - span_list[ii].bottom
            sum_spacing += line_spacing
            line_spacing = int(round(line_spacing / 2))
            span_list[ii+1].top -= line_spacing
            span_list[ii].set_bottom(span_list[ii].bottom + line_spacing)
        
        if len(span_list) >= 2:
            mean_spacing = int(0.5 * round(sum_spacing / (len(span_list)-1)))
            span_list[0].top = max(0, span_list[0].top-mean_spacing)
            span_list[0].set_bottom(span_list[0].bottom)
            span_list[-1].set_bottom(min(src_img.shape[0], span_list[-1].bottom))

    left_var, middle_var = -1, -1
    if shrink_hor_space:
        left_pnts, middle_pnts = [], []
        for ii in range(len(span_list)):
            s = span_list[ii]
            im = src_img[s.top: s.bottom, 0: width]
            sumby_yaxis = np.mean(im, axis=0)
            content_array = np.where(sumby_yaxis > 10)[0].tolist()
            left, right = 0, width
            if len(content_array) != 0:
                left, right = content_array[0], content_array[-1]
            span_list[ii].set_left(left)
            span_list[ii].set_right(right)
            s = span_list[ii]
            left_pnts.append(left)
            middle_pnts.append((left+right)/2)
        left_var, middle_var = np.var(np.array(left_pnts)), np.var(np.array(middle_pnts))
            
    return span_list, (left_var, middle_var)
        
        
        
def find_span(span_list, max_or_min=max, key="height"):
    if key=="height":
        return max_or_min(enumerate(span_list), key=(lambda x: span_list[x[0]].height), default = -1)
    else:
        return max_or_min(enumerate(span_list), key=(lambda x: span_list[x[0]].width), default = -1)



def discard_spans(span_list, thresh_ratio=0.3):
    index, max_span = find_span(span_list, max)
    max_height = max_span.height
    height_thresh = max_height * thresh_ratio
    new_spanlist = []
    for sp in span_list:
        if sp.height < height_thresh:
            continue
        new_spanlist.append(sp)

    return new_spanlist



def plot_mapresult(sumbyvector, xlength, span_list=None, thresh=None):
    '''for experiment'''
    try:
        import matplotlib.pyplot as plt
        plt.plot(sumbyvector)
        plt.ylabel('div pnt value')
        plt.xlabel('div pnt coord')
        s = [0, 255]
        x_cords = []
        if span_list is not None:
            for sp in span_list:
                x_cords.append(sp.top)
                x_cords.append(sp.bottom)
        if thresh is not None:
            for tr in thresh:
                plt.vlines(x = x_cords, ymin = 0, ymax = max(s), 
                        colors = 'purple', 
                        label = 'vline_multiple - full height')
                plt.hlines(y = tr * sumbyvector.mean(), xmin = 0, xmax = xlength, linestyles='--')
        plt.show()
    except:
        pass



def box(width, height):
    return np.ones((height, width), dtype=np.uint8)


def crop_img(img, crop_ratio=0.2, clip_width=True, dilate=False):
    h, w = img.shape[:2]
    moments = cv2.moments(img)
    area = moments['m00']
    if area != 0:
        mean_x = int(round(moments['m10'] / area))
        mean_y = int(round(moments['m01'] / area))
        crop_r = int(round(crop_ratio * w))
        if clip_width:
            crop_x0 = np.clip(mean_x - crop_r, 0, w)
            crop_x1 = np.clip(mean_x + crop_r, 0, w)
            if crop_x1 > crop_x0:
                img = img[:, crop_x0: crop_x1]
        else:
            crop_r = np.clip(crop_r * 2, 0, w - 1)
            img = img[:, crop_r:]
    img = np.copy(img)
    if clip_width and dilate:
        w = int(round(w/7))
        if w > 1:
            img = cv2.dilate(img, box(w, 1), 1)
    return img, img.shape[0], img.shape[1]



def split_textblock(src_img, crop_ratio=0.2, blur=False, show_process=False, discard=True, shrink=True, recheck=False, clip_width=True, dilate=True):
    
    if blur:
        src_img = cv2.GaussianBlur(src_img,(3,3),cv2.BORDER_DEFAULT)
    if crop_ratio > 0:
        img, height, width = crop_img(src_img, crop_ratio=crop_ratio, clip_width=clip_width, dilate=dilate)
    else:
        img, height, width = src_img, src_img.shape[0], src_img.shape[1]
    
    sumby_yaxis = img.mean(axis=1)
    bound0 = np.where(sumby_yaxis > sumby_yaxis.mean() * 0.1)[0].tolist()
    vars = (-1, -1)
    
    if len(bound0) < 2:
        return [TextSpan(0, height-1, 0, width - 1)], vars

    base_span = TextSpan(bound0[0], bound0[-1])
    meanby_yaxis = sumby_yaxis.mean()

    thresh_ratio = [0.4, 0.8]
    thresh0 = meanby_yaxis * thresh_ratio[0]
    thresh2 = meanby_yaxis * thresh_ratio[1]

    span_list = split_step0(base_span, thresh0, sumby_yaxis, thresh2=thresh2)
    if span_list is None:
        return None, None
    if discard:
        span_list = discard_spans(span_list)
    if shrink:
        span_list, vars = shrink_span_list(src_img, span_list)

    '''for experiment'''
    if show_process:
        plot_mapresult(sumby_yaxis, height, span_list=span_list, thresh=thresh_ratio)

    if recheck and len(span_list) == 1 and crop_ratio > 0:
        return split_textblock(src_img, crop_ratio==-1, show_process=show_process, discard=discard, shrink=shrink, recheck=False)
    
    valid_span_list = []
    for span in span_list:
        if span.top is None:
            span.set_top(0)
        if span.left is None:
            span.set_left(0)
        if span.right is None:
            span.set_right(width)
        if span.bottom is None:
            span.set_bottom(height)
        valid_span_list.append(span)

    return valid_span_list, vars



# def tessocr_img2text(img, lang):
#     img = Image.fromarray(img)
#     if re.findall("vert", lang):
#         psm = PSM.SINGLE_BLOCK_VERT_TEXT
#     else:
#         psm = PSM.SINGLE_LINE
#     return tesserocr.image_to_text(img, psm=psm, lang=lang, path=TESSDATA_PATH)

# def tessocr_img2text(img, lang):
#     psm = "5" if re.findall("vert", lang) else "7"
#     config = r'--tessdata-dir "models\tessdata" --psm ' + psm
#     return pytesseract.image_to_string(img, lang=lang, config=config)


def textspan2list(span_list):
    converted_list = []
    for ii, s in enumerate(span_list):
        converted_list.append([])
        converted_list[ii].append(s.top)
        converted_list[ii].append(s.left)
        converted_list[ii].append(s.bottom)
        converted_list[ii].append(s.right)
    return converted_list



def manga_split(img, bbox=None, show_process=False, clip_width=False) -> list[TextSpan]:

    im = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
    imh, imw = im.shape[:2]

    if bbox is None:
        bbox = [0, 0, im.shape[1], im.shape[0]]
    bboxes = [bbox]

    span_list, _ = split_textblock(im, show_process=show_process, shrink=False, recheck=True, discard=False, crop_ratio=0)
    if span_list is None:
        return [TextSpan(0, 0, im.shape[1], im.shape[0])]
    # span_list, _ = shrink_span_list(im, span_list, shrink_vert_space=False)
        
    for ii, span in enumerate(span_list):
        left = span.left
        right = span.right
        if ii == 0:
            span.left = 0
        else:
            span.left = span.top
        if ii == len(span_list) - 1:
            span.right = im.shape[0]
        else:
            span.right = span.bottom
        span.top =  imw - right
        span.bottom = imw - left
        span.height = span.bottom - span.top
        span.width = span.right - span.left

    return span_list


def tessocr_img2text_linemode(img, span_list=None, combine_lines=True, show_process=False, gen_data=False, lang="comic6k", jpn_vert=False):
    if jpn_vert:
        lang = "jpn_vert"
        img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) 
    hig = img.shape[0]
    wid = img.shape[1]
    if hig * wid < 5:
        return '', -1, -1

    bw = 3
    text = ''
    alignment, vars = 0, (-1, -1)
    if span_list is None:
        span_list, vars = split_textblock(img, show_process=show_process)
        _, maxspan = find_span(span_list, max)
        maxh = bw*2 + maxspan.height
    else:
        maxh = max([s[2]-s[0] for s in span_list])
        maxh = bw*2 + maxh
    
    long_line = []
    word_space = int(round(maxh / 8))
    img = 255 - img
    for ind, s in enumerate(span_list):
        if isinstance(s, list):
            im = img[s[0]: s[2], s[1]: s[3]]
        else:
            im = img[s.top: s.bottom, s.left: s.right]
        
        hw1 = int(round((maxh - im.shape[0])/2))
        hw2 = maxh - hw1 - im.shape[0]
        dst = cv2.copyMakeBorder(im, hw1, hw2, word_space, word_space, cv2.BORDER_CONSTANT, None, value=[255, 255, 255])

        if not combine_lines:
            text += tessocr_img2text(dst, lang=lang) +'\n'
        else:
            long_line.append(dst)
        if show_process:
            cv2.imshow(str(ind), dst)

    if combine_lines:
        long_line = cv2.hconcat(long_line)
        if jpn_vert:
            long_line = cv2.rotate(long_line, cv2.ROTATE_90_CLOCKWISE) 
        if show_process:
            cv2.namedWindow("long line:", cv2.WINDOW_NORMAL)
            cv2.imshow("long line:", long_line)
        if gen_data:
            return long_line
        res = tessocr_img2text(long_line, lang=lang)
    mean_height = -1
    if len(span_list) != 0:
        if isinstance(span_list[0], list):
            mean_height = np.mean(np.array([s[2]-s[0] for s in span_list]))
        else:
            mean_height = np.mean(np.array([s.height for s in span_list]))
        alignment = 1 if vars[1] < vars[0] else 0
    return res, mean_height, alignment