├── .gitignore ├── LICENSE ├── README.md ├── __init__.py ├── config.py ├── image.py ├── img ├── __init__.py ├── table-ceil.png ├── table-detect.jpg ├── table-detect.png ├── table-detect.xlsx ├── table-detectceil.png └── table-line.png ├── models └── table-detect.cfg ├── requirements.txt ├── table_build.py ├── table_ceil.py ├── table_detect.py ├── table_line.py ├── train ├── __init__.py ├── dataset-line │ └── 0 │ │ ├── 0.jpg │ │ ├── 0.json │ │ ├── 1.jpg │ │ ├── 1.json │ │ ├── 13.jpg │ │ ├── 13.json │ │ ├── 2.jpg │ │ ├── 2.json │ │ ├── 5.jpg │ │ └── 5.json └── train.py └── utils.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Python: 2 | *.py[cod] 3 | *.so 4 | *.egg 5 | *.egg-info 6 | *.pth 7 | *.pb 8 | *.h5 9 | *.pbtxt 10 | *.hdf5 11 | *.weights 12 | *.traineddata 13 | dist 14 | buil 15 | .DS_Store* 16 | .ipynb_checkpoints 17 | __pycache__ 18 | dump.rdb 19 | define 20 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 chineseocr 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # table-detect 2 | 3 | ## table detect(yolo) , table line(unet) （表格检测/表格单元格定位） 4 | 5 | links(下载链接): http://gofile.me/4Nlqh/fNHlWzVWo 6 | download models weights and move to ./modes 7 | 8 | ### test table detect（表格检测） 9 | 10 | ` 11 | python table_detect.py --jpgPath img/table-detect.jpg 12 | ` 13 | 14 | ### test table ceil detect with unet（表格识别输出到excel） 15 | 16 | ` 17 | python table_ceil.py --isToExcel True --jpgPath img/table-detect.jpg 18 | ` 19 | 20 | 21 | ## train table line(训练表格) 22 | ### label table with labelme(https://github.com/wkentaro/labelme) 23 | ` 24 | python train/train.py 25 | ` 26 | 27 | 28 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | @author: chineseocr 6 | """ 7 | -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | 6 | @author: chineseocr 7 | """ 8 | 9 | tableModelDetectPath = 'models/table-detect.weights' 10 | tableModeLinePath = "models/table-line.h5" 11 | -------------------------------------------------------------------------------- /image.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | image 6 | @author: chineseocr 7 | """ 8 | 9 | import base64 10 | import json 11 | 12 | import cv2 13 | import numpy as np 14 | import six 15 | from PIL import Image 16 | 17 | 18 | def plot_lines(img, lines, linetype=2): 19 | tmp = np.copy(img) 20 | for line in lines: 21 | p1, p2 = line 22 | cv2.line(tmp, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])), (0, 0, 0), linetype, lineType=cv2.LINE_AA) 23 | 24 | return Image.fromarray(tmp) 25 | 26 | 27 | def base64_to_PIL(string): 28 | try: 29 | 30 | base64_data = base64.b64decode(string) 31 | buf = six.BytesIO() 32 | buf.write(base64_data) 33 | buf.seek(0) 34 | img = Image.open(buf).convert('RGB') 35 | return img 36 | except: 37 | return None 38 | 39 | 40 | def read_json(p): 41 | with open(p) as f: 42 | jsonData = json.loads(f.read()) 43 | shapes = jsonData.get('shapes') 44 | imageData = jsonData.get('imageData') 45 | lines = [] 46 | labels = [] 47 | for shape in shapes: 48 | lines.append(shape['points']) 49 | [x0, y0], [x1, y1] = shape['points'] 50 | label = shape['label'] 51 | if label == '0': 52 | if abs(y1 - y0) > 500: 53 | label = '1' 54 | elif label == '1': 55 | if abs(x1 - x0) > 500: 56 | label = '0' 57 | 58 | labels.append(label) 59 | img = base64_to_PIL(imageData) 60 | return img, lines, labels 61 | 62 | 63 | from numpy import cos, sin, pi 64 | 65 | 66 | def rotate(x, y, angle, cx, cy): 67 | """ 68 | 点(x,y) 绕(cx,cy)点旋转 69 | """ 70 | angle = angle * pi / 180 71 | x_new = (x - cx) * cos(angle) - (y - cy) * sin(angle) + cx 72 | y_new = (x - cx) * sin(angle) + (y - cy) * cos(angle) + cy 73 | return x_new, y_new 74 | 75 | 76 | def box_rotate(box, angle=0, imgH=0, imgW=0): 77 | """ 78 | 对坐标进行旋转 逆时针方向 0\90\180\270, 79 | """ 80 | x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] 81 | if angle == 90: 82 | x1_, y1_ = y2, imgW - x2 83 | x2_, y2_ = y3, imgW - x3 84 | x3_, y3_ = y4, imgW - x4 85 | x4_, y4_ = y1, imgW - x1 86 | 87 | elif angle == 180: 88 | x1_, y1_ = imgW - x3, imgH - y3 89 | x2_, y2_ = imgW - x4, imgH - y4 90 | x3_, y3_ = imgW - x1, imgH - y1 91 | x4_, y4_ = imgW - x2, imgH - y2 92 | 93 | elif angle == 270: 94 | x1_, y1_ = imgH - y4, x4 95 | x2_, y2_ = imgH - y1, x1 96 | x3_, y3_ = imgH - y2, x2 97 | x4_, y4_ = imgH - y3, x3 98 | else: 99 | x1_, y1_, x2_, y2_, x3_, y3_, x4_, y4_ = x1, y1, x2, y2, x3, y3, x4, y4 100 | 101 | return (x1_, y1_, x2_, y2_, x3_, y3_, x4_, y4_) 102 | 103 | 104 | def angle_transpose(p, angle, w, h): 105 | x, y = p 106 | if angle == 90: 107 | x, y = y, w - x 108 | elif angle == 180: 109 | x, y = w - x, h - y 110 | elif angle == 270: 111 | x, y = h - y, x 112 | return x, y 113 | 114 | 115 | def img_argument(img, lines, labels, size=(512, 512)): 116 | w, h = img.size 117 | if np.random.randint(0, 100) > 80: 118 | degree = np.random.uniform(-5, 5) 119 | else: 120 | degree = 0 121 | # degree = np.random.uniform(-5,5) 122 | newlines = [] 123 | for line in lines: 124 | p1, p2 = line 125 | p1 = rotate(p1[0], p1[1], degree, w / 2, h / 2) 126 | p2 = rotate(p2[0], p2[1], degree, w / 2, h / 2) 127 | newlines.append([p1, p2]) 128 | # img = img.rotate(-degree,center=(w/2,h/2),resample=Image.BILINEAR,fillcolor=(128,128,128)) 129 | img = img.rotate(-degree, center=(w / 2, h / 2), resample=Image.BILINEAR) 130 | angle = np.random.choice([0, 90, 180, 270], 1)[0] 131 | newlables = [] 132 | for i in range(len(newlines)): 133 | p1, p2 = newlines[i] 134 | p1 = angle_transpose(p1, angle, w, h) 135 | p2 = angle_transpose(p2, angle, w, h) 136 | newlines[i] = [p1, p2] 137 | if angle in [90, 270]: 138 | if labels[i] == '0': 139 | newlables.append('1') 140 | else: 141 | newlables.append('0') 142 | else: 143 | newlables.append(labels[i]) 144 | 145 | if angle == 90: 146 | img = img.transpose(Image.ROTATE_90) 147 | elif angle == 180: 148 | img = img.transpose(Image.ROTATE_180) 149 | elif angle == 270: 150 | img = img.transpose(Image.ROTATE_270) 151 | 152 | return img, newlines, newlables 153 | 154 | 155 | def fill_lines(img, lines, linetype=2): 156 | tmp = np.copy(img) 157 | for line in lines: 158 | p1, p2 = line 159 | cv2.line(tmp, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])), 255, linetype, lineType=cv2.LINE_AA) 160 | 161 | return tmp 162 | 163 | 164 | def get_img_label(p, size, linetype=1): 165 | img, lines, labels = read_json(p) 166 | img, lines = img_resize(img, lines, target_size=512, max_size=1024) 167 | img, lines, labels = img_argument(img, lines, labels, size) 168 | img, lines, labels = get_random_data(img, lines, labels, size=size) 169 | 170 | lines = np.array(lines) 171 | labels = np.array(labels) 172 | labelImg0 = np.zeros(size[::-1], dtype='uint8') 173 | labelImg1 = np.zeros(size[::-1], dtype='uint8') 174 | 175 | ind = np.where(labels == '0')[0] 176 | labelImg0 = fill_lines(labelImg0, lines[ind], linetype=linetype) 177 | ind = np.where(labels == '1')[0] 178 | labelImg1 = fill_lines(labelImg1, lines[ind], linetype=linetype) 179 | 180 | labelY = np.zeros((size[1], size[0], 2), dtype='uint8') 181 | labelY[:, :, 0] = labelImg0 182 | labelY[:, :, 1] = labelImg1 183 | labelY = labelY > 0 184 | return np.array(img), lines, labelY 185 | 186 | 187 | from matplotlib.colors import rgb_to_hsv, hsv_to_rgb 188 | 189 | 190 | def rand(a=0, b=1): 191 | return np.random.rand() * (b - a) + a 192 | 193 | 194 | def get_random_data(image, lines, labels, size=(1024, 1024), jitter=.3, hue=.1, sat=1.5, val=1.5): 195 | '''random preprocessing for real-time data augmentation''' 196 | 197 | iw, ih = image.size 198 | 199 | # resize image 200 | w, h = size 201 | new_ar = w / h * rand(1 - jitter, 1 + jitter) / rand(1 - jitter, 1 + jitter) 202 | # scale = rand(.2, 2) 203 | scale = rand(0.2, 3) 204 | if new_ar < 1: 205 | nh = int(scale * h) 206 | nw = int(nh * new_ar) 207 | else: 208 | nw = int(scale * w) 209 | nh = int(nw / new_ar) 210 | image = image.resize((nw, nh), Image.BICUBIC) 211 | 212 | # place image 213 | dx = int(rand(0, w - nw)) 214 | dy = int(rand(0, h - nh)) 215 | new_image = Image.new('RGB', (w, h), (128, 128, 128)) 216 | new_image.paste(image, (dx, dy)) 217 | image = new_image 218 | 219 | # distort image 220 | hue = rand(-hue, hue) 221 | sat = rand(1, sat) if rand() < .5 else 1 / rand(1, sat) 222 | val = rand(1, val) if rand() < .5 else 1 / rand(1, val) 223 | x = rgb_to_hsv(np.array(image) / 255.) 224 | x[..., 0] += hue 225 | x[..., 0][x[..., 0] > 1] -= 1 226 | x[..., 0][x[..., 0] < 0] += 1 227 | x[..., 1] *= sat 228 | x[..., 2] *= val 229 | x[x > 1] = 1 230 | x[x < 0] = 0 231 | image_data = hsv_to_rgb(x) # numpy array, 0 to 1 232 | N = len(lines) 233 | for i in range(N): 234 | p1, p2 = lines[i] 235 | p1 = p1[0] * nw / iw + dx, p1[1] * nh / ih + dy 236 | p2 = p2[0] * nw / iw + dx, p2[1] * nh / ih + dy 237 | lines[i] = [p1, p2] 238 | return image_data, lines, labels 239 | 240 | 241 | def gen(paths, batchsize=2, linetype=2): 242 | num = len(paths) 243 | i = 0 244 | while True: 245 | # sizes = [512,512,512,512,640,1024] ##多尺度训练 246 | # size = np.random.choice(sizes,1)[0] 247 | size = 640 248 | 249 | X = np.zeros((batchsize, size, size, 3)) 250 | Y = np.zeros((batchsize, size, size, 2)) 251 | for j in range(batchsize): 252 | if i >= num: 253 | i = 0 254 | np.random.shuffle(paths) 255 | p = paths[i] 256 | i += 1 257 | 258 | # linetype=2 259 | img, lines, labelImg = get_img_label(p, size=(size, size), linetype=linetype) 260 | X[j] = img 261 | Y[j] = labelImg 262 | 263 | yield X, Y 264 | 265 | 266 | def img_resize(im, lines, target_size=600, max_size=1500): 267 | w, h = im.size 268 | im_size_min = np.min(im.size) 269 | im_size_max = np.max(im.size) 270 | 271 | im_scale = float(target_size) / float(im_size_min) 272 | if max_size is not None: 273 | if np.round(im_scale * im_size_max) > max_size: 274 | im_scale = float(max_size) / float(im_size_max) 275 | 276 | im = im.resize((int(w * im_scale), int(h * im_scale)), Image.BICUBIC) 277 | N = len(lines) 278 | for i in range(N): 279 | p1, p2 = lines[i] 280 | p1 = p1[0] * im_scale, p1[1] * im_scale 281 | p2 = p2[0] * im_scale, p2[1] * im_scale 282 | lines[i] = [p1, p2] 283 | 284 | return im, lines 285 | -------------------------------------------------------------------------------- /img/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 10 01:33:19 2020 5 | 6 | @author: chineseocr 7 | """ 8 | -------------------------------------------------------------------------------- /img/table-ceil.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-ceil.png -------------------------------------------------------------------------------- /img/table-detect.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-detect.jpg -------------------------------------------------------------------------------- /img/table-detect.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-detect.png -------------------------------------------------------------------------------- /img/table-detect.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-detect.xlsx -------------------------------------------------------------------------------- /img/table-detectceil.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-detectceil.png -------------------------------------------------------------------------------- /img/table-line.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/img/table-line.png -------------------------------------------------------------------------------- /models/table-detect.cfg: -------------------------------------------------------------------------------- 1 | [net] 2 | # Testing 3 | # batch=1 4 | # subdivisions=1 5 | # Training 6 | batch=32 7 | subdivisions=16 8 | width=608 9 | height=608 10 | channels=3 11 | momentum=0.9 12 | decay=0.0005 13 | angle=0 14 | saturation = 1.5 15 | exposure = 1.5 16 | hue=.1 17 | 18 | learning_rate=0.0001 19 | burn_in=1000 20 | max_batches = 50200 21 | policy=steps 22 | steps=40000,45000 23 | scales=.1,.1 24 | 25 | 26 | 27 | [convolutional] 28 | batch_normalize=1 29 | filters=32 30 | size=3 31 | stride=1 32 | pad=1 33 | activation=leaky 34 | 35 | # Downsample 36 | 37 | [convolutional] 38 | batch_normalize=1 39 | filters=64 40 | size=3 41 | stride=2 42 | pad=1 43 | activation=leaky 44 | 45 | [convolutional] 46 | batch_normalize=1 47 | filters=32 48 | size=1 49 | stride=1 50 | pad=1 51 | activation=leaky 52 | 53 | [convolutional] 54 | batch_normalize=1 55 | filters=64 56 | size=3 57 | stride=1 58 | pad=1 59 | activation=leaky 60 | 61 | [shortcut] 62 | from=-3 63 | activation=linear 64 | 65 | # Downsample 66 | 67 | [convolutional] 68 | batch_normalize=1 69 | filters=128 70 | size=3 71 | stride=2 72 | pad=1 73 | activation=leaky 74 | 75 | [convolutional] 76 | batch_normalize=1 77 | filters=64 78 | size=1 79 | stride=1 80 | pad=1 81 | activation=leaky 82 | 83 | [convolutional] 84 | batch_normalize=1 85 | filters=128 86 | size=3 87 | stride=1 88 | pad=1 89 | activation=leaky 90 | 91 | [shortcut] 92 | from=-3 93 | activation=linear 94 | 95 | [convolutional] 96 | batch_normalize=1 97 | filters=64 98 | size=1 99 | stride=1 100 | pad=1 101 | activation=leaky 102 | 103 | [convolutional] 104 | batch_normalize=1 105 | filters=128 106 | size=3 107 | stride=1 108 | pad=1 109 | activation=leaky 110 | 111 | [shortcut] 112 | from=-3 113 | activation=linear 114 | 115 | # Downsample 116 | 117 | [convolutional] 118 | batch_normalize=1 119 | filters=256 120 | size=3 121 | stride=2 122 | pad=1 123 | activation=leaky 124 | 125 | [convolutional] 126 | batch_normalize=1 127 | filters=128 128 | size=1 129 | stride=1 130 | pad=1 131 | activation=leaky 132 | 133 | [convolutional] 134 | batch_normalize=1 135 | filters=256 136 | size=3 137 | stride=1 138 | pad=1 139 | activation=leaky 140 | 141 | [shortcut] 142 | from=-3 143 | activation=linear 144 | 145 | [convolutional] 146 | batch_normalize=1 147 | filters=128 148 | size=1 149 | stride=1 150 | pad=1 151 | activation=leaky 152 | 153 | [convolutional] 154 | batch_normalize=1 155 | filters=256 156 | size=3 157 | stride=1 158 | pad=1 159 | activation=leaky 160 | 161 | [shortcut] 162 | from=-3 163 | activation=linear 164 | 165 | [convolutional] 166 | batch_normalize=1 167 | filters=128 168 | size=1 169 | stride=1 170 | pad=1 171 | activation=leaky 172 | 173 | [convolutional] 174 | batch_normalize=1 175 | filters=256 176 | size=3 177 | stride=1 178 | pad=1 179 | activation=leaky 180 | 181 | [shortcut] 182 | from=-3 183 | activation=linear 184 | 185 | [convolutional] 186 | batch_normalize=1 187 | filters=128 188 | size=1 189 | stride=1 190 | pad=1 191 | activation=leaky 192 | 193 | [convolutional] 194 | batch_normalize=1 195 | filters=256 196 | size=3 197 | stride=1 198 | pad=1 199 | activation=leaky 200 | 201 | [shortcut] 202 | from=-3 203 | activation=linear 204 | 205 | 206 | [convolutional] 207 | batch_normalize=1 208 | filters=128 209 | size=1 210 | stride=1 211 | pad=1 212 | activation=leaky 213 | 214 | [convolutional] 215 | batch_normalize=1 216 | filters=256 217 | size=3 218 | stride=1 219 | pad=1 220 | activation=leaky 221 | 222 | [shortcut] 223 | from=-3 224 | activation=linear 225 | 226 | [convolutional] 227 | batch_normalize=1 228 | filters=128 229 | size=1 230 | stride=1 231 | pad=1 232 | activation=leaky 233 | 234 | [convolutional] 235 | batch_normalize=1 236 | filters=256 237 | size=3 238 | stride=1 239 | pad=1 240 | activation=leaky 241 | 242 | [shortcut] 243 | from=-3 244 | activation=linear 245 | 246 | [convolutional] 247 | batch_normalize=1 248 | filters=128 249 | size=1 250 | stride=1 251 | pad=1 252 | activation=leaky 253 | 254 | [convolutional] 255 | batch_normalize=1 256 | filters=256 257 | size=3 258 | stride=1 259 | pad=1 260 | activation=leaky 261 | 262 | [shortcut] 263 | from=-3 264 | activation=linear 265 | 266 | [convolutional] 267 | batch_normalize=1 268 | filters=128 269 | size=1 270 | stride=1 271 | pad=1 272 | activation=leaky 273 | 274 | [convolutional] 275 | batch_normalize=1 276 | filters=256 277 | size=3 278 | stride=1 279 | pad=1 280 | activation=leaky 281 | 282 | [shortcut] 283 | from=-3 284 | activation=linear 285 | 286 | # Downsample 287 | 288 | [convolutional] 289 | batch_normalize=1 290 | filters=512 291 | size=3 292 | stride=2 293 | pad=1 294 | activation=leaky 295 | 296 | [convolutional] 297 | batch_normalize=1 298 | filters=256 299 | size=1 300 | stride=1 301 | pad=1 302 | activation=leaky 303 | 304 | [convolutional] 305 | batch_normalize=1 306 | filters=512 307 | size=3 308 | stride=1 309 | pad=1 310 | activation=leaky 311 | 312 | [shortcut] 313 | from=-3 314 | activation=linear 315 | 316 | 317 | [convolutional] 318 | batch_normalize=1 319 | filters=256 320 | size=1 321 | stride=1 322 | pad=1 323 | activation=leaky 324 | 325 | [convolutional] 326 | batch_normalize=1 327 | filters=512 328 | size=3 329 | stride=1 330 | pad=1 331 | activation=leaky 332 | 333 | [shortcut] 334 | from=-3 335 | activation=linear 336 | 337 | 338 | [convolutional] 339 | batch_normalize=1 340 | filters=256 341 | size=1 342 | stride=1 343 | pad=1 344 | activation=leaky 345 | 346 | [convolutional] 347 | batch_normalize=1 348 | filters=512 349 | size=3 350 | stride=1 351 | pad=1 352 | activation=leaky 353 | 354 | [shortcut] 355 | from=-3 356 | activation=linear 357 | 358 | 359 | [convolutional] 360 | batch_normalize=1 361 | filters=256 362 | size=1 363 | stride=1 364 | pad=1 365 | activation=leaky 366 | 367 | [convolutional] 368 | batch_normalize=1 369 | filters=512 370 | size=3 371 | stride=1 372 | pad=1 373 | activation=leaky 374 | 375 | [shortcut] 376 | from=-3 377 | activation=linear 378 | 379 | [convolutional] 380 | batch_normalize=1 381 | filters=256 382 | size=1 383 | stride=1 384 | pad=1 385 | activation=leaky 386 | 387 | [convolutional] 388 | batch_normalize=1 389 | filters=512 390 | size=3 391 | stride=1 392 | pad=1 393 | activation=leaky 394 | 395 | [shortcut] 396 | from=-3 397 | activation=linear 398 | 399 | 400 | [convolutional] 401 | batch_normalize=1 402 | filters=256 403 | size=1 404 | stride=1 405 | pad=1 406 | activation=leaky 407 | 408 | [convolutional] 409 | batch_normalize=1 410 | filters=512 411 | size=3 412 | stride=1 413 | pad=1 414 | activation=leaky 415 | 416 | [shortcut] 417 | from=-3 418 | activation=linear 419 | 420 | 421 | [convolutional] 422 | batch_normalize=1 423 | filters=256 424 | size=1 425 | stride=1 426 | pad=1 427 | activation=leaky 428 | 429 | [convolutional] 430 | batch_normalize=1 431 | filters=512 432 | size=3 433 | stride=1 434 | pad=1 435 | activation=leaky 436 | 437 | [shortcut] 438 | from=-3 439 | activation=linear 440 | 441 | [convolutional] 442 | batch_normalize=1 443 | filters=256 444 | size=1 445 | stride=1 446 | pad=1 447 | activation=leaky 448 | 449 | [convolutional] 450 | batch_normalize=1 451 | filters=512 452 | size=3 453 | stride=1 454 | pad=1 455 | activation=leaky 456 | 457 | [shortcut] 458 | from=-3 459 | activation=linear 460 | 461 | # Downsample 462 | 463 | [convolutional] 464 | batch_normalize=1 465 | filters=1024 466 | size=3 467 | stride=2 468 | pad=1 469 | activation=leaky 470 | 471 | [convolutional] 472 | batch_normalize=1 473 | filters=512 474 | size=1 475 | stride=1 476 | pad=1 477 | activation=leaky 478 | 479 | [convolutional] 480 | batch_normalize=1 481 | filters=1024 482 | size=3 483 | stride=1 484 | pad=1 485 | activation=leaky 486 | 487 | [shortcut] 488 | from=-3 489 | activation=linear 490 | 491 | [convolutional] 492 | batch_normalize=1 493 | filters=512 494 | size=1 495 | stride=1 496 | pad=1 497 | activation=leaky 498 | 499 | [convolutional] 500 | batch_normalize=1 501 | filters=1024 502 | size=3 503 | stride=1 504 | pad=1 505 | activation=leaky 506 | 507 | [shortcut] 508 | from=-3 509 | activation=linear 510 | 511 | [convolutional] 512 | batch_normalize=1 513 | filters=512 514 | size=1 515 | stride=1 516 | pad=1 517 | activation=leaky 518 | 519 | [convolutional] 520 | batch_normalize=1 521 | filters=1024 522 | size=3 523 | stride=1 524 | pad=1 525 | activation=leaky 526 | 527 | [shortcut] 528 | from=-3 529 | activation=linear 530 | 531 | [convolutional] 532 | batch_normalize=1 533 | filters=512 534 | size=1 535 | stride=1 536 | pad=1 537 | activation=leaky 538 | 539 | [convolutional] 540 | batch_normalize=1 541 | filters=1024 542 | size=3 543 | stride=1 544 | pad=1 545 | activation=leaky 546 | 547 | [shortcut] 548 | from=-3 549 | activation=linear 550 | 551 | ###################### 552 | 553 | [convolutional] 554 | batch_normalize=1 555 | filters=512 556 | size=1 557 | stride=1 558 | pad=1 559 | activation=leaky 560 | 561 | [convolutional] 562 | batch_normalize=1 563 | size=3 564 | stride=1 565 | pad=1 566 | filters=1024 567 | activation=leaky 568 | 569 | [convolutional] 570 | batch_normalize=1 571 | filters=512 572 | size=1 573 | stride=1 574 | pad=1 575 | activation=leaky 576 | 577 | [convolutional] 578 | batch_normalize=1 579 | size=3 580 | stride=1 581 | pad=1 582 | filters=1024 583 | activation=leaky 584 | 585 | [convolutional] 586 | batch_normalize=1 587 | filters=512 588 | size=1 589 | stride=1 590 | pad=1 591 | activation=leaky 592 | 593 | [convolutional] 594 | batch_normalize=1 595 | size=3 596 | stride=1 597 | pad=1 598 | filters=1024 599 | activation=leaky 600 | 601 | [convolutional] 602 | size=1 603 | stride=1 604 | pad=1 605 | filters=21 606 | activation=linear 607 | 608 | [yolo] 609 | mask = 6,7,8 610 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 611 | classes=2 612 | num=9 613 | jitter=.3 614 | ignore_thresh = .5 615 | truth_thresh = 1 616 | random=1 617 | 618 | [route] 619 | layers = -4 620 | 621 | [convolutional] 622 | batch_normalize=1 623 | filters=256 624 | size=1 625 | stride=1 626 | pad=1 627 | activation=leaky 628 | 629 | [upsample] 630 | stride=2 631 | 632 | [route] 633 | layers = -1, 61 634 | 635 | 636 | 637 | [convolutional] 638 | batch_normalize=1 639 | filters=256 640 | size=1 641 | stride=1 642 | pad=1 643 | activation=leaky 644 | 645 | [convolutional] 646 | batch_normalize=1 647 | size=3 648 | stride=1 649 | pad=1 650 | filters=512 651 | activation=leaky 652 | 653 | [convolutional] 654 | batch_normalize=1 655 | filters=256 656 | size=1 657 | stride=1 658 | pad=1 659 | activation=leaky 660 | 661 | [convolutional] 662 | batch_normalize=1 663 | size=3 664 | stride=1 665 | pad=1 666 | filters=512 667 | activation=leaky 668 | 669 | [convolutional] 670 | batch_normalize=1 671 | filters=256 672 | size=1 673 | stride=1 674 | pad=1 675 | activation=leaky 676 | 677 | [convolutional] 678 | batch_normalize=1 679 | size=3 680 | stride=1 681 | pad=1 682 | filters=512 683 | activation=leaky 684 | 685 | [convolutional] 686 | size=1 687 | stride=1 688 | pad=1 689 | filters=21 690 | activation=linear 691 | 692 | [yolo] 693 | mask = 3,4,5 694 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 695 | classes=2 696 | num=9 697 | jitter=.3 698 | ignore_thresh = .5 699 | truth_thresh = 1 700 | random=1 701 | 702 | [route] 703 | layers = -4 704 | 705 | [convolutional] 706 | batch_normalize=1 707 | filters=128 708 | size=1 709 | stride=1 710 | pad=1 711 | activation=leaky 712 | 713 | [upsample] 714 | stride=2 715 | 716 | [route] 717 | layers = -1, 36 718 | 719 | 720 | 721 | [convolutional] 722 | batch_normalize=1 723 | filters=128 724 | size=1 725 | stride=1 726 | pad=1 727 | activation=leaky 728 | 729 | [convolutional] 730 | batch_normalize=1 731 | size=3 732 | stride=1 733 | pad=1 734 | filters=256 735 | activation=leaky 736 | 737 | [convolutional] 738 | batch_normalize=1 739 | filters=128 740 | size=1 741 | stride=1 742 | pad=1 743 | activation=leaky 744 | 745 | [convolutional] 746 | batch_normalize=1 747 | size=3 748 | stride=1 749 | pad=1 750 | filters=256 751 | activation=leaky 752 | 753 | [convolutional] 754 | batch_normalize=1 755 | filters=128 756 | size=1 757 | stride=1 758 | pad=1 759 | activation=leaky 760 | 761 | [convolutional] 762 | batch_normalize=1 763 | size=3 764 | stride=1 765 | pad=1 766 | filters=256 767 | activation=leaky 768 | 769 | [convolutional] 770 | size=1 771 | stride=1 772 | pad=1 773 | filters=21 774 | activation=linear 775 | 776 | [yolo] 777 | mask = 0,1,2 778 | anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 779 | classes=2 780 | num=9 781 | jitter=.3 782 | ignore_thresh = .5 783 | truth_thresh = 1 784 | random=1 785 | 786 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-contrib-python==4.0.0.21 2 | pillow 3 | h5py 4 | scipy 5 | numpy 6 | tensorflow-gpu==2.5 7 | scikit-learn 8 | matplotlib 9 | scikit-image 10 | xlwt -------------------------------------------------------------------------------- /table_build.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Wed Jan 13 17:31:37 2021 5 | 6 | @author: lywen 7 | """ 8 | 9 | 10 | class tableBuid: 11 | ##表格重建 12 | def __init__(self, ceilbox, interval=10): 13 | """ 14 | ceilboxes:[[x0,y0,x1,y1,x2,y2,x3,y3,x4,y4]] 15 | """ 16 | diagBoxes =[[int(x[0]), int(x[1]), int(x[4]), int(x[5])] for x in ceilbox] 17 | 18 | self.diagBoxes = diagBoxes 19 | self.interval = interval 20 | self.batch() 21 | 22 | def batch(self): 23 | self.cor = [] 24 | rowcor = self.table_line_cor(self.diagBoxes, axis='row', interval=self.interval) 25 | colcor = self.table_line_cor(self.diagBoxes, axis='col', interval=self.interval) 26 | cor = [{'row': line[1], 'col': line[0]} for line in zip(rowcor, colcor)] 27 | self.cor = cor 28 | 29 | def table_line_cor(self, lines, axis='col', interval=10): 30 | 31 | if axis == 'col': 32 | edges = [[line[1], line[3]] for line in lines] 33 | else: 34 | edges = [[line[0], line[2]] for line in lines] 35 | 36 | edges = sum(edges, []) 37 | edges = sorted(edges) 38 | 39 | nedges = len(edges) 40 | edgesMap = {} 41 | for i in range(nedges): 42 | if i == 0: 43 | edgesMap[edges[i]] = edges[i] 44 | continue 45 | else: 46 | if edges[i] - edgesMap[edges[i - 1]] < interval: 47 | edgesMap[edges[i]] = edgesMap[edges[i - 1]] 48 | else: 49 | edgesMap[edges[i]] = edges[i] 50 | 51 | edgesMapList = [[key, edgesMap[key]] for key in edgesMap] 52 | edgesMapIndex = [line[1] for line in edgesMapList] 53 | edgesMapIndex = list(set(edgesMapIndex)) 54 | edgesMapIndex = {x: ind for ind, x in enumerate(sorted(edgesMapIndex))} 55 | 56 | if axis == 'col': 57 | cor = [[edgesMapIndex[edgesMap[line[1]]], edgesMapIndex[edgesMap[line[3]]]] for line in lines] 58 | else: 59 | cor = [[edgesMapIndex[edgesMap[line[0]]], edgesMapIndex[edgesMap[line[2]]]] for line in lines] 60 | return cor 61 | 62 | import xlwt 63 | def to_excel(res, workbook=None): 64 | ##res:[{'text': '购 买 方', 'cx': 192.0, 'w': 58.0, 'h': 169.0, 'cy': 325.5, 'angle': 0.0, 'row': [0, 1], 'col': [0, 1]}] 65 | row = 0 66 | if workbook is None: 67 | workbook = xlwt.Workbook() 68 | if len(res) == 0: 69 | worksheet = workbook.add_sheet('table') 70 | worksheet.write_merge(0, 0, 0, 0, "无数据") 71 | else: 72 | worksheet = workbook.add_sheet('page') 73 | pageRow = 0 74 | for line in res: 75 | row0, row1 = line['row'] 76 | col0, col1 = line['col'] 77 | text = line.get('text','') 78 | try: 79 | pageRow = max(row1 - 1, pageRow) 80 | worksheet.write_merge(row + row0, row + row1 - 1, col0, col1 - 1, text) 81 | except: 82 | pass 83 | return workbook 84 | 85 | 86 | if __name__=='__main__': 87 | pass 88 | -------------------------------------------------------------------------------- /table_ceil.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | table ceil 6 | @author: chineseocr 7 | """ 8 | 9 | import cv2 10 | import numpy as np 11 | from table_detect import table_detect 12 | from table_line import table_line 13 | from table_build import tableBuid,to_excel 14 | from utils import minAreaRectbox, measure, eval_angle, draw_lines 15 | 16 | class table: 17 | def __init__(self, img, tableSize=(416, 416), tableLineSize=(1024, 1024), isTableDetect=False, isToExcel=False): 18 | self.img = img 19 | self.tableSize = tableSize 20 | self.tableLineSize = tableLineSize 21 | self.isTableDetect = isTableDetect 22 | self.isToExcel = isToExcel 23 | self.img_degree() 24 | self.table_boxes_detect() ##表格定位 25 | self.table_ceil() ##表格单元格定位 26 | 27 | self.table_build() 28 | 29 | def img_degree(self): 30 | img, degree = eval_angle(self.img, angleRange=[-15, 15]) 31 | self.img = img 32 | self.degree = degree 33 | 34 | def table_boxes_detect(self): 35 | h, w = self.img.shape[:2] 36 | 37 | if self.isTableDetect: 38 | boxes, adBoxes, scores = table_detect(self.img, sc=self.tableSize, thresh=0.2, NMSthresh=0.3) 39 | if len(boxes) == 0: 40 | boxes = [[0, 0, w, h]] 41 | adBoxes = [[0, 0, w, h]] 42 | scores = [0] 43 | else: 44 | boxes = [[0, 0, w, h]] 45 | adBoxes = [[0, 0, w, h]] 46 | scores = [0] 47 | 48 | self.boxes = boxes 49 | self.adBoxes = adBoxes 50 | self.scores = scores 51 | 52 | def table_ceil(self): 53 | ###表格单元格 54 | n = len(self.adBoxes) 55 | self.tableCeilBoxes = [] 56 | self.childImgs = [] 57 | for i in range(n): 58 | xmin, ymin, xmax, ymax = [int(x) for x in self.adBoxes[i]] 59 | 60 | childImg = self.img[ymin:ymax, xmin:xmax] 61 | rowboxes, colboxes = table_line(childImg[..., ::-1], size=self.tableLineSize, hprob=0.5, vprob=0.5) 62 | tmp = np.zeros(self.img.shape[:2], dtype='uint8') 63 | tmp = draw_lines(tmp, rowboxes + colboxes, color=255, lineW=2) 64 | labels = measure.label(tmp < 255, connectivity=2) # 8连通区域标记 65 | regions = measure.regionprops(labels) 66 | ceilboxes = minAreaRectbox(regions, False, tmp.shape[1], tmp.shape[0], True, True) 67 | ceilboxes = np.array(ceilboxes) 68 | ceilboxes[:, [0, 2, 4, 6]] += xmin 69 | ceilboxes[:, [1, 3, 5, 7]] += ymin 70 | self.tableCeilBoxes.extend(ceilboxes) 71 | self.childImgs.append(childImg) 72 | 73 | def table_build(self): 74 | tablebuild = tableBuid(self.tableCeilBoxes) 75 | cor = tablebuild.cor 76 | for line in cor: 77 | line['text'] = 'table-test'##ocr 78 | if self.isToExcel: 79 | workbook = to_excel(cor, workbook=None) 80 | else: 81 | workbook=None 82 | self.res = cor 83 | self.workbook = workbook 84 | 85 | 86 | def table_ocr(self): 87 | """use ocr and match ceil""" 88 | pass 89 | 90 | 91 | 92 | if __name__ == '__main__': 93 | import argparse 94 | import os 95 | import time 96 | from utils import draw_boxes 97 | 98 | parser = argparse.ArgumentParser(description='tabel to excel demo') 99 | parser.add_argument('--isTableDetect', default=False, type=bool, help="是否先进行表格检测") 100 | parser.add_argument('--tableSize', default='416,416', type=str, help="表格检测输入size") 101 | parser.add_argument('--tableLineSize', default='1024,1024', type=str, help="表格直线输入size") 102 | parser.add_argument('--isToExcel', default=False, type=bool, help="是否输出到excel") 103 | parser.add_argument('--jpgPath', default='img/table-detect.jpg',type=str, help="测试图像地址") 104 | args = parser.parse_args() 105 | args.tableSize = [int(x) for x in args.tableSize.split(',')] 106 | args.tableLineSize = [int(x) for x in args.tableLineSize.split(',')] 107 | print(args) 108 | img = cv2.imread(args.jpgPath) 109 | t = time.time() 110 | tableDetect = table(img,tableSize=args.tableSize, 111 | tableLineSize=args.tableLineSize, 112 | isTableDetect=args.isTableDetect, 113 | isToExcel=args.isToExcel 114 | ) 115 | tableCeilBoxes = tableDetect.tableCeilBoxes 116 | tableJson = tableDetect.res 117 | workbook = tableDetect.workbook 118 | img = tableDetect.img 119 | tmp = np.zeros_like(img) 120 | img = draw_boxes(tmp, tableDetect.tableCeilBoxes, color=(255, 255, 255)) 121 | print(time.time() - t) 122 | pngP = os.path.splitext(args.jpgPath)[0]+'ceil.png' 123 | cv2.imwrite(pngP, img) 124 | if workbook is not None: 125 | workbook.save(os.path.splitext(args.jpgPath)[0]+'.xlsx') 126 | -------------------------------------------------------------------------------- /table_detect.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | table detect with yolo 6 | @author: chineseocr 7 | """ 8 | import cv2 9 | import numpy as np 10 | 11 | from config import tableModelDetectPath 12 | from utils import nms_box, letterbox_image, rectangle 13 | 14 | tableDetectNet = cv2.dnn.readNetFromDarknet(tableModelDetectPath.replace('.weights', '.cfg'), tableModelDetectPath) # 15 | 16 | 17 | def table_detect(img, sc=(416, 416), thresh=0.5, NMSthresh=0.3): 18 | """ 19 | 表格检测 20 | img:GBR 21 | 22 | """ 23 | scale = sc[0] 24 | img_height, img_width = img.shape[:2] 25 | inputBlob, fx, fy = letterbox_image(img[..., ::-1], (scale, scale)) 26 | inputBlob = cv2.dnn.blobFromImage(inputBlob, scalefactor=1.0, size=(scale, scale), swapRB=True, crop=False); 27 | tableDetectNet.setInput(inputBlob / 255.0) 28 | outputName = tableDetectNet.getUnconnectedOutLayersNames() 29 | outputs = tableDetectNet.forward(outputName) 30 | class_ids = [] 31 | confidences = [] 32 | boxes = [] 33 | for output in outputs: 34 | for detection in output: 35 | scores = detection[5:] 36 | class_id = np.argmax(scores) 37 | confidence = scores[class_id] 38 | if confidence > thresh: 39 | center_x = int(detection[0] * scale / fx) 40 | center_y = int(detection[1] * scale / fy) 41 | width = int(detection[2] * scale / fx) 42 | height = int(detection[3] * scale / fy) 43 | left = int(center_x - width / 2) 44 | top = int(center_y - height / 2) 45 | if class_id == 1: 46 | class_ids.append(class_id) 47 | confidences.append(float(confidence)) 48 | xmin, ymin, xmax, ymax = left, top, left + width, top + height 49 | xmin = max(xmin, 1) 50 | ymin = max(ymin, 1) 51 | xmax = min(xmax, img_width - 1) 52 | ymax = min(ymax, img_height - 1) 53 | boxes.append([xmin, ymin, xmax, ymax]) 54 | 55 | boxes = np.array(boxes) 56 | 57 | confidences = np.array(confidences) 58 | if len(boxes) > 0: 59 | boxes, confidences = nms_box(boxes, confidences, score_threshold=thresh, nms_threshold=NMSthresh) 60 | 61 | boxes, adBoxes = fix_table_box_for_table_line(boxes, confidences, img) 62 | return boxes, adBoxes, confidences 63 | 64 | 65 | def point_in_box(p, box): 66 | x, y = p 67 | xmin, ymin, xmax, ymax = box 68 | if xmin <= x <= xmin and ymin <= y <= ymax: 69 | return True 70 | else: 71 | return False 72 | 73 | 74 | def fix_table_box_for_table_line(boxes, confidences, img): 75 | ### 修正表格用于表格线检测 76 | h, w = img.shape[:2] 77 | n = len(boxes) 78 | adBoxes = [] 79 | 80 | for i in range(n): 81 | prob = confidences[i] 82 | 83 | xmin, ymin, xmax, ymax = boxes[i] 84 | padx = (xmax - xmin) * (1 - prob) 85 | padx = padx 86 | 87 | pady = (ymax - ymin) * (1 - prob) 88 | pady = pady 89 | xminNew = max(xmin - padx, 1) 90 | yminNew = max(ymin - pady, 1) 91 | xmaxNew = min(xmax + padx, w) 92 | ymaxNew = min(ymax + pady, h) 93 | 94 | adBoxes.append([xminNew, yminNew, xmaxNew, ymaxNew]) 95 | 96 | return boxes, adBoxes 97 | 98 | 99 | if __name__ == '__main__': 100 | import time 101 | import argparse 102 | parser = argparse.ArgumentParser(description='tabel to excel demo') 103 | parser.add_argument('--tableSize', default='416,416', type=str, help="表格检测输入size") 104 | parser.add_argument('--jpgPath', default='img/table-detect.jpg', type=str, help="测试图像地址") 105 | args = parser.parse_args() 106 | args.tableSize = [int(x) for x in args.tableSize.split(',')] 107 | 108 | p = 'img/table-detect.jpg' 109 | img = cv2.imread(args.jpgPath) 110 | t = time.time() 111 | boxes, adBoxes, scores = table_detect(img, sc=(416, 416), thresh=0.5, NMSthresh=0.3) 112 | print(time.time() - t, boxes, adBoxes, scores) 113 | img = rectangle(img, adBoxes) 114 | img.save('img/table-detect.png') 115 | -------------------------------------------------------------------------------- /table_line.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | table line detect 6 | @author: chineseocr 7 | """ 8 | 9 | from tensorflow.keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D 10 | from tensorflow.keras.layers import LeakyReLU 11 | from tensorflow.keras.models import Model 12 | 13 | 14 | def table_net(input_shape=(512, 512, 3), num_classes=1): 15 | inputs = Input(shape=input_shape) 16 | # 512 17 | use_bias = False 18 | down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs) 19 | down0a = BatchNormalization()(down0a) 20 | down0a = LeakyReLU(alpha=0.1)(down0a) 21 | down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0a) 22 | down0a = BatchNormalization()(down0a) 23 | down0a = LeakyReLU(alpha=0.1)(down0a) 24 | down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a) 25 | # 256 26 | 27 | down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0a_pool) 28 | down0 = BatchNormalization()(down0) 29 | 30 | down0 = LeakyReLU(alpha=0.1)(down0) 31 | down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0) 32 | down0 = BatchNormalization()(down0) 33 | down0 = LeakyReLU(alpha=0.1)(down0) 34 | down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0) 35 | # 128 36 | 37 | down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool) 38 | down1 = BatchNormalization()(down1) 39 | down1 = LeakyReLU(alpha=0.1)(down1) 40 | down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1) 41 | down1 = BatchNormalization()(down1) 42 | down1 = LeakyReLU(alpha=0.1)(down1) 43 | down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1) 44 | # 64 45 | 46 | down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool) 47 | down2 = BatchNormalization()(down2) 48 | down2 = LeakyReLU(alpha=0.1)(down2) 49 | down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2) 50 | down2 = BatchNormalization()(down2) 51 | down2 = LeakyReLU(alpha=0.1)(down2) 52 | down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2) 53 | # 32 54 | 55 | down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down2_pool) 56 | down3 = BatchNormalization()(down3) 57 | down3 = LeakyReLU(alpha=0.1)(down3) 58 | down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down3) 59 | down3 = BatchNormalization()(down3) 60 | down3 = LeakyReLU(alpha=0.1)(down3) 61 | down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3) 62 | # 16 63 | 64 | down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down3_pool) 65 | down4 = BatchNormalization()(down4) 66 | down4 = LeakyReLU(alpha=0.1)(down4) 67 | down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down4) 68 | down4 = BatchNormalization()(down4) 69 | down4 = LeakyReLU(alpha=0.1)(down4) 70 | down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4) 71 | # 8 72 | 73 | center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(down4_pool) 74 | center = BatchNormalization()(center) 75 | center = LeakyReLU(alpha=0.1)(center) 76 | center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(center) 77 | center = BatchNormalization()(center) 78 | center = LeakyReLU(alpha=0.1)(center) 79 | # center 80 | 81 | up4 = UpSampling2D((2, 2))(center) 82 | up4 = concatenate([down4, up4], axis=3) 83 | up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4) 84 | up4 = BatchNormalization()(up4) 85 | up4 = LeakyReLU(alpha=0.1)(up4) 86 | up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4) 87 | up4 = BatchNormalization()(up4) 88 | up4 = LeakyReLU(alpha=0.1)(up4) 89 | up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4) 90 | up4 = BatchNormalization()(up4) 91 | up4 = LeakyReLU(alpha=0.1)(up4) 92 | # 16 93 | 94 | up3 = UpSampling2D((2, 2))(up4) 95 | up3 = concatenate([down3, up3], axis=3) 96 | up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3) 97 | up3 = BatchNormalization()(up3) 98 | up3 = LeakyReLU(alpha=0.1)(up3) 99 | up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3) 100 | up3 = BatchNormalization()(up3) 101 | up3 = LeakyReLU(alpha=0.1)(up3) 102 | up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3) 103 | up3 = BatchNormalization()(up3) 104 | up3 = LeakyReLU(alpha=0.1)(up3) 105 | # 32 106 | 107 | up2 = UpSampling2D((2, 2))(up3) 108 | up2 = concatenate([down2, up2], axis=3) 109 | up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2) 110 | up2 = BatchNormalization()(up2) 111 | up2 = LeakyReLU(alpha=0.1)(up2) 112 | up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2) 113 | up2 = BatchNormalization()(up2) 114 | up2 = LeakyReLU(alpha=0.1)(up2) 115 | up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2) 116 | up2 = BatchNormalization()(up2) 117 | up2 = LeakyReLU(alpha=0.1)(up2) 118 | # 64 119 | 120 | up1 = UpSampling2D((2, 2))(up2) 121 | up1 = concatenate([down1, up1], axis=3) 122 | up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1) 123 | up1 = BatchNormalization()(up1) 124 | up1 = LeakyReLU(alpha=0.1)(up1) 125 | up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1) 126 | up1 = BatchNormalization()(up1) 127 | up1 = LeakyReLU(alpha=0.1)(up1) 128 | up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1) 129 | up1 = BatchNormalization()(up1) 130 | up1 = LeakyReLU(alpha=0.1)(up1) 131 | # 128 132 | 133 | up0 = UpSampling2D((2, 2))(up1) 134 | up0 = concatenate([down0, up0], axis=3) 135 | up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0) 136 | up0 = BatchNormalization()(up0) 137 | up0 = LeakyReLU(alpha=0.1)(up0) 138 | up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0) 139 | up0 = BatchNormalization()(up0) 140 | up0 = LeakyReLU(alpha=0.1)(up0) 141 | up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0) 142 | up0 = BatchNormalization()(up0) 143 | up0 = LeakyReLU(alpha=0.1)(up0) 144 | # 256 145 | 146 | up0a = UpSampling2D((2, 2))(up0) 147 | up0a = concatenate([down0a, up0a], axis=3) 148 | up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a) 149 | up0a = BatchNormalization()(up0a) 150 | up0a = LeakyReLU(alpha=0.1)(up0a) 151 | up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a) 152 | up0a = BatchNormalization()(up0a) 153 | up0a = LeakyReLU(alpha=0.1)(up0a) 154 | up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a) 155 | up0a = BatchNormalization()(up0a) 156 | up0a = LeakyReLU(alpha=0.1)(up0a) 157 | # 512 158 | 159 | classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0a) 160 | 161 | model = Model(inputs=inputs, outputs=classify) 162 | 163 | return model 164 | 165 | 166 | from config import tableModeLinePath 167 | from utils import letterbox_image, get_table_line, adjust_lines, line_to_line 168 | import numpy as np 169 | import cv2 170 | 171 | model = table_net((None, None, 3), 2) 172 | model.load_weights(tableModeLinePath) 173 | 174 | 175 | def table_line(img, size=(512, 512), hprob=0.5, vprob=0.5, row=50, col=30, alph=15): 176 | sizew, sizeh = size 177 | inputBlob, fx, fy = letterbox_image(img[..., ::-1], (sizew, sizeh)) 178 | pred = model.predict(np.array([np.array(inputBlob) / 255.0])) 179 | pred = pred[0] 180 | vpred = pred[..., 1] > vprob ##竖线 181 | hpred = pred[..., 0] > hprob ##横线 182 | vpred = vpred.astype(int) 183 | hpred = hpred.astype(int) 184 | colboxes = get_table_line(vpred, axis=1, lineW=col) 185 | rowboxes = get_table_line(hpred, axis=0, lineW=row) 186 | ccolbox = [] 187 | crowlbox = [] 188 | if len(rowboxes) > 0: 189 | rowboxes = np.array(rowboxes) 190 | rowboxes[:, [0, 2]] = rowboxes[:, [0, 2]] / fx 191 | rowboxes[:, [1, 3]] = rowboxes[:, [1, 3]] / fy 192 | xmin = rowboxes[:, [0, 2]].min() 193 | xmax = rowboxes[:, [0, 2]].max() 194 | ymin = rowboxes[:, [1, 3]].min() 195 | ymax = rowboxes[:, [1, 3]].max() 196 | ccolbox = [[xmin, ymin, xmin, ymax], [xmax, ymin, xmax, ymax]] 197 | rowboxes = rowboxes.tolist() 198 | 199 | if len(colboxes) > 0: 200 | colboxes = np.array(colboxes) 201 | colboxes[:, [0, 2]] = colboxes[:, [0, 2]] / fx 202 | colboxes[:, [1, 3]] = colboxes[:, [1, 3]] / fy 203 | 204 | xmin = colboxes[:, [0, 2]].min() 205 | xmax = colboxes[:, [0, 2]].max() 206 | ymin = colboxes[:, [1, 3]].min() 207 | ymax = colboxes[:, [1, 3]].max() 208 | colboxes = colboxes.tolist() 209 | crowlbox = [[xmin, ymin, xmax, ymin], [xmin, ymax, xmax, ymax]] 210 | 211 | rowboxes += crowlbox 212 | colboxes += ccolbox 213 | 214 | rboxes_row_, rboxes_col_ = adjust_lines(rowboxes, colboxes, alph=alph) 215 | rowboxes += rboxes_row_ 216 | colboxes += rboxes_col_ 217 | nrow = len(rowboxes) 218 | ncol = len(colboxes) 219 | for i in range(nrow): 220 | for j in range(ncol): 221 | rowboxes[i] = line_to_line(rowboxes[i], colboxes[j], 10) 222 | colboxes[j] = line_to_line(colboxes[j], rowboxes[i], 10) 223 | 224 | return rowboxes, colboxes 225 | 226 | 227 | if __name__ == '__main__': 228 | import time 229 | 230 | p = 'img/table-detect.jpg' 231 | from utils import draw_lines 232 | 233 | img = cv2.imread(p) 234 | t = time.time() 235 | rowboxes, colboxes = table_line(img[..., ::-1], size=(512, 512), hprob=0.5, vprob=0.5) 236 | img = draw_lines(img, rowboxes + colboxes, color=(255, 0, 0), lineW=2) 237 | 238 | print(time.time() - t, len(rowboxes), len(colboxes)) 239 | cv2.imwrite('img/table-line.png', img) 240 | -------------------------------------------------------------------------------- /train/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 10 02:52:45 2020 5 | @author: chineseocr 6 | """ 7 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pf/P1rP8A4N7r/wCOUAdHRXOf8ITpf/P1rP8A4N7r/wCOUf8ACE6X/wA/Ws/+De6/+OUAdHRXOf8ACE6X/wA/Ws/+De6/+OUf8ITpf/P1rP8A4N7r/wCOUAT+NP8AkRfEP/YMuf8A0U1aWm/8gqz/AOuCf+giuP8AFfhDTbbwdrlxHcasXi0+d1EmqXDqSI2PKlyCPY8Guu0lBHo1ii5wtvGBk5P3R3PWgC5RRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXM/EP/knmvf8AXm/8q6auZ+If/JPNe/683/lQBsajbajcGP7BqEdoFzv324l3en8Qx3qj/Z3iH/oYIP8AwXj/AOLrcooAw/7O8Q/9DBB/4Lx/8XR/Z3iH/oYIP/BeP/i63KKAM7T7TVYJ2a+1SK6iK4CJaiMg5HOdx9+PetGiigAooooAKKKKAOc8bf8AIDt/+wnY/wDpTHXR1znjb/kB2/8A2E7H/wBKY66OgAooooAK5mD/AJKfff8AYGt//R01dNXMwf8AJT77/sDW/wD6OmoA6aiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArmdV/5KH4b/6877+cFdNXM6r/AMlD8N/9ed9/OCgDpqKKKACiiigDm/CH/Me/7DFx/wCy10lc34Q/5j3/AGGLj/2WukoAKKKKACuZg/5Kfff9ga3/APR01dNXMwf8lPvv+wNb/wDo6agDpqKKKACiiigAooooA5nwT/yD9V/7DN9/6PeumrmfBP8AyD9V/wCwzff+j3rpqACiiigDm9F/5HbxP/26f+izXSVzei/8jt4n/wC3T/0Wa6SgAooooAKKKKAOZ1X/AJKH4b/6877+cFdNXM6r/wAlD8N/9ed9/OCumoAKKKKACiiigAooooAKKKKAMPxp/wAiL4h/7Blz/wCimrS03/kFWf8A1wT/ANBFZvjT/kRfEP8A2DLn/wBFNWlpv/IKs/8Argn/AKCKALVFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFc18Qhn4e66ByTZuB+VdLXMfEQBvh5rwIBH2RzzQBu/wBpWH/P7bf9/V/xo/tKw/5/bb/v6v8AjWb/AMIX4V/6FrRv/ACL/wCJo/4Qvwr/ANC1o3/gBF/8TQBpf2lYf8/tt/39X/Gj+0rD/n9tv+/q/wCNZv8AwhfhX/oWtG/8AIv/AImj/hC/Cv8A0LWjf+AEX/xNAGl/aVh/z+23/f1f8aP7SsP+f22/7+r/AI1m/wDCF+Ff+ha0b/wAi/8AiaP+EL8K/wDQtaN/4ARf/E0AaX9pWH/P7bf9/V/xo/tKw/5/bb/v6v8AjWb/AMIX4V/6FrRv/ACL/wCJo/4Qvwr/ANC1o3/gBF/8TQBpf2lYf8/tt/39X/Gj+0rD/n9tv+/q/wCNZv8AwhfhX/oWtG/8AIv/AImj/hC/Cv8A0LWjf+AEX/xNAFDxnf2cmiW6x3cDH+0bI4WQHgXMZJ/Kuh/tKw/5/bb/AL+r/jXH+L/Cfhy20aCS38P6VE51CzQtHZxqSrXEYYZA6EEgjuDW9/whfhX/AKFrRv8AwAi/+JoA0v7SsP8An9tv+/q/40f2lYf8/tt/39X/ABrN/wCEL8K/9C1o3/gBF/8AE0f8IX4V/wCha0b/AMAIv/iaANL+0rD/AJ/bb/v6v+Nc5DfWg+JV7KbqDyzo9uofzBgkTTZGfXkfnWj/AMIX4V/6FrRv/ACL/wCJrnofCnhw/EW8tToGlG3XSYJFi+xx7AxmmBYDGMkADPsKAOz/ALSsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/40f2lYf8AP7bf9/V/xrN/4Qvwr/0LWjf+AEX/AMTR/wAIX4V/6FrRv/ACL/4mgDS/tKw/5/bb/v6v+NH9pWH/AD+23/f1f8azf+EL8K/9C1o3/gBF/wDE0f8ACF+Ff+ha0b/wAi/+JoA0v7SsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/40f2lYf8AP7bf9/V/xrN/4Qvwr/0LWjf+AEX/AMTR/wAIX4V/6FrRv/ACL/4mgDS/tKw/5/bb/v6v+NH9pWH/AD+23/f1f8azf+EL8K/9C1o3/gBF/wDE0f8ACF+Ff+ha0b/wAi/+JoA0v7SsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/40f2lYf8AP7bf9/V/xrN/4Qvwr/0LWjf+AEX/AMTR/wAIX4V/6FrRv/ACL/4mgDS/tKw/5/bb/v6v+NH9pWH/AD+23/f1f8azf+EL8K/9C1o3/gBF/wDE0f8ACF+Ff+ha0b/wAi/+JoA0v7SsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/40f2lYf8AP7bf9/V/xrN/4Qvwr/0LWjf+AEX/AMTR/wAIX4V/6FrRv/ACL/4mgDS/tKw/5/bb/v6v+NH9pWH/AD+23/f1f8azf+EL8K/9C1o3/gBF/wDE0f8ACF+Ff+ha0b/wAi/+JoA0v7SsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/40f2lYf8AP7bf9/V/xrN/4Qvwr/0LWjf+AEX/AMTR/wAIX4V/6FrRv/ACL/4mgDS/tKw/5/bb/v6v+NH9pWH/AD+23/f1f8azf+EL8K/9C1o3/gBF/wDE0f8ACF+Ff+ha0b/wAi/+JoA0v7SsP+f22/7+r/jR/aVh/wA/tt/39X/Gs3/hC/Cv/QtaN/4ARf8AxNH/AAhfhX/oWtG/8AIv/iaANL+0rD/n9tv+/q/41zmp31o3j/w7ILqAotpehmEgwCTBjJ/A/lWj/wAIX4V/6FrRv/ACL/4mue1Lwp4cj8daBbpoGlLBLaXjSRizjCuVMO0kYwSMnHpk0Adn/aVh/wA/tt/39X/Gj+0rD/n9tv8Av6v+NZv/AAhfhX/oWtG/8AIv/iaP+EL8K/8AQtaN/wCAEX/xNAGl/aVh/wA/tt/39X/Gj+0rD/n9tv8Av6v+NZv/AAhfhX/oWtG/8AIv/iaP+EL8K/8AQtaN/wCAEX/xNAGf4SvrOP8AtzfdQLu1e4YZkAyPl5rov7SsP+f22/7+r/jXHeFvCnh25/trz9A0qXy9VnjTfZxttQbcKMjgD0rf/wCEL8K/9C1o3/gBF/8AE0AaX9pWH/P7bf8Af1f8aP7SsP8An9tv+/q/41m/8IX4V/6FrRv/AAAi/wDiaP8AhC/Cv/QtaN/4ARf/ABNAGl/aVh/z+23/AH9X/GuchvrQfEq9lN1B5Z0e3UP5gwSJpsjPryPzrR/4Qvwr/wBC1o3/AIARf/E1z0PhTw4fiLeWp0DSjbrpMEixfY49gYzTAsBjGSABn2FAHZ/2lYf8/tt/39X/ABo/tKw/5/bb/v6v+NZv/CF+Ff8AoWtG/wDACL/4mj/hC/Cv/QtaN/4ARf8AxNAGl/aVh/z+23/f1f8AGj+0rD/n9tv+/q/41m/8IX4V/wCha0b/AMAIv/iaP+EL8K/9C1o3/gBF/wDE0AaX9pWH/P7bf9/V/wAaP7SsP+f22/7+r/jWb/whfhX/AKFrRv8AwAi/+Jo/4Qvwr/0LWjf+AEX/AMTQBneDL60isNUEl1AhOsXrANIBkGdiD9K6P+0rD/n9tv8Av6v+NcZ4Q8KeHLmx1NrjQNKlZNWvY1MlnGxVVmYKoyOAAMAdq6H/AIQvwr/0LWjf+AEX/wATQBpf2lYf8/tt/wB/V/xo/tKw/wCf22/7+r/jWb/whfhX/oWtG/8AACL/AOJo/wCEL8K/9C1o3/gBF/8AE0AZ+j31mvjPxK7XUAV/su1jIMHEZziui/tKw/5/bb/v6v8AjXHaT4U8Oy+L/EUEmgaU8MP2byo2s4yqZjJO0Y4yfSt//hC/Cv8A0LWjf+AEX/xNAGl/aVh/z+23/f1f8aP7SsP+f22/7+r/AI1m/wDCF+Ff+ha0b/wAi/8AiaP+EL8K/wDQtaN/4ARf/E0AaX9pWH/P7bf9/V/xo/tKw/5/bb/v6v8AjWb/AMIX4V/6FrRv/ACL/wCJo/4Qvwr/ANC1o3/gBF/8TQBnanfWjeP/AA7ILqAotpehmEgwCTBjJ/A/lXR/2lYf8/tt/wB/V/xrjNS8KeHI/HWgW6aBpSwS2l40kYs4wrlTDtJGMEjJx6ZNdD/whfhX/oWtG/8AACL/AOJoA0v7SsP+f22/7+r/AI0f2lYf8/tt/wB/V/xrN/4Qvwr/ANC1o3/gBF/8TR/whfhX/oWtG/8AACL/AOJoA0v7SsP+f22/7+r/AI0f2lYf8/tt/wB/V/xrN/4Qvwr/ANC1o3/gBF/8TR/whfhX/oWtG/8AACL/AOJoA0v7SsP+f22/7+r/AI0f2lYf8/tt/wB/V/xrN/4Qvwr/ANC1o3/gBF/8TR/whfhX/oWtG/8AACL/AOJoA0v7SsP+f22/7+r/AI0f2lYf8/tt/wB/V/xrN/4Qvwr/ANC1o3/gBF/8TR/whfhX/oWtG/8AACL/AOJoAr+ML+zk8Ea/HHdwO7adcBVWQEkmNuAK2tMIOk2ZByDAnP8AwEVyvi3wl4btvBmuzweHtJimi0+4eOSOyjVkYRsQQQMgg966nSkWPR7JEUKq28YCgYAG0cUAW6KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5n4h/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641 | "imageHeight": 300, 642 | "imageWidth": 619 643 | } -------------------------------------------------------------------------------- /train/dataset-line/0/5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/chineseocr/table-detect/92488f30ffaf486d29791aab63802beeb1eaca32/train/dataset-line/0/5.jpg -------------------------------------------------------------------------------- /train/train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | 6 | @author: chineseocr 7 | """ 8 | import sys 9 | 10 | sys.path.append('.') 11 | from table_line import model 12 | from tensorflow.keras.optimizers import Adam 13 | from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau 14 | from sklearn.model_selection import train_test_split 15 | from glob import glob 16 | from image import gen 17 | 18 | if __name__ == '__main__': 19 | filepath = './models/table-line-fine.h5' ##模型权重存放位置 20 | 21 | checkpointer = ModelCheckpoint(filepath=filepath, monitor='loss', verbose=0, save_weights_only=True, 22 | save_best_only=True) 23 | rlu = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=5, verbose=0, mode='auto', cooldown=0, min_lr=0) 24 | model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['acc']) 25 | 26 | paths = glob('./train/dataset-line/*/*.json') ##table line dataset label with labelme 27 | trainP, testP = train_test_split(paths, test_size=0.1) 28 | print('total:', len(paths), 'train:', len(trainP), 'test:', len(testP)) 29 | batchsize = 4 30 | trainloader = gen(trainP, batchsize=batchsize, linetype=1) 31 | testloader = gen(testP, batchsize=batchsize, linetype=1) 32 | model.fit_generator(trainloader, 33 | steps_per_epoch=max(1, len(trainP) // batchsize), 34 | callbacks=[checkpointer], 35 | validation_data=testloader, 36 | validation_steps=max(1, len(testP) // batchsize), 37 | epochs=30) 38 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Sep 9 23:11:51 2020 5 | utils 6 | @author: chineseocr 7 | """ 8 | import cv2 9 | import numpy as np 10 | 11 | 12 | def nms_box(boxes, scores, score_threshold=0.5, nms_threshold=0.3): 13 | ##nms box 14 | boxes = np.array(boxes) 15 | scores = np.array(scores) 16 | ind = scores > score_threshold 17 | boxes = boxes[ind] 18 | scores = scores[ind] 19 | 20 | def box_to_center(box): 21 | xmin, ymin, xmax, ymax = [round(float(x), 4) for x in box] 22 | w = xmax - xmin 23 | h = ymax - ymin 24 | return [round(xmin, 4), round(ymin, 4), round(w, 4), round(h, 4)] 25 | 26 | newBoxes = [box_to_center(box) for box in boxes] 27 | newscores = [round(float(x), 6) for x in scores] 28 | 29 | index = cv2.dnn.NMSBoxes(newBoxes, newscores, score_threshold=score_threshold, nms_threshold=nms_threshold) 30 | if len(index) > 0: 31 | index = index.reshape((-1,)) 32 | return boxes[index], scores[index] 33 | else: 34 | return np.array([]), np.array([]) 35 | 36 | 37 | from scipy.ndimage import filters, interpolation 38 | from numpy import amin, amax 39 | 40 | 41 | def resize_im(im, scale, max_scale=None): 42 | f = float(scale) / min(im.shape[0], im.shape[1]) 43 | if max_scale != None and f * max(im.shape[0], im.shape[1]) > max_scale: 44 | f = float(max_scale) / max(im.shape[0], im.shape[1]) 45 | return cv2.resize(im, (0, 0), fx=f, fy=f) 46 | 47 | 48 | def estimate_skew_angle(raw, angleRange=[-15, 15]): 49 | """ 50 | 估计图像文字偏转角度, 51 | angleRange:角度估计区间 52 | """ 53 | raw = resize_im(raw, scale=600, max_scale=900) 54 | image = raw - amin(raw) 55 | image = image / amax(image) 56 | m = interpolation.zoom(image, 0.5) 57 | m = filters.percentile_filter(m, 80, size=(20, 2)) 58 | m = filters.percentile_filter(m, 80, size=(2, 20)) 59 | m = interpolation.zoom(m, 1.0 / 0.5) 60 | # w,h = image.shape[1],image.shape[0] 61 | w, h = min(image.shape[1], m.shape[1]), min(image.shape[0], m.shape[0]) 62 | flat = np.clip(image[:h, :w] - m[:h, :w] + 1, 0, 1) 63 | d0, d1 = flat.shape 64 | o0, o1 = int(0.1 * d0), int(0.1 * d1) 65 | flat = amax(flat) - flat 66 | flat -= amin(flat) 67 | est = flat[o0:d0 - o0, o1:d1 - o1] 68 | angles = range(angleRange[0], angleRange[1]) 69 | estimates = [] 70 | for a in angles: 71 | roest = interpolation.rotate(est, a, order=0, mode='constant') 72 | v = np.mean(roest, axis=1) 73 | v = np.var(v) 74 | estimates.append((v, a)) 75 | 76 | _, a = max(estimates) 77 | return a 78 | 79 | 80 | def eval_angle(img, angleRange=[-5, 5]): 81 | """ 82 | 估计图片文字的偏移角度 83 | """ 84 | im = Image.fromarray(img) 85 | degree = estimate_skew_angle(np.array(im.convert('L')), angleRange=angleRange) 86 | im = im.rotate(degree, center=(im.size[0] / 2, im.size[1] / 2), expand=1, fillcolor=(255, 255, 255)) 87 | img = np.array(im) 88 | return img, degree 89 | 90 | 91 | def letterbox_image(image, size, fillValue=[128, 128, 128]): 92 | ''' 93 | resize image with unchanged aspect ratio using padding 94 | ''' 95 | image_h, image_w = image.shape[:2] 96 | w, h = size 97 | new_w = int(image_w * min(w * 1.0 / image_w, h * 1.0 / image_h)) 98 | new_h = int(image_h * min(w * 1.0 / image_w, h * 1.0 / image_h)) 99 | 100 | resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_CUBIC) 101 | # cv2.imwrite('tmp/test.png', resized_image[...,::-1]) 102 | if fillValue is None: 103 | fillValue = [int(x.mean()) for x in cv2.split(np.array(image))] 104 | boxed_image = np.zeros((size[1], size[0], 3), dtype=np.uint8) 105 | boxed_image[:] = fillValue 106 | boxed_image[:new_h, :new_w, :] = resized_image 107 | 108 | return boxed_image, new_w / image_w, new_h / image_h 109 | 110 | 111 | from skimage import measure 112 | 113 | 114 | def get_table_line(binimg, axis=0, lineW=10): 115 | ##获取表格线 116 | ##axis=0 横线 117 | ##axis=1 竖线 118 | labels = measure.label(binimg > 0, connectivity=2) # 8连通区域标记 119 | regions = measure.regionprops(labels) 120 | if axis == 1: 121 | lineboxes = [minAreaRect(line.coords) for line in regions if line.bbox[2] - line.bbox[0] > lineW] 122 | else: 123 | lineboxes = [minAreaRect(line.coords) for line in regions if line.bbox[3] - line.bbox[1] > lineW] 124 | return lineboxes 125 | 126 | 127 | def sqrt(p1, p2): 128 | return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2) 129 | 130 | 131 | def adjust_lines(RowsLines, ColsLines, alph=50): 132 | ##调整line 133 | 134 | nrow = len(RowsLines) 135 | ncol = len(ColsLines) 136 | newRowsLines = [] 137 | newColsLines = [] 138 | for i in range(nrow): 139 | 140 | x1, y1, x2, y2 = RowsLines[i] 141 | cx1, cy1 = (x1 + x2) / 2, (y1 + y2) / 2 142 | for j in range(nrow): 143 | if i != j: 144 | x3, y3, x4, y4 = RowsLines[j] 145 | cx2, cy2 = (x3 + x4) / 2, (y3 + y4) / 2 146 | if (x3 < cx1 < x4 or y3 < cy1 < y4) or (x1 < cx2 < x2 or y1 < cy2 < y2): 147 | continue 148 | else: 149 | r = sqrt((x1, y1), (x3, y3)) 150 | if r < alph: 151 | newRowsLines.append([x1, y1, x3, y3]) 152 | r = sqrt((x1, y1), (x4, y4)) 153 | if r < alph: 154 | newRowsLines.append([x1, y1, x4, y4]) 155 | 156 | r = sqrt((x2, y2), (x3, y3)) 157 | if r < alph: 158 | newRowsLines.append([x2, y2, x3, y3]) 159 | r = sqrt((x2, y2), (x4, y4)) 160 | if r < alph: 161 | newRowsLines.append([x2, y2, x4, y4]) 162 | 163 | for i in range(ncol): 164 | x1, y1, x2, y2 = ColsLines[i] 165 | cx1, cy1 = (x1 + x2) / 2, (y1 + y2) / 2 166 | for j in range(ncol): 167 | if i != j: 168 | x3, y3, x4, y4 = ColsLines[j] 169 | cx2, cy2 = (x3 + x4) / 2, (y3 + y4) / 2 170 | if (x3 < cx1 < x4 or y3 < cy1 < y4) or (x1 < cx2 < x2 or y1 < cy2 < y2): 171 | continue 172 | else: 173 | r = sqrt((x1, y1), (x3, y3)) 174 | if r < alph: 175 | newColsLines.append([x1, y1, x3, y3]) 176 | r = sqrt((x1, y1), (x4, y4)) 177 | if r < alph: 178 | newColsLines.append([x1, y1, x4, y4]) 179 | 180 | r = sqrt((x2, y2), (x3, y3)) 181 | if r < alph: 182 | newColsLines.append([x2, y2, x3, y3]) 183 | r = sqrt((x2, y2), (x4, y4)) 184 | if r < alph: 185 | newColsLines.append([x2, y2, x4, y4]) 186 | 187 | return newRowsLines, newColsLines 188 | 189 | 190 | def minAreaRect(coords): 191 | """ 192 | 多边形外接矩形 193 | """ 194 | rect = cv2.minAreaRect(coords[:, ::-1]) 195 | box = cv2.boxPoints(rect) 196 | box = box.reshape((8,)).tolist() 197 | 198 | box = image_location_sort_box(box) 199 | 200 | x1, y1, x2, y2, x3, y3, x4, y4 = box 201 | degree, w, h, cx, cy = solve(box) 202 | if w < h: 203 | xmin = (x1 + x2) / 2 204 | xmax = (x3 + x4) / 2 205 | ymin = (y1 + y2) / 2 206 | ymax = (y3 + y4) / 2 207 | 208 | else: 209 | xmin = (x1 + x4) / 2 210 | xmax = (x2 + x3) / 2 211 | ymin = (y1 + y4) / 2 212 | ymax = (y2 + y3) / 2 213 | # degree,w,h,cx,cy = solve(box) 214 | # x1,y1,x2,y2,x3,y3,x4,y4 = box 215 | # return {'degree':degree,'w':w,'h':h,'cx':cx,'cy':cy} 216 | return [xmin, ymin, xmax, ymax] 217 | 218 | 219 | def fit_line(p1, p2): 220 | """A = Y2 - Y1 221 | B = X1 - X2 222 | C = X2*Y1 - X1*Y2 223 | AX+BY+C=0 224 | 直线一般方程 225 | """ 226 | x1, y1 = p1 227 | x2, y2 = p2 228 | A = y2 - y1 229 | B = x1 - x2 230 | C = x2 * y1 - x1 * y2 231 | return A, B, C 232 | 233 | 234 | def point_line_cor(p, A, B, C): 235 | ##判断点与之间的位置关系 236 | # 一般式直线方程(Ax+By+c)=0 237 | x, y = p 238 | r = A * x + B * y + C 239 | return r 240 | 241 | 242 | def line_to_line(points1, points2, alpha=10): 243 | """ 244 | 线段之间的距离 245 | """ 246 | x1, y1, x2, y2 = points1 247 | ox1, oy1, ox2, oy2 = points2 248 | A1, B1, C1 = fit_line((x1, y1), (x2, y2)) 249 | A2, B2, C2 = fit_line((ox1, oy1), (ox2, oy2)) 250 | flag1 = point_line_cor([x1, y1], A2, B2, C2) 251 | flag2 = point_line_cor([x2, y2], A2, B2, C2) 252 | 253 | if (flag1 > 0 and flag2 > 0) or (flag1 < 0 and flag2 < 0): 254 | 255 | x = (B1 * C2 - B2 * C1) / (A1 * B2 - A2 * B1) 256 | y = (A2 * C1 - A1 * C2) / (A1 * B2 - A2 * B1) 257 | p = (x, y) 258 | r0 = sqrt(p, (x1, y1)) 259 | r1 = sqrt(p, (x2, y2)) 260 | 261 | if min(r0, r1) < alpha: 262 | 263 | if r0 < r1: 264 | points1 = [p[0], p[1], x2, y2] 265 | else: 266 | points1 = [x1, y1, p[0], p[1]] 267 | 268 | return points1 269 | 270 | 271 | from scipy.spatial import distance as dist 272 | 273 | 274 | def _order_points(pts): 275 | # 根据x坐标对点进行排序 276 | """ 277 | --------------------- 278 | 作者：Tong_T 279 | 来源：CSDN 280 | 原文：https://blog.csdn.net/Tong_T/article/details/81907132 281 | 版权声明：本文为博主原创文章，转载请附上博文链接！ 282 | """ 283 | x_sorted = pts[np.argsort(pts[:, 0]), :] 284 | 285 | left_most = x_sorted[:2, :] 286 | right_most = x_sorted[2:, :] 287 | left_most = left_most[np.argsort(left_most[:, 1]), :] 288 | (tl, bl) = left_most 289 | 290 | distance = dist.cdist(tl[np.newaxis], right_most, "euclidean")[0] 291 | (br, tr) = right_most[np.argsort(distance)[::-1], :] 292 | 293 | return np.array([tl, tr, br, bl], dtype="float32") 294 | 295 | 296 | def image_location_sort_box(box): 297 | x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] 298 | pts = (x1, y1), (x2, y2), (x3, y3), (x4, y4) 299 | pts = np.array(pts, dtype="float32") 300 | (x1, y1), (x2, y2), (x3, y3), (x4, y4) = _order_points(pts) 301 | return [x1, y1, x2, y2, x3, y3, x4, y4] 302 | 303 | 304 | def solve(box): 305 | """ 306 | 绕 cx,cy点 w,h 旋转 angle 的坐标 307 | x = cx-w/2 308 | y = cy-h/2 309 | x1-cx = -w/2*cos(angle) +h/2*sin(angle) 310 | y1 -cy= -w/2*sin(angle) -h/2*cos(angle) 311 | 312 | h(x1-cx) = -wh/2*cos(angle) +hh/2*sin(angle) 313 | w(y1 -cy)= -ww/2*sin(angle) -hw/2*cos(angle) 314 | (hh+ww)/2sin(angle) = h(x1-cx)-w(y1 -cy) 315 | 316 | """ 317 | x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] 318 | cx = (x1 + x3 + x2 + x4) / 4.0 319 | cy = (y1 + y3 + y4 + y2) / 4.0 320 | w = (np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + np.sqrt((x3 - x4) ** 2 + (y3 - y4) ** 2)) / 2 321 | h = (np.sqrt((x2 - x3) ** 2 + (y2 - y3) ** 2) + np.sqrt((x1 - x4) ** 2 + (y1 - y4) ** 2)) / 2 322 | # x = cx-w/2 323 | # y = cy-h/2 324 | sinA = (h * (x1 - cx) - w * (y1 - cy)) * 1.0 / (h * h + w * w) * 2 325 | angle = np.arcsin(sinA) 326 | return angle, w, h, cx, cy 327 | 328 | 329 | def xy_rotate_box(cx, cy, w, h, angle=0, degree=None, **args): 330 | """ 331 | 绕 cx,cy点 w,h 旋转 angle 的坐标 332 | x_new = (x-cx)*cos(angle) - (y-cy)*sin(angle)+cx 333 | y_new = (x-cx)*sin(angle) + (y-cy)*sin(angle)+cy 334 | """ 335 | if degree is not None: 336 | angle = degree 337 | cx = float(cx) 338 | cy = float(cy) 339 | w = float(w) 340 | h = float(h) 341 | angle = float(angle) 342 | x1, y1 = rotate(cx - w / 2, cy - h / 2, angle, cx, cy) 343 | x2, y2 = rotate(cx + w / 2, cy - h / 2, angle, cx, cy) 344 | x3, y3 = rotate(cx + w / 2, cy + h / 2, angle, cx, cy) 345 | x4, y4 = rotate(cx - w / 2, cy + h / 2, angle, cx, cy) 346 | return x1, y1, x2, y2, x3, y3, x4, y4 347 | 348 | 349 | from numpy import cos, sin 350 | 351 | 352 | def rotate(x, y, angle, cx, cy): 353 | angle = angle # *pi/180 354 | x_new = (x - cx) * cos(angle) - (y - cy) * sin(angle) + cx 355 | y_new = (x - cx) * sin(angle) + (y - cy) * cos(angle) + cy 356 | return x_new, y_new 357 | 358 | 359 | def minAreaRectbox(regions, flag=True, W=0, H=0, filtersmall=False, adjustBox=False): 360 | """ 361 | 多边形外接矩形 362 | """ 363 | boxes = [] 364 | for region in regions: 365 | rect = cv2.minAreaRect(region.coords[:, ::-1]) 366 | 367 | box = cv2.boxPoints(rect) 368 | box = box.reshape((8,)).tolist() 369 | box = image_location_sort_box(box) 370 | x1, y1, x2, y2, x3, y3, x4, y4 = box 371 | angle, w, h, cx, cy = solve(box) 372 | if adjustBox: 373 | x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(cx, cy, w + 5, h + 5, angle=0, degree=None) 374 | 375 | if w > 32 and h > 32 and flag: 376 | if abs(angle / np.pi * 180) < 20: 377 | if filtersmall and w < 10 or h < 10: 378 | continue 379 | boxes.append([x1, y1, x2, y2, x3, y3, x4, y4]) 380 | else: 381 | if w * h < 0.5 * W * H: 382 | if filtersmall and w < 8 or h < 8: 383 | continue 384 | boxes.append([x1, y1, x2, y2, x3, y3, x4, y4]) 385 | return boxes 386 | 387 | 388 | from PIL import Image 389 | 390 | 391 | def rectangle(img, boxes): 392 | tmp = np.copy(img) 393 | for box in boxes: 394 | xmin, ymin, xmax, ymax = box[:4] 395 | cv2.rectangle(tmp, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 0, 0), 1, lineType=cv2.LINE_AA) 396 | return Image.fromarray(tmp) 397 | 398 | 399 | def draw_lines(im, bboxes, color=(0, 0, 0), lineW=3): 400 | """ 401 | boxes: bounding boxes 402 | """ 403 | tmp = np.copy(im) 404 | c = color 405 | h, w = im.shape[:2] 406 | 407 | for box in bboxes: 408 | x1, y1, x2, y2 = box[:4] 409 | cv2.line(tmp, (int(x1), int(y1)), (int(x2), int(y2)), c, lineW, lineType=cv2.LINE_AA) 410 | 411 | return tmp 412 | 413 | 414 | def draw_boxes(im, bboxes, color=(0, 0, 0)): 415 | """ 416 | boxes: bounding boxes 417 | """ 418 | tmp = np.copy(im) 419 | c = color 420 | h, w, _ = im.shape 421 | 422 | for box in bboxes: 423 | if type(box) is dict: 424 | x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(**box) 425 | else: 426 | x1, y1, x2, y2, x3, y3, x4, y4 = box[:8] 427 | 428 | cv2.line(tmp, (int(x1), int(y1)), (int(x2), int(y2)), c, 1, lineType=cv2.LINE_AA) 429 | cv2.line(tmp, (int(x2), int(y2)), (int(x3), int(y3)), c, 1, lineType=cv2.LINE_AA) 430 | cv2.line(tmp, (int(x3), int(y3)), (int(x4), int(y4)), c, 1, lineType=cv2.LINE_AA) 431 | cv2.line(tmp, (int(x4), int(y4)), (int(x1), int(y1)), c, 1, lineType=cv2.LINE_AA) 432 | 433 | return tmp 434 | --------------------------------------------------------------------------------