Initial commit

.gitignore

+.idea
+.DS_Store

LICENSE

+MIT License
+
+Copyright (c) 2020 chineseocr
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

+### test table detect（表格检测）  
+
+`
+python table_detect.py --jpgPath img/table-detect.jpg
+`
+
+### test table ceil detect with unet（表格识别输出到excel）
+
+`
+python table_ceil.py --isToExcel True --jpgPath img/table-detect.jpg
+`
+
+
+## train table line(训练表格)
+### label table with labelme(https://github.com/wkentaro/labelme)
+`
+python train/train.py
+
+
+`
+
+

__init__.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 9 23:11:51 2020
+@author: chineseocr
+"""

config.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 9 23:11:51 2020
+
+@author: chineseocr
+"""
+
+tableModelDetectPath = 'models/table-detect.weights'
+tableModeLinePath = "models/table-line.h5"

image.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 9 23:11:51 2020
+image
+@author: chineseocr
+"""
+
+import base64
+import json
+import tesserocr
+import cv2
+import numpy as np
+import six
+from PIL import Image
+from cnocr import CnOcr
+
+def plot_lines(img, lines, linetype=2):
+    tmp = np.copy(img)
+    for line in lines:
+        p1, p2 = line
+        cv2.line(tmp, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])), (0, 0, 0), linetype, lineType=cv2.LINE_AA)
+
+    return Image.fromarray(tmp)
+
+
+def base64_to_PIL(string):
+    try:
+
+        base64_data = base64.b64decode(string)
+        buf = six.BytesIO()
+        buf.write(base64_data)
+        buf.seek(0)
+        img = Image.open(buf).convert('RGB')
+        return img
+    except:
+        return None
+
+
+def read_json(p):
+    with open(p) as f:
+        jsonData = json.loads(f.read())
+    shapes = jsonData.get('shapes')
+    imageData = jsonData.get('imageData')
+    lines = []
+    labels = []
+    for shape in shapes:
+        lines.append(shape['points'])
+        [x0, y0], [x1, y1] = shape['points']
+        label = shape['label']
+        if label == '0':
+            if abs(y1 - y0) > 500:
+                label = '1'
+        elif label == '1':
+            if abs(x1 - x0) > 500:
+                label = '0'
+
+        labels.append(label)
+    img = base64_to_PIL(imageData)
+    return img, lines, labels
+
+
+from numpy import cos, sin, pi
+
+
+def rotate(x, y, angle, cx, cy):
+    """
+    点(x,y) 绕(cx,cy)点旋转
+    """
+    angle = angle * pi / 180
+    x_new = (x - cx) * cos(angle) - (y - cy) * sin(angle) + cx
+    y_new = (x - cx) * sin(angle) + (y - cy) * cos(angle) + cy
+    return x_new, y_new
+
+
+def box_rotate(box, angle=0, imgH=0, imgW=0):
+    """
+    对坐标进行旋转 逆时针方向 0\90\180\270,
+    """
+    x1, y1, x2, y2, x3, y3, x4, y4 = box[:8]
+    if angle == 90:
+        x1_, y1_ = y2, imgW - x2
+        x2_, y2_ = y3, imgW - x3
+        x3_, y3_ = y4, imgW - x4
+        x4_, y4_ = y1, imgW - x1
+
+    elif angle == 180:
+        x1_, y1_ = imgW - x3, imgH - y3
+        x2_, y2_ = imgW - x4, imgH - y4
+        x3_, y3_ = imgW - x1, imgH - y1
+        x4_, y4_ = imgW - x2, imgH - y2
+
+    elif angle == 270:
+        x1_, y1_ = imgH - y4, x4
+        x2_, y2_ = imgH - y1, x1
+        x3_, y3_ = imgH - y2, x2
+        x4_, y4_ = imgH - y3, x3
+    else:
+        x1_, y1_, x2_, y2_, x3_, y3_, x4_, y4_ = x1, y1, x2, y2, x3, y3, x4, y4
+
+    return (x1_, y1_, x2_, y2_, x3_, y3_, x4_, y4_)
+
+
+def angle_transpose(p, angle, w, h):
+    x, y = p
+    if angle == 90:
+        x, y = y, w - x
+    elif angle == 180:
+        x, y = w - x, h - y
+    elif angle == 270:
+        x, y = h - y, x
+    return x, y
+
+
+def img_argument(img, lines, labels, size=(512, 512)):
+    w, h = img.size
+    if np.random.randint(0, 100) > 80:
+        degree = np.random.uniform(-5, 5)
+    else:
+        degree = 0
+    # degree = np.random.uniform(-5,5)
+    newlines = []
+    for line in lines:
+        p1, p2 = line
+        p1 = rotate(p1[0], p1[1], degree, w / 2, h / 2)
+        p2 = rotate(p2[0], p2[1], degree, w / 2, h / 2)
+        newlines.append([p1, p2])
+    # img = img.rotate(-degree,center=(w/2,h/2),resample=Image.BILINEAR,fillcolor=(128,128,128))
+    img = img.rotate(-degree, center=(w / 2, h / 2), resample=Image.BILINEAR)
+    angle = np.random.choice([0, 90, 180, 270], 1)[0]
+    newlables = []
+    for i in range(len(newlines)):
+        p1, p2 = newlines[i]
+        p1 = angle_transpose(p1, angle, w, h)
+        p2 = angle_transpose(p2, angle, w, h)
+        newlines[i] = [p1, p2]
+        if angle in [90, 270]:
+            if labels[i] == '0':
+                newlables.append('1')
+            else:
+                newlables.append('0')
+        else:
+            newlables.append(labels[i])
+
+    if angle == 90:
+        img = img.transpose(Image.ROTATE_90)
+    elif angle == 180:
+        img = img.transpose(Image.ROTATE_180)
+    elif angle == 270:
+        img = img.transpose(Image.ROTATE_270)
+
+    return img, newlines, newlables
+
+
+def fill_lines(img, lines, linetype=2):
+    tmp = np.copy(img)
+    for line in lines:
+        p1, p2 = line
+        cv2.line(tmp, (int(p1[0]), int(p1[1])), (int(p2[0]), int(p2[1])), 255, linetype, lineType=cv2.LINE_AA)
+
+    return tmp
+
+
+def get_img_label(p, size, linetype=1):
+    img, lines, labels = read_json(p)
+    img, lines = img_resize(img, lines, target_size=512, max_size=1024)
+    img, lines, labels = img_argument(img, lines, labels, size)
+    img, lines, labels = get_random_data(img, lines, labels, size=size)
+
+    lines = np.array(lines)
+    labels = np.array(labels)
+    labelImg0 = np.zeros(size[::-1], dtype='uint8')
+    labelImg1 = np.zeros(size[::-1], dtype='uint8')
+
+    ind = np.where(labels == '0')[0]
+    labelImg0 = fill_lines(labelImg0, lines[ind], linetype=linetype)
+    ind = np.where(labels == '1')[0]
+    labelImg1 = fill_lines(labelImg1, lines[ind], linetype=linetype)
+
+    labelY = np.zeros((size[1], size[0], 2), dtype='uint8')
+    labelY[:, :, 0] = labelImg0
+    labelY[:, :, 1] = labelImg1
+    labelY = labelY > 0
+    return np.array(img), lines, labelY
+
+
+from matplotlib.colors import rgb_to_hsv, hsv_to_rgb
+
+
+def rand(a=0, b=1):
+    return np.random.rand() * (b - a) + a
+
+
+def get_random_data(image, lines, labels, size=(1024, 1024), jitter=.3, hue=.1, sat=1.5, val=1.5):
+    '''random preprocessing for real-time data augmentation'''
+
+    iw, ih = image.size
+
+    # resize image
+    w, h = size
+    new_ar = w / h * rand(1 - jitter, 1 + jitter) / rand(1 - jitter, 1 + jitter)
+    # scale = rand(.2, 2)
+    scale = rand(0.2, 3)
+    if new_ar < 1:
+        nh = int(scale * h)
+        nw = int(nh * new_ar)
+    else:
+        nw = int(scale * w)
+        nh = int(nw / new_ar)
+    image = image.resize((nw, nh), Image.BICUBIC)
+
+    # place image
+    dx = int(rand(0, w - nw))
+    dy = int(rand(0, h - nh))
+    new_image = Image.new('RGB', (w, h), (128, 128, 128))
+    new_image.paste(image, (dx, dy))
+    image = new_image
+
+    # distort image
+    hue = rand(-hue, hue)
+    sat = rand(1, sat) if rand() < .5 else 1 / rand(1, sat)
+    val = rand(1, val) if rand() < .5 else 1 / rand(1, val)
+    x = rgb_to_hsv(np.array(image) / 255.)
+    x[..., 0] += hue
+    x[..., 0][x[..., 0] > 1] -= 1
+    x[..., 0][x[..., 0] < 0] += 1
+    x[..., 1] *= sat
+    x[..., 2] *= val
+    x[x > 1] = 1
+    x[x < 0] = 0
+    image_data = hsv_to_rgb(x)  # numpy array, 0 to 1
+    N = len(lines)
+    for i in range(N):
+        p1, p2 = lines[i]
+        p1 = p1[0] * nw / iw + dx, p1[1] * nh / ih + dy
+        p2 = p2[0] * nw / iw + dx, p2[1] * nh / ih + dy
+        lines[i] = [p1, p2]
+    return image_data, lines, labels
+
+
+def gen(paths, batchsize=2, linetype=2):
+    num = len(paths)
+    i = 0
+    while True:
+        # sizes = [512,512,512,512,640,1024] ##多尺度训练
+        # size = np.random.choice(sizes,1)[0]
+        size = 640
+
+        X = np.zeros((batchsize, size, size, 3))
+        Y = np.zeros((batchsize, size, size, 2))
+        for j in range(batchsize):
+            if i >= num:
+                i = 0
+                np.random.shuffle(paths)
+            p = paths[i]
+            i += 1
+
+            # linetype=2
+            img, lines, labelImg = get_img_label(p, size=(size, size), linetype=linetype)
+            X[j] = img
+            Y[j] = labelImg
+
+        yield X, Y
+
+
+def img_resize(im, lines, target_size=600, max_size=1500):
+    w, h = im.size
+    im_size_min = np.min(im.size)
+    im_size_max = np.max(im.size)
+
+    im_scale = float(target_size) / float(im_size_min)
+    if max_size is not None:
+        if np.round(im_scale * im_size_max) > max_size:
+            im_scale = float(max_size) / float(im_size_max)
+
+    im = im.resize((int(w * im_scale), int(h * im_scale)), Image.BICUBIC)
+    N = len(lines)
+    for i in range(N):
+        p1, p2 = lines[i]
+        p1 = p1[0] * im_scale, p1[1] * im_scale
+        p2 = p2[0] * im_scale, p2[1] * im_scale
+        lines[i] = [p1, p2]
+
+    return im, lines
+
+from cnocr.utils import read_img
+from cnocr import CnOcr
+
+ocr = CnOcr()
+
+if __name__ == '__main__':
+    # ocr = CnOcr()
+    # img_fp = 'img/gou.png'
+    # res = ocr.ocr(img_fp)
+    # print("Predicted Chars:", res)
+
+    from PIL import Image
+    img = Image.open('img/gou.png')
+    result = tesserocr.image_to_text(img)
+    print(result)
+    # im = read_img(img_fp)
+    # if(min(im.shape[0],im.shape[1])<32):
+    #     w, h = im.shape[0],im.shape[1]
+    #     im_size_min = min(im.shape[0],im.shape[1])
+    #     im_size_max = max(im.shape[0],im.shape[1])
+    #
+    #     im_scale = float(32) / float(im_size_min)
+    #     max_scale = max((int(w * im_scale), int(h * im_scale)))
+    #     zeros = np.ones((max_scale,max_scale,3),dtype = np.uint8)
+    #     zeros = zeros*255
+    #     zeros[:w,:h] = im
+    #
+    #     x = Image.fromarray(zeros)
+    #     x.save("test.jpg")
+    #     print(zeros.shape)
+    #     res = ocr.ocr(zeros)
+    #
+    #     print("Predicted Chars:", res)
\ No newline at end of file

img/__init__.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 10 01:33:19 2020
+
+@author: chineseocr
+"""

models/table-detect.cfg

+[net]
+# Testing
+# batch=1
+# subdivisions=1
+# Training
+batch=32
+subdivisions=16
+width=608
+height=608
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.0001
+burn_in=1000
+max_batches = 50200
+policy=steps
+steps=40000,45000
+scales=.1,.1
+
+
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=21
+activation=linear
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=2
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=21
+activation=linear
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=2
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=21
+activation=linear
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=2
+num=9
+jitter=.3
+ignore_thresh = .5
+truth_thresh = 1
+random=1
+

table_build.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jan 13 17:31:37 2021
+
+@author: lywen
+"""
+
+
+class tableBuid:
+    ##表格重建
+    def __init__(self, ceilbox, interval=10):
+        """
+        ceilboxes:[[x0,y0,x1,y1,x2,y2,x3,y3,x4,y4]]
+        """
+        diagBoxes =[[int(x[0]), int(x[1]), int(x[4]), int(x[5])] for x in ceilbox]
+
+        self.diagBoxes = diagBoxes
+        self.interval = interval
+        self.batch()
+
+    def batch(self):
+        self.cor = []
+        rowcor = self.table_line_cor(self.diagBoxes, axis='row', interval=self.interval)
+        colcor = self.table_line_cor(self.diagBoxes, axis='col', interval=self.interval)
+        cor = [{'row': line[1], 'col': line[0]} for line in zip(rowcor, colcor)]
+        self.cor = cor
+
+    def table_line_cor(self, lines, axis='col', interval=10):
+
+        if axis == 'col':
+            edges = [[line[1], line[3]] for line in lines]
+        else:
+            edges = [[line[0], line[2]] for line in lines]
+
+        edges = sum(edges, [])
+        edges = sorted(edges)
+
+        nedges = len(edges)
+        edgesMap = {}
+        for i in range(nedges):
+            if i == 0:
+                edgesMap[edges[i]] = edges[i]
+                continue
+            else:
+                if edges[i] - edgesMap[edges[i - 1]] < interval:
+                    edgesMap[edges[i]] = edgesMap[edges[i - 1]]
+                else:
+                    edgesMap[edges[i]] = edges[i]
+
+        edgesMapList = [[key, edgesMap[key]] for key in edgesMap]
+        edgesMapIndex = [line[1] for line in edgesMapList]
+        edgesMapIndex = list(set(edgesMapIndex))
+        edgesMapIndex = {x: ind for ind, x in enumerate(sorted(edgesMapIndex))}
+
+        if axis == 'col':
+            cor = [[edgesMapIndex[edgesMap[line[1]]], edgesMapIndex[edgesMap[line[3]]]] for line in lines]
+        else:
+            cor = [[edgesMapIndex[edgesMap[line[0]]], edgesMapIndex[edgesMap[line[2]]]] for line in lines]
+        return cor
+
+import xlwt
+def to_excel(res, workbook=None):
+    ##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]}]
+    row = 0
+    if workbook is None:
+        workbook = xlwt.Workbook()
+    if len(res) == 0:
+        worksheet = workbook.add_sheet('table')
+        worksheet.write_merge(0, 0, 0, 0, "无数据")
+    else:
+        worksheet = workbook.add_sheet('page')
+        pageRow = 0
+        for line in res:
+                row0, row1 = line['row']
+                col0, col1 = line['col']
+                text = line.get('text','')
+                try:
+                    pageRow = max(row1 - 1, pageRow)
+                    worksheet.write_merge(row + row0, row + row1 - 1, col0, col1 - 1, text)
+                except:
+                    pass
+    return workbook
+
+
+if __name__=='__main__':
+    pass

table_ceil.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import cv2
+import numpy as np
+from table_detect import table_detect
+from table_line import table_line
+from table_build import tableBuid,to_excel
+from utils import minAreaRectbox, measure, eval_angle, draw_lines
+from cnocr import CnOcr
+from scipy.ndimage import filters
+
+class table:
+    def __init__(self, img, tableSize=(416, 416), tableLineSize=(1024, 1024), isTableDetect=False, isToExcel=False):
+        self.img = img
+        self.tableSize = tableSize
+        self.tableLineSize = tableLineSize
+        self.isTableDetect = isTableDetect
+        self.isToExcel = isToExcel
+        self.img_degree()
+        self.table_boxes_detect()  ##表格定位
+        self.table_ceil()  ##表格单元格定位
+        self.table_build()
+        self.cnocr = CnOcr()
+        self.table_ocr()
+
+    def img_degree(self):
+        img, degree = eval_angle(self.img, angleRange=[-15, 15])
+        self.img = img
+        self.degree = degree
+
+    def table_boxes_detect(self):
+        h, w = self.img.shape[:2]
+
+        if self.isTableDetect:
+            boxes, adBoxes, scores = table_detect(self.img, sc=self.tableSize, thresh=0.2, NMSthresh=0.3)
+            if len(boxes) == 0:
+                boxes = [[0, 0, w, h]]
+                adBoxes = [[0, 0, w, h]]
+                scores = [0]
+        else:
+            boxes = [[0, 0, w, h]]
+            adBoxes = [[0, 0, w, h]]
+            scores = [0]
+
+        self.boxes = boxes
+        self.adBoxes = adBoxes
+        self.scores = scores
+
+    def table_ceil(self):
+        ###表格单元格
+        n = len(self.adBoxes)
+        self.tableCeilBoxes = []
+        self.childImgs = []
+        for i in range(n):
+            xmin, ymin, xmax, ymax = [int(x) for x in self.adBoxes[i]]
+
+            childImg = self.img[ymin:ymax, xmin:xmax]
+            rowboxes, colboxes = table_line(childImg[..., ::-1], size=self.tableLineSize, hprob=0.5, vprob=0.5)
+            tmp = np.zeros(self.img.shape[:2], dtype='uint8')
+            tmp = draw_lines(tmp, rowboxes + colboxes, color=255, lineW=2)
+            labels = measure.label(tmp < 255, connectivity=2)  # 8连通区域标记
+            regions = measure.regionprops(labels)
+            ceilboxes = minAreaRectbox(regions, False, tmp.shape[1], tmp.shape[0], True, True)
+            ceilboxes = np.array(ceilboxes)
+            ceilboxes[:, [0, 2, 4, 6]] += xmin
+            ceilboxes[:, [1, 3, 5, 7]] += ymin
+            self.tableCeilBoxes.extend(ceilboxes)
+            self.childImgs.append(childImg)
+
+    def table_build(self):
+        tablebuild = tableBuid(self.tableCeilBoxes)
+        cor = tablebuild.cor
+        for line in cor:
+            line['text'] = 'table-test'##ocr
+        # if self.isToExcel:
+        #     workbook = to_excel(cor, workbook=None)
+        # else:
+        #     workbook=None
+        self.res = cor
+        # self.workbook = workbook
+
+
+    def table_ocr(self):
+        res = []
+        """use ocr and match ceil"""
+        i = 0
+        for ceil in zip(self.tableCeilBoxes,self.res):
+            cor = {}
+            i = i + 1
+            ceilBoxes = list(ceil[0])
+            # image[600:1200, 750:1500]
+            left,right,top,bottom = ceilBoxes[0],ceilBoxes[2],ceilBoxes[1],ceilBoxes[5]
+            # print(int(left),int(right),int(top),int(bottom))
+            tmpImg = img[int(top)+4:int(bottom)-4,int(left)+4:int(right)-4,:]
+            cv2.imwrite("img/{0}.png".format(str(i)),tmpImg)
+            # tmpImg = mx.image.imread("img/tmp.png",1)
+            content = self.cnocr.ocr(tmpImg)
+
+            # print(content)
+            s_content = ""
+            for x in content:
+                s = "".join(x[0])
+                s_content += "\n"
+                s_content += s
+            print(s_content)
+            cor['row'] = ceil[1]['row']
+            cor['col'] = ceil[1]['col']
+            cor['text'] = s_content
+            res.append(cor)
+        if self.isToExcel:
+            pass
+            # workbook = to_excel(res, workbook=None)
+        else:
+            workbook=None
+        # self.workbook = workbook
+
+
+
+
+
+if __name__ == '__main__':
+    import argparse
+    import os
+    import time
+    from utils import draw_boxes
+
+    parser = argparse.ArgumentParser(description='tabel to excel demo')
+    parser.add_argument('--isTableDetect', default=False, type=bool, help="是否先进行表格检测")
+    parser.add_argument('--tableSize', default='416,416', type=str, help="表格检测输入size")
+    parser.add_argument('--tableLineSize', default='1024,1024', type=str, help="表格直线输入size")
+    parser.add_argument('--isToExcel', default=False, type=bool, help="是否输出到excel")
+    parser.add_argument('--jpgPath', default='img/table-detect.jpg',type=str, help="测试图像地址")
+    args = parser.parse_args()
+    args.tableSize = [int(x) for x in args.tableSize.split(',')]
+    args.tableLineSize = [int(x) for x in args.tableLineSize.split(',')]
+    print(args)
+    img = cv2.imread(args.jpgPath)
+    t = time.time()
+    tableDetect = table(img,tableSize=args.tableSize,
+                        tableLineSize=args.tableLineSize,
+                        isTableDetect=args.isTableDetect,
+                        isToExcel=args.isToExcel
+                        )
+    tableCeilBoxes = tableDetect.tableCeilBoxes
+    tableJson = tableDetect.res
+    workbook =  tableDetect.workbook
+    img = tableDetect.img
+    tmp = np.zeros_like(img)
+    img = draw_boxes(tmp, tableDetect.tableCeilBoxes, color=(255, 255, 255))
+    print(time.time() - t)
+    pngP = os.path.splitext(args.jpgPath)[0]+'ceil.png'
+    cv2.imwrite(pngP, img)
+    if workbook is not None:
+        workbook.save(os.path.splitext(args.jpgPath)[0]+'.xlsx')

table_detect.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+
+from config import tableModelDetectPath
+from utils import nms_box, letterbox_image, rectangle
+
+tableDetectNet = cv2.dnn.readNetFromDarknet(tableModelDetectPath.replace('.weights', '.cfg'), tableModelDetectPath)  #
+
+
+def table_detect(img, sc=(416, 416), thresh=0.5, NMSthresh=0.3):
+    """
+    表格检测
+    img:GBR
+    
+    """
+    scale = sc[0]
+    img_height, img_width = img.shape[:2]
+    inputBlob, fx, fy = letterbox_image(img[..., ::-1], (scale, scale))
+    inputBlob = cv2.dnn.blobFromImage(inputBlob, scalefactor=1.0, size=(scale, scale), swapRB=True, crop=False);
+    tableDetectNet.setInput(inputBlob / 255.0)
+    outputName = tableDetectNet.getUnconnectedOutLayersNames()
+    outputs = tableDetectNet.forward(outputName)
+    class_ids = []
+    confidences = []
+    boxes = []
+    for output in outputs:
+        for detection in output:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > thresh:
+                center_x = int(detection[0] * scale / fx)
+                center_y = int(detection[1] * scale / fy)
+                width = int(detection[2] * scale / fx)
+                height = int(detection[3] * scale / fy)
+                left = int(center_x - width / 2)
+                top = int(center_y - height / 2)
+                if class_id == 1:
+                    class_ids.append(class_id)
+                    confidences.append(float(confidence))
+                    xmin, ymin, xmax, ymax = left, top, left + width, top + height
+                    xmin = max(xmin, 1)
+                    ymin = max(ymin, 1)
+                    xmax = min(xmax, img_width - 1)
+                    ymax = min(ymax, img_height - 1)
+                    boxes.append([xmin, ymin, xmax, ymax])
+
+    boxes = np.array(boxes)
+
+    confidences = np.array(confidences)
+    if len(boxes) > 0:
+        boxes, confidences = nms_box(boxes, confidences, score_threshold=thresh, nms_threshold=NMSthresh)
+
+    boxes, adBoxes = fix_table_box_for_table_line(boxes, confidences, img)
+    return boxes, adBoxes, confidences
+
+
+def point_in_box(p, box):
+    x, y = p
+    xmin, ymin, xmax, ymax = box
+    if xmin <= x <= xmin and ymin <= y <= ymax:
+        return True
+    else:
+        return False
+
+
+def fix_table_box_for_table_line(boxes, confidences, img):
+    ### 修正表格用于表格线检测 
+    h, w = img.shape[:2]
+    n = len(boxes)
+    adBoxes = []
+
+    for i in range(n):
+        prob = confidences[i]
+
+        xmin, ymin, xmax, ymax = boxes[i]
+        padx = (xmax - xmin) * (1 - prob)
+        padx = padx
+
+        pady = (ymax - ymin) * (1 - prob)
+        pady = pady
+        xminNew = max(xmin - padx, 1)
+        yminNew = max(ymin - pady, 1)
+        xmaxNew = min(xmax + padx, w)
+        ymaxNew = min(ymax + pady, h)
+
+        adBoxes.append([xminNew, yminNew, xmaxNew, ymaxNew])
+
+    return boxes, adBoxes
+
+
+if __name__ == '__main__':
+    import time
+    import argparse
+    parser = argparse.ArgumentParser(description='tabel to excel demo')
+    parser.add_argument('--tableSize', default='416,416', type=str, help="表格检测输入size")
+    parser.add_argument('--jpgPath', default='img/table-detect.jpg', type=str, help="测试图像地址")
+    args = parser.parse_args()
+    args.tableSize = [int(x) for x in args.tableSize.split(',')]
+
+    p = 'img/table-detect.jpg'
+    img = cv2.imread(args.jpgPath)
+    t = time.time()
+    boxes, adBoxes, scores = table_detect(img, sc=(416, 416), thresh=0.5, NMSthresh=0.3)
+    print(time.time() - t, boxes, adBoxes, scores)
+    img = rectangle(img, adBoxes)
+    img.save('img/table-detect.png')

table_line.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+
+from tensorflow.keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D
+from tensorflow.keras.layers import LeakyReLU
+from tensorflow.keras.models import Model
+
+
+def table_net(input_shape=(512, 512, 3), num_classes=1):
+    inputs = Input(shape=input_shape)
+    # 512
+    use_bias = False
+    down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(inputs)
+    down0a = BatchNormalization()(down0a)
+    down0a = LeakyReLU(alpha=0.1)(down0a)
+    down0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(down0a)
+    down0a = BatchNormalization()(down0a)
+    down0a = LeakyReLU(alpha=0.1)(down0a)
+    down0a_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0a)
+    # 256
+
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0a_pool)
+    down0 = BatchNormalization()(down0)
+
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(down0)
+    down0 = BatchNormalization()(down0)
+    down0 = LeakyReLU(alpha=0.1)(down0)
+    down0_pool = MaxPooling2D((2, 2), strides=(2, 2))(down0)
+    # 128
+
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down0_pool)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(down1)
+    down1 = BatchNormalization()(down1)
+    down1 = LeakyReLU(alpha=0.1)(down1)
+    down1_pool = MaxPooling2D((2, 2), strides=(2, 2))(down1)
+    # 64
+
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down1_pool)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(down2)
+    down2 = BatchNormalization()(down2)
+    down2 = LeakyReLU(alpha=0.1)(down2)
+    down2_pool = MaxPooling2D((2, 2), strides=(2, 2))(down2)
+    # 32
+
+    down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down2_pool)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(down3)
+    down3 = BatchNormalization()(down3)
+    down3 = LeakyReLU(alpha=0.1)(down3)
+    down3_pool = MaxPooling2D((2, 2), strides=(2, 2))(down3)
+    # 16
+
+    down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down3_pool)
+    down4 = BatchNormalization()(down4)
+    down4 = LeakyReLU(alpha=0.1)(down4)
+    down4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(down4)
+    down4 = BatchNormalization()(down4)
+    down4 = LeakyReLU(alpha=0.1)(down4)
+    down4_pool = MaxPooling2D((2, 2), strides=(2, 2))(down4)
+    # 8
+
+    center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(down4_pool)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+    center = Conv2D(1024, (3, 3), padding='same', use_bias=use_bias)(center)
+    center = BatchNormalization()(center)
+    center = LeakyReLU(alpha=0.1)(center)
+    # center
+
+    up4 = UpSampling2D((2, 2))(center)
+    up4 = concatenate([down4, up4], axis=3)
+    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
+    up4 = BatchNormalization()(up4)
+    up4 = LeakyReLU(alpha=0.1)(up4)
+    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
+    up4 = BatchNormalization()(up4)
+    up4 = LeakyReLU(alpha=0.1)(up4)
+    up4 = Conv2D(512, (3, 3), padding='same', use_bias=use_bias)(up4)
+    up4 = BatchNormalization()(up4)
+    up4 = LeakyReLU(alpha=0.1)(up4)
+    # 16
+
+    up3 = UpSampling2D((2, 2))(up4)
+    up3 = concatenate([down3, up3], axis=3)
+    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+    up3 = Conv2D(256, (3, 3), padding='same', use_bias=use_bias)(up3)
+    up3 = BatchNormalization()(up3)
+    up3 = LeakyReLU(alpha=0.1)(up3)
+    # 32
+
+    up2 = UpSampling2D((2, 2))(up3)
+    up2 = concatenate([down2, up2], axis=3)
+    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+    up2 = Conv2D(128, (3, 3), padding='same', use_bias=use_bias)(up2)
+    up2 = BatchNormalization()(up2)
+    up2 = LeakyReLU(alpha=0.1)(up2)
+    # 64
+
+    up1 = UpSampling2D((2, 2))(up2)
+    up1 = concatenate([down1, up1], axis=3)
+    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+    up1 = Conv2D(64, (3, 3), padding='same', use_bias=use_bias)(up1)
+    up1 = BatchNormalization()(up1)
+    up1 = LeakyReLU(alpha=0.1)(up1)
+    # 128
+
+    up0 = UpSampling2D((2, 2))(up1)
+    up0 = concatenate([down0, up0], axis=3)
+    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
+    up0 = BatchNormalization()(up0)
+    up0 = LeakyReLU(alpha=0.1)(up0)
+    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
+    up0 = BatchNormalization()(up0)
+    up0 = LeakyReLU(alpha=0.1)(up0)
+    up0 = Conv2D(32, (3, 3), padding='same', use_bias=use_bias)(up0)
+    up0 = BatchNormalization()(up0)
+    up0 = LeakyReLU(alpha=0.1)(up0)
+    # 256
+
+    up0a = UpSampling2D((2, 2))(up0)
+    up0a = concatenate([down0a, up0a], axis=3)
+    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
+    up0a = BatchNormalization()(up0a)
+    up0a = LeakyReLU(alpha=0.1)(up0a)
+    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
+    up0a = BatchNormalization()(up0a)
+    up0a = LeakyReLU(alpha=0.1)(up0a)
+    up0a = Conv2D(16, (3, 3), padding='same', use_bias=use_bias)(up0a)
+    up0a = BatchNormalization()(up0a)
+    up0a = LeakyReLU(alpha=0.1)(up0a)
+    # 512
+
+    classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(up0a)
+    print(classify.shape)
+    print(inputs.shape)
+    model = Model(inputs=inputs, outputs=classify)
+    return model
+
+
+from config import tableModeLinePath
+from utils import letterbox_image, get_table_line, adjust_lines, line_to_line
+import numpy as np
+import cv2
+
+model = table_net((None, None, 3), 2)
+model.load_weights(tableModeLinePath)
+
+
+def table_line(img, size=(512, 512), hprob=0.5, vprob=0.5, row=50, col=30, alph=15):
+    sizew, sizeh = size
+    inputBlob, fx, fy = letterbox_image(img[..., ::-1], (sizew, sizeh))
+    pred = model.predict(np.array([np.array(inputBlob) / 255.0]))
+
+    pred = pred[0]
+    # shape = [1024,1024,2]
+
+    vpred = pred[..., 1] > vprob  ##竖线
+    hpred = pred[..., 0] > hprob  ##横线
+    vpred = vpred.astype(int)
+    hpred = hpred.astype(int)
+    colboxes = get_table_line(vpred, axis=1, lineW=col)
+    rowboxes = get_table_line(hpred, axis=0, lineW=row)
+    ccolbox = []
+    crowlbox = []
+    if len(rowboxes) > 0:
+        rowboxes = np.array(rowboxes)
+        rowboxes[:, [0, 2]] = rowboxes[:, [0, 2]] / fx
+        rowboxes[:, [1, 3]] = rowboxes[:, [1, 3]] / fy
+        xmin = rowboxes[:, [0, 2]].min()
+        xmax = rowboxes[:, [0, 2]].max()
+        ymin = rowboxes[:, [1, 3]].min()
+        ymax = rowboxes[:, [1, 3]].max()
+        ccolbox = [[xmin, ymin, xmin, ymax], [xmax, ymin, xmax, ymax]]
+        rowboxes = rowboxes.tolist()
+
+    if len(colboxes) > 0:
+        colboxes = np.array(colboxes)
+        colboxes[:, [0, 2]] = colboxes[:, [0, 2]] / fx
+        colboxes[:, [1, 3]] = colboxes[:, [1, 3]] / fy
+
+        xmin = colboxes[:, [0, 2]].min()
+        xmax = colboxes[:, [0, 2]].max()
+        ymin = colboxes[:, [1, 3]].min()
+        ymax = colboxes[:, [1, 3]].max()
+        colboxes = colboxes.tolist()
+        crowlbox = [[xmin, ymin, xmax, ymin], [xmin, ymax, xmax, ymax]]
+
+    rowboxes += crowlbox
+    colboxes += ccolbox
+
+    rboxes_row_, rboxes_col_ = adjust_lines(rowboxes, colboxes, alph=alph)
+    rowboxes += rboxes_row_
+    colboxes += rboxes_col_
+    nrow = len(rowboxes)
+    ncol = len(colboxes)
+    for i in range(nrow):
+        for j in range(ncol):
+            rowboxes[i] = line_to_line(rowboxes[i], colboxes[j], 10)
+            colboxes[j] = line_to_line(colboxes[j], rowboxes[i], 10)
+
+    return rowboxes, colboxes
+
+
+if __name__ == '__main__':
+    import time
+
+    p = 'img/table-detect.jpg'
+    from utils import draw_lines
+
+    img = cv2.imread(p)
+    t = time.time()
+    rowboxes, colboxes = table_line(img[..., ::-1], size=(512, 512), hprob=0.5, vprob=0.5)
+    img = draw_lines(img, rowboxes + colboxes, color=(255, 0, 0), lineW=2)
+
+    print(time.time() - t, len(rowboxes), len(colboxes))
+    cv2.imwrite('img/table-line.png', img)

train/__init__.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 10 02:52:45 2020
+@author: chineseocr
+"""
+import sys
+
+sys.path.append('.')

train/dataset-line/0/0.json

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+  "imageHeight": 300,
+  "imageWidth": 619
+}
\ No newline at end of file

train/train.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 9 23:11:51 2020
+
+@author: chineseocr
+"""
+import sys
+
+sys.path.append('.')
+from table_line import model
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
+from sklearn.model_selection import train_test_split
+from glob import glob
+from image import gen
+
+if __name__ == '__main__':
+    filepath = './models/table-line-fine.h5'  ##模型权重存放位置
+
+    checkpointer = ModelCheckpoint(filepath=filepath, monitor='loss', verbose=0, save_weights_only=True,
+                                   save_best_only=True)
+    rlu = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=5, verbose=0, mode='auto', cooldown=0, min_lr=0)
+    model.compile(optimizer=Adam(lr=0.0001), loss='binary_crossentropy', metrics=['acc'])
+
+    paths = glob('./train/dataset-line/*/*.json')  ##table line dataset label with labelme
+    trainP, testP = train_test_split(paths, test_size=0.1)
+    print('total:', len(paths), 'train:', len(trainP), 'test:', len(testP))
+    batchsize = 4
+    trainloader = gen(trainP, batchsize=batchsize, linetype=1)
+    testloader = gen(testP, batchsize=batchsize, linetype=1)
+    model.fit_generator(trainloader,
+                        steps_per_epoch=max(1, len(trainP) // batchsize),
+                        callbacks=[checkpointer],
+                        validation_data=testloader,
+                        validation_steps=max(1, len(testP) // batchsize),
+                        epochs=30)

utils.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+
+def nms_box(boxes, scores, score_threshold=0.5, nms_threshold=0.3):
+    ##nms box
+    boxes = np.array(boxes)
+    scores = np.array(scores)
+    ind = scores > score_threshold
+    boxes = boxes[ind]
+    scores = scores[ind]
+
+    def box_to_center(box):
+        xmin, ymin, xmax, ymax = [round(float(x), 4) for x in box]
+        w = xmax - xmin
+        h = ymax - ymin
+        return [round(xmin, 4), round(ymin, 4), round(w, 4), round(h, 4)]
+
+    newBoxes = [box_to_center(box) for box in boxes]
+    newscores = [round(float(x), 6) for x in scores]
+
+    index = cv2.dnn.NMSBoxes(newBoxes, newscores, score_threshold=score_threshold, nms_threshold=nms_threshold)
+    if len(index) > 0:
+        index = index.reshape((-1,))
+        return boxes[index], scores[index]
+    else:
+        return np.array([]), np.array([])
+
+
+from scipy.ndimage import filters, interpolation
+from numpy import amin, amax
+
+
+def resize_im(im, scale, max_scale=None):
+    f = float(scale) / min(im.shape[0], im.shape[1])
+    if max_scale != None and f * max(im.shape[0], im.shape[1]) > max_scale:
+        f = float(max_scale) / max(im.shape[0], im.shape[1])
+    return cv2.resize(im, (0, 0), fx=f, fy=f)
+
+
+def estimate_skew_angle(raw, angleRange=[-15, 15]):
+    """
+    估计图像文字偏转角度,
+    angleRange:角度估计区间
+    """
+    raw = resize_im(raw, scale=600, max_scale=900)
+    image = raw - amin(raw)
+    image = image / amax(image)
+    m = interpolation.zoom(image, 0.5)
+    m = filters.percentile_filter(m, 80, size=(20, 2))
+    m = filters.percentile_filter(m, 80, size=(2, 20))
+    m = interpolation.zoom(m, 1.0 / 0.5)
+    # w,h = image.shape[1],image.shape[0]
+    w, h = min(image.shape[1], m.shape[1]), min(image.shape[0], m.shape[0])
+    flat = np.clip(image[:h, :w] - m[:h, :w] + 1, 0, 1)
+    d0, d1 = flat.shape
+    o0, o1 = int(0.1 * d0), int(0.1 * d1)
+    flat = amax(flat) - flat
+    flat -= amin(flat)
+    est = flat[o0:d0 - o0, o1:d1 - o1]
+    angles = range(angleRange[0], angleRange[1])
+    estimates = []
+    for a in angles:
+        roest = interpolation.rotate(est, a, order=0, mode='constant')
+        v = np.mean(roest, axis=1)
+        v = np.var(v)
+        estimates.append((v, a))
+
+    _, a = max(estimates)
+    return a
+
+
+def eval_angle(img, angleRange=[-5, 5]):
+    """
+    估计图片文字的偏移角度
+    """
+    im = Image.fromarray(img)
+    degree = estimate_skew_angle(np.array(im.convert('L')), angleRange=angleRange)
+    im = im.rotate(degree, center=(im.size[0] / 2, im.size[1] / 2), expand=1, fillcolor=(255, 255, 255))
+    img = np.array(im)
+    return img, degree
+
+
+def letterbox_image(image, size, fillValue=[128, 128, 128]):
+    '''
+    resize image with unchanged aspect ratio using padding
+    '''
+    image_h, image_w = image.shape[:2]
+    w, h = size
+    new_w = int(image_w * min(w * 1.0 / image_w, h * 1.0 / image_h))
+    new_h = int(image_h * min(w * 1.0 / image_w, h * 1.0 / image_h))
+
+    resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
+    # cv2.imwrite('tmp/test.png', resized_image[...,::-1])
+    if fillValue is None:
+        fillValue = [int(x.mean()) for x in cv2.split(np.array(image))]
+    boxed_image = np.zeros((size[1], size[0], 3), dtype=np.uint8)
+    boxed_image[:] = fillValue
+    boxed_image[:new_h, :new_w, :] = resized_image
+
+    return boxed_image, new_w / image_w, new_h / image_h
+
+
+from skimage import measure
+
+
+def get_table_line(binimg, axis=0, lineW=10):
+    ##获取表格线
+    ##axis=0 横线
+    ##axis=1 竖线
+    # 把每个连通图的元素赋值一个标记，从1开始到N
+    labels = measure.label(binimg > 0, connectivity=2)  # 8连通区域标记
+    # 获取连通区域
+    regions = measure.regionprops(labels)
+    if axis == 1:
+        # 多边型的外接矩形
+        lineboxes = [minAreaRect(line.coords) for line in regions if line.bbox[2] - line.bbox[0] > lineW]
+    else:
+        lineboxes = [minAreaRect(line.coords) for line in regions if line.bbox[3] - line.bbox[1] > lineW]
+    return lineboxes
+
+
+def sqrt(p1, p2):
+    return np.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)
+
+
+def adjust_lines(RowsLines, ColsLines, alph=50):
+    ##调整line
+
+    nrow = len(RowsLines)
+    ncol = len(ColsLines)
+    newRowsLines = []
+    newColsLines = []
+    for i in range(nrow):
+
+        x1, y1, x2, y2 = RowsLines[i]
+        cx1, cy1 = (x1 + x2) / 2, (y1 + y2) / 2
+        for j in range(nrow):
+            if i != j:
+                x3, y3, x4, y4 = RowsLines[j]
+                cx2, cy2 = (x3 + x4) / 2, (y3 + y4) / 2
+                if (x3 < cx1 < x4 or y3 < cy1 < y4) or (x1 < cx2 < x2 or y1 < cy2 < y2):
+                    continue
+                else:
+                    r = sqrt((x1, y1), (x3, y3))
+                    if r < alph:
+                        newRowsLines.append([x1, y1, x3, y3])
+                    r = sqrt((x1, y1), (x4, y4))
+                    if r < alph:
+                        newRowsLines.append([x1, y1, x4, y4])
+
+                    r = sqrt((x2, y2), (x3, y3))
+                    if r < alph:
+                        newRowsLines.append([x2, y2, x3, y3])
+                    r = sqrt((x2, y2), (x4, y4))
+                    if r < alph:
+                        newRowsLines.append([x2, y2, x4, y4])
+
+    for i in range(ncol):
+        x1, y1, x2, y2 = ColsLines[i]
+        cx1, cy1 = (x1 + x2) / 2, (y1 + y2) / 2
+        for j in range(ncol):
+            if i != j:
+                x3, y3, x4, y4 = ColsLines[j]
+                cx2, cy2 = (x3 + x4) / 2, (y3 + y4) / 2
+                if (x3 < cx1 < x4 or y3 < cy1 < y4) or (x1 < cx2 < x2 or y1 < cy2 < y2):
+                    continue
+                else:
+                    r = sqrt((x1, y1), (x3, y3))
+                    if r < alph:
+                        newColsLines.append([x1, y1, x3, y3])
+                    r = sqrt((x1, y1), (x4, y4))
+                    if r < alph:
+                        newColsLines.append([x1, y1, x4, y4])
+
+                    r = sqrt((x2, y2), (x3, y3))
+                    if r < alph:
+                        newColsLines.append([x2, y2, x3, y3])
+                    r = sqrt((x2, y2), (x4, y4))
+                    if r < alph:
+                        newColsLines.append([x2, y2, x4, y4])
+
+    return newRowsLines, newColsLines
+
+
+def minAreaRect(coords):
+    """
+    多边形外接矩形
+    """
+    rect = cv2.minAreaRect(coords[:, ::-1])
+    box = cv2.boxPoints(rect)
+    box = box.reshape((8,)).tolist()
+
+    box = image_location_sort_box(box)
+
+    x1, y1, x2, y2, x3, y3, x4, y4 = box
+    degree, w, h, cx, cy = solve(box)
+    if w < h:
+        xmin = (x1 + x2) / 2
+        xmax = (x3 + x4) / 2
+        ymin = (y1 + y2) / 2
+        ymax = (y3 + y4) / 2
+
+    else:
+        xmin = (x1 + x4) / 2
+        xmax = (x2 + x3) / 2
+        ymin = (y1 + y4) / 2
+        ymax = (y2 + y3) / 2
+    # degree,w,h,cx,cy = solve(box)
+    # x1,y1,x2,y2,x3,y3,x4,y4 = box
+    # return {'degree':degree,'w':w,'h':h,'cx':cx,'cy':cy}
+    return [xmin, ymin, xmax, ymax]
+
+
+def fit_line(p1, p2):
+    """A = Y2 - Y1
+       B = X1 - X2
+       C = X2*Y1 - X1*Y2
+       AX+BY+C=0
+    直线一般方程
+    """
+    x1, y1 = p1
+    x2, y2 = p2
+    A = y2 - y1
+    B = x1 - x2
+    C = x2 * y1 - x1 * y2
+    return A, B, C
+
+# 判断点是在线段的哪一侧，只有正负有含义，大小无含义
+def point_line_cor(p, A, B, C):
+    ##判断点与之间的位置关系
+    # 一般式直线方程(Ax+By+c)=0
+    x, y = p
+    r = A * x + B * y + C
+    return r
+
+
+def line_to_line(points1, points2, alpha=10):
+    """
+    线段之间的距离
+    """
+    x1, y1, x2, y2 = points1
+    ox1, oy1, ox2, oy2 = points2
+    # 通过一般直线方程计算线段
+    A1, B1, C1 = fit_line((x1, y1), (x2, y2))
+    A2, B2, C2 = fit_line((ox1, oy1), (ox2, oy2))
+    flag1 = point_line_cor([x1, y1], A2, B2, C2)
+    flag2 = point_line_cor([x2, y2], A2, B2, C2)
+
+    # 如果x1,y1 和 x2,y2 在线段的同一侧
+    if (flag1 > 0 and flag2 > 0) or (flag1 < 0 and flag2 < 0):
+
+        x = (B1 * C2 - B2 * C1) / (A1 * B2 - A2 * B1)
+        y = (A2 * C1 - A1 * C2) / (A1 * B2 - A2 * B1)
+        p = (x, y)
+        r0 = sqrt(p, (x1, y1))
+        r1 = sqrt(p, (x2, y2))
+
+        if min(r0, r1) < alpha:
+
+            if r0 < r1:
+                points1 = [p[0], p[1], x2, y2]
+            else:
+                points1 = [x1, y1, p[0], p[1]]
+
+    return points1
+
+
+from scipy.spatial import distance as dist
+
+
+def _order_points(pts):
+    # 根据x坐标对点进行排序
+    """
+    ---------------------
+    作者：Tong_T
+    来源：CSDN
+    原文：https://blog.csdn.net/Tong_T/article/details/81907132
+    版权声明：本文为博主原创文章，转载请附上博文链接！
+    """
+    x_sorted = pts[np.argsort(pts[:, 0]), :]
+
+    left_most = x_sorted[:2, :]
+    right_most = x_sorted[2:, :]
+    left_most = left_most[np.argsort(left_most[:, 1]), :]
+    (tl, bl) = left_most
+
+    distance = dist.cdist(tl[np.newaxis], right_most, "euclidean")[0]
+    (br, tr) = right_most[np.argsort(distance)[::-1], :]
+
+    return np.array([tl, tr, br, bl], dtype="float32")
+
+
+def image_location_sort_box(box):
+    x1, y1, x2, y2, x3, y3, x4, y4 = box[:8]
+    pts = (x1, y1), (x2, y2), (x3, y3), (x4, y4)
+    pts = np.array(pts, dtype="float32")
+    (x1, y1), (x2, y2), (x3, y3), (x4, y4) = _order_points(pts)
+    return [x1, y1, x2, y2, x3, y3, x4, y4]
+
+
+def solve(box):
+    """
+    绕 cx,cy点 w,h 旋转 angle 的坐标
+    x = cx-w/2
+    y = cy-h/2
+    x1-cx = -w/2*cos(angle) +h/2*sin(angle)
+    y1 -cy= -w/2*sin(angle) -h/2*cos(angle)
+
+    h(x1-cx) = -wh/2*cos(angle) +hh/2*sin(angle)
+    w(y1 -cy)= -ww/2*sin(angle) -hw/2*cos(angle)
+    (hh+ww)/2sin(angle) = h(x1-cx)-w(y1 -cy)
+
+    """
+    x1, y1, x2, y2, x3, y3, x4, y4 = box[:8]
+    cx = (x1 + x3 + x2 + x4) / 4.0
+    cy = (y1 + y3 + y4 + y2) / 4.0
+    w = (np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + np.sqrt((x3 - x4) ** 2 + (y3 - y4) ** 2)) / 2
+    h = (np.sqrt((x2 - x3) ** 2 + (y2 - y3) ** 2) + np.sqrt((x1 - x4) ** 2 + (y1 - y4) ** 2)) / 2
+    # x = cx-w/2
+    # y = cy-h/2
+    sinA = (h * (x1 - cx) - w * (y1 - cy)) * 1.0 / (h * h + w * w) * 2
+    angle = np.arcsin(sinA)
+    return angle, w, h, cx, cy
+
+
+def xy_rotate_box(cx, cy, w, h, angle=0, degree=None, **args):
+    """
+    绕 cx,cy点 w,h 旋转 angle 的坐标
+    x_new = (x-cx)*cos(angle) - (y-cy)*sin(angle)+cx
+    y_new = (x-cx)*sin(angle) + (y-cy)*sin(angle)+cy
+    """
+    if degree is not None:
+        angle = degree
+    cx = float(cx)
+    cy = float(cy)
+    w = float(w)
+    h = float(h)
+    angle = float(angle)
+    x1, y1 = rotate(cx - w / 2, cy - h / 2, angle, cx, cy)
+    x2, y2 = rotate(cx + w / 2, cy - h / 2, angle, cx, cy)
+    x3, y3 = rotate(cx + w / 2, cy + h / 2, angle, cx, cy)
+    x4, y4 = rotate(cx - w / 2, cy + h / 2, angle, cx, cy)
+    return x1, y1, x2, y2, x3, y3, x4, y4
+
+
+from numpy import cos, sin
+
+
+def rotate(x, y, angle, cx, cy):
+    angle = angle  # *pi/180
+    x_new = (x - cx) * cos(angle) - (y - cy) * sin(angle) + cx
+    y_new = (x - cx) * sin(angle) + (y - cy) * cos(angle) + cy
+    return x_new, y_new
+
+
+def minAreaRectbox(regions, flag=True, W=0, H=0, filtersmall=False, adjustBox=False):
+    """
+    多边形外接矩形
+    """
+    boxes = []
+    for region in regions:
+        rect = cv2.minAreaRect(region.coords[:, ::-1])
+
+        box = cv2.boxPoints(rect)
+        box = box.reshape((8,)).tolist()
+        box = image_location_sort_box(box)
+        x1, y1, x2, y2, x3, y3, x4, y4 = box
+        angle, w, h, cx, cy = solve(box)
+        if adjustBox:
+            x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(cx, cy, w + 5, h + 5, angle=0, degree=None)
+
+        if w > 32 and h > 32 and flag:
+            if abs(angle / np.pi * 180) < 20:
+                if filtersmall and w < 10 or h < 10:
+                    continue
+                boxes.append([x1, y1, x2, y2, x3, y3, x4, y4])
+        else:
+            if w * h < 0.5 * W * H:
+                if filtersmall and w < 8 or h < 8:
+                    continue
+                boxes.append([x1, y1, x2, y2, x3, y3, x4, y4])
+    return boxes
+
+
+from PIL import Image
+
+
+def rectangle(img, boxes):
+    tmp = np.copy(img)
+    for box in boxes:
+        xmin, ymin, xmax, ymax = box[:4]
+        cv2.rectangle(tmp, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 0, 0), 1, lineType=cv2.LINE_AA)
+    return Image.fromarray(tmp)
+
+
+def draw_lines(im, bboxes, color=(0, 0, 0), lineW=3):
+    """
+        boxes: bounding boxes
+    """
+    tmp = np.copy(im)
+    c = color
+    h, w = im.shape[:2]
+
+    for box in bboxes:
+        x1, y1, x2, y2 = box[:4]
+        cv2.line(tmp, (int(x1), int(y1)), (int(x2), int(y2)), c, lineW, lineType=cv2.LINE_AA)
+
+    return tmp
+
+
+def draw_boxes(im, bboxes, color=(0, 0, 0)):
+    """
+        boxes: bounding boxes
+    """
+    tmp = np.copy(im)
+    c = color
+    h, w, _ = im.shape
+
+    for box in bboxes:
+        if type(box) is dict:
+            x1, y1, x2, y2, x3, y3, x4, y4 = xy_rotate_box(**box)
+        else:
+            x1, y1, x2, y2, x3, y3, x4, y4 = box[:8]
+
+        cv2.line(tmp, (int(x1), int(y1)), (int(x2), int(y2)), c, 1, lineType=cv2.LINE_AA)
+        cv2.line(tmp, (int(x2), int(y2)), (int(x3), int(y3)), c, 1, lineType=cv2.LINE_AA)
+        cv2.line(tmp, (int(x3), int(y3)), (int(x4), int(y4)), c, 1, lineType=cv2.LINE_AA)
+        cv2.line(tmp, (int(x4), int(y4)), (int(x1), int(y1)), c, 1, lineType=cv2.LINE_AA)
+
+    return tmp
+if __name__ == '__main__':
+    print(solve([1,1,7,1,1,5,7,4]))
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