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TableUNet.py
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TableUNet.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, concatenate, Conv2D, MaxPooling2D, BatchNormalization, UpSampling2D
from tensorflow.keras.layers import LeakyReLU
import numpy as np
import time
from utils import draw_lines, letterbox_image, get_table_line, adjust_lines, line_to_line, LineCluster, Timer
import cv2
tableModeLinePath = './table-line.h5'
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)
model = Model(inputs=inputs, outputs=classify)
return model
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))
with Timer('predict table lines'):
pred = model.predict(np.array([np.array(inputBlob) / 255.0]))
pred = pred[0]
vpred = pred[..., 1] > vprob # 竖线 boolean
hpred = pred[..., 0] > hprob # 横线 boolean
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__':
DEBUG = False
p = 'merged.jpg'
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)
if DEBUG:
blank = np.zeros(img.shape[:2], dtype=np.uint8)
blank = draw_lines(blank, rowboxes + colboxes, lineW=2)
cv2.namedWindow('hello', cv2.WINDOW_AUTOSIZE)
cv2.imshow('hello', blank)
cv2.waitKey(0)
# 合并rowboxes中相近的直线
clusters = [LineCluster(len(rowboxes)) for _ in range(len(rowboxes))]
for i in range(len(rowboxes), -1, -1):
pass
# fld = cv2.ximgproc.createFastLineDetector()
# lines = fld.detect(blank)
# drawn_img = fld.drawSegments(blank, lines)
# cv2.imshow("LSD", drawn_img)
# cv2.waitKey(0)
print(time.time() - t, len(rowboxes), len(colboxes))
cv2.imwrite('merged_lined.jpg', img)