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eval_Dibco_2010.py
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eval_Dibco_2010.py
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import os
os.environ["PYTHONIOENCODING"] = "utf-8"
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from tensorflow.keras import regularizers
from tensorflow.keras import metrics
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import CSVLogger, TensorBoard, ModelCheckpoint
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.constraints import MaxNorm
from network.layers import FullGatedConv2D, GatedConv2D, OctConv2D
from tensorflow.keras.layers import Conv2D, Bidirectional, LSTM, GRU, Dense
from tensorflow.keras.layers import Dropout, BatchNormalization, LeakyReLU, PReLU
from tensorflow.keras.layers import Input, Add, Activation, Lambda, MaxPooling2D, Reshape
from tensorflow.keras.models import load_model
import imageio
import math
import tensorflow as tf
from PIL import Image
from tqdm import tqdm
import random
import sys
import codecs
import re
import cv2
import tqdm
from glob import glob
from tqdm import tqdm
from data import preproc as pp
##########################################################################################################
##########################################################################################################
##########################################################################################################
rootPath='/content/drive/MyDrive/workIAM/'
DatabasePath='/content/drive/MyDrive/workIAM/TL_DIBCO'
scenario='S2_W10_IAM'
# define parameters
source = "khatt"
arch = "flor" ########ne pas modifier, nous utilisons architeture crnn de flor
batch_size=32
# define paths
source_path = os.path.join("..", "data", f"{source}.hdf5")
output_path = os.path.join("..", "output-crnn-gan-" + scenario , source, arch)
target_path = os.path.join(output_path, "checkpoint_weights.hdf5")
os.makedirs(output_path, exist_ok=True)
source_path2 = os.path.join("..", "data", f"{source}.hdf5")
output_path2 = os.path.join("..", "output-crnn-gan-progressive-" + scenario, source, arch)
target_path2 = os.path.join(output_path2, "checkpoint_weights.hdf5")
os.makedirs(output_path2, exist_ok=True)
# define input size, number max of chars per line and list of valid chars
max_text_length = 128 ####not change this value
img_width=1024 #########for crnn
img_height=128 #########for crnn
input_size_crnn = (1024,128, 1)
input_size = (128,1024, 1) #############for the GAN
i =1
flag = 0
def preproc(impath):
# Load image, create blank mask, convert to grayscale, Gaussian blur
# then adaptive threshold to obtain a binary image
img = cv2.imread(impath)
#== Parameters =======================================================================
b,g,r = cv2.split(img) # get b,g,r
rgb_img = cv2.merge([r,g,b]) # switch it to rgb
# Denoising
#dst = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,8)
dst = cv2.fastNlMeansDenoisingColored(img,None,12,12,1,8)
b,g,r = cv2.split(dst) # get b,g,r
rgb_dst = cv2.merge([r,g,b]) # switch it to rgb
# save resulting masked image
cv2.imwrite('result.png', rgb_dst)
return rgb_dst
##########################################################################################################
##########################################################################################################
def psnr(img1, img2):
mse = np.mean( (img1 - img2) ** 2 )
if (mse == 0):
return (100)
PIXEL_MAX = 1.0
return (20 * math.log10(PIXEL_MAX / math.sqrt(mse)))
def read_file(list_file_path):
char_file = codecs.open(list_file_path, 'r', 'utf-8')
list0 = []
for l in char_file:
list0.append(l.strip())
return list0
def read_file_char(list_file_path):
char_file = codecs.open(list_file_path, 'r', 'utf-8')
list0 = []
for l in char_file:
list0.append(l.strip())
return list0
def unet(pretrained_weights=None, input_size=(128, 1024, 1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
bn = BatchNormalization(momentum=0.8)(conv1)
bn.trainable=False
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv1)
bn.trainable=False
pool1 = MaxPooling2D(pool_size=(2, 2))(bn)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
bn = BatchNormalization(momentum=0.8)(conv2)
bn.trainable=False
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv2)
bn.trainable=False
pool2 = MaxPooling2D(pool_size=(2, 2))(bn)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
bn = BatchNormalization(momentum=0.8)(conv3)
bn.trainable=False
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv3)
bn.trainable=False
pool3 = MaxPooling2D(pool_size=(2, 2))(bn)
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
bn = BatchNormalization(momentum=0.8)(conv4)
bn.trainable=False
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv4)
bn.trainable=False
drop4 = Dropout(0.5)(bn)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
bn = BatchNormalization(momentum=0.8)(conv5)
bn.trainable=False
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv5)
bn.trainable=False
drop5 = Dropout(0.5)(bn)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
# merge6 = merge([drop4,up6], mode = 'concat', concat_axis = 3)
bn = BatchNormalization(momentum=0.8)(up6)
bn.trainable=False
merge6 = concatenate([drop4, bn])
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
bn = BatchNormalization(momentum=0.8)(conv6)
bn.trainable=False
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv6)
bn.trainable=False
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(bn))
bn = BatchNormalization(momentum=0.8)(up7)
bn.trainable=False
merge7 = concatenate([conv3, bn])
# merge7 = merge([conv3,up7], mode = 'concat', concat_axis = 3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
bn = BatchNormalization(momentum=0.8)(conv7)
bn.trainable=False
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv7)
bn.trainable=False
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(bn))
bn = BatchNormalization(momentum=0.8)(up8)
bn.trainable=False
merge8 = concatenate([conv2, bn])
# merge8 = merge([conv2,up8], mode = 'concat', concat_axis = 3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
bn = BatchNormalization(momentum=0.8)(conv8)
bn.trainable=False
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv8)
bn.trainable=False
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(bn))
# merge9 = merge([conv1,up9], mode = 'concat', concat_axis = 3)
bn = BatchNormalization(momentum=0.8)(up9)
bn.trainable=False
merge9 = concatenate([conv1, bn])
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
bn = BatchNormalization(momentum=0.8)(conv9)
bn.trainable=False
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv9)
bn.trainable=False
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv9)
bn.trainable=False
conv10 = Conv2D(1, 1, activation='sigmoid')(bn)
model = Model(inputs=inputs, outputs=conv10)
# model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
def unetorigine(pretrained_weights=None, input_size=(128,1024, 1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
bn = BatchNormalization(momentum=0.8)(conv1)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(bn)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
bn = BatchNormalization(momentum=0.8)(conv2)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(bn)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
bn = BatchNormalization(momentum=0.8)(conv3)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(bn)
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
bn = BatchNormalization(momentum=0.8)(conv4)
conv4 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv4)
drop4 = Dropout(0.5)(bn)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
bn = BatchNormalization(momentum=0.8)(conv5)
conv5 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv5)
drop5 = Dropout(0.5)(bn)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(drop5))
# merge6 = merge([drop4,up6], mode = 'concat', concat_axis = 3)
bn = BatchNormalization(momentum=0.8)(up6)
merge6 = concatenate ([drop4, bn])
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
bn = BatchNormalization(momentum=0.8)(conv6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(bn))
bn = BatchNormalization(momentum=0.8)(up7)
merge7 = concatenate ([conv3, bn])
# merge7 = merge([conv3,up7], mode = 'concat', concat_axis = 3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
bn = BatchNormalization(momentum=0.8)(conv7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(bn))
bn = BatchNormalization(momentum=0.8)(up8)
merge8 = concatenate ([conv2, bn])
# merge8 = merge([conv2,up8], mode = 'concat', concat_axis = 3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
bn = BatchNormalization(momentum=0.8)(conv8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(UpSampling2D(size=(2, 2))(bn))
# merge9 = merge([conv1,up9], mode = 'concat', concat_axis = 3)
bn = BatchNormalization(momentum=0.8)(up9)
merge9 = concatenate ([conv1, bn])
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
bn = BatchNormalization(momentum=0.8)(conv9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(bn)
bn = BatchNormalization(momentum=0.8)(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(bn)
model = Model(inputs=inputs, outputs=conv10)
# model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
return model
def get_optimizer():
return Adam(lr=1e-4)
def vconcat_resize(img_list, interpolation
=cv2.INTER_CUBIC):
# take minimum width
w_min = min(img.shape[1]
for img in img_list)
# resizing images
im_list_resize = [cv2.resize(img,
(w_min, int(img.shape[0] * w_min / img.shape[1])),
interpolation=interpolation)
for img in img_list]
# return final image
return cv2.vconcat(im_list_resize)
def split2(dataset,size,h,w):
newdataset=[]
nsize1=128
nsize2=1024
for i in range (size):
im=dataset[i]
for ii in range(0,h,nsize1): #2048
for iii in range(0,w,nsize2): #1536
newdataset.append(im[ii:ii+nsize1,iii:iii+nsize2,:])
return np.array(newdataset)
def merge_image2(splitted_images, h,w):
image=np.zeros(((h,w,1)))
nsize1=128
nsize2=1024
ind =0
for ii in range(0,h,nsize1):
for iii in range(0,w,nsize2):
image[ii:ii+nsize1,iii:iii+nsize2,:]=splitted_images[ind]
ind=ind+1
return np.array(image)
def readGrayPair(deg_image_path):
original_image = Image.open(deg_image_path)
grey_image = original_image.convert('L')
grey_image.save("deg_imageqq.tif")
deg_image = plt.imread("deg_imageqq.tif")
return deg_image
def predict_gan_dibco(generator_path,dest):
DatabasePathDistorted='handwritten-text-recognition/datasetsDIBCO/2010/H_DIBCO2010_test_images/'
generator = unet()
generator.load_weights(generator_path)
count_image=1
b=0
for i in range(1,11):
ext='.tiff'
if i == 1 or i==3 or i==6:
ext='.tif'
elif i == 4:
ext='.bmp'
else:
ext='.jpg'
noisy_image_path=DatabasePathDistorted + '/H' + str(i).zfill(2) + ext
#preproc(noisy_image_path)
original_image = Image.open(noisy_image_path)
##############end padding
#original_image = Image.open(noisy_image_path)
grey_image = original_image.convert('L')
grey_image.save("deg_imagexw.png")
test_image = plt.imread("deg_imagexw.png")
h = ((test_image.shape [0] // 128) +1)*128
w = ((test_image.shape [1] // 1024 ) +1)*1024
test_padding=np.zeros((h,w))+1
test_padding[:test_image.shape[0],:test_image.shape[1]]=test_image
test_image_p=split2(test_padding.reshape(1,h,w,1),1,h,w)
predicted_list=[]
n=0
for l in range(test_image_p.shape[0]):
print(str(i).zfill(2))
i2=test_image_p[l]#*255
i2=i2.astype('float64')
imageio.imwrite('imbn.png', i2)
deg_image =readGrayPair('imbn.png')
pr=generator.predict(deg_image.reshape(1,128,1024,1))
#plt.imsave('/content/drive/MyDrive/workIAM/TL_DIBCO/chunks/predicted/'+ str(b)+ '.png',pr.reshape(128, 1024),cmap='gray')
#plt.imsave('/content/drive/MyDrive/workIAM/TL_DIBCO/chunks/distorted/'+ str(b)+ '.png',deg_image,cmap='gray')
predicted_list.append(pr)
#imageio.imwrite('/content/drive/MyDrive/workIAM/TL_DIBCO/chunks/GT/'+ str(b)+ '.png',i1)
n=n+1
b=b+1
predicted_image = np.array(predicted_list)#.reshape()
predicted_image=merge_image2(predicted_image,h,w)
predicted_image=predicted_image[:test_image.shape[0],:test_image.shape[1]]
predicted_image=predicted_image.reshape(predicted_image.shape[0],predicted_image.shape[1])
sauv=predicted_image[:,:]
predicted_image = (predicted_image[:,:])#*255
predicted_image_float=predicted_image
predicted_image=predicted_image.astype('float64')
#print(predicted_image)
os.makedirs('handwritten-text-recognition/src/' + dest, exist_ok=True)
task='binarize'
plt.imsave('handwritten-text-recognition/src/'+ dest + '/predicted_'+str(i)+'.png', predicted_image.reshape(
predicted_image.shape[0],predicted_image.shape[1]), cmap='gray')
if task == 'binarize':
bin_thresh = 0.0
gray=sauv
Binary = (gray[:,:]>bin_thresh)*1
imageio.imwrite('handwritten-text-recognition/src/'+dest+'/b_predicted_'+str(i)+'.png',Binary)
i=i+1
count_image=count_image+1
def get_psnr(dest):
DatabasePathGT='handwritten-text-recognition/datasetsDIBCO/2010/H_DIBCO2010_GT'
count_image=1
qo=0
recap=0
for i in range(1,11):
original_path_image_gt=DatabasePathGT + '/H' + str(i).zfill(2) + '.tiff'
original_image = Image.open(original_path_image_gt)
grey_image = original_image.convert('1')
grey_image.save("gta.png")
gt = plt.imread("gta.png")
enhanced_image_path='handwritten-text-recognition/src/'+dest+'/b_predicted_' + str(i) + '.png'
im2=Image.open(enhanced_image_path)
im2=im2.convert('1')
im2.save('im2a.png')
predicted = plt.imread('im2a.png')
psnrv=psnr(predicted,gt)
print(psnrv)
recap=recap+psnrv
qo+=1
av=recap/qo
print('average psnr epoch 1 ')
print(av)
def resul_tuning(generator_path,dest):
predict_gan_dibco(generator_path,dest)
get_psnr(dest)
if __name__ == '__main__':
generator_path="weights/generator_weights.h5"
resul_tuning(generator_path,'DIBCO2010_TL')