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main.py
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main.py
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from __future__ import division
from __future__ import print_function
import sys
import argparse
import cv2
import editdistance
from DataLoader import DataLoader, Batch
from Model import Model, DecoderType
from SamplePreprocessor import preprocess
import pandas as pd
import nltk
import sklearn
import os
class FilePaths:
import sklearn
"filenames and paths to data"
fnCharList = '../model/charList.txt'
fnAccuracy = '../model/accuracy.txt'
fnTrain = '../data/'
fnInfer ='../data/test.tif'
fnCorpus = '../data/corpus.txt'
def train(model, loader):
"train NN"
epoch = 0 # number of training epochs since start
bestCharErrorRate = float('inf') # best valdiation character error rate
noImprovementSince = 0 # number of epochs no improvement of character error rate occured
earlyStopping = 5 # stop training after this number of epochs without improvement
while True:
epoch += 1
print('Epoch:', epoch)
# train
print('Train NN')
loader.trainSet()
while loader.hasNext():
iterInfo = loader.getIteratorInfo()
batch = loader.getNext()
loss = model.trainBatch(batch)
print('Batch:', iterInfo[0],'/', iterInfo[1], 'Loss:', loss)
# validate
charErrorRate = validate(model, loader)
# if best validation accuracy so far, save model parameters
if charErrorRate < bestCharErrorRate:
print('Character error rate improved, save model')
bestCharErrorRate = charErrorRate
noImprovementSince = 0
model.save()
open(FilePaths.fnAccuracy, 'w').write('Validation character error rate of saved model: %f%%' % (charErrorRate*100.0))
else:
print('Character error rate not improved')
noImprovementSince += 1
# stop training if no more improvement in the last x epochs
if noImprovementSince >= earlyStopping:
print('No more improvement since %d epochs. Training stopped.' % earlyStopping)
break
def validate(model, loader):
"validate NN"
print('Validate NN')
loader.validationSet()
numCharErr = 0
numCharTotal = 0
numWordOK = 0
numWordTotal = 0
while loader.hasNext():
iterInfo = loader.getIteratorInfo()
print('Batch:', iterInfo[0],'/', iterInfo[1])
batch = loader.getNext()
(recognized, _) = model.inferBatch(batch)
print('Ground truth -> Recognized')
for i in range(len(recognized)):
numWordOK += 1 if batch.gtTexts[i] == recognized[i] else 0
numWordTotal += 1
dist = editdistance.eval(recognized[i], batch.gtTexts[i])
numCharErr += dist
numCharTotal += len(batch.gtTexts[i])
#print('[OK]' if dist==0 else '[ERR:%d]' % dist,'"' + batch.gtTexts[i] + '"', '->', '"' + recognized[i] + '"')
# print validation result
charErrorRate = numCharErr / numCharTotal
wordAccuracy = numWordOK / numWordTotal
print('Character error rate: %f%%. Word accuracy: %f%%.' % (charErrorRate*100.0, wordAccuracy*100.0))
return charErrorRate
def infer(model, fnImg):
"recognize text in image provided by file path"
img = preprocess(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE), Model.imgSize)
batch = Batch(None, [img]* Model.batchSize)
(recognized, probability) = model.inferBatch(batch, True)
words = '../data/test.txt'
f=open(words, "w+", encoding="utf-8")
f.write(recognized[0])
print('Probability:', probability[0]);
return recognized[0]
def inferBatch(model):
"recognize text in set of images provided by file path"
col_names = ['Jacard Similarity', 'Cosine similarity', 'Levenstein Similarity', 'Euclidian distance', 'Character Level Accuracy', 'Actual image' , 'Predicted Text']
df = pd.DataFrame(columns = col_names)
f=open(FilePaths.fnTrain+'validation.txt')
for line in f:
# ignore comment line
if not line or line[0]=='#':
continue
lineSplit = line.strip().split(' ')
assert len(lineSplit) >= 9
# filename: part1-part2-part3 --> part1/part1-part2/part1-part2-part3.png
fileNameSplit = lineSplit[0].split('-')
fileName = FilePaths.fnTrain + 'validation/' + fileNameSplit[0] + '/' + fileNameSplit[0] + '-' + fileNameSplit[1] + '/' + lineSplit[0] + '.png'
# GT text are columns starting at 9
gtText = DataLoader.truncateLabel('self', ' '.join(lineSplit[8:]), Model.maxTextLen)
# check if image is not empty
if not os.path.getsize(fileName):
#bad_samples.append(lineSplit[0] + '.png')
continue
# put sample into list
#self.samples.append(Sample(gtText, fileName))
img = preprocess(cv2.imread(fileName, cv2.IMREAD_GRAYSCALE), Model.imgSize)
batch = Batch(None, [img])
try:
(recognized, probability) = model.inferBatch(batch, True)
print('Recognized:', '"' + recognized[0] + '"')
print('Probability:', probability[0]);
jaccard_score,cosine_score,levens_score,euclidean_score = get_AccuracyMetrics(gtText, recognized[0])
acc = getCharacterLevelAccuracy(gtText,recognized[0])
print('Character Accuracy:', acc)
df.loc[len(df)] = [jaccard_score, cosine_score, levens_score, euclidean_score, acc, gtText, recognized[0]]
except:
continue
df.to_csv(r'C:\Projects\Handwriting recognition\Validation_IAM_HAN.csv')
def getCharacterLevelAccuracy(a,b):
length = len(a)
correctCount = 0
for x, y in zip(a, b):
if x == y:
correctCount = correctCount+1
if correctCount == 0:
return '0%'
else:
per = (correctCount/length)*100
return str(per)+'%'
def get_AccuracyMetrics(actual_text,detected_text):
news_headline1 = a = actual_text
news_headline2 = b = detected_text
news_headlines = [news_headline1, news_headline2]
news_headline1_tokens = nltk.word_tokenize(news_headline1)
news_headline2_tokens = nltk.word_tokenize(news_headline2)
try:
transformed_results = transform([news_headline1_tokens, news_headline2_tokens])
except:
return None,None,None,None
print('Euclidian Distance (lower the distance, more is the acuracy\n')
print('======================')
print('Master Sentence: %s' % news_headlines[0])
for i, news_headline in enumerate(news_headlines):
euclidean_score = sklearn.metrics.pairwise.euclidean_distances([transformed_results[i]], [transformed_results[0]])[0][0]
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (euclidean_score, news_headline))
print('\nCosine Similarity\n')
print('======================')
print('Master Sentence: %s' % news_headlines[0])
for i, news_headline in enumerate(news_headlines):
cosine_score = sklearn.metrics.pairwise.cosine_similarity([transformed_results[i]], [transformed_results[0]])[0][0]
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (cosine_score, news_headline))
print('\nJaccard Similarity\n')
print('======================')
print('Master Sentence: %s' % a)
score = get_jaccard_sim(a,a)
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (score, a))
jaccard_score = get_jaccard_sim(a,b)
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (jaccard_score, b))
print('\nLevenshtein Similarity\n')
print('======================')
print('Master Sentence: %s' % a)
score = levens_similarity(a,a)
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (score, a))
levens_score = levens_similarity(a,b)
print('-----')
print('Score: %.2f, Comparing Sentence: %s' % (levens_score, b))
return jaccard_score,cosine_score,levens_score,euclidean_score
def get_jaccard_sim(str1, str2):
a = set(str1.split())
b = set(str2.split())
c = a.intersection(b)
return float(len(c)) / (len(a) + len(b) - len(c))
def cosine_similarity(actual_text,detected_text):
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix_detected = tfidf_vectorizer.fit_transform([detected_text])
tfidf_matrix_actual = tfidf_vectorizer.fit_transform([actual_text])
return cosine_similarity(tfidf_matrix_actual, tfidf_matrix_detected)
def euclidian_measure(actual_text,detected_text):
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import euclidean_distances
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix_detected = tfidf_vectorizer.fit_transform([detected_text])
tfidf_matrix_actual = tfidf_vectorizer.fit_transform([actual_text])
return euclidean_distances(tfidf_matrix_actual, tfidf_matrix_detected)
def levens_similarity(actual_text,detected_text):
import Levenshtein
return Levenshtein.ratio(actual_text,detected_text)
def transform(headlines):
tokens = [w for s in headlines for w in s ]
#print()
#print('All Tokens:')
#print(tokens)
import sklearn
results = []
label_enc = sklearn.preprocessing.LabelEncoder()
onehot_enc = sklearn.preprocessing.OneHotEncoder()
encoded_all_tokens = label_enc.fit_transform(list(set(tokens)))
encoded_all_tokens = encoded_all_tokens.reshape(len(encoded_all_tokens), 1)
onehot_enc.fit(encoded_all_tokens)
for headline_tokens in headlines:
#print()
#print('Original Input:', headline_tokens)
encoded_words = label_enc.transform(headline_tokens)
#print('Encoded by Label Encoder:', encoded_words)
encoded_words = onehot_enc.transform(encoded_words.reshape(len(encoded_words), 1))
#print('Encoded by OneHot Encoder:')
#print(encoded_words)
import numpy as np
results.append(np.sum(encoded_words.toarray(), axis=0))
return results
def main():
import nltk
import sklearn
from numpy import argmax
import numpy as np
# optional command line args
parser = argparse.ArgumentParser()
#parser.add_argument('--input', help='Path to test image', action='store_true')
parser.add_argument('--train', help='train the NN', action='store_true')
parser.add_argument('--validate', help='validate the NN', action='store_true')
parser.add_argument('--beamsearch', help='use beam search instead of best path decoding', action='store_true')
parser.add_argument('--wordbeamsearch', help='use word beam search instead of best path decoding', action='store_true')
parser.add_argument('--dump', help='dump output of NN to CSV file(s)', action='store_true')
parser.add_argument('--infer', help='dump output of NN to CSV file(s)', action='store_true')
args = parser.parse_args()
decoderType = DecoderType.BestPath
if args.beamsearch:
decoderType = DecoderType.BeamSearch
elif args.wordbeamsearch:
decoderType = DecoderType.WordBeamSearch
# train or validate on IAM dataset
if args.train or args.validate or args.infer:
# load training data, create TF model
loader = DataLoader(FilePaths.fnTrain, Model.batchSize, Model.imgSize, Model.maxTextLen)
# save characters of model for inference mode
open(FilePaths.fnCharList, 'w', encoding="utf-8").write(str().join(loader.charList))
# save words contained in dataset into file
open(FilePaths.fnCorpus, 'w', encoding="utf-8").write(str(' ').join(loader.trainWords + loader.validationWords))
# execute training or validation
if args.train:
model = Model(loader.charList, decoderType)
train(model, loader)
elif args.validate:
model = Model(loader.charList, decoderType, mustRestore=True)
validate(model, loader)
elif args.infer:
model = Model(open(FilePaths.fnCharList, encoding='utf8').read(), decoderType, mustRestore=True, dump=args.dump)
inferBatch(model)
# infer text on test image
else:
print(open(FilePaths.fnAccuracy).read())
model = Model(open(FilePaths.fnCharList, encoding='utf8').read(), decoderType, mustRestore=True, dump=args.dump)
detected_text = infer(model, FilePaths.fnInfer)
if __name__ == '__main__':
main()