def getNext(self):
batchRange = range(self.currIdx, self.currIdx + self.batchSize)
gtTexts = [self.samples[i].gtText for i in batchRange]
imgs = [preprocessor(cv2.imread(self.samples[i].filePath, cv2.IMREAD_GRAYSCALE), self.imgSize)
for i in batchRange]
self.currIdx += self.batchSize
return Batch(gtTexts, imgs)
def image_generator(files, max_label_length, batch_size=16, imgSize=(256, 32)):
index_ = 0
while True:
# Select files (paths/indices) for the batch
batch_paths = files[index_ * batch_size:(index_ + 1) * batch_size]
training_img = []
training_txt = []
train_label_length = []
train_input_length = []
max_label_len = max_label_length
# Read in each input, perform preprocessing and get labels
for input_path in batch_paths:
gtText1 = input_path.split('/')[-1].split('.')[0]
# print(gtText1)
encode_to_labels(gtText1)
training_txt.append(encode_to_labels(gtText1))
train_label_length.append(len(gtText1))
img = preprocessor(filePath=input_path, imgSize=imgSize, rotation=True)
training_img.append(img)
train_input_length.append(max_label_len)
train_padded_txt = pad_sequences(training_txt, maxlen=max_label_len, padding='post', value=len(char_list))
train_input_length = np.array(train_input_length)
train_label_length = np.array(train_label_length)
training_img = np.array(training_img)
output = np.zeros(len(training_img))
input_ = [training_img, train_padded_txt, train_input_length, train_label_length]
index_ = index_ + 1
if index_ >= len(files) // batch_size:
index_ = 0
yield (input_, output)
def _getData(self, ):
self.gtTexts = []
self.imgs = []
for sample in self.samples:
self.gtTexts.append(sample.gtText)
self.imgs.append(
preprocessor(cv2.imread(sample.filePath, cv2.IMREAD_GRAYSCALE),
self.imgSize))
def load_different_image():
imgs = []
for i in range(1, Model.batchSize):
imgs.append(
preprocessor(cv2.imread("../data/check_image/a ({}).png".format(i),
cv2.IMREAD_GRAYSCALE),
Model.imgSize,
enhance=False))
return imgs
def load_different_image():
imgs = []
for i in range(1, Model.batchSize):
imgs.append(
preprocessor(cv2.imread(
"/content/drive/My Drive/Handwritten-Line-Text-Recognition-using-Deep-Learning-with-Tensorflow-master/data/check_image/a ({}).png"
.format(i), cv2.IMREAD_GRAYSCALE),
Model.imgSize,
enhance=False))
return imgs
def infer(model, fnImg):
""" Recognize text in image provided by file path """
img = preprocessor(cv2.imread(fnImg, cv2.IMREAD_GRAYSCALE), imgSize=Model.imgSize)
if img is None:
print("Image not found")
imgs = load_different_image()
imgs = [img] + imgs
batch = Batch(None, imgs)
recognized = model.inferBatch(batch) # recognize text
print("Without Correction", recognized[0])
print("With Correction", correct_sentence(recognized[0]))
return recognized[0]
def image_generator_test(files, imgSize=(256, 32), batch_size=16):
index_ = 0
while True:
# Select files (paths/indices) for the batch
batch_paths = files[index_ * batch_size:(index_ + 1) * batch_size]
training_img = []
# Read in each input, perform preprocessing and get labels
for input_path in batch_paths:
img = preprocessor(filePath=input_path, imgSize=imgSize, rotation=True)
training_img.append(img)
training_img = np.array(training_img)
index_ = index_ + 1
if index_ >= len(files) // batch_size:
index_ = 0
yield training_img
valid_input_length = []
valid_label_length = []
valid_orig_txt = []
max_label_len = 56
i = 1
flag = 0
train_paths = open(fnTrain, 'r').read().split('\n')[:-1]
test_paths = open(fnTest, 'r').read().split('\n')[:-1]
for i1 in train_paths:
gtText1 = i1.split('_')[-1].split('.')[0]
training_txt.append(encode_to_labels(gtText1))
train_label_length.append(len(gtText1))
img = preprocessor(i1, (256, 64), rotation=True)
training_img.append(img)
train_input_length.append(max_label_len)
for i2 in test_paths:
gtText2 = i2.split('_')[-1].split('.')[0]
valid_orig_txt.append(gtText2)
valid_txt.append(encode_to_labels(gtText2))
valid_label_length.append(len(gtText2))
img = preprocessor(i2, (256, 64), rotation=True)
valid_img.append(img)
valid_input_length.append(max_label_len)
train_padded_txt = pad_sequences(training_txt, maxlen=max_label_len, padding='post', value=len(char_list))
valid_padded_txt = pad_sequences(valid_txt, maxlen=max_label_len, padding='post', value=len(char_list))
