def batch_generator(self, queue):
"""Takes a queue and enqueue batches in it
"""
generator = GeneratorFromDict(language=self.language)
while True:
batch = []
while len(batch) < self.batch_size:
img, lbl = generator.next()
batch.append((
resize_image(np.array(img.convert("L")),
self.max_image_width)[0],
lbl,
label_to_array(lbl, self.char_vector),
))
raw_batch_x, raw_batch_y, raw_batch_la = zip(*batch)
batch_y = np.reshape(np.array(raw_batch_y), (-1))
batch_dt = sparse_tuple_from(
np.reshape(np.array(raw_batch_la), (-1)))
raw_batch_x = np.swapaxes(raw_batch_x, 1, 2)
batch_x = np.reshape(
np.array(raw_batch_x),
(len(raw_batch_x), self.max_image_width, 32, 1))
if queue.qsize() < 20:
queue.put((batch_y, batch_dt, batch_x))
else:
pass
def __getitem__(self, index):
try:
img, label = self.generator.next()
except StopIteration:
self.generator = GeneratorFromDict(**self.args)
img, label = self.generator.next()
return img, label
def test_generator_from_dict(self):
generator = GeneratorFromDict()
i = 0
while i < 100:
img, lbl = next(generator)
self.assertTrue(img.size[1] == 32, "Shape is not right")
i += 1
def gen(index):
print("PID {0}: {1}".format(index, os.getpid()))
count = numImageGen // numProcess
if index == 0:
count = count + (numImageGen % numProcess)
generator[index] = GeneratorFromDict(count=count)
with open("filenames.txt", 'a') as fileObj:
for img, lbl in generator[index]:
fileObj.write(img[1] + " " + lbl + "\n")
def __init__(self, count, textlength, dictpath):
self.count = count
fonts_dir = "/home/ldl/桌面/python-notebook/My_trdg/trdg/fonts/cn"
fonts = [os.path.join(fonts_dir, i) for i in os.listdir(fonts_dir)]
# dictpath = "/home/ldl/桌面/论文/文本识别/TextRecognitionDataGenerator/trdg/mydicts/all_4068.txt"
img_dir = "/home/ldl/桌面/论文/文本识别/TextRecognitionDataGenerator/trdg/images"
self.args = dict(count=self.count,
length=textlength,
allow_variable=True,
fonts=fonts,
language=dictpath,
size=64,
blur=2,
random_blur=True,
image_dir=img_dir,
background_type=[0, 1, 2, 3],
distorsion_type=[0, 1, 2],
text_color="#000000,#FF8F8F",
image_mode="L",
char_cat="",
space_width=[1, 2, 3, 4],
character_spacing=[0, 1, 2, 3, 4, 5])
self.generator = GeneratorFromDict(**self.args)
def __init__(self, batch_size=1, alphabet=string.printable):
super(FakeTextImageGenerator).__init__()
self.batch_size = batch_size
self.alphabet = alphabet
self.alphabet_size = len(alphabet)
self.height = 32
self.generator = GeneratorFromDict(
length=5,
allow_variable=True,
language="en",
size=32,
background_type=1,
fit=True,
text_color="#000000,#888888",
)
def __init__(self, dir, transform=None, artificial=False):
super().__init__()
self.names = os.listdir(dir)
self.images_names = get_files_with_extension(dir, self.names, ".jpg")
self.annotation_names = get_files_with_extension(
dir, self.names, ".txt")
self.transform = transform
self.images = []
self.texts = []
self.texts_length = []
self.texts_encoded = []
print("Loading train data ...")
for (image_name, annotation_name) in tqdm(
zip(self.images_names, self.annotation_names)):
images, texts, texts_length, texts_encoded = self.load_train_data(
image_name, annotation_name)
self.images += images
self.texts_length += texts_length
self.texts += texts
self.texts_encoded += texts_encoded
# generate additional train data
print(f"Train data: {len(self.texts)}")
if artificial:
generator = GeneratorFromDict(count=len(self.images), width=256)
for image, text in generator:
self.texts.append(text)
text = ''.join(c for c in text if c in char_list)
self.texts_length.append(len(text))
self.texts_encoded.append(encode_to_labels(text))
image = image.convert("L")
self.images.append(image)
print(f"Train data with artificial data: {len(self.texts)}")
max_text_length = max(map(len, self.texts_encoded))
print(f"max_length: {max_text_length}")
# do not pad with zero
self.texts_encoded = np.array(
[t + [1] * (max_text_length - len(t)) for t in self.texts_encoded])
def test_generator_from_dict_stops(self):
generator = GeneratorFromDict(count=1)
next(generator)
self.assertRaises(StopIteration, generator.next)
def __init__(self, args, training=True, semi_amount=0.0):
"""
Initialize the constructor.
"""
self.args = args
self.is_training = training
self.batch_size = args.batch_size
self.current_iter = 0
self.fake_generators = [
GeneratorFromRandom(
count=-1,
length=1,
language="fr",
size=64,
background_type=1,
skewing_angle=2,
margins=(2, 1, 1, 1),
use_letters=False,
use_symbols=False,
use_numbers=True,
random_skew=True,
text_color='#000000,#888888',
),
GeneratorFromRandom(
count=-1,
length=1,
language="fr",
size=48,
background_type=1,
skewing_angle=2,
margins=(2, 1, 1, 1),
use_letters=True,
use_symbols=True, # false
use_numbers=False,
random_skew=True,
text_color='#000000,#888888',
),
GeneratorFromRandom(
count=-1,
length=3,
language="fr",
size=24,
background_type=3,
skewing_angle=2,
fit=True,
random_skew=True,
text_color='#000000,#888888',
),
GeneratorFromRandom(
count=-1,
length=3,
language="fr",
size=32,
background_type=3,
skewing_angle=2,
space_width=2,
use_symbols=False,
margins=(8, 8, 8, 8),
random_skew=False,
),
GeneratorFromRandom(
count=-1,
length=2,
language="fr",
size=55,
background_type=1,
skewing_angle=3,
use_symbols=True, # false
fit=True,
random_skew=True,
text_color='#0171ff',
),
GeneratorFromRandom(
count=-1,
length=3,
language="fr",
size=43,
background_type=1,
skewing_angle=2,
margins=(4, 2, 10, 6),
random_skew=False,
text_color='#000000,#888888',
),
GeneratorFromRandom(
count=-1,
length=5,
language="fr",
size=37,
space_width=3,
background_type=1,
use_symbols=False,
fit=True,
text_color='#000000,#888888',
),
GeneratorFromRandom(
count=-1,
length=5,
language="fr",
size=28,
background_type=1,
use_symbols=False,
fit=True,
text_color='#000000,#888888',
),
]
self.dict_generators = [
GeneratorFromDict(
length=5,
allow_variable=True,
language="fr",
size=32,
background_type=1,
fit=True,
text_color='#000000,#888888',
),
GeneratorFromDict(
length=5,
allow_variable=True,
language="fr",
size=32,
background_type=1,
margins=(7, 4, 6, 4),
text_color='#000000,#888888',
),
GeneratorFromDict(
length=5,
allow_variable=True,
language="fr",
size=32,
background_type=1,
fit=True,
text_color='#000000,#888888',
),
]
self.classic_gen = [
GeneratorFromDict(
length=3,
allow_variable=True,
language="fr",
size=32,
background_type=0,
fit=True,
),
GeneratorFromRandom(
count=-1,
length=5,
language="fr",
size=28,
background_type=0,
use_symbols=False,
fit=True,
),
]
self.height = 32
self.width = None
self.alphabet = ALPHABET
# self.alphabet = string.printable
self.alphabet_size = len(self.alphabet)
def gen(index):
print("PID {0}: {1}".format(index, os.getpid()))
generator[index - 1] = GeneratorFromDict(count=1000)
with open("filenames.txt", 'w') as fileObj:
for img, lbl in generator[index - 1]:
pass
from trdg.generators import (
GeneratorFromDict,
GeneratorFromRandom,
GeneratorFromStrings,
GeneratorFromWikipedia,
)
generator = GeneratorFromDict(
language=,
count=2,
blur=2,
random_blur=True
)
for ret, lbl in generator:
img = ret[0]
box = ret[1]
img.show()
print(box)